Clinical Research Redesign
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Joe Adams, Michael Chen, Lindsay Kaplan, Tracy V. Nunnery, Shujen Yeh
Capstone Consulting: Clinical Research Redesign Proposal
MMI 498-DL
Spring 2013
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Table of Contents
Executive Summary....................................................................................................................................... 4
Key Deliverables ........................................................................................................................................ 5
Project Timeline and Milestones .............................................................................................................. 5
Project Costs ............................................................................................................................................. 6
Project ROI ................................................................................................................................................ 7
Introduction .................................................................................................................................................. 8
Project Scope ............................................................................................................................................ 9
Stakeholders ........................................................................................................................................... 10
Current State ............................................................................................................................................... 12
AHS Research Initiatives ......................................................................................................................... 12
Workflow Challenges .............................................................................................................................. 13
Figure 1: Operational Model – AHS Current State .................................................................................. 13
Patient Recruitment Challenges ............................................................................................................. 14
Figure 2: Study Enrollment Use Case: Current State .............................................................................. 15
Absence of Clinical Research and EHR Integration ................................................................................. 16
CTMS Solution ............................................................................................................................................. 17
EHR Integration ....................................................................................................................................... 17
Future State ............................................................................................................................................ 18
Figure 3: Study Enrollment Use Case: AHS future state ......................................................................... 20
Data Architecture .................................................................................................................................... 21
Figure 5: Operational Data Model (ODM) – Future State ....................................................................... 22
Technology Standards ............................................................................................................................. 23
Figure 6: Example ODM Data File ........................................................................................................... 24
CTMS Functionalities............................................................................................................................... 26
Minimizing Re-Keying and Re-Entering of Data ...................................................................................... 28
Patient Portal .......................................................................................................................................... 29
Streamlining Workflows.......................................................................................................................... 31
Clinical Implementation .............................................................................................................................. 31
CTMS Training ......................................................................................................................................... 34
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Support Following Implementation ........................................................................................................ 35
Privacy, Legal, and Ethical Issues ................................................................................................................ 36
Privacy & Confidentiality of Health Records ........................................................................................... 36
Ethical Considerations............................................................................................................................. 36
Infrastructure .......................................................................................................................................... 37
Identifying Potential Study Participants ................................................................................................. 38
Informed Consent ................................................................................................................................... 40
Financial Considerations ............................................................................................................................. 41
Clinical Research Costs ............................................................................................................................ 41
Initial Cost ............................................................................................................................................... 42
Table A: First Year Budget Analysis for CTMS ......................................................................................... 42
Savings and Earnings ............................................................................................................................... 44
Staffing Concerns .................................................................................................................................... 46
Funding ................................................................................................................................................... 47
Conclusion ................................................................................................................................................... 48
References .................................................................................................................................................. 49
Appendix A: Supported CTMS Standards.................................................................................................... 56
Appendix B: Regulatory Compliance........................................................................................................... 60
Appendix C: Annotated Bibliography .......................................................................................................... 64
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Executive Summary
In addition to advancing the quality and delivery of care, by investing in a system-wide
Electronic Health Record (EHR), Anytown Health System (AHS) has uniquely positioned itself
to take advantage of the yet-unrealized potential of research informatics. “The enormity of data
generated from new diagnostic and measurement technologies, increasing ability to collect data
rapidly from patients or external data sources, and the scope and scale of today’s research
enterprises have led to a bewildering array and amount of data and information” (Richesson &
Andrews, 2012). Though tremendous strides have been made in harnessing this data to optimize
clinical practice, like many organizations, AHS still suffers from historically uncoordinated and
inefficient research activities, error-prone data collection within disparate systems, and
complicated and underwhelming patient recruitment efforts. This proposal aims “to increase
coordination between patient care and patient-oriented research activities, while reducing the
burden on physicians, patients, and healthcare delivery” (Weng, et al., 2012).
The lack of coordinated research efforts may be addressed by integrating Capstone
Consulting’s comprehensive clinical trial management system (CTMS) into the existing AHS
electronic health record. Clinicians and investigators will utilize the new CTMS to access and
manage all aspects of clinical research. This can result in improved patient care and coordination
among the care team, more efficient dissemination of emerging clinical care guidelines,
encouragement of research activities, and can be used to promote a patient-centered approach to
precision medicine.
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Key Deliverables
CTMS core functionalities:
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Recruitment and enrollment management integrated with the existing EHR
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Integrated electronic data capture minimizing re-keying and minimizing data
capture on paper
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Systematic, standard-conforming data base management and transmission
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Data extraction and the ability to generate reports
EHR functionality enhancements for integration with CTMS:
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Expansion of structured data capture
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Trial eligibility alerts and initial intent response mechanism to CTM
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Applicable elements of CDISC standards
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Toward standardized clinical terminology suitable for research
Project Timeline and Milestones
Capstone Consulting recommends a multi-phased implementation focusing on key
functionality that offers the greatest upfront benefit with the least clinical impact. Not only will
this allow clinicians to continue to provide the outstanding care which AHS patients have come
to value, but it will minimize resource and budgetary burdens while engendering useracceptance. After a Steering Committee and Project Vision are established, the initial phase of
collaboration between Capstone Consulting and AHS stakeholders including needs assessment
and functionality prioritization could be completed by early to mid-August. Assuming AHS
concurs with the recommended four core functionalities, site-specific customization, testing, and
training could begin at this time, culminating in an October go-live for AHS Hospitals. In
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January of 2014, feedback from the initial implementation will position ambulatory sites for this
same process with go-live in March. Future functionality, including a participant portal, could be
explored beginning in early spring of next year.
Project Costs
Type
Estimated Cost
Analysis
Initial software
$30,000 - $200,000
acquisition and setup
Software installation, hardware and other
infrastructure improvements, and in-house staff
may be required.
Licensing
$1,500 annual peruser fee
Larger organizations need more accessibility and
more users, which may affect licensing costs.
Ongoing subscription costs must be negotiated
carefully based on the needs of the organization.
Support and
Maintenance
$25,000 – $50,000
Costs can come from a per case basis or a fixed
support cost. These costs are ongoing and will
continue throughout the lifecycle of the software
use. Contracting for 24/7 support is also an option
that will increase cost
Training
$10,000 - $50,000
Exhaustive training requires hiring of personnel to
train staff, but lower cost training may result in
lower proficiency or higher employee
commitment. Cost is also determined by loss of
productivity during training.
Data Migration
$50,000 - $150,000
Interfacing with the existing software (EHR, HR,
Accounting, Billing) and migrating pertinent data
must be done for the system to work properly
Total
$200,000 - $500,000
The initial cost is daunting, but the return on
investment could be much more. For larger
organizations, the low-end estimates may not be
attainable.
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Project ROI
Improvement Area
Year 2 Return
Rationale
Billing – Improved
materials management
and tracked billing of
sponsor.
$25,000 - $50,000
No more missed billing opportunities
Reduced burden on
research staff
$50,000 - $75,000
Research has more time to commit to
research rather than manual cataloguing and
procedural duties. Potential to reduce staff
and increase amount of studies
Increased study
participation
$100,000 - $300,000
CTMS can reduce the number of studies
missed due to participant issues.
Improved negotiation
and contracting
$0 - $100,000
The ability to quickly determine which
studies AHS is capable of undertaking and
producing the most equitable contract will
improve the number of studies and the
reimbursements from sponsors
Total
$175,000 - $525,000
Other organizations have reported
improvements well past this conservative
estimate for return within the first year. One
organization reported a staggering $2M
increase in annual clinical trial revenue in a
few years (Miller, 2006)
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Introduction
Anytown University Health System (AHS) is a not-for-profit hospitals and affiliated
physician’s health group. The physicians and specialists who are part of AHS include more than
300 providers and 25 specialties across 80 locations. AHS provides a complete, coordinated and
comprehensive sphere of care for the region it serves. The broad array of services includes
emergency medicine, cardiology, obstetrics/gynecology, pediatrics, oncology, surgery,
neurology as well as laboratory, radiology, and pathology. Intensive care facilities provide
critical care for adult, pediatric, and neonatal patients as well as coronary care and post-op
intensive care.
Anytown University Health System has implemented an integrated, system-wide
Electronic Health Record (EHR) system in its hospitals and ambulatory care settings. The
hospitals and affiliated ambulatory care professionals have met EHR Meaningful Use Stage 1
objectives and are well on their way toward meeting Stage 2 objectives and beyond. With the
quickly accumulating data from electronic records, new opportunities are presented for the reach,
scale, efficiency and innovation in clinical research. The ability to “generate lists of patients by
specific conditions to use for quality improvement, reduction of disparities, research, or
outreach” and to “identify and report specific cases to a State cancer registry or a specialized
registry” is an impetus for improving clinical research with the use of EHR (Centers for
Medicare & Medicaid Services, 2013). Toward this end, it is time for AHS to leverage the
inherent potential within the existing EHR and implement a comprehensive Clinical Trial
Management System (CTMS).
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Project Scope
A comprehensive Clinical Trial Management System (CTMS) will include functionalities
that address the needs in all stages of clinical research at AHS. Common clinical research
processes where opportunities exist for informatics solutions include (Payne, 2012):
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Identifying Potential Study Participants
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Screening and Enrolling Participants in a Clinical Study
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Scheduling and Tracking Study-Related Participant Events
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Executing Study Encounters and Associated Data Collection Tasks
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Ensuring the Quality of Study Data
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Regulatory and Sponsor Reporting and Administrative Tracking/Compliance
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Budgeting and Fiscal Reconciliation
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Human Subjects Protection Reporting and Monitoring
These processes represent a complex, information-intensive endeavor that incorporates a broad
variety of professionals and participants. Informatics solutions have the potential to address this
challenging environment and barriers to the efficient, effective, high-quality, and timely conduct
of clinical research programs (Embi & Payne, 2009). “The challenges in clinical research – and
the opportunities for informatics support – arise from many different objectives and
requirements, including the need for optimal protocol design, regulatory compliance, sufficient
patient recruitment, efficient protocol management, and data collection and acquisition; data
storage, transfer, processing, and analysis; and impeccable patient safety throughout” (Richesson
& Andrews, 2012).
The CTMS deliverable that Capstone Consulting is proposing for AHS is a
comprehensive solution that will be fully integrated with the existing EHR. It includes
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customizable functionalities that will be interoperable and standards-based, allowing for
information exchange within and outside of the AHS organization and will improve efficiencies
in AHS clinical research by streamlining processes and minimizing duplicative efforts.
Stakeholders
In considering the integration of Clinical Capstone’s CTMS with the existing EHR at
Anytown University Health System, it is important to not only consider the research goals of the
organization but also the collective, long-term goals of the organization. In looking at the
organization’s plan for growth, “Consider where you see your organization going in both the
short and long term future. Most organizations strive to increase profitability. One of the best
ways to do this is to increase the number of studies and the size of studies conducted” (Burke,
2013). One of the best ways to manage research functions in order to increase both the size of
studies and the number of studies being conducted is by utilizing a CTMS. “An effective CTMS
should become the backbone of an organization’s clinical development efforts, as it serves as the
central repository for all trial data. Since clinical trials are multi-functional efforts that require
the coordination of a huge number of specialized functions, the CTMS must be useful to all”
(Tyson & Lynch, 2008).
Each stakeholder group for this project offers unique interests and perspectives. Primary
stakeholders for this project include study management, clinical operations, clinical data
management, and medical affairs. Secondary users of the system include: finance, IT, regulatory,
contracting drug supply, and trial master file management (Tyson & Lynch, 2008). Capstone
Consulting is capable of not only meeting the needs of the primary stakeholders but of meeting
the needs of all stakeholders within the organization. However, for the CTMS to be fully
functional and effective at AHS, it is crucial to have buy-in from all users. “Without buy-in from
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all of the key stakeholders, CTMS implementation flounders because non-engaged stakeholders
fail to use the system effectively, making it less useful even for those functions motivated to use
the system” (Tyson & Lynch, 2008).
Prior to selecting and implementing the CTMS, it is recommended that Anytown
Health’s clinical development leadership collaborate on two key strategies: appointing a Steering
Committee and developing a common CTMS Vision. Led by an Executive Project Sponsor the
Steering Committee will convene key AHS organizational leadership and clinical research
stakeholders—from translational, interventional, and clinical researches to outcome and care
quality researchers, in various specialties and disease areas—to “agree on and prioritize system
and user needs, select the CTMS, and map the operational approach” (Tyson & Lynch, 2008).
The Committee may then develop the common vision for the CTMS as it pertains to the
organizational vision of AHS in order to determine how the CTMS will be used within the
organization, to decrease the challenges of the system since a diverse group of stakeholders are
involved, and to specify exactly what AHS hopes to accomplish with the CTMS (Tyson &
Lynch, 2008). In discussing clinical research commonality and special needs, communicating
and developing the vision for future clinical research, and achieving broad buy-ins and
enthusiasm toward the future clinical research information system, the Steering Committee will
be able to develop a comprehensive vision for clinical research at AHS. It is important to focus
on the goal of having one system for various study designs, trial-stages, and sponsor-type.
Building a study management system specific to certain study types or disease/conditions
without considering its fit in the continuum and overall model of clinical research processes and
the AHS vision will likely lead to heterogeneous solutions of duplicating or even conflicting
functions.
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Current State
AHS Research Initiatives
As a partner to an academic research institution, Anytown University Health System is
heavily invested in teaching as well as research efforts. In 2012, AHS received over $75 million
in research funding from the NIH, not including projects funded by the American Recovery and
Reinvestment Act of 2009. As research needs become an increasingly important part of the
organization, it is clear that future goals for long-term improvement in patient care require an
investment in research. Currently, AHS has a research model which is very dispersed and
uncoordinated. Investigators and researchers tend to use the tools with which they are most
familiar and, as a result, there are hundreds of home-grown systems which aren't interoperable,
are not standards-based, and have no means of sharing data. The environment has created many
research activities which are insulated from each other with a great deal of duplicated effort, and
a lack of efficiency, standardization, and coordination among research endeavors. Tools
currently in use include both home-grown and third-party point solutions, employed to solve
particular problems without regard to organizational issues or needs. Some investigators have
implemented server-based data repositories and analytics. Others have relied upon desktop tools,
Excel, Access, and custom-created applications while some depend on paper-based records for
conducting research. There are no enterprise-wide data standards, mechanisms for sharing data
or organizational protocols for research activities. Careful “consideration needs to be given to the
complexity of the research question since this can have an impact on how easily issues of using
EMR data for research can be overcome” (Terry, 2010).
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Workflow Challenges
Common workflow challenges in Anytown University Health System’s current clinical
research environment include: paper-based information management practices; complex
technical and communication processes including a mixture of papers, telephones, computers
and other electronic medium, and face-to-face communications; task interruptions due to the
environment or other study-related activities; and a single point of information exchange—who
most frequently is the Clinical Research Coordinator (CRC). The CRC often becomes the
primary limiting component of overall research productivity. These characteristics add to
increased cognitive complexity in clinical research processes and lead to increased errors and
reduced efficiency (Payne, 2012).
Figure 1: Operational Model – AHS Current State
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Patient Recruitment Challenges
Currently at AHS, the patient recruitment process is complicated, prone to error, and
utilizes a significant number of labor-intensive manual processes. According to recent statistics,
86% of all clinical trials fail to enroll on time, 85-95% of study days beyond the original study
timetable are due to not recruiting subjects on time, and only 7% of eligible patients enroll in a
clinical trial (Kahn, 2006). With a Partial Waiver in place and after IRB approval, the
recruitment process begins at AHS with a study coordinator who must search through multiple
electronic systems looking for patients who may be a potential match for a given study. These
systems must be cross-referenced with each other to ensure the data being reviewed is the most
accurate and current available. Study coordinators must navigate an electronic medical record as
well as other data repositories for billing, lab, scheduling, mortality, and prior or current study
participation. Coordinators also access multiple electronic file shares containing individual
databases containing information of activity in current and historical research studies. Some
information regarding patients is also captured via paper documents which must also be
reviewed by study coordinators. This is a labor-intensive process and prone to delay since some
documents may be in other locations, misfiled, or checked out by other staff members.
Once the study coordinator has identified a potential research study candidate, the
coordinator then schedules a time to meet with the primary care physician of the patient. The
coordinator and the physician then review the case to assess eligibility. At this point, the
physician can decide whether to pursue the patient for enrollment or to take no further action. If
the physician chooses to pursue enrollment, the physician contacts the patient to determine if
they are interested in participating. If so, the physician contacts the study coordinator to follow-
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up with the patient. The coordinator then contacts the patient and, if the patient is still interested
in participation, he or she is enrolled in the research study.
Figure 2: Study Enrollment Use Case: Current State
This example use case highlights the arduous task faced by study coordinators. Currently,
they are required to search multiple paper and electronic sources, maintain contact with both the
patient and the physician and ensure that there is follow-through on each step of the process.
Given other work constraints of study coordinators as well as heavy workloads typical of
physicians, potential study participants are not enrolled because they are “lost” at some point in
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the time-consuming and laborious process. The current uncoordinated system does not leverage
electronic systems and presents more opportunities for failure than for a successful enrollment.
Absence of Clinical Research and EHR Integration
Much of what contributes to the challenges in clinical research at AHS and the current
lack of integration between clinical research and the existing EHR is that there are differences in
the functionalities needed for clinical research information management and for EHRs. Unique
functions of the AHS clinical research information system that are not in its EHR include
(Nadkarni, Marenco, & Brandt, 2012):
●
Clinical research information management systems must be able to represent
study designs (i.e. protocols) electronically, including randomized double-blinded
design in which neither the patient nor the care-givers knows the medication the
patient is receiving.
●
Clinical research information systems must support the monitoring function and
break the blinding when serious adverse effects develop for a particular patient.
●
Clinical research information systems must support selective access to an
individual patient’s data and selective access to only relevant parts of a patient’s
data based on user roles in clinical studies, where users may cross institutional or
even national boundaries, and are not limited to health care providers tending the
patients or study subjects.
While many differences exist in the functionalities needed for clinical research
information management and for electronic health records, to optimize AHS clinical research
efforts, integration of clinical research and the EHR is necessary.
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CTMS Solution
EHR Integration
In order to integrate Anytown University Health System’s clinical research initiatives
with the existing electronic health record system, the EHR will have an essential role in all stages
of clinical study, from study formation and study enrollment, to data capture and transfer, to
assisting study execution compliance, to the dissemination of study-substantiated knowledge and
evidence (Michael G. Kahn, 2006; Michael G Kahn & Weng, 2012). In the study design phase,
AHS researchers may query the EHR database for potential candidates to assess the feasibility of
study design or to adjust the study design. In the study recruitment phase, eligibility alerts will be
implemented in the EHR, and candidate of-interest responses will be sent back to the clinical
management system. In the study execution phase, study specific data capture is incorporated
into routine clinical care documentation workflow, with embedded structured data entry tab and
data range check. The US FDA guidance on Computerized Systems Used in Clinical
Investigations (US FDA, 2007) recommends “use of prompts, flags, or other help features in the
system to encourage consistent use of clinical terminology and to alert the user to data that are
out of acceptable range”. The US FDA draft guidance on Electronic Source Data in Clinical
Investigations (US FDA, 2012) stipulates EHR as one of the potential electronic data originators,
and that data elements originating in an EHR can be automatically transmitted directly into the
electronic Case Report Form (eCRF). Kahn has also identified potential roles of EHR in clinical
trials submission and reporting (to regulatory agencies), in assessing congruence of new findings
with current practice and outcomes, and in implementing validated study findings and evidences
as clinical documentation, order sets, and rules/alerts.
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Future State
While a system to address the pressing research needs could be built-in house by hiring
the appropriate support staff, there are many advantages to leveraging a third party solution
utilizing a Service-oriented Architecture (SOA) and Software as a Service (SaaS) model.
“Traditionally, companies buy software and then install and maintain these applications on their
own machines. That model is giving way to one where companies will buy subscriptions and
access services over the Internet from software developers that host their own applications”
(Dubey, 2007). SaaS architectures provide a web-based delivery model to serve multiple clients
using a multi-tenancy infrastructure “so as to get great benefit from economies of scale” (Sun,
2008).
Benefits of utilizing an integrated solution from Capstone Consulting include: cost
savings through reduced overhead and operating costs, the ability to trade higher fixed costs for
lower, variable costs, a reduction in the need for capital investment, elimination of the expense
for under-utilized equipment, fewer lab and support staff, lower training and equipment costs,
physical space can be repurposed, access to state-of-the-art technology and sophisticated services
offered by the vendor and, most importantly, outsourcing allows organizations to focus on the
core mission. Capstone Consulting incorporates best-of-breed tools including proprietary as well
as open-source modules. There are commercial as well as open-source products which can be
implemented as an enterprise-scale solution at a lower cost. “Open-source CTMS are viable
alternatives to the more expensive commercial systems to conduct, record and manage clinical
studies” (Leroux, 2011).
The integrated solution provided by Capstone Consulting addresses a number of issues
and limitation in the current system. As a comparison to the previous use case of patient
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enrollment, the proposed process flow leverages existing systems and technologies, decreases
manual processes significantly, improves the targeting and retention of potential study
candidates, promotes follow-up, reduces the chance for errors and creates a process which can be
completed in a much shorter period of time.
The proposed solution for study recruitment begins with an automated, clinical trial alert,
generated by the existing EHR. This clinical decision support system (CDSS) contains all of the
business logic for study eligibility and also has access to multiple data stores to assess eligibility
criteria. As potential study participants are identified, physicians are alerted automatically. This
alert allows the physician to approach the patient and discuss participating in the research study
during the visit rather than waiting to locate the patient at a later date. If the physician decides
that the patient is a good match and the patient is willing to participate, the CTMS notifies the
study coordinator. The coordinator then follows-up with the patient to complete enrollment. In
the patient’s after–visit summary, they are informed of potential contact within 2 weeks by a
study coordinator, informed that they are under no obligation to proceed, and are provided the
study coordinator’s contact information. (Embi et al., 2005)
This process can be completed in a matter of minutes as compared to current AHS
processes which may span many days or even weeks. The potential for errors is significantly
reduced, the tedious process of patient-matching is automated, and the disruption to clinical
workflow is minimized, which all allow physicians and study staff to concentrate their efforts on
more important tasks. The study enrollment workflow utilizing the CTMS is show in the figure
below followed by a screen shot of clinical trial eligibility alert:
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Figure 3: Study Enrollment Use Case: AHS future state
Figure 4: Future Clinical Trial Eligibility Alert
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Data Architecture
A distributed system, such as those using a Service Oriented Architecture (SOA), can be
shared among many systems and are built in such a way that allows for disparate systems to
connect to various modules. The SOA approach is a manufacturing model and a method of
software design and construction. Different systems are able to connect to only the functionality
they need, regardless of their operating system or the application languages used locally. Legacy
systems are a major concern when new system implementations are being considered. Capstone
Consulting utilizes a means of encapsulating existing services using XML wrappers to transform
and maintain these functions as web services, available to all existing and future users and
systems. This XML encapsulation technology allows existing system assets to be preserved and
delivered as needed through the use of web services, XML, WSDL and the SOA model.
Using SOA principles, services can be provided from a centralized location, ensuring that
all AHS users who connect are accessing the same information and using the same data sources
and ontologies. (Nunnery, 2012). Taken a step further, the SaaS model is a distribution model
intended to provide a flexible means to deliver software solutions to end users. It is typically a
subscription-based model where the resources and functionality are available via the Internet or
“cloud.” It requires little or no local installation, maintenance or management on the part of the
end user and can be delivered quickly, as long as the connectivity and infrastructure can support
the bandwidth requirements.
The management of AHS research data consists of four main areas: “planning,
specification, implementation and analysis” (Shankar, 2006). The Operational Data Model
(ODM) was developed by the Clinical Data Interchange Standards Consortium (CDISC). CDISC
is an “open, multidisciplinary, non-profit organization committed to the development of industry
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standards to support the electronic acquisition, exchange, submission, and archiving of clinical
trials data and metadata for medical and biopharmaceutical product development. The mission of
CDISC is to lead the development of global, vendor-neutral, platform-independent standards to
improve data quality and accelerate product development in our industry” (CDISC, 2012).
The ODM provides a robust framework to support each of these needs in a standardized
and system-agnostic manner. “ODM is a vendor neutral, platform independent format for
interchange and archive of clinical study data. The model includes the clinical data along with its
associated metadata, administrative data, reference data and audit information. All of the
information that needs to be shared among different software systems during the setup,
operation, analysis, submission or for long term retention as part of an archive is included in the
model” (CDISC Standards, 2013).
Figure 5: Operational Data Model (ODM) – Future State
(adapted from Medidata, 2013; Sneed, 2006)
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The proposal for an integrated Capstone Consulting system of clinical research functions
within the existing EHR in place at Anytown University Hospital System will provide
measurable benefits in efficiency, risk management, data accessibility and security. AHS seeks a
solution to “increase the efficiency of the recruiting process and to handle a set of protocols
running concurrently at multiple recruiting centers by various groups of researchers”
(Vahabzadeh, 2007). A SaaS-based solution can integrate disparate financial, clinical, research
and administrative systems into an interconnected system where “clinical teams can eliminate
double data entry and access real-time outcomes data, improving the safety and accuracy of
studies” (Velos, 2012).
Technology Standards
An additional consideration is the use of standards-based integration for a CTMS solution of this
scope.
“The task of transmitting or linking data across multiple biomedical data
sources is often difficult because of the multitude of different formats and
systems that are available for storing data. Standard methods are thus
needed for both representing and exchanging information across disparate
data sources to link potentially related data across the spectrum of
translational medicine from laboratory data at the bench to patient charts
at the bedside to linkage and availability of clinical data across a
community to the development of aggregate statistics of populations.
These standards need to accommodate the range of heterogeneous data
storage systems that may be required for clinical or research purposes,
while enabling the data to be accessible for subsequent linkage and
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retrieval. Standards are thus an essential element in the representation of
data in a form that can be readily exchanged with other systems” (Sarkar,
2010).
To facilitate data sharing within the AHS organization and across institutional
boundaries, it is imperative that the clinical research information system and supporting
functions in the EHR adhere to terminology and data exchange standards. Standards in
terminology (such as SNOMED-CT, LOINC, RxNorm) and in data elements and transmission
(including CDISC standards, HL7 standards, and IHE profiles) lay the foundation for data
sharing, in addition to the benefits of enhancing between-system workflow integration and
efficiency of cross-institution collaboration. (See Appendix A for a table of other standards the
CTMS supports.)
Figure 6: Example ODM Data File
(Verplancke, 2007)
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Capstone Consulting’s CTMS offers an Operational Data Model-compliant (ODM),
standards-based solution for AHS which provides a cost-effective approach to incrementally
implement an integrated, interoperable, flexible, and extensible system into the AHS
environment. Capstone Consulting’s CTMS is ODM-certified and “is designed to facilitate the
archive and interchange of the metadata and data for clinical research” (CDISC Standards,
2013). The ODM provides a system-agnostic format for representing study data, metadata and
administrative information pertaining to clinical trials.
The ODM standard was created as a method to represent research study data in the
context of data capture. Leveraging the XML format, the ODM syntax is meant to describe the
communication of data from a source to a destination rather than being a format for storage.
Another crucial component of the Capstone Consulting CTMS is the incorporation of the CDISC
Study Data Tabulation Model (SDTM). The STDM is a generalizable framework for collecting,
storing and organizing study data. “Unlike ODM [STDM] focuses on groupings of data…by the
use of data” (Bain, 2004). The use of an ODM-STDM design enables datasets to be
automatically generated, facilitates integration with other systems and enables reuse of study
metadata. The STDM model “is built around the concept of observations, which consist of
discrete pieces of information collected during a study. Observations are reported in a series of
domains, usually corresponding to data that were collected together. A domain is defined as a
collection of observations with a topic-specific commonality about a subject” (Godoy, 2004).
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Figure 7: STDM Data Domains
(Adapted from Godoy, 2004).
CTMS Functionalities
When designing and choosing potential solutions, usability factors should not be
overlooked. Navigation and workflow presentation issues are closely related to system adoption
rate, frequency of errors, and productivity. Experience and research in medical data systems have
offered the lesson that “it is often easier to add functionality to a usable system rather than
making a functional system usable” (Choi, et. al., 2005). With that, Capstone Consulting offers a
product that focuses on a core set of functionalities with the opportunity for future system
enhancements to works towards the AHS vision for clinical research. In focusing on the core
functions of the CTMS, there will be certain clinical research functions embedded in the existing
EHR.
CTMS core functionalities include:

Recruitment and enrollment management integrated with existing EHR
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
Integrated electronic data capture minimizing re-keying and minimizing data
capture on paper

Systematic, standard-conforming data base management and transmission

Data extraction and the ability to generate reports
EHR functionality enhancements for integration with CTMS include:
●
Expansion of structured data capture - Clinical encounter data are less structured
and have a higher likelihood of missing data (Meiman & Freund, 2012). A good
percentage of trial required data elements are already available in EHR but may
not be as rigorously structured (Kahn 2006).
●
Trial eligibility alerts and initial intent response mechanism to CTMS (Embi,
2007; Embi, 2012; Kopcke, 2013; Embi, 2013)
●
Applicable elements of CDISC standards
●
Toward standardized clinical terminology suitable for research
Following the initial implementation phase, future CTMS functionalities for the AHS are
customizable based on the organization’s needs but may include:
●
Study design and planning—including intervention branches, study sizes,
inclusion/exclusion criteria representations, etc.
●
Trial execution management—focusing on participant scheduling and retention
●
Research finance management—including budgeting, expense capturing, and
tracking
●
Report generation demonstrating AHS regulatory compliance
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Minimizing Re-Keying and Re-Entering of Data
While trial eligibility alerts in the EHR simplifies recruitment workflow and increases
recruitment success rate, integrating trial data capture functionality in the EHR can substantially
reduce re-keying and re-entering of trial data. The Case Report Form (CRF) is the principal
mechanism for data collection in AHS clinical trials. Its purpose is to capture required data
elements that are necessary to answer the research question as defined in the study protocol. It is
also used to document any adverse events occurring during the trial. Currently, AHS research
processes include multiple transcribing and re-entering of data that is time consuming, hard to
track, and prone to error. In the future state, the CTMS allows for data collection to use best
practice standards, and data collection is streamlined, greatly reducing manual entry.
Case Report Form—Current State
1. The study initiator designs and communicates the Case Reform Form in paper format.
CRF is reviewed and approved by the IRB along with the study protocol.
2. With each participating patient, the Research Coordinator initiates and fills out one CRF.
3. The Coordinator enters or transcribes patient data on the paper CRF. The original data
was printed and handed to the Coordinator, at request. There may be a parallel ad-hoc
electronic version, but in most cases not on a validated system. In most cases the paper
copy is considered the “original” and “true” version.
4. The Coordinator controls and manages the CRF files, pulling out the form and filling in
additional data, much like the paper medical record system.
5. Eventually the data are re-entered into an electronic file in preparation for data analysis.
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Case Report Form—Future State
1. The study initiator designs electronic Case Report Form (cCRF) in the clinical trial
management system. Widgets of commonly used standard elements are available in the
system. Templates from the National Cancer Institute and other organizations are also
available for use in the system.
2. Once the eCRF is complete and reviewed, and approved by the IRB, the system is set up
to extract predefined data elements from EHR for enrolled patients.
3. The Clinical Research Coordinator initiates an eCRF for each confirmed enrolled patient,
entering initial information and verifying Consent is in place. The Coordinator and the
Investigator sign off pulling of data for the patient from EHR as well as other connected
sources.
4. Entering the data analysis phase, the system produces data tables from the collections of
eCRF for the study.
Patient Portal
While not a core function of the CTMS, an additional feature aimed at enhancing patient
participation and satisfaction is the incorporation of a Patient Portal. The percentage of patients
who are enrolled in clinical trials is strikingly low. “Even in fields like oncology where clinical
trial enrollment is considered the optimal management approach for many patients, only about
3% of patients enroll in clinical trials” (Embi, et al., 2005). Combined with the Clinical Trial
Alert for improving the patient recruitment process and aiding in enrollment, the addition of a
Patient Portal to the AHS research initiatives could also increase patient participation. This
feature would positively impact recruiting, as it would allow patients to be actively involved in
the clinical research process.
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Through the Patient Portal, potential participants would have the opportunity to seek out
studies, determine eligibility for studies, and ask about inclusion. Patients will not only be able to
search for research occurring at AHS but also national and international clinical trials. Links will
be provided on the Patient Portal to two key sites where patients and families may go to learn
about ongoing research and clinical trials and educational information outside of AHS:
http://clinicaltrials.gov/and http://medlineplus.gov. Provided by the U.S. National Institutes of
Health, http://clinicaltrials.gov/ is a user-friendly websites with over 146,000 studies listed,
including studies both in the United States as well as worldwide (2013). The National Institutes
of Health’s MedlinePlus website, produced by the National Library of Medicine, is also a
resourceful site featuring free access to an abundance of information about “diseases, conditions,
and wellness issues” including medical research about topics and clinical trials related to a
disease or condition (2013).
Active research participants would utilize the Patient Portal to fill out surveys or
questionnaires, report vital information immediately, and communicate with their physicians and
study coordinators with ease (Weng, et al., 2012). Patients in other (non-research) settings that
were given the opportunity to use portals “demonstrated increased satisfaction with
communication and overall care… [and] valued the portal’s convenience” (Lin, et al., 2005).
Marketing this enhancement and describing to patients how it could empower them as active
participants in research and simplify communication with physicians and study coordinators
could markedly increase enrollment and hospital reputation. Care that satisfies patients is always
a priority, and this is certainly a step in the right direction.
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Streamlining Workflows
The proposed CTMS functionalities for the AHS clinical research initiatives are focused
on implementing a usable system that streamlines clinical research efforts, obtains buy-in from
stakeholders, has widespread user-acceptance, and maintains a patient-centered approach.
However, it is also essential that the CTMS integrates with daily workflows (Pfotenhauer, 2012).
“Streamlining operations can reduce stress and help [AHS] to be more productive and efficient”
(Systems, 2013). Specific ways that this CTMS will streamline the AHS research process would
include:
●
Easier subject visit management
●
Simplified reporting process
●
Instant visibility into patient recruitment activities
●
Centralized access to documents and key study data
●
Integrated workload tracking
●
Standardized process (Systems, 2013)
The use cases described throughout this proposal illustrate that Capstone Consulting’s CTMS not
only acknowledges daily clinical workflow at AHS but streamlines processes to provide a more
efficient workflow and manage clinical research initiatives.
Clinical Implementation
Given the complexity of clinical research processes, the diverse AHS stakeholders
involved, and the wide range of informatics needs identified in the creation and implementation
of a comprehensive research management solution, Capstone Consulting recommends a phased
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realization of the functionalities envisioned. Multi-phased realization reduces business and
operational risks and short-term initial resource and financial burdens. Benefits of an integrated
system can be reaped with a limited set of well-designed, highly usable functionalities. Initial
return can then be used for investment in future phases. Additionally, a phased approach
focusing on a core set of functions will minimize disruption in workflow during initial
implementation and increase user-acceptance.
Capstone Consulting will collaborate with Anytown University Health System’s CTMS
Project Sponsor in order to develop an organized plan for implementing the CTMS. A realistic
timeline will be established, but the key aspect remains that the implementation plan will be a
phased approach. In the first phase of the CTMS implementation, it is crucial to assist AHS in
selecting “...the smallest subset of functionality that will provide the most value-add to the
organization” (Tyson & Lynch, 2008). Based on the stakeholder priorities and as discussed
previously in the CTMS Functionalities section, it is strongly recommended that AHS focuses on
implementing the four, core CTMS functionalities. Within the first phase of implementation, it
will be subdivided into a Phase I-A and Phase I-B implementation. This will allow for training
and go-live of the CTMS solution to take place in the AHS hospitals first followed by training
and go-live in AHS ambulatory settings.
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Figure 8: Implementation Phases
Figure 9: Implementation Timeline
While the “attempt to implement all or even most of the capabilities of a CTMS at once
[could cause] significant implementation delays” (Tyson & Lynch, 2008), it is expected that
AHS will find many benefits by implementing the CTMS using a staged approach. This includes
users who are “more likely to result in regular use of the CTMS because it is more likely that
those using the system will find the CTMS to be more beneficial than their previous approach”
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(Tyson & Lynch, 2008). In future phases, once the CTMS is being used frequently by users, the
additional complex functionalities will be added. Future phases of CTMS implementation will
include the go-live for the Patient Portal as well as additional functionalities and enhancements,
as prioritized by AHS.
CTMS Training
As with the implementation plan for the CTMS, a specific and detailed plan will also be
developed with Anytown University Health System’s Project Sponsor to train all users on the
CTMS product. It is essential that a sufficient amount of the budget be allocated to training in
order to ensure successful implementation and effectiveness of the product. According to Parem
Singh of BioPharm Systems, “[CTMS] Projects where 7% of the project budget was spent on
training were significantly more successful than projects where training took up only 4% of the
budget” (2011). Additionally, Singh notes that the amount of time spent on training is also
crucial. “User groups who had twice the amount of training had a far higher level of project
success” (Singh, 2011).
With a key focus on adult learners, the CTMS training will consist of on-site, instructorled training involving every user to the system. Researcher Malcolm Knowles revealed that adult
learners are “Relevance-Oriented, Experience-Oriented, and Goal-Oriented” (Singh, 2011). This
will be the focus of training the users, which will include clinical research coordinators, principal
investigators, physicians, nurses, pharmacy staff, and regulatory and finance personnel, among
others (Pfotenhauer, 2012). While training the users, there are six main goals that will be central
to the training approach:
●
Familiarizing users with system functions
●
Teaching tools to start using CTMS right away
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●
Focusing on system navigation
●
Knowing system features
●
Using real-life scenarios/Anytown Health System research data as examples
●
Maintaining an open environment to ask questions and provide feedback
(Pfotenhauer, 2012)
Throughout the training, a plan will be in place “…for collecting and (later) evaluating requests
for system enhancements” (Singh, 2011). As requests are expected to occur during training, this
plan on addressing requests for system enhancements acknowledges the requests and concerns of
the users and “also prevents training from being derailed” (Singh, 2011). Another focus of the
training will be in getting the clinical development leadership trained and using the system as
early as possible. This “drives user adoption and consistent use of the system” (Singh, 2011).
Support Following Implementation
Numerous resources will be available for support following the CTMS go-live. Anytown
Health will have designated subject matter experts (SMEs) who will collaborate with the Project
Manager and be a resource for the users. They will be tasked with compiling questions and
concerns of the users and with staying current on new CTMS features. Long-term
recommendations include the use of computer based training (CBT) for updates or refreshers,
quick reference guides available to all users, and focus sessions as needed to address concerns.
In addition to the on-site training, Capstone Consulting will provide 24/7 software
support for the amount of time outline in the negotiated software package. Many user features
are also available on the Capstone Consulting “Connections,” accessible via the Capstone
Consulting website. Anytown Health users will receive free access to online video training
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materials, a frequently asked question (FAQ) page, a customer feedback page, an online forum,
and live, internet-based support available during specified hours.
In combining the initial training program with the ongoing resources provided by
Capstone Consulting, Anytown Health will be well-equipped to fulfill their vision for a CTMS
within their organization.
Privacy, Legal, and Ethical Issues
Privacy & Confidentiality of Health Records
Concerns of privacy regarding coexistence in the EHR of patient-related data elements
pertinent to clinical trials or to clinical encounters can be addressed through recognizing the
different roles of care providers and individuals in research conduct, and granting of differential
access privileges accordingly and controlled through the CTMS. More importantly, there is
strong public and private interest in leveraging clinical data captured in the health records during
episodes of care and using this data to supplement data collected for other purposes, including
research. “Innovative research and clinical opportunities may arise from the ability to combine
clinical and geospatial data at the regional scale in large, integrated health care delivery
systems.” Also, “the National Institutes of Health (NIH) Common Fund initiated the Health
Systems Research Collaboratory based on pragmatic clinical trials that include integrated health
systems and their EHRs as the common source of data” (Califf, Sanderson, & Miranda, 2012).
Ethical Considerations
Clinical research involves human volunteers. As such, ethical principles (voluntary
participation, informed consent, purpose and necessity, protection of participant wellbeing, risk
and benefit) must be upheld (NIH Clinical Trials and You,
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http://www.nih.gov/health/clinicaltrials/basics.htm; Umscheid, Margolis, & Grossman, 2011).
The CTMS solution that Capstone Consulting is proposing for AHS complies with current
regulatory requirements regarding privacy, safety reporting, data security and integrity. Specific
regulatory requirements the CTMS complies with include: the Health Insurance Portability and
Accountability Act of 1996; the Office for Human Research Protections (OHRP) human subjects
protection (or the “Common Rule”); FDA Protection of Human Subjects; and FDA guidelines on
electronic records and electronic signatures (See Appendix B Regulatory Compliance for
description of each regulation).
Infrastructure
“The primary functionality of commercial applications today is essentially concerned
with the delivery of valid and accurate data in conformity with the Good Clinical Practice (GCP)
guidelines” ( To facilitate acceptance and auditing of clinical trials in accordance with 21 CFR
11, Capstone Consulting’s Clinical Trial Management System (CTMS) employs the following
guidelines established by Leroux, McBride, and Gibson (2011):
1. Implements security measures and protocols that prohibit unauthorized access to
the study and data.
2. Provides adequate audit trail to ensure that all changes pertaining to the conduct
of the trial are well documented.
3. Incorporates features to encourage the consistent use of clinical terminology and
to alert users that data is out of range.
4. Provides suitable safeguards to isolate identifiable information from the study and
ensure that retrieved data regarding each subject is only attributable to that
subject.
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5. Provides satisfactory backup and recovery protocols to guard against data loss.
6. Provides support for several types of fields (such as dates, text, numerical values)
and in various formats (such as files, x-ray images).
7. Facilitates data extraction and the ability to swiftly generate reports.
8. Upholds the cost effectiveness of the system.
9. Endorses minimal development efforts.
10. Advocates an advantageous type of licensing.
11. Promote adherence to industry standards, such as the Clinical Data Interchange
Standards Consortium (CDISC). (Refer to Technology standards section.)
In addition, with regards to electronic protected health information (e-PHI), Capstone
Consulting’s CTMS is fully compliant with HIPAA’s Security Rule:
1. Ensuring the confidentiality, integrity, and availability of all e-PHI which is
created, received, maintained or transmitted;
2. Identifying and protecting against reasonably anticipated threats to the security or
integrity of the information;
3. Protecting against reasonably anticipated, impermissible uses or disclosures; and
4. Ensuring compliance by the workforce. (U.S. Department of Health and Human
Services, 2003)
Identifying Potential Study Participants
Advancement in medical science has been impaired by inadequate recruitment to clinical
trials. ( This has been further complicated by patient privacy regulations. “HIPAA forbids
nonconsensual release of patient information to a third party not involved with treatment,
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payment, or other routine operations associated with the provision of healthcare to the patient;
therefore, concerns regarding privacy represent a growing barrier to electronic screening for
clinical trials accrual” (Weng & Embi, 2012).
The EHR-based Clinical Trial Alerts (CTAs), described in the Clinical Trial Alert Use
Case, present an opportunity to identify a large number of eligible study participants as they
enable point-of-care recruitment by presenting trial eligibility to the patient’s care provider
during visits. A crucial legal and ethical component to the CTAs is that they are HIPAAcompliant. This is accomplished in the following manner:
“Because all information is sent within the secure EHR environment
between personnel with a legitimate reason and patient authorization to
view the information, this approach does not compromise the patient’s
privacy” (Embi et al., 2005). According to the Privacy Rule, “Covered
entities may permit researchers to review PHI in medical records or
elsewhere during reviews preparatory to research. These reviews allow the
researcher to determine, for example, whether there is a sufficient number
or type of records to conduct the research” (U.S. Department of Health
and Human Services National Institutes of Health, 2004).
Capstone Consulting’s CTMS also facilitates the collection of PHI using both deidentified data sets and limited data sets in conformity with the standards set by the Privacy Rule.
The former method removes the eighteen distinguishable elements that could be used to identify
a patient, including name, social security number, e-mail address, etc. In cases such as
population-based case-control studies, the latter allows for the de-identification of at least sixteen
identifiers with the retention of such items as zip code, state, or date of birth which may be
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correlated. While there are no restrictions on the release of the de-identified set, the researcher
must enter into a Data Use Agreement for Limited Data Set with the Anytown University Health
System (the covered entity). “These written agreements have to include the specific ways that the
data will be used and protected from improper disclosure by the researcher” (Erlen, 2005).
Informed Consent
Once a participant meets all applicable study eligibility criteria they may be enrolled in
the trial after an IRB necessitated informed consent process, “a process by which potential
participants are informed of the nature of a study, its risks, and benefits, in a way that allows
them to weigh such factors before voluntarily engaging in a study” (Payne, 2012). This
documentation can be stored within Capstone Consulting’s CTMS.
“The Privacy Rule necessitates that additional information be made available to a
potential subject when protected health information will be obtained for study purposes and that
there be a signed authorization” (Erlen, 2005). Under the Privacy Rule, an Authorization may be
combined with the informed consent document for research. If the informed consent document is
combined with an Authorization meeting the Privacy Rule's requirements, 45 CFR part 46 and/or
21 CFR parts 50 and 56 would require IRB review of the combined document (U.S. Department
of Health and Human Services National Institutes of Health, 2003).
Capstone Consulting’s CTMS centralizes access not only to study data but also allows for
informed consent form tracking (ADCS Clinical Trial Management, 2013) and re-consenting
management (Forte Research Systems, 2012).
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Financial Considerations
Clinical Research Costs
The costs for clinical research have been increasing at a staggering 7.4% annually over
inflation to the tune of roughly $800,000,000 per approved drug (DiMasi et al., 2003). While
these estimates have been questioned by other researchers due to possible conflicts of interests,
additional studies have replicated the results and even suggest that those estimates may be
conservative. One such paper produced by the Federal Trade Commission suggests that Phase III
clinical trials cost on average $27m per year for new drugs (Adams et al., 2010). Specific cost
estimates for research related to clinical practice interventions, outcomes, and care quality
measures are more difficult to determine. Although clinical research is more than drug or
medical device trials, those statistics illustrate the cost issue and lack of efficiency in general in
clinical researches. Developing strategies to improve efficiency through site and personnel
management and electronic data capture can dramatically lower costs. The short term benefit is
increased return on investment for research institutions and hospitals, but there may even be a
long term benefit of reducing clinical trial costs and creating a market for increased research
funding.
Although overhaul of the clinical research systems at healthcare organizations can take
large initial investments, eventual costs savings and revenue growth can offset this cost. With a
robust clinical trial management system, realized savings can come from improved budgeting,
increased research efficiency, and improved billing and inventory management. Electronic
systems can increase trial inclusion and lower subject attrition to ensure a reliable stream of
revenue from clinical trials.
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Initial Cost
With any system enhancement, there are initial costs that must be considered. Especially
with healthcare IT acquisitions, these costs can be dauntingly high. Committing the resources for
funding and staff to develop a clinical trial management system and properly use it is a large
investment, and careful consideration must be given to determine whether or not this investment
is the right solution for AHS. Table A reviews the budget for the first-year acquisition of a
clinical trial management system (SimpleCTMS, 2010). Implementation costs money. Beyond
the obvious costs in software acquisition and licensing paid to the vendor, employees must be
trained properly, additional staff may need to be hired to use or maintain the software, and data
must be migrated and integrated with existing systems. Proper planning and working with vendor
provided consulting teams can mitigate these costs and improve efficiency. Key stakeholders
must also test the system to make sure it is improving productivity in specified areas to ensure
that gaps are properly attended to early in the process with vendor support.
Table A: First Year Budget Analysis for CTMS
Type
Analysis
Initial software Depending on the needs of an organization, these costs
acquisition and can vary greatly. For an enterprise system that is
setup
maintained by an organization, there are additional costs.
Software installation, hardware and other infrastructure
improvements, and in-house staff may be required.
Implementation and training costs can also be quite
expensive. Choosing the right software and process for
an organization can change pricing.
Estimated Cost
$30,000 - $200,000
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Licensing
The size of the organization can greatly affect the price.
$1,500 annual per-
Larger organizations need more accessibility and more
user fee
users, which may affect licensing costs. Ongoing
subscription costs must be negotiated carefully based on
the needs of the organization.
Training
Training packages are commonly included in the initial
$10,000 - $50,000
setup cost. Depending on the vendor, this process may
be quite expensive. Exhaustive training requires hiring
of personnel to train staff, but lower cost training may
result in lower proficiency or higher employee
commitment. Cost is also determined by loss of
productivity during training.
Support and
Some vendors may not even supply support and
Maintenance
maintenance while others may charge a staggering sum.
$25,000 – $50,000
Costs can come from a per case basis or a fixed support
cost. These costs are ongoing and will continue
throughout the lifecycle of the software use. Contracting
for 24/7 support is also an option that will increase cost
Data
Interfacing with the existing software (EHR, HR,
Migration
Accounting, Billing) and migrating pertinent data must
$50,000 - $150,000
be done for the system to work properly
Contract
Though not an initial cost, a CTMS will require ongoing
Commitment
contractual commitment to a vendor.
Total
The initial cost is daunting, but the return on investment
$200,000 -
could be much more. For larger organizations, the low-
$500,000
end estimates may not be attainable.
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Savings and Earnings
With a CTMS in place, clinical trial functions become more efficient and can translate
into large cost savings. Capstone Consulting recognizes that for an investment to be made, the
return on investment must justify the cost. While each healthcare organization is different, we
believe that in utilizing the CTMS, the ease of management for clinical research will make
Anytown University Health System a leader in clinical research and ultimately increase
profitability.
Understanding the clinical research process better ensures that the appropriate resources
are put into it. With a CTMS in place, contracting and negotiation with government or private
payers is handled more effectively. Better information is available regarding revenues associated
with each type of clinical trial and each payer and can help AHS decide who to do business with.
Accurate cost and budget analysis from existing clinical trials can help determine important
negotiation points that more adequate cover the cost of the trial (Stier, 2011). Easier budget
negotiations and contracts can also conceivably increase the number of contracts with better
reimbursement deals. The same functions from the CTMS can address billing issues once the
clinical trial has begun. The CTMS can assist in tracking materials and assist in invoicing trial
sponsors for goods and services. Rather than having research personnel meticulously tracking
and billing, the CTMS system will provide automated processes that will ensure more attention
is focused on clinical research. Prebuilt alerts can also assist providers in understanding which
portions of the research are covered by sponsors and what, if any, sources of income can come
from billing private insurers or Medicare. Charting procedures properly and billing for them
hassle-free will drive down the operational costs of research and ensure that no revenue is lost
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(Stier, 2011). Managing the clinical trials from an administrative perspective becomes simple
and driven by protocol.
Efficiency in operating clinical trials can also be increased dramatically. Most
organizations have many FTEs supporting clinical research in completely inefficient ways.
Manually handling data, invoices, coverage analysis, and billing requires time and effort that can
be reduced with the adoption of a CTMS (Stier, 2011). While these will still be considerations,
research teams will have the benefit of saving time and focusing attention to more important
details more directly related to research. Automated participant identification will also
dramatically increase the efficiency of research teams. This automated process can decrease
costs from advertising for participants, choosing the wrong patients or missing enrollment
periods due to lack of participants. The Holston Medical Group recently suggested that the
participant recruitment benefit alone increased clinical trial revenues by over $2M annually for
their organization (Miller, 2006).
By realizing the full potential for how to more sensibly approach clinical trial
management, AHS can cut costs significantly. The initial investment is undoubtedly large and
entails some risk, but the benefits greatly outweigh the costs. Some estimates put cost savings
from clinical trial redesign suggest that costs for clinical trials in all phases can be reduced by
59% (Eisenstein et al., 2008). “Most large research programs using a good CTMS can justify
their investment as a result of improved research service reimbursement alone — usually inside a
year or two” (McIlwain, 2004). In just a few years’ time, the return on investment for AHS will
more than cover the initial costs and the overhaul of the system and will continue to produce
revenue and lower costs.
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Staffing Concerns
The question remains whether or not AHS will require the addition of full-time
equivalents and what expertise they may require. Our estimate is that in its initial phases, staff
increase costs can range from $60,000 - $200,000 in the first year, but will soon taper off to
nothing within 3 years of implementation.
Every department, from management to billing to care providers, will need a designated
super-user to ensure that all involved use the CTMS to its full functionality. This may not
necessarily mean a staff increase, as current employees can be designated as super-users, but it is
likely that three to four new FTEs will be needed at early stages of implementation. These
employees must be familiar with clinical research management, but the level of expertise they
will need can be limited, as they will simply be working with existing staff to convert a manual
process into a software solution. Choosing a correct system—such as the proposed CTMS—that
has friendly interfaces and intuitive functionality may reduce this burden.
The administrative burden that is taken off of research teams eventually translates into
providing the time needed to adequately learn and utilize the software. Especially because billing
and other financial concerns were manually and inefficiently handled, there is a possibility that
improving the system will actually reduce staffing needs in the long-term. Capstone Consulting
estimates that the cost for employees added during early stages of implementation will be offset
by one of two possibilities: either the system becomes much more efficient and less research
staff are necessary; or the management system increases research capacity and the increased
revenue pays for those additional employees.
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Funding
Clinical Capstone understands that undertaking such a large project will require vast
amounts of funding and has made recommendations to find sources of funding that will offset
the initial costs:
●
The National Institutes of Health has offered a grant that is meant specifically to fund the
acquisition and development of clinical training management systems by healthcare
organizations. The NIH R34 Grant funds the “development of tools for data management
and research oversight” for an organization. This grant can fund up to $100,000 of the
costs. Additional details for the application process can be found at
http://grants.nih.gov/grants/funding/r34.htm.
●
The Centers for Medicare & Medicaid Services Medicare EHR Incentive Program
provides hospitals with a base of $2M in incentives for eligible hospitals. Although a
majority of that funding goes to offset the costs of EHR introduction and deployment,
many of these costs are associated with building the infrastructure to support the EHR.
The same infrastructure improvements may in fact be ample to deploy the CTMS, which
may lower the estimated cost for implementation of a CTMS. Once the Meaningful Use
incentive payments have repaid the cost of the EHR, it is possible to divert some of these
funds into new projects, including the CTMS project.
●
The Patient Protection and Affordable Care Act also guarantees by 2014 that insurers will
not have the option of not covering patients who enter into clinical trials. Whereas this
may have been a cost associated with clinical trials in the past, in the future these patients
will still be insured and will have access to the same billable medical services. The more
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participants in clinical trials, the more these savings will be realized and can be used to
cover costs of the CTMS.
Conclusion
Capstone Consulting is eager to assist Anytown University Hospital System in this
endeavor to restructure Clinical Research to meet the needs of the changing healthcare
environment. Our team is dedicated to making the transition seamless and ensuring that Anytown
University Hospital System is equipped to handle the challenges that lie ahead. Driving
innovation through use of emerging technologies is no small feat, but it is our belief that AHS is
ready to take this step forward. We believe that this investment in the Hospital System will foster
an attitude of efficiency, improve relations with patients and the community, contribute to the
accumulation of medical knowledge with needed efficiency, and help AHS grow as a leader in
healthcare through clinical researches that produce better evidences and gap-filling interventions.
Ultimately, it will help AHS and the medical community serve its population to create better
health outcomes by advancing evidence-based medicine. It is simply an opportunity that should
not be missed.
Clinical Research Redesign
49
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Appendix A: Supported CTMS Standards
Standard
Description
CDASH
“The [CDASH] standard describes the basic recommended data
collection fields for 18 domains; these include common header fields,
demographic, adverse events, and other safety domains that are
common to all therapeutic areas and phases of clinical research”
(CDISC CDASH, 2013).
LAB
LAB is a CDISC-developed standard specification defining
requirements for interchange of laboratory data.
SEND
The Standard for Exchange of Nonclinical Data (SEND) is a
standardized format which defines the structure and format of
nonclinical datasets for purposes of exchange.
ADaM
The Analysis Data Model (ADaM) is a standard developed to
facilitate the transfer of datasets between research organizations,
partners, regulatory agencies and independent monitoring committees
(CDISC ADaM, 2013).
XML
“XML is a standard, simple, self-describing way of encoding both
text and data so that content can be processed with relatively little
human intervention and exchanged across diverse hardware,
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operating systems, and applications” (TechCommunity, 2013).
LOINC
LOINC (Logical Observation Identifiers Names and Codes) is a
database and standard for measuring laboratory results. Vreeman, D.
(2010) states LOINC was developed to provide a definitive standard
for identifying clinical observation in electronic reports. This
standard has been designated for use in the U. S. Federal Government
systems for the exchange of clinical health information, (U.S.
National Library of Medicine)
SNOMED-CT
SNOMED-CT (Systemized Nomenclature of Medicine-Clinical
Terms), according to the International Health Terminology Standards
Development Organization, is the most comprehensive, multilingual
healthcare terminology in the world . This standard is able to crossmap to other international standards and is used in more than fifty
countries. SNOMED can assist in recording, storing and retrieving
data within the EMR as well (Nunnery, 2012).
RxNorm
According to the National Library of Medicine, RxNorm provides
normalized names for clinical drugs and links its names to many of
the drug vocabularies commonly used in pharmacies. NLM adds that
RxNorm now includes the National Drug File-Reference
Terminology (NDF-RT) from the Veterans Health Administration
(National Library of Medicine).
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DICOM
DICOM is designed to create interoperability of systems used to
produce, store, retrieve and view medical images. This standard
ensures interoperability among many medical departments and units.
Examples include radiology, cardiology and neurology (Nunnery,
2012).
HL-7
HL-7 provides a comprehensive framework and related standards for
the exchange, integration, sharing, and retrieval of electronic health
information (Health Level 7 International). HL-7 standards define
how information is packaged and communicated from one party to
another, setting the language, structure and data types required for
seamless integration between systems (Nunnery, 2012).
CCD
A continuity of care document is an electronic summary of all of a
patient’s clinical information. This standard provides physicians with
the ability to share a patient’s medical history and current condition
in a comprehensive representation. CCD is typically used in among
other capacities, emergency departments. CCD is one of two formats
required by the government to achieve meaningful use (Astin, 2012).
WSDL
WSDL, Web Services Description Language, is an XML-based
format for facilitating the access of network services.
ICD-9-CM
“The International Classification of Diseases, Ninth Revision,
Clinical Modification (ICD-9-CM) is based on the World Health
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Organization's Ninth Revision, International Classification of
Diseases (ICD-9). ICD-9-CM is the official system of assigning
codes to diagnoses and procedures associated with hospital utilization
in the United States. The ICD-9 was used to code and classify
mortality data from death certificates until 1999, when use of ICD-10
for mortality coding started” (CDC, 2012).
CPT
Current Procedural Terminology (CPT) codes are developed and
maintained by the American Medical Association (AMA). According
to the AMA, CPT codes are “the most widely accepted medical
nomenclature used to report medical procedures and services under
public and private health insurance programs” (AMA, 2012).
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Appendix B: Regulatory Compliance
The Health Insurance Portability and Accountability Act of 1996 (HIPAA)
The Privacy Rule (derived from 45 CFR x160 and x164), which specifically addresses
security and privacy considerations regarding individually identifiable patient
information, referred to as Protected Health Information (PHI) by the Privacy Rule. The
Privacy Rule requirements apply to any research that requires PHI, whether it is federally
related or not. (Ramachandran & Kheterpal, 2011)
The Privacy Rule permits a covered entity to use or disclose PHI for research under the
following circumstances and conditions:
●
If the subject of the PHI has granted specific written permission through an
Authorization that satisfies section 164.508
●
For reviews preparatory to research with representations obtained from the
researcher that satisfy section 164.512(i)(1)(ii) of the Privacy Rule
●
For research solely on decedents' information with certain representations and, if
requested, documentation obtained from the researcher that satisfies section
164.512(i)(1)(iii) of the Privacy Rule
●
If the covered entity receives appropriate documentation that an IRB or a Privacy
Board has granted a waiver of the Authorization requirement that satisfies section
164.512(i)
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●
If the covered entity obtains documentation of an IRB or Privacy Board's
alteration of the Authorization requirement as well as the altered Authorization
from the individual
●
If the PHI has been de-identified in accordance with the standards set by the
Privacy Rule at section 164.514(a)-(c) (in which case, the health information is no
longer PHI)
●
If the information is released in the form of a limited data set, with certain
identifiers removed and with a data use agreement between the researcher and the
covered entity, as specified under section 164.514(e)
●
Under a "grandfathered" informed consent of the individual to participate in the
research, an IRB waiver of such informed consent, or Authorization or other
express legal permission to use or disclose the information for research as
specified under the transition provisions of the Privacy Rule at section 164.532(c)
(U.S. Department of Health and Human Services National Institutes of Health,
2004)
The Security Standards for the Protection of Electronic Protected Health Information (the
Security Rule) establish a national set of security standards for protecting certain health
information that is held or transferred in electronic form. The Security Rule
operationalizes the protections contained in the Privacy Rule by addressing the technical
and non-technical safeguards that organizations called “covered entities” must put in
place to secure individuals’ “electronic protected health information” (e-PHI). (U.S.
Department of Health and Human Services, 2003)
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Office for Human Research Protections (OHRP) Human Subjects Protection
45 CFR Part 46 - Federal Policy for the Protection of Human Subjects or the
“Common Rule”
The Common Rule, derived from subpart A of 45 CFR x46. This Common Rule is used
as an overarching regulatory principle governing human subjects research conducted,
supported, or otherwise subject to regulation by any federal department or agency “45
CFR x46.101(a),” and it includes requirements for IRB review and patient informed
consent. Although the Common Rule only regulates federally supported research, many
academic medical centers apply the Common Rule to all research by policy.
(Ramachandran & Kheterpal, 2011)
FDA Protection of Human Subjects
21 CFR Part 50 - PROTECTION OF HUMAN SUBJECTS
This regulation applies to all clinical investigations regulated by the Food and Drug
Administration. Subpart B addresses informed consent of human subjects. Some of the
key elements addressed within subpart B include the following:
●
General requirements of informed consent
●
Exceptions from the general requirements
●
Exceptions from informed consent requirements for emergency research
●
Elements of informed consent
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●
Documentation of informed consent (University of Connecticut Health Center
Human Subjects Protection Office, 2013)
21 CFR Part 56 - INSTITUTIONAL REVIEW BOARDS
This regulation is very similar to 45 CFR 46 in that it addresses several of the same key
elements, including:
●
IRB membership
●
IRB functions and operations
●
IRB review of research
●
Expedited review procedures
●
Criteria for IRB approval (University of Connecticut Health Center Human
Subjects Protection Office, 2013)
FDA guidelines on electronic records and electronic signatures
21 CFR Part 11 - ELECTRONIC RECORDS; ELECTRONIC SIGNATURES
21 CFR 11 consists of FDA regulations for electronic records and electronic signatures to
be considered trustworthy and equivalent to paper records and handwritten signatures.
Part 11 requires various controls, including audits and validation systems, to be
implemented as part of a regulated entity's operations. (Manion, Robbins, Weems, &
Crowley, 2009)
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Appendix C: Annotated Bibliography
Adams, C. P., & Brantner, V. V. (2010). Spending on new drug development1.Health
Economics, 19(2), 130-141.
This paper discusses the costs of drug development and specifically addresses costs of
clinical trials from Phase I through Phase III. It seeks to replicate the DiMasi study from
2003 to determine if the numbers provided by DiMasi et al. are appropriate estimates for
the cost of research and clinical trials. The paper suggests that the costs discussed in
DiMasi are realistic and that costs run as high as $70-80M annually for research.
ADCS Clinical Trial Management. (2013). ADCS Clinical Trial Management. Retrieved May,
11, 2013, from https://adcs.ucsd.edu/default.aspx
The ADCS Clinical Trial Management System (CTMS) is a flexible, scalable, and secure
web-based software solution which empowers the ADCS to demonstrate Good Clinical
Practice (GCP) and manage all aspects of clinical trial activities including Regulatory
Affairs and Ethics, Trial Master Files, Clinical Monitoring and Safety, Laboratory and
Biospecimen Information, Supply Management, Site Payments, and Study Document
Management. By leveraging a strong business process management approach, these
solutions improve inspection readiness and allow the ADCS to expedite clinical trial
operations in a more transparent, efficient and compliant manner. The CTMS allows
users to seamlessly access EDC data through a built-in module which imports data
collected through the data portals to the CTMS data warehouse.
Mentions solution for Informed Consent Tracking.
Bruland P, Breil B, Fritz F, Dugas M. (2012). Interoperability in clinical research: from
metadata registries to semantically annotated CDISC ODM. Stud Health Technol
Inform. 2012;180:564-8.
Planning case report forms for data capture in clinical trials is a labor-insensitive and not
formalized process. These CRFs are often neither standardized nor using defined data
elements. Metadata registries as the NCI caDSR provide the capability to create forms
based on common data elements. However, an exchange of these forms into clinical trial
management systems through a standardized format like CDISC ODM is currently not
offered. Thus, our objectives were to develop a mapping model between NCI forms and
ODM. We analyzed 3012 NCI forms and included common data elements regarding their
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frequency and uniqueness. In this paper, we have created a mapping model between both
formats and identified limitations in the conversion process: Semantic codes requested
from the caDSR registry did not allow a proper mapping to ODM items and information
like the number of module repetitions got lost. Summarized, it can be stated that our
mapping model is feasible. However, mapping of semantic concepts in ODM needs to be
specified more precisely.
Califf, R., Sanderson, I., & Miranda, M. (2012). The future of cardiovascular clinical
research: Informatics, clinical investigators, and community engagement. JAMA,
308(17), 1747-1748. doi: 10.1001/jama.2012.28745
Debate continues about how current EHR user interfaces help or hinder patient outcomes.
However, the overall efficiency of medicine and the rational basis for decision making
will be enhanced as the practical issues of integrating data, information, and knowledge
into clinical care and public health practice are solved. Two projects provide examples,
integrating clinical and geospatially mapped data with the purpose of improving
individual and population health in geographically defined regions. Project One: Use
geospatial methods to connect clinical data from Duke Medicine, the Durham County
Health Department, and Lincoln Community Health Center (Durham’s Federally
Qualified Health Center) with data on housing, neighborhoods, social stressors,
environmental exposures, and culture. Project Two: Similar approach, focusing on adults
living with type 2 diabetes mellitus and extends the work to other counties in North
Carolina, Mississippi, and West Virginia. Projects leverage informatics platforms to
understand phenotypic and geographic patterns of diabetes and its outcomes, with
detailed analysis of community-based care interventions at the individual and
neighborhood scale in areas characterized by the highest risk of adverse outcomes. “If all
Americans have an EHR that supports individual care, and data are collected using
common standards and housed in data warehouses jointly owned by health care delivery
systems and local communities, this resource could be used to design and conduct health
interventions; investigate the intersection of biology, culture, and environment; and
provide a continuous learning environment.”
Cascade E, Marr P, Winslow M, Burgess A, Nixon M. (2012). Conducting research on
the Internet: medical record data integration with patient-reported outcomes. J Med
Internet Res. 2012 Oct 11;14(5):e137.
BACKGROUND: The growth in the number of patients seeking health information
online has given rise to new direct-to-patient research methods, including direct patient
recruitment and study conduct without use of physician sites. While such patient-centric
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designs offer time and cost efficiencies, the absence of physician-reported data is a key
concern, with potential impact on both scientific rigor and operational feasibility.
OBJECTIVE: To (1) gain insight into the viability of collecting patient-reported
outcomes and medical record information in a sample of gout patients through a directto-patient approach (ie, without the involvement of physician sites), and (2) evaluate the
validity of patient-reported diagnoses collected during a patient-reported outcomes plus
medical record (PRO+MR) direct-to-patient study.
METHODS: We invited a random sample of MediGuard.org members aged 18 to 80
years to participate via email based on a gout treatment or diagnosis in their online
profiles. Interested members clicked on an email link to access study information,
consent to participate electronically, and be screened for eligibility. The first 50
consenting participants completed an online survey and provided electronic and wet
signatures on medical record release forms for us to obtain medical charts from their
managing physicians.
RESULTS: A total of 108 of 1250 MediGuard.org members (8.64%) accessed study
information before we closed the study at 50 completed surveys. Of these 108 members
who took the screener, 50 (46.3%) completed the study, 19 (17.6%) did not pass the
screening, 5 (4.6%) explicitly declined to participate due to the medical record
requirement, and 34 (31.5%) closed the browser without completing the survey screener.
Ultimately, we obtained 38 of 50 charts (76%): 28 collected using electronic signature
and 10 collected based on wet signature on a paper form. Of the 38 charts, 37 cited a gout
diagnosis (35 charts) or use of a gout medication (2 charts). Only 1 chart lacked any
mention of gout.
CONCLUSIONS: Patients can be recruited directly for observational study designs that
include patient-reported outcomes and medical record data with over 75% data
completeness. Although the validity of self-reported diagnosis is often a concern in
Internet-based studies, in this PRO+MR study pilot, nearly all (37 of 38) charts
confirmed patient-reported data.
CDISC. (Standards, 2013). Standards and Implementations. CDISC website. Retrieved from
http://www.cdisc.org/standards-and-implementations.
CDISC catalyzes productive collaboration to develop industry-wide data standards
enabling the harmonization of clinical data and streamlining research processes from
protocol (study plan) through analysis and reporting, including the use of electronic
health records to facilitate study recruitment, study conduct and the collection of high
quality research data. CDISC standards, implementations and innovations can improve
the time/cost/quality ratio of medical research, to speed the development of safer and
more effective medical products and enable a learning healthcare system.
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CDISC. (STDM, 2013). Study Data Tabulation Model (SDTM). CDISC website. Retrieved
from http://www.cdisc.org/sdtm.
This Study Data Tabulation Model (STDM) standard defines recommended standards for
the submission of data from clinical trials in which medical devices are used. The
document includes seven new domains, developed by a team comprised of medical
device experts, CDISC experts, and the FDA (CDRH and CBER), and represents years of
work by the members of the CDISC Medical Device team.
Choi, B., et al. (2005). “Usability comparison of three clinical trial management systems.” AMIA
Annu Symp Proc.: 921. Retrieved from
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1560441/.
To advise in the selection of a clinical trial management system (CTMS), three three
candidate applications were evaluated. After preliminary analyses, heuristic evaluation
and usability testing to assess system’s usability, Velos eResearch, a commercial CTMS,
had the best usability outcome despite having fewer features in comparison. In the
decision process, the “ease-of-use” aspect was more valued than functionality.
Davidson, S., et al. (2004). Where's the Beef? The Promise and the Reality of Clinical
Documentation. Academic Emergency Medicine. 11(11); 1127-1134.
Physician-generated emergency department clinical documentation (information obtained
from clinician observations and summarized decision processes inclusive of all manner of
electronic systems capturing, storing, and presenting clinical documentation) serves four
purposes: recording of medical care and communication among providers; payment for
hospital and physician; legal defense from medical negligence allegations; and
symptom/disease surveillance, public health, and research functions.
de Lusignan S, Cashman J, Poh N, Michalakidis G, Mason A, Desombre T, Krause P.
(2012). Conducting requirements analyses for research using routinely collected health
data: a model driven approach. Stud Health Technol Inform. 2012;180:1105-7.
BACKGROUND: Medical research increasingly requires the linkage of data from
different sources. Conducting a requirements analysis for a new application is an
established part of software engineering, but rarely reported in the biomedical literature;
and no generic approaches have been published as to how to link heterogeneous health
data.
METHODS: Literature review, followed by a consensus process to define how
requirements for research, using, multiple data sources might be modeled.
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RESULTS: We have developed a requirements analysis: i-ScheDULEs - The first
components of the modeling process are indexing and create a rich picture of the research
study. Secondly, we developed a series of reference models of progressive complexity:
Data flow diagrams (DFD) to define data requirements; unified modeling language
(UML) use case diagrams to capture study specific and governance requirements; and
finally, business process models, using business process modeling notation (BPMN).
DISCUSSION: These requirements and their associated models should become part of
research study protocols.
DiMasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003). The price of innovation: new
estimates of drug development costs. Journal of health economics, 22(2), 151-186.
DiMasi et al. discuss the increases in research costs for new drugs over the last few years.
Each drug is estimated to cost hundreds of millions of dollars for research and
development with the majority of expenditures in Phase III clinical trials. The paper
proposes various reasons for why the costs are so high, such as participant attrition. The
costs differ markedly for drugs that are approved, drugs that are investigational and not
approved, and trends for increasing costs in drug research and development.
Dubey, A. and Wagle, D. (2007). Delivering Software as a Service. The McKinsey Quarterly.
Retrieved from http://ai.kaist.ac.kr/~jkim/cs4892007/Resources/DeliveringSWasaService.pdf.
Traditionally, companies buy software and then install and maintain these applications on
their own machines. That model is giving way to one where companies will buy
subscriptions and access services over the Internet from software developers that host
their own applications.
Eisenstein, E. L., Collins, R., Cracknell, B. S., Podesta, O., Reid, E. D., Sandercock, P. & Diaz,
R. (2008). Sensible approaches for reducing clinical trial costs. Clinical Trials, 5(1), 7584.
The paper proposes various solutions to reducing the costs of clinical trials. Eisenstein et
al. suggest that there can be a 59 – 90% reduction in costs for clinical trials with their
methods. Electronic data capture is the most important innovation for lowering costs, but
there are also suggestions for site management that are supposed to reduce costs. The
paper concentrates on large scale clinical trials (the study was of 20,000 patients at 1,000
sites).
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Elliott AF, Davidson A, Lum F, Chiang MF, Saaddine JB, Zhang X, Crews JE, Chou CF.
(2012). Use of electronic health records and administrative data for public health
surveillance of eye health and vision-related conditions in the United States. Am J
Ophthalmol. 2012 Dec;154(6 Suppl):S63-70.
PURPOSE: To discuss the current trend toward greater use of electronic health records
and how these records could enhance public health surveillance of eye health and visionrelated conditions.
DESIGN: Perspective, comparing systems.
METHODS: We describe 3 currently available sources of electronic health data (Kaiser
Permanente, the Veterans Health Administration, and the Centers for Medicare &
Medicaid Services) and how these sources can contribute to a comprehensive vision and
eye health surveillance system.
RESULTS: Each of the 3 sources of electronic health data can contribute meaningfully to
a comprehensive vision and eye health surveillance system, but none currently provide all
the information required. The use of electronic health records for vision and eye health
surveillance has both advantages and disadvantages.
CONCLUSIONS: Electronic health records may provide additional information needed
to create a comprehensive vision and eye health surveillance system. Recommendations
for incorporating electronic health records into such a system are presented.
Embi, P. J., Jain, A., Clark, J., & Harris, C. M. (2005). Development of an electronic health
record-based Clinical Trial Alert system to enhance recruitment at the point of care.
AMIA .. Annual Symposium Proceedings/AMIA Symposium., 231-235.
Clinical trials are essential to the progress of medical science. Physician participation in
trial recruitment is vital, but most do not participate. Few approaches to improve
physician participation in trial recruitment have been described or proven successful.
Previously described approaches have largely relied on locally developed technology or
been designed for use in specialized settings, thereby limiting their generalizability. We
describe the design, operation and initial testing of a new Clinical Trial Alert (CTA)
system built upon the existing capabilities of a commercial EHR in use across a large
academic healthcare system. Given the trend toward implementation of similarly capable
EHRs in institutions engaged in clinical research, this approach should be widely
applicable and may represent a solution to the common problem of inadequate clinical
trial recruitment. Further study of this system is ongoing.
Excellent resource for EHR-integrated clinical trial alerts at the point of care.
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Embi, P. J., Jain, A., & Harris, C. (2008). Physicians' perceptions of an electronic health
record-based clinical trial alert approach to subject recruitment: A survey. BMC Medical
Informatics & Decision Making, 8(1), 1-8. doi:10.1186/1472-6947-8-13
This survey with a response by sixty-nine physicians looked at the decision-making by
physicians for clinical research recruitment in EHR-equipped settings or using EHRbased approaches. Physician perceptions about clinical research recruitment in general
were assessed as well as perceptions to using a Clinical Trial Alert (CTA) built into the
EHR. Conclusions showed that physicians thought an EHR-based CTA approach would
be helpful. The inclusion of an EHR-based CTA approach should be considered in the
clinical research redesign for the academic health system. Perhaps a CTA could be
instrumental in the recruitment of patients for existing studies.
Embi, P. J., & Leonard, A. C. (2012). FOCUS on clinical research informatics: Evaluating alert
fatigue over time to EHR-based clinical trial alerts: findings from a randomized
controlled study. Journal of the American Medical Informatics Association: JAMIA,
19(e1), e145.
Clinical research is a necessary tool to advance medicine, but finding participants that
match the inclusion criteria is difficult. Physicians and other clinicians can assist in
recruiting patients, but many are not aware of the current clinical trials available or
simply do not have the time to participate. This paper studies the use of clinical trial
alerts in an EHR system to see if recruitment rates increase. The major focus of the study
was alert fatigue, and the paper suggests that while alert fatigue did occur, response rates
were still decently high. It seems that a CTA system embedded in an EHR could
reasonably increase recruitment rates, even in spite of alert fatigue. In the randomized
controlled study, physicians responded at a rate of 50%, which dropped off to roughly
35% by 36 months. Even a 35% response rate for clinical trial alerts seems fairly high
and could increase the participants in clinical trials by a considerable amount, especially
if these CTA systems are used ubiquitously by university and hospital EHR systems.
Embi, P. J., & Payne, P. R. (2009). Clinical research informatics: challenges, opportunities and
definition for an emerging domain. [Research Support, N.I.H., Extramural]. Journal of
the American Medical Informatics Association : JAMIA, 16(3), 316-327. doi:
10.1197/jamia.M3005
OBJECTIVES: Clinical Research Informatics, an emerging sub-domain of Biomedical
Informatics, is currently not well defined. A formal description of CRI including major
challenges and opportunities is needed to direct progress in the field. DESIGN: The
authors engaged in series of qualitative studies with key stakeholders and opinion leaders
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to determine the range of challenges and opportunities facing CRI. These phases
employed complimentary methods to triangulate upon our findings. MEASUREMENTS:
Study phases included: 1) a group interview with key stakeholders, 2) an email follow-up
survey with a larger group of self-identified CRI professionals, and 3) validation of our
results via electronic peer-debriefing and member-checking with a group of CRI-related
opinion leaders. Data were collected, transcribed, and organized for formal, independent
content analyses by experienced qualitative investigators, followed by an iterative
process to identify emergent categorizations and thematic descriptions of the data.
RESULTS: We identified a range of challenges and opportunities facing the CRI domain.
These included 13 distinct themes spanning academic, practical, and organizational
aspects of CRI. These findings also informed the development of a formal definition of
CRI and supported further representations that illustrate areas of emphasis critical to
advancing the domain. CONCLUSIONS: CRI has emerged as a distinct discipline that
faces multiple challenges and opportunities. The findings presented summarize those
challenges and opportunities and provide a framework that should help inform next steps
to advance this important new discipline.
Speaks to challenges met by research informatics, especially in the regulatory area.
Erlen, J. A. (2005). HIPAA--Implications for research. Orthopaedic Nursing, 24(2), 139-142.
Privacy, anonymity, and informed consent are the hallmarks of current research conduct.
How do the Health Insurance Portability and Accountability Act regulations regarding
individually identified health information and protected health information affect
research? The purpose of this article is to discuss ways that the Health Insurance
Portability and Accountability Act is influencing the conduct of research, including the
implications for institutional review boards, recruitment of subjects, obtaining consent,
access to data, de-identification of data, authorization to disclose data, and the processing,
transmission, and storage of collected data.
IRB conducted HIPAA training for researchers. Discusses difficulties for recruiting
research subjects imposed by Privacy Rule. Consent documentation lengthier and more
complex. Discusses disclosure authorization vs waiver from the IRB or a privacy board.
The eighteen identifiers that need to be stripped are described and which can be retained
when limited data set are needed. Mentions data use agreement researchers have with the
covered entity.
Etheredge, L. M. (2007). A rapid-learning health system. Health Affairs, 26(2), w107-w118.
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Etheredge discusses using EHR systems and data abstraction to perform research. EHRs
“make it possible to include clinical experience from tens of millions of patients annually
in computer-searchable databases for collaborative research.” Abstractors can test
hypotheses with real data for millions of subjects, including subpopulations that may
react differently to treatments. Etheredge notes that clinical trials will not be replaced, but
rather can be supplemented with this large database of health information. She then
suggests where rapid-learning via EHR records could potentially improve clinical
research and change the evidence base we use to determine best practice methods.
Suggestions include national clinical trial databases, national assessments of new
technologies based on patient outcomes on a large scale, and using the information
available to reassess payment policies for healthcare.
Forte Research Systems. (2012). Winter 2011 Release of Allegro CTMS@Site. Retrieved May
11, 2013, from http://forteresearch.com/news/winter-2011-release-of-allegro-ctmssite/
Has functionality for re-consenting management.
Ganslandt, T., Mate, S., Helbing, K., Sax, U., & Prokosch, H. U. (2008). Unlocking Data for
Clinical Research–The German i2b2 Experience. Methods of Information in Medicine,
47(2), 117-123.
This paper discusses the informatics for integrating biology and bedside (i2b2) project
funded by the NIH. Using data from medical records to do research is fraught with
difficulties. Data that are spread between many disparate systems, from EMR to lab, need
to be consolidated in a single format and de-duplicated for proper use. The i2b2 approach
is a modular approach that supposedly makes querying and exporting data easier and
includes natural language processing to assist further analysis. There are even tools in it
to deidentify information and improve patient protection. In some cases, i2b2 showed an
increase in results when querying for patients in comparison to native SQL, but the
process was 5 to 10 times slower than SQL (due to the relational (SQL) vs single (i2b2)
database models used). The paper also discusses adding CDISC ontology for clinical trial
use of i2b2. I’m not too sure about the i2b2 stuff, but the information about how data is
housed and extracted seems relevant. Embedding CDISC ontology into data queries for
clinical trials to improve extraction of data into standardized formats is also discussed,
though briefly.
Gelfond, J. A., Heitman, E., Pollock, B. H., & Klugman, C. M. (2011). Principles for the ethical
analysis of clinical and translational research. Statistics in Medicine, 30(23), 2785-2792.
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Statistical analysis is a cornerstone of the scientific method and evidence-based medicine,
and statisticians serve an increasingly important role in clinical and translational research
by providing objective evidence concerning the risks and benefits of novel therapeutics.
Researchers rely on statistics and informatics as never before to generate and test
hypotheses and to discover patterns of disease hidden within overwhelming amounts of
data. Too often, clinicians and biomedical scientists are not adequately proficient in
statistics to analyze data or interpret results, and statistical expertise may not be properly
incorporated within the research process. We argue for the ethical imperative of statistical
standards, and we present ten nontechnical principles that form a conceptual framework
for the ethical application of statistics in clinical and translational research. These
principles are drawn from the literature on the ethics of data analysis and the American
Statistical Association Ethical Guidelines for Statistical Practice. Copyright 2011 John
Wiley & Sons, Ltd.
Ethics in Biostatistics. Discusses prevalence of analytical errors and deficiencies and the
harm caused by biased or faulty analysis. Demonstrates the need for statistical expertise
especially with regard to unfamiliar statistical and epidemiological challenges that are
faced potentially leading to invalid conclusions. Speaks to mistakes, negligence, and
ethical violations. Reviews American Statistical Association's ethical guidelines as
applied to clinical and translational research.
Hurdle JF, Smith KR, Mineau GP. (2013). Mining electronic health records: an additional
perspective. Nat Rev Genet. 2013 Jan;14(1):75.
Response to the Jensen & Jensen (2012), offering examples of additional systems
Jensen PB, Jensen LJ, Brunak S. (2012). Mining electronic health records: towards better
research applications and clinical care. Nat Rev Genet. 2012 May 2;13(6):395-405.
Clinical data describing the phenotypes and treatment of patients represents an underused
data source that has much greater research potential than is currently realized. Mining of
electronic health records (EHRs) has the potential for establishing new patientstratification principles and for revealing unknown disease correlations. Integrating EHR
data with genetic data will also give a finer understanding of genotype-phenotype
relationships. However, a broad range of ethical, legal and technical reasons currently
hinder the systematic deposition of these data in EHRs and their mining. Here, we
consider the potential for furthering medical research and clinical care using EHR data
and the challenges that must be overcome before this is a reality.
Summary
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·Electronic health record (EHR) systems are increasingly being implemented all over the
world, but represent a vast, underused data resource for biomedical research.
·Structured EHR data, such as encoded diagnosis and medication information, are the
easiest data sources to process, but advances in text-mining methods has made it
possible to also use the narrative parts of patient records.
· Statistical studies of the distribution and co-occurrence of clinical features in large
collections of patient records enables identification of correlations between, for
example, diseases (comorbidities) or between medications and adverse drug reactions.
· Knowledge-discovery and machine-learning methods can be used both for discovering
novel patterns in patient data and for classification and predictive purposes, such as
outcome or risk assessment. This has the potential to extend current EHR decision
support systems, which integrate available patient data with clinical guidelines to
provide assistance to the physician at the point of care.
· Research platforms built on EHR data, alone or coupled to genotype data, provide an
inexpensive and timely way to sample relevant case and control cohorts based on
relevant clinical features. As EHR and DNA databases become increasingly interlinked,
genotype–phenotype association studies may be designed and conducted by re-using
existing data.
· The growing political focus on the adoption of EHR systems must be accompanied by
funding and strategic research into data standards, interoperability and security. Legal
matters such as data ownership, privacy and consent need to be addressed to find the
right balance between public demands for autonomy and privacy, and manageable
procedures for researchers to access data.
· Fulfilling the full potential of electronic health data for scientific discovery and
improved public health will require collaboration across stakeholders and research
groups.
Kahn, M. G. (2006). Integrating Electronic Health Records and Clinical Trials. Paper presented
at the National Center for Research Resources Workshop: Ensuring the Inclusion of
Clinical Research in the National Health Information Network.
Regulatory issues are confusing and complex. Author’s hospital (TCH)
“standard”ambulatory EMR contains 30-50% of CRF elements from 3 randomly selected
pediatric protocols. List of EHR potential roles in clinical trials front-end and back-end
steps:
- Query EHR database to establish number of potential study candidates. - Incorporate
study manual or special instructions into EHR “clinical content” for study encounters.
- Implement study screening parameters into patient registration and scheduling. - Query
EHR database to contact/recruit potential candidates and notify the patient’s provider(s)
of potential study eligibility.
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- Incorporate study-specific data capture as part of routine clinical care / clinical
documentation workflows. - Auto-populate study data elements into care report forms
from other parts of the EHR database. - Embed study-specific data requirements (case
record forms) as special tabs/documentation templates using structured data entry. Implement rules/alerts to ensure compliance with study data collection requirements. Create range checks and structured documentation checks to ensure valid data entry,
- Provide data extraction formats that support data exchange standards. - Document and
report adverse events.
- Assess congruence of new findings and existing evidence with current practice and
outcomes (incorporate into meta-analyses). - Submit findings to electronic trial banks
using published standards.
- Implement study findings as study findings as clinical documentation, orders sets, pointof-care rules/alerts. - Monitor changes in care and outcomes in response to evidencebased clinical decision support. - Provide easy access to detailed clinical care data for
motivating new clinical trial hypotheses.
Kahn MG, Raebel MA, Glanz JM, Riedlinger K, Steiner JF. (2012). A pragmatic framework for
single-site and multisite data quality assessment in electronic health record-based clinical
research. Med Care. 2012 Jul;50 Suppl:S21-9. doi: 10.1097/MLR.0b013e318257dd67.
INTRODUCTION: Answers to clinical and public health research questions increasingly
require aggregated data from multiple sites. Data from electronic health records and other
clinical sources are useful for such studies, but require stringent quality assessment. Data
quality assessment is particularly important in multisite studies to distinguish true
variations in care from data quality problems. METHODS: We propose a "fit-for-use"
conceptual model for data quality assessment and a process model for planning and
conducting single-site and multisite data quality assessments. These approaches are
illustrated using examples from prior multisite studies. APPROACH: Critical
components of multisite data quality assessment include: thoughtful prioritization of
variables and data quality dimensions for assessment; development and use of
standardized approaches to data quality assessment that can improve data utility over
time; iterative cycles of assessment within and between sites; targeting assessment
toward data domains known to be vulnerable to quality problems; and detailed
documentation of the rationale and outcomes of data quality assessments to inform data
users. The assessment process requires constant communication between site-level data
providers, data coordinating centers, and principal investigators. DISCUSSION: A
conceptually based and systematically executed approach to data quality assessment is
essential to achieve the potential of the electronic revolution in health care. High-quality
data allow "learning health care organizations" to analyze and act on their own
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information, to compare their outcomes to peers, and to address critical scientific
questions from the population perspective.
(General, Current Status)
Kahn, M. G., & Weng, C. (2012). Clinical research informatics: a conceptual perspective. J Am
Med Inform Assoc, published on-line April 20, 2012, doi: 10.1136/amiajnl-2012-000968,
http://jamia.bmj.com/content/early/2012/04/19/amiajnl-2012-000968.full.html
Clinical research informatics is the rapidly evolving sub-discipline within biomedical
informatics that focuses on developing new informatics theories, tools, and solutions to
accelerate the full translational continuum: basic research to clinical trials (T1), clinical
trials to academic health center practice (T2), diffusion and implementation to
community practice (T3), and ‘real world’ outcomes (T4). Figure 1 is a conceptual model
consisting of an informatics-enabled clinical research workflow, integration across
heterogeneous data sources, and core informatics tools and platforms.
Kawamoto K ., et al. (2005). Improving clinical practice using clinical decision support systems:
a systematic review of trials to identify features critical to success. BMJ 2005;330:765.
Several features were closely correlated with decision support systems' ability to improve
patient care significantly. Clinicians and other stakeholders should implement clinical
decision support systems that incorporate these features whenever feasible and
appropriate.
Köpcke, F., Trinczek, B., Majeed, R. W., Schreiweis, B., Wenk, J., Leusch, T., & ... Prokosch,
H.
(2013). Evaluation of data completeness in the electronic health record for the purpose of
patient recruitment into clinical trials: a retrospective analysis of element presence. BMC
Medical Informatics & Decision Making, 13(1), 1-8. doi:10.1186/1472-6947-13-37
This study examined eligibility criteria for defined in clinical trial protocols with patient
data available in the EHR. Gaps were found in the existing structure and content of data
documented during patient care and data required for patient eligibility assessment. This
gap could be considered further in proposing a clinical research redesign. Data that is
more consistent with clinical trial criteria and patient documentation could increase
involvement in clinical trials. Decreasing this gap and incorporating something like the
EHR-based Clinical Trial Alert approach (Embi, et al. 2013) could increase knowledge of
clinical trials and provide an easier method for determining patients who are eligible for
specific clinical trials.
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Kuchinke W, Wiegelmann S, Verplancke P, Ohmann C. (2006). Extended cooperation in
clinical studies through exchange of CDISC metadata between different study software
solutions. Methods Inf Med. 2006;45(4):441-6.
OBJECTIVES: Our objectives were to analyze the possibility of an exchange of an entire
clinical study between two different and independent study software solutions. The
question addressed was whether a software-independent transfer of study metadata can be
performed without programming efforts and with software routinely used for clinical
research.
METHODS: Study metadata was transferred with ODM standard (CDISC). Study
software systems employed were MACRO (InferMed) and XTrial (XClinical). For the
Proof of Concept, a test study was created with MACRO and exported as ODM. For
modification and validation of the ODM export file XML-Spy (Altova) and ODMChecker (XML4Pharma) were used.
RESULTS: Through exchange of a complete clinical study between two different study
software solutions, a Proof of Concept of the technical feasibility of a systemindependent metadata exchange was conducted successfully. The interchange of study
metadata between two different systems at different centers was performed with minimal
expenditure. A small number of mistakes had to be corrected in order to generate a
syntactically correct ODM file and a "vendor extension" had to be inserted. After these
modifications, XTrial exhibited the study, including all data fields, correctly. However,
the optical appearance of both CRFs (case report forms) was different.
CONCLUSIONS: ODM can be used as an exchange format for clinical studies between
different study software. Thus, new forms of cooperation through exchange of metadata
seem possible, for example the joint creation of electronic study protocols or CRFs at
different research centers. Although the ODM standard represents a clinical study
completely, it contains no information about the representation of data fields in CRFs.
Leroux, H. et al. (2011). "On selecting a clinical trial management system for
large scale, multi-centre, multi-modal clinical research study". Studies in Health
Technology and Informatics 168: 89–95. PMID 21893916. Retrieved from
http://www.ncbi.nlm.nih.gov/pubmed/21893916.
Clinical research studies offer many challenges for their supporting information systems.
This paper discusses the shortcomings of the clinical trial management system chosen to
record the results of a study and a set of guidelines was devised and a critique of five
systems ensued. The paper concluded that open-source CTMS are viable alternatives to
the more expensive commercial systems to conduct, record and manage clinical studies.
Existing data issues, data from disparate source systems, and heterogeneity of data: data
quality and integrity, usability, flexibility, lack of audit trail and defects introduction.
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Systems evaluated:
Oracle Clinical – integrated system rich functionality
Medidata Rave -- SaaS
Phase Forward InForm -- SOA
DADOS-Prospective – modular solutions
OpenClinica -- Mainly electronic data capture, eCRF
Criteria used:
1- security measures
2- audit trail
3- consistent use of clinical terminology /data validation check at input
4- subject identifiable information
5- backup and recovery
6- versatile data field types
7- data extraction and the ability to generate reports
8- cost
9- development efforts
10- type of licensing (open source or proprietary, etc)
11- industry standards (CDISC)
Speaks to the infrastructure to support FDA 21 CFR 11.
Liao, K., et al. (2010). Electronic medical records for discovery research in rheumatoid arthritis.
Arthritis Care & Research. 62(8); 1120-1127.
Electronic medical records (EMRs) are a rich data source for discovery research but are
underutilized due to the difficulty of extracting highly accurate clinical data. We assessed
whether a classification algorithm incorporating narrative EMR data (typed physician
notes) more accurately classifies subjects with rheumatoid arthritis (RA) compared with
an algorithm using codified EMR data alone.
Manion, F. J., Robbins, R. J., Weems, W. A., & Crowley, R. S. (2009). Security and privacy
requirements for a multi-institutional cancer research data grid: an interview-based study.
BMC Medical Informatics & Decision Making, 9, 31.
BACKGROUND: Data protection is important for all information systems that deal with
human-subjects data. Grid-based systems--such as the cancer Biomedical Informatics
Grid (caBIG)--seek to develop new mechanisms to facilitate real-time federation of
cancer-relevant data sources, including sources protected under a variety of regulatory
laws, such as HIPAA and 21CFR11. These systems embody new models for data sharing,
and hence pose new challenges to the regulatory community, and to those who would
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develop or adopt them. These challenges must be understood by both systems developers
and system adopters. In this paper, we describe our work collecting policy statements,
expectations, and requirements from regulatory decision makers at academic cancer
centers in the United States. We use these statements to examine fundamental
assumptions regarding data sharing using data federations and grid computing.
METHODS: An interview-based study of key stakeholders from a sample of US cancer
centers. Interviews were structured, and used an instrument that was developed for the
purpose of this study. The instrument included a set of problem scenarios--difficult policy
situations that were derived during a full-day discussion of potentially problematic issues
by a set of project participants with diverse expertise. Each problem scenario included a
set of open-ended questions that were designed to elucidate stakeholder opinions and
concerns. Interviews were transcribed verbatim and used for both qualitative and
quantitative analysis. For quantitative analysis, data was aggregated at the individual or
institutional unit of analysis, depending on the specific interview question.
RESULTS: Thirty-one (31) individuals at six cancer centers were contacted to
participate. Twenty-four out of thirty-one (24/31) individuals responded to our requestyielding a total response rate of 77%. Respondents included IRB directors and policymakers, privacy and security officers, directors of offices of research, information
security officers and university legal counsel. Nineteen total interviews were conducted
over a period of 16 weeks. Respondents provided answers for all four scenarios (a total of
87 questions). Results were grouped by broad themes, including among others:
governance, legal and financial issues, partnership agreements, de-identification,
institutional technical infrastructure for security and privacy protection, training, risk
management, auditing, IRB issues, and patient/subject consent.
CONCLUSION: The findings suggest that with additional work, large scale federated
sharing of data within a regulated environment is possible. A key challenge is developing
suitable models for authentication and authorization practices within a federated
environment. Authentication--the recognition and validation of a person's identity--is in
fact a global property of such systems, while authorization - the permission to access data
or resources--mimics data sharing agreements in being best served at a local level. Nine
specific recommendations result from the work and are discussed in detail. These
include: (1) the necessity to construct separate legal or corporate entities for governance
of federated sharing initiatives on this scale; (2) consensus on the treatment of foreign
and commercial partnerships; (3) the development of risk models and risk management
processes; (4) development of technical infrastructure to support the credentialing process
associated with research including human subjects; (5) exploring the feasibility of
developing large-scale, federated honest broker approaches; (6) the development of
suitable, federated identity provisioning processes to support federated authentication and
authorization; (7) community development of requisite HIPAA and research ethics
training modules by federation members; (8) the recognition of the need for central
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auditing requirements and authority, and; (9) use of two-protocol data exchange models
where possible in the federation.
Describes 21 CFR 11 in a concise manner.
Marler JR. (2012). A strategic plan to accelerate development of acute stroke treatments.
Ann NY Acad Sci. 2012 Sep;1268:152-6.
In order to reenergize acute stroke research and accelerate the development of new
treatments, we need to transform the usual design and conduct of clinical trials to test for
small but significant improvements in effectiveness, and treat patients as soon as possible
after stroke onset when treatment effects are most detectable. This requires trials that
include thousands of acute stroke patients. A plan to make these trials possible is
proposed. There are four components: (1) free access to the electronic medical record; (2)
a large stroke emergency network and clinical trial coordinating center connected in real
time to hundreds of emergency departments; (3) a clinical trial technology development
center; and (4) strategic leadership to raise funds, motivate clinicians to participate, and
interact with politicians, insurers, legislators, and other national and international
organizations working to advance the quality of stroke care.
McCarty, C., et al. (2011). The eMERGE Network: A consortium of biorepositories linked to
electronic medical records data for conducting genomic studies. BMC Medical Genomics
2011, 4:13.
The eMERGE (electronic MEdical Records and GEnomics) Network is an NHGRIsupported consortium of five institutions to explore the utility of DNA repositories
coupled to Electronic Medical Record (EMR) systems for advancing discovery in
genome science. eMERGE also includes a special emphasis on the ethical, legal and
social issues related to these endeavors.
McIlwain, J. (2004). Clinical Trial Management Systems (CTMS) System Selection
Considerations. Velos Voice: News and Views for the Next Generation Researcher.
March, 2004. Retrieved from http://velos.com/whitepaper/.
Recognition of the need for clinical research information systems has begun to move into
the mainstream of the investigator market. As a result, questions relating to defining
one’s system requirements and differentiating vendor capabilities are emerging. The
objective of this paper is to provide an informative discussion and guidelines to help
readers clearly define CTMS system needs and evaluate product alternatives.
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Meiman J, Freund JE. (2012). Large data sets in primary care research. Ann Fam Med.
2012 Sep-Oct;10(5):473-4.
“Networked EHRs provide new opportunities for obtaining more comprehensive data
regarding health services received.” “EHR data are gathered for the purposes of health
care delivery, and as such, do not adhere to the rigorous standards of scientific
studies.Although the sheer volume of data can overcome isolated inaccuracies, large
systematic errors can occur.” “Missing data is a common issue with EHRs, and simply
ignoring these gaps can lead to very biased results.”
Miller, J. L. (2006). The EHR solution to clinical trial recruitment in physician groups. Health
management technology, 27(12), 22.
The article describes the use of EHR data mining to recruit participants to clinical trials at
Holston Medical Group. Use of EHR has helped HMG recruit thousands of patients over
the span of a decade into clinical trials. It saves sponsors money because they don’t have
to spend advertising dollars on recruitment. It eliminates the risk that enrollment falls
short of the necessary minimum. It ensures that patients meet all criteria necessary before
recruitment process begins so it requires reduced cost for screening potential applicants
once chosen. These all translate into increased revenue, and the paper puts that estimate
at $2.5M annually for the group (which covers the cost of the research by a large margin).
The speed and accuracy of the recruitment process is greatly enhanced by EHR
screening. Interoperability also affects clinical research because the entire research study
is well documented and adverse events are reported and analyzed. Information gathered
from the research is easily monitored if reported properly in the EHR.
Nadkarni, P. M., Marenco, L. N., & Brandt, C. A. (2012). Clinical Research Information
Systems. In R. L. Richesson & J. E. Andrews (Eds.), Clinical Research Informatics.
London: Springer-Verlag
- Regards Clinical Research Information Systems (CRISs) are a type of specialized
software application which are designed to support clinical research. Emphasizes
distinctions in funcitonality and requirements between CRIS and EMR.
- Presents various CRIS vendor models, including open-source systems.
- Describes issues and workflows unique to clinical research that mandate the use of a
Clinical Research Information System, and distinguish its functionality from that
provided by Electronic Medical Record (EMR) Systems.
- Describes the operations of a CRIS during different phases of a study, including
determining patient recruitment and eligibility, protocol management, patient monitoring
and safety, and analysis and reporting.
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Olson, S. and Downye, A. S., Rapporteurs. (2013). Sharing Clinical Research Data: Workshop
Summary. Washington, D.C.: The National Academies Press. Retrieved from
http://www.nap.edu/catalog.php?record_id=18267.
This prepublication copy summarizes workshop information related to sharing clinical
research data. Benefits and barriers to data sharing are discussed, models of data sharing
are provided, and Standards are considered. A list of current data-sharing initiatives is
also provided. While the data sharing seems to focus on sharing among large
organizations for clinical research purposes, many of the themes are applicable to and
probably should be considered when proposing a clinical research redeisgn for a specific
academic institution.
Ouagne D, Hussain S, Sadou E, Jaulent MC, Daniel C. (2012). The Electronic
Healthcare Record for Clinical Research (EHR4CR) information model and terminology.
Stud Health Technol Inform. 2012;180:534-8.
A major barrier to repurposing routinely collected data for clinical research is the
heterogeneity of healthcare information systems. Electronic Healthcare Record for
Clinical Research (EHR4CR) is a European platform designed to improve the efficiency
of conducting clinical trials. In this paper, we propose an initial architecture of the
EHR4CR Semantic Interoperability Framework. We used a model-driven engineering
approach to build a reference HL7-based multidimensional model bound to a set of
reference clinical terminologies acting as a global as view model. We then conducted an
evaluation of its expressiveness for patient eligibility. The EHR4CR information model
consists in one fact table dedicated to clinical statement and 4 dimensions. The EHR4CR
terminology integrates reference terminologies used in patient care (e.g LOINC, ICD-10,
SNOMED CT, etc). We used the Object Constraint Language (OCL) to represent patterns
of eligibility criteria as constraints on the EHR4CR model to be further transformed in
SQL statements executed on different clinical data warehouses.
Papazoglou, M. (Dec. 2003). Service-oriented computing: concepts, characteristics and
directions. Web Information Systems Engineering, 2003. WISE 2003. Proceedings of the
Fourth International Conference. Dec. 10-12, 2003, pp. 3-12. Retrieved from
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1254461.
Service-oriented computing (SOC) is the computing paradigm that utilizes services as
fundamental elements for developing applications/solutions. To build the service model,
SOC relies on the service oriented architecture (SOA), which is a way of reorganizing
software applications and infrastructure into a set of interacting services. However, the
basic SOA does not address overarching concerns such as management, service
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orchestration, service transaction management and coordination, security, and other
concerns that apply to all components in a service architecture. This paper introduces an
extended service oriented architecture that provides separate tiers for composing and
coordinating services and for managing services in an open marketplace by employing
grid services.
Payne, P., et al. (2005). Breaking the Translational Barriers: The Value of Integrating
Biomedical Informatics and Translational Research. Journal of Investigative Medicine.
53(4); 192-201.
The conduct of translational health research has become a vital national enterprise.
However, multiple barriers prevent the effective translation of basic science discoveries
into clinical and community practice. New information technology (IT) applications
could help address these barriers. Unfortunately, owing to a combination of
organizational, technical, and social factors, neither physician‐investigators and research
staff nor their clinical and community counterparts have harnessed such applications.
Recently, at the request of the Institute of Medicine's Clinical Research Roundtable, a
qualitative study of these factors was conducted at several leading academic medical
centers. The current status of IT in the translational research domain is explored, the
qualitative results are described and a proposed set of initiatives to further increase the
integration of IT into translational research is presented.
Payne, P. R. O. (2012). The Clinical Research Environment. In R. L. Richesson & J. E.
Andrews (Eds.), Clinical Research Informatics. London: Springer-Verlag.
- Describes clinical research processes, stakeholders, actors, and goals.
- Clinical research is an information and resource intensive endeavor, incorporating a
broad variety of stakeholders spanning from patients to providers to policymakers.
Increasingly, the modern clinical research environment incorporates a number of
informatics methods and technologies, informed by socio-technical and informationtheoretic frameworks.
- Challenges in clinical research workflow: paper-based information management,
complex technical and communication processes; interruptions due to the nature in the
environment or setting; single point of information exchange.
- Trends in research funding: large-scale research consortia; shift to community practice
and global setting.
- Describes informed consent in a concise manner.
Price RC, Huth D, Smith J, Harper S, Pace W, Pulver G, Kahn MG, Schilling LM, Facelli
JC. (2012). Federated queries for comparative effectiveness research: performance
analysis. Stud Health Technol Inform. 2012;175:9-18.
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This paper presents a study of the performance of federated queries implemented in a
system that simulates the architecture proposed for the Scalable Architecture for
Federated Translational Inquiries Network (SAFTINet). Performance tests were
conducted using both physical hardware and virtual machines within the test laboratory
of the Center for High Performance Computing at the University of Utah. Tests were
performed on SAFTINet networks ranging from 4 to 32 nodes with databases containing
synthetic data for several million patients. The results show that the caGrid FQE
(Federated Query Engine) is capable and suitable for comparative effectiveness research
(CER) federated queries given its nearly linear scalability as partner nodes increase in
number. The results presented here are also important for the specification of the
hardware required to run a CER grid.
Ramachandran, S. K., & Kheterpal, S. (2011). Outcomes research using quality improvement
databases: evolving opportunities and challenges. Anesthesiology Clinics, 29(1), 71-81.
The challenges to prospective randomized controlled trials have necessitated the
exploration of observational data sets that support research into the predictors and
modulators of preoperative adverse events. The primary purpose and design of quality
improvement databases is quality assessment and improvement at the local, regional, or
national level. However, these data can also provide the opportunity to robustly study
specific questions related to patient outcomes with no additional clinical risk to the
patient. The virtual explosion of anesthesia-related registries has opened seemingly
limitless opportunities for outcomes research in addition to generating hypothesis for
more rigorous prospective analysis. Copyright 2011 Elsevier Inc. All rights reserved.
QI research. Mentions The Belmont Report’s 3 fundamental ethical principles of human
subject research. Speaks to federal regulations including Privacy Rule and Common
Rule. Discusses 3 questions to determine when IRB review and patient consent are
required. Mentions handling of missing data and ensuring data integrity.
Richesson, R. L., & Andrews, J. E. (2012). Introduction to Clinical Research Informatics. In R.
L. Richesson & J. E. Andrews (Eds.), Clinical Research Informatics. London: SpringerVerlag.
“The challenges in clinical research – and the opportunities for informatics support –
arise from many different objectives and requirements, including the need for optimal
protocol design, regulatory compliance, sufficient patient recruitment, efficient protocol
management, and data collection and acquisition; data storage, transfer, processing, and
analysis; and impeccable patient safety throughout.”
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Describes conformity with the Good Clinical Practice (GCP) guidelines.
Ross, J. S., & Krumholz, H. M. (2013). Ushering in a new era of open science through data
sharing: the wall must come down. JAMA, 309(13), 1355-1356. doi:
10.1001/jama.2013.1299
- Data Sharing: Sharing maximizes the value of collected data, promoting follow-up
studies of secondary research questions; minimizes duplicative data collection, which in
turn reduces research costs and lowers the burden on human research participants while
positioning clinical trial data as a public good and respecting the contributions of
participating patients.
- Proposed ways to address some of the concerns: Credit giving mechanism, disclosing
intervention and protocol details to prevent misuse misinterpretation; responsible
evaluation of products risks and benefits.
- Funders, such as the Gates Foundation and the National Institutes of Health, start to
adopt policies to promote data sharing. The European Medicines Agency has been
releasing clinical trial reports on request since 2010, and has recently announced that it
will provide full access to complete clinical trial data sets to outside investigators
beginning in 2014.
- Future - Create a culture of that promotes sharing and provides credit to those who do
and consequences for those who do not.
Shankar, R., et al. (2006). “Towards Semantic Interoperability in a Clinical Trials Management
System.” Lecture Notes in Computer Science 4273: 901–912.
Managing a clinical trial from its inception to completion typically involves multiple
disparate applications facilitating activities such as trial design specification, clinical sites
management, participants tracking, and trial data analysis. There remains however a
strong impetus to integrate these diverse applications – each supporting different but
related functions of clinical trial management – at syntactic and semantic levels so as to
improve clarity, consistency and correctness in specifying clinical trials, and in acquiring
and analyzing clinical data. The situation becomes especially critical with the need to
manage multiple clinical trials at various sites, and to facilitate meta-analyses on trials.
This paper introduces a knowledge-based framework to support a suite of clinical trial
management applications using semantic technologies to provide a consistent basis for
the application interoperability.
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SimpleCTMS Team (2010). The True Cost of a Clinical Trial Management System. Trial By
Fire Solutions.
http://www.simplectms.com/storage/media/True_Cost_of_CTMS_report2v.pdf
The SimpleCTMS paper is a proposal by SimpleCTMS for a SaaS based clinical trial
management system. It provides budget analysis and discusses the benefits of having a
CTMS. The brunt of the paper discusses the implementation process and the difference
between a enterprise system and a SaaS module. The paper recommends a SaaS model,
though that is because they are trying to sell their SaaS technology. I actually based a
considerable portion of my budget on their recommendations and their estimates for
CTMS implementation.
Sneed, H. (March, 2006). Integrating legacy Software into a Service oriented Architecture.
Software Maintenance and Reengineering, 2006. CSMR 2006. Proceedings of the 10th
European Conference. March 22-24, 2006, pp. 11-14. Retrieved from
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1602353&tag=1.
Legacy programs, i. e. programs which have been developed with an outdated technology
make-up for the vast majority of programs in many user application environments.
Moving to a new technology such as service oriented architecture is impossible without
taking these programs along. This contribution presents a tool supported method for
achieving that goal. Legacy code is wrapped behind an XML shell which allows
individual functions within the programs, to be offered as Web services to any external
user. By means of this wrapping technology, a significant part of the company software
assets can be preserved within the framework of a service oriented architecture.
Stausberg J, Pritzkuleit R, Schmidt CO, Schrader T, Nonnemacher M. (2012). Indicators
of data quality: revision of a guideline for networked medical research. Stud Health
Technol Inform. 2012;180:711-5.
Data quality significantly impacts the reliability and validity of empirical medical
research. Specific measures can be used to check the quality of data during operation of a
research project like a register. Furthermore these indicators allow an assessment of data
quality independently from the institution responsible for data recording. A previously
developed set of 24 data quality indicators was compared with measures of three research
projects, each representing a specific view on the topic. The structure of the set was
confirmed, being able to capture most of the projects' measures under the headings
plausibility, organization, and correctness. Solely two indicators about metadata could
not be appropriately mapped. However, several measures not considered so far were
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added to reach a number of 51 quality indicators in a first draft of a revised set. Most of
the new indicators refine existing ones; e. g. the indicator "allowed values for missings"
refines the existing indicator "allowed values for qualitative data elements". Seven
projects' measures contribute supplementary aspects of data quality. The draft of the
revised set of quality indicators will now be reviewed within and beyond the group.
Stier, N., Staman, M. (2011). Clinical Trial Management: Making the Business Case for CRMS.
Huron Education. https://wiki.duke.edu/download/attachments/14723021/W5+Clinical+Trial+Managemen-+Making+the+Business+Case.pdf?version=1
This was not a paper and more a presentation provided by a consulting firm that
discussed the need for clinical trial management systems. The presenation went over
points on why a CTMS would be useful and how it would affect different areas of
research, from management and administration to the trials themselves. The presentation
also evaluated how a CTMS could be used to increase revenue, lower costs, and increase
efficiency. It provided easy to absorb information regarding what the noticeable benefits
of a CTMS system are.
Sun, Wei. (Sept. 2008). Software as a Service: Configuration and Customization Perspectives.
Congress on Services Part II, 2008. SERVICES-2. IEEE. Sept. 23-26, 2008, pp. 18-25.
Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4700495.
Software as a service (SaaS) provides software application vendors a Web based delivery
model to serve clients with multi-tenancy based infrastructure and application sharing
architecture so as to get great benefit from the economy of scale. Due to the subscription
based model, SaaS vendors need take a well-designed strategy to enable self-serve
configuration and customization by their customers without changing the SaaS
application source code for any individual customer. A competency model and a
methodology framework have been developed to help SaaS vendors to plan and evaluate
their capabilities and strategies for service configuration and customization.
Terry, A., et al. (2010). Using your electronic medical record for research: a primer
for avoiding pitfalls. Family Practice (2010) 27(1); 121-126.
Additional time is required for providers to undertake EMR training and to standardize
the way data are entered into the EMR and EMRs which are designed for clinical care,
not research. Based on these experiences, we offer our thoughts about how EMRs may,
nonetheless, be used for research.
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Tierney WM, Rotich JK, Smith FE, Bii J, Einterz RM, Hannan TJ. (2002). Crossing the
"digital divide:" implementing an electronic medical record system in a rural Kenyan
health center to support clinical care and research. Proc AMIA Symp. 2002:792-5.
To improve care, one must measure it. In the US, electronic medical record systems have
been installed in many institutions to support health care management, quality
improvement, and research. Developing countries lack such systems and thus have
difficulties managing scarce resources and investigating means of improving health care
delivery and outcomes. We describe the implementation and use of the first documented
electronic medical record system in ambulatory care in sub-Saharan Africa. After one
year, it has captured data for more than 13,000 patients making more than 26,000 visits.
We present lessons learned and modifications made to this system to improve its capture
of data and ability to support a comprehensive clinical care and research agenda.
(General, Current Status)
Treweek, S. (2003). The potential of electronic medical record systems to support quality
improvement work and research in Norwegian general practice. BMC Health Services
Research 2003, 3:10.
Electronic medical record (EMR) systems are used for many purposes including patient
care, administration, research, quality improvement and reimbursement. This study aimed
to test a data extraction tool (QTools) and to provide information to support the
interpretation of EMR data.
Tyson, Gary, & Lynch, Marybeth. (2008). Avoiding the Five Common Errors Made in
Implementing a
CTMS. Retrieved from VIEW on Clinical Operations website:
http://www.campbellalliance.com/articles/PharmaVoice%20View%20on%20CD%20%20CTMS%20-%20June%202008.pdf
This article provided by the Campbell Alliance discusses five common erros that are
made while implementing a CTMS. Focusing on not enough stakeholde involvement, an
insufficient alignmnet of resources, competing visions, setting unreasonable expectations,
and inerface mania, many common pitfalls and resolutions are discussed that
organizations should consider when purchasing and implementing a CTMS.
Umscheid CA. Margolis DJ. Grossman CE. (2011). Key concepts of clinical trials: a narrative
review. Postgraduate Medicine. 123(5):194-204, 2011 Sep.
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The recent focus of federal funding on comparative effectiveness research underscores
the importance of clinical trials in the practice of evidence-based medicine and health
care reform. The impact of clinical trials not only extends to the individual patient by
establishing a broader selection of effective therapies, but also to society as a whole by
enhancing the value of health care provided. However, clinical trials also have the
potential to pose unknown risks to their participants, and biased knowledge extracted
from flawed clinical trials may lead to the inadvertent harm of patients. Although
conducting a well-designed clinical trial may appear straightforward, it is founded on
rigorous methodology and oversight governed by key ethical principles. In this review,
we provide an overview of the ethical foundations of trial design, trial oversight, and the
process of obtaining approval of a therapeutic, from its pre-clinical phase to postmarketing surveillance. This narrative review is based on a course in clinical trials
developed by one of the authors (DJM), and is supplemented by a PubMed search
predating January 2011 using the keywords "randomized controlled trial,"
"patient/clinical research," "ethics," "phase IV," "data and safety monitoring board," and
"surrogate endpoint." With an understanding of the key principles in designing and
implementing clinical trials, health care providers can partner with the pharmaceutical
industry and regulatory bodies to effectively compare medical therapies and thereby meet
one of the essential goals of health care reform.
US FDA (2012) Guidance for Industry Draft Guidance on Electronic Source Data in Clinical
Investigations
http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guid
ances/UCM328691.pdf Retrieved May 03 2013
o Examples of electronic source data originators:
 Investigators
 Clinical investigation site staff
 Clinical investigation subjects
 Consulting services (e.g., a radiologist reporting on a computed
tomography (CT) scan)
o Medical devices (e.g., electrocardiograph (ECG) machine and other medical
instruments such as a blood pressure machine)
 Electronic health records (EHR)
 Automated laboratory reporting systems
 Barcode readers (e.g., that are used to record medications or devices)
o A list of authorized data originators (i.e., persons, systems, devices, and
instruments) should be co-developed and maintained by the sponsor and the
investigator(s). Each of the authorized originators should have a unique identifier.
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o The list should identify the systems, devices, and instruments that transmit data
elements directly into the eCRF.
o When a system, device, or instrument automatically populates a data element field
in the eCRF, a data element identifier should be created that automatically
identifies the particular system, device, or instrument as the originator of the data
element.
o Data elements originating in an EHR can be transmitted directly into the eCRF
automatically.
o EHRs may use intervening processes (e.g., algorithms for the selection of the
appropriate data elements). For this reason the EHR is the source and should be
made available for review during an FDA inspection.
o The ability of sponsors and/or monitors to access health records in clinical
information systems should not differ from their ability to access health records
recorded on paper.
o eCRF data elements need to have metadata for each element, containing:
originator, date time, and study subject.
US FDA (2007) guidance for industry on Computerized Systems Used in Clinical Investigations
http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guid
ances/UCM070266.pdf
- Source documentation requirements, Part 11 requirements
- Limited access, audit trails, date time stamps
-Recommend use of prompts, flags, or other help features in system to encourage
consistent use of clinical terminology and to alert the user to data that are out of
acceptable range.
- No default value entering; careful with auto population of data.
- Retrieved data can be attributed to study subject
- System control, backup, recovery
- Software change control
U.S. Department of Health and Human Services. (2003). Summary of the HIPAA Security Rule.
Retrieved May 10, 2013, from
http://www.hhs.gov/ocr/privacy/hipaa/understanding/srsummary.html
This is a summary of key elements of the Security Rule including who is covered, what
information is protected, and what safeguards must be in place to ensure appropriate
protection of electronic protected health information.
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U.S. Department of Health and Human Services National Institutes of Health. (2003, 7/8/2004).
Institutional Review Boards and the HIPAA Privacy Rule. Retrieved May 11, 2013,
2013
Speaks to combining HIPAA Authorization with Informed Consent
U.S. Department of Health and Human Services National Institutes of Health. (2004, 6/22/2004).
Clinical Research and the HIPAA Privacy Rule. Retrieved May 11, 2013, 2013, from
http://privacyruleandresearch.nih.gov/clin_research.asp
Summary of PHI disclosure
U.S. National Institutes of Health. (2013). Retrieved June 4, 2013, from ClinicalTrials.gov:
http://clinicaltrials.gov/
This website is a registry and results database of publicly and privately supported clinical
studies of human participants conducted around the world. With a listing of over 140,000
studies, it is a user-friendly site offering search tips for Patients and Families,
Researchers, and Study Record Managers.
U.S. National Library of Medicine. (2013). MedlinePlus. Retrieved June 4, 2013, from
MedlinePlus:
Trusted health information for you: http://www.nlm.nih.gov/medlineplus/
MedlinePlus is the National Institutes of Health's Web site for patients and their families
and friends. Produced by the National Library of Medicine, it brings information about
diseases, conditions, and wellness issues in a language that is easily understood.
MedlinePlus offers reliable, up-to-date health information, anytime, anywhere, for free.
This comprehensive website also provides research and clinical trials for diseases and
conditions.
University of Connecticut Health Center Human Subjects Protection Office. (2013). Federal
Regulations. Retrieved May 11, 2013, from
http://hspo.uchc.edu/investigators/regulations/index.html
Describes FDA 21 CFR Part 50 and 21 CFR Part 56 in a concise manner.
Vawdrey, DK, Hripcsak, G. (2013). Publication bias in clinical trials of electronic health
records, Journal of Biomedical Informatics. Volume 46, Issue 1, February 2013, Pages
139-141, ISSN 1532-0464, 10.1016/j.jbi.2012.08.007.
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(http://www.sciencedirect.com/science/article/pii/S1532046412001475)
“ClinicalTrials.gov is an information resource maintained by the United States National
Library of Medicine that provides a registry of both federally and privately funded
clinical trials since February 2000. Journals whose editors belong to the International
Committee of Medical Journal Editors (ICMJEs) will only publish clinical trial results if
the trial is registered with ClinicalTrials.gov or another ICMJE approved trial registry
before the first patient is recruited. “
Objective: To measure the rate of non-publication and assess possible publication bias in
clinical trials of electronic health records.
Methods: We searched ClinicalTrials.gov to identify registered clinical trials of electronic
health records and searched the biomedical literature and contacted trial investigators to
determine whether the results of the trials were published. Publications were judged as
positive, negative, or neutral according to the primary outcome.
Results: Seventy-six percent of trials had publications describing trial results; of these,
74% were positive, 21% were neutral, and 4% were negative (harmful). Of unpublished
studies for which the investigator responded, 43% were positive, 57% were neutral, and
none were negative; the lower rate of positive results was significant.
Conclusion: The rate of non-publication in electronic health record studies is similar to
that in other biomedical studies. There appears to be a bias toward publication of positive
trials in this domain.
(General, Background)
Weiskopf NG, Weng C. (2013). Methods and dimensions of electronic health record data quality
assessment: enabling reuse for clinical research. J Am Med Inform Assoc. 2013 Jan
1;20(1):144-51. doi: 10.1136/amiajnl-2011-000681. Epub 2012 Jun 25.
OBJECTIVE: To review the methods and dimensions of data quality assessment in the
context of electronic health record (EHR) data reuse for research. MATERIALS AND
METHODS: A review of the clinical research literature discussing data quality
assessment methodology for EHR data was performed. Using an iterative process, the
aspects of data quality being measured were abstracted and categorized, as well as the
methods of assessment used. RESULTS: Five dimensions of data quality were identified,
which are completeness, correctness, concordance, plausibility, and currency, and seven
broad categories of data quality assessment methods: comparison with gold standards,
data element agreement, data source agreement, distribution comparison, validity checks,
log review, and element presence. DISCUSSION: Examination of the methods by which
clinical researchers have investigated the quality and suitability of EHR data for research
shows that there are fundamental features of data quality, which may be difficult to
measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data
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for clinical research are recommended to consider the adoption of a consistent taxonomy
of EHR data quality, to remain aware of the task-dependence of data quality, to integrate
work on data quality assessment from other fields, and to adopt systematic, empirically
driven, statistically based methods of data quality assessment. CONCLUSION: There is
currently little consistency or potential generalizability in the methods used to assess
EHR data quality. If the reuse of EHR data for clinical research is to become accepted,
researchers should adopt validated, systematic methods of EHR data quality assessment.
(General, Current Status)
Weng, C., & Embi, P. (2012). Informatics Approaches to Participant Recruitment. In R. L.
Richesson & J. E. Andrews (Eds.), Clinical Research Informatics (pp. 81-93): Springer
London.
Clinical research is essential to the advancement of medical science and is a priority for
academic health centers, research funding agencies, and industries working to develop
and deploy new treatments. In addition, the growing rate of biomedical discoveries makes
conducting high-quality and efficient clinical research increasingly important. Participant
recruitment continues to represent a major bottleneck in the successful conduct of human
studies. Barriers to clinical research enrollment include patient factors and physician
factors, as well as recruitment challenges added by patient privacy regulations such as the
Health Insurance Portability and Accountability Act (HIPAA) in the USA. Another major
deterrent to enrollment is the challenge of identifying eligible patients, which has
traditionally been a labor-intensive procedure. In this chapter, we review the informatics
interventions for improving the efficiency and accuracy of eligibility determination and
trial recruitment that have been used in the past and that are maturing as the underlying
technologies improve, and we summarize the common sociotechnical challenges that
need continuous dedicated work in the future.
Describes HIPAA implication to identifying potential study participants.
West SL, Blake C, Liu Zhiwen, McKoy JN, Oertel MD, Carey TS. (2009). Reflections on the use
of electronic health record data for clinical research. Health Informatics J. 2009
Jun;15(2):108-21.
The adoption of electronic health records (EHRs) offers the potential to improve the
delivery, quality, and continuity of clinical care, but widespread use has not yet occurred.
In this article, we describe our use of clinical (production) data that were derived from
outpatient and inpatient visits at a university teaching hospital for clinical research, a use
for which the data and their structure were not originally designed. Similar data exist at
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many outpatient and inpatient clinical facilities, and we believe that our insights are
relevant to electronically captured medical data regardless of their origin. We describe
the approaches taken to ensure compliance with the Health Insurance Portability and
Accountability Act (HIPAA) and to leverage the vast stores of structured and
unstructured data that are currently underused. We conclude by reflecting on what we
would have done differently and by making recommendations to streamline the process.
(General, Current Status)
Speaks to obtaining limited dataset to comply with HIPAA privacy rule. Discusses
challenges to ensuring deidentification of structured vs unstructured data. One key
recommendation was to use structured data fields to reduce need for text mining. Another
was to avoid patient identifiers in transcribed notes.
Wipke-Tevis, D. D., & Pickett, M. A. (2008). Impact of the Health Insurance Portability and
Accountability Act on participant recruitment and retention.Western journal of nursing
research, 30(1), 39-53.
This meta analysis reviews previous studies that suggest that HIPAA regulations
negatively impact the recruitment and retention of research subjects. The analysis breaks
down the process into 7 distinct categories in which research may be hampered by
HIPAA regulations. I will go over a few of them. In preparing for research or trials, the
researchers would take data from EHRs to see if they could recruit participants.
Researchers must submit a Prepatory to Research form to the IRB, but in it must outline
exactly what data and for what purpose they are using the PHI. Thus, they could not
access any of the material that was not included due to the minimum necessary rule. They
could not take the information out of the location and if they were not employees of the
covered entitity (business associate) they had to have a co-investigator who was part of
the covered entity to assist. They also have difficulty contacting patients. Before, patient
lists with certain conditions could be given to researchers and the researchers could
contact the potential subjects. However, under HIPAA regulations, healthcare providers
cannot disclose patient names without consent, so the providers were required to get a
HIPAA Waiver before researchers could contact patients at all and ask them questions.
Some of the other reasons outlined that make HIPAA a bane on clinical research I found
to be less legitimate concerns. An issue mentioned is that conventional payment methods
or recruitment methods for clinical research had to be redesigned and were more difficult
to do. Things like not sending patients postcards with PHI on them were listed as things
that made research more difficult, but that sort of practice is not difficult to change and is
really not good for patient privacy.
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