The Diabe-DS Proof-of-Concept for “Collect Once, Use Many” Project:
A Strategy for Defining Common Data Elements to Support Clinical Care and Secondary
Use
A pilot demonstration project sponsored by the HL7 EHR Workgroup
Team member list attached
Overview
The objective of the Diabe-DS (Diabetes Data Strategy) proof-of-concept project is to
demonstrate a repeatable process that identifies important data elements for clinical care and
secondary use. The process provides a framework for the “collect once, repurpose many times”
paradigm, which is vital to support the next generation of clinical and translational research.
The project focuses on Type 1 Diabetes (T1D) assessment in an ambulatory setting to
document the process and produce artifacts.
Background
There are several data standards and reporting requirements for healthcare organizations to
consider in the collection of healthcare data. The standards and requirements are vast in scope
(most cover all disease areas) and therefore complex to implement. The healthcare industry
needs clinical content data standards (e.g., data elements, definitions and value sets,
information models) which can support both patient care and secondary data uses, such as
quality measurement and a spectrum of clinical research and population health activities. These
clinical content data standards will likely be developed in disease- or therapeutic-specific
contexts, but need to be harmonized with complex national and international data standards and
specifications. The learning curve and technical skills required for a disease-specific content
data standards project are significant – especially in the context of a focused domain – and the
potential for duplication of effort and for conflicting data standards across clinical content areas
is very real. This creates a need for a standards-based process that specifically includes: a
thorough review of existing standards (of many types), an analysis of the data requirements for
both patient care and secondary uses, and formal linkage of these requirements and data
standards to Electronic Health Record (EHR) information models and functional specifications.
Methods
The Diabe-DS (Diabetes Data Strategy) project was formed in early 2009 by the HL7 EHR
Working Group and includes representatives from academic medical centers, research
organizations, professional societies, government entities, EHR developers, pharmaceutical
industry, standards development organizations and international health-related organizations.
This project developed narratives to describe the capture and use of the data elements in
primary (patient care) and secondary (research and reporting) settings. Concurrently the project
collected and harmonized relevant data elements from a variety of sources. The data elements
were grouped by subject and organized in an information model. The information model is
mapped to the US-based HITSP specification and the HL7 EHR Functional Model.
The artifacts of the Diabe-DS include the harmonized data definitions in a HITSP format, the
identification of atomic data elements in the EHR (leveraging the work of the National Quality
Forum and HITEP), mappings of these data elements (via a Domain Analysis Model) to HL7
Detailed Clinical Models (DCMs) and EHR system functions for both patient care (e.g., EHR-S
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FM, Child Health Functional Profile) and clinical research and quality measurement (via the
EHR Clinical Research Functional Profile).
Results
The team has written approximately 10 mini use cases and assembled a collection of over 150
data elements (question, value set, and narrative definition) from various sources including
clinical note and EHR specifications, observational and interventional clinical research data
forms, and standard (federally approved) quality measures. Collectively, these elements are
represented in an Information Model that is suitable for review and discussion by broader T1D
stakeholders. There are two outcomes of this project—the production of data content (i.e., a
preliminary set of T1D data elements sufficient for both clinical and secondary uses), and
technical specifications that tie T1D domain-specific data capture needs to functional
requirements.
Significance
The Diabe-DS is a novel project that is harmonizing data requirements for multiple secondary
uses, and in turn harmonizing those elements with clinical capture data representations. Much
of the process and lessons learned is applicable to other disease areas. Further, this project
harmonizes data definitions, which are required to thoughtfully apply these standards and to
validly re-use technical components or re-purpose data. If successful as a proof of concept,
these artifacts and methodologies can be applied to the development of common data elements
for other disease domains. Additionally, this work provides solution to the “collect once,
repurpose many times” paradigm which can increase speed and efficiency of evidence-based
care, population surveillance, and quality monitoring, and ultimately benefit patients everywhere.
More Information
Project wiki:
http://wiki.hl7.org/index.php?title=EHR_Diabetes_Data_Strategy (overview)
http://wiki.hl7.org/index.php?title=EHR_Diabetes:_Working_Documents (documents)
Contacts:
Crystal Kallem, crystal.kallem@ahima.org
Rachel Richesson, Rachel.Richesson@epi.usf.edu
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Diabe-DS Project Team Members
Project Facilitators
Rachel Richesson, PhD, MPH
University of South Florida (USF)
Rachel.Richesson@epi.usf.edu
Donald T. Mon, PhD, FHIMSS
American Health Information Management Association (AHIMA)
donald.mon@ahima.org
Crystal Kallem, RHIA, CPHQ
American Health Information Management Association (AHIMA)
crystal.kallem@ahima.org
Active Project Contributors
Donna DuLong, RN, BSN
Apelon
ddulong@apelon.com
Luigi Sison
Consultant (?)
lsison@yahoo.com
Wendy Huang
Canada Health Infoway, Inc.
whuang@infoway-inforoute.ca
Patricia Van Dyke
The ODS Companies
vandykp@odscompanies.com
Patricia Gunter, MS
Duke Translational Medicine Institute
patricia.gunter@duke.edu
Scott Bolte, MS
GE Healthcare
scott.bolte@med.ge.com
Maryanne Quinn, MD, MPH
Children's Hospital Boston
Maryanne.Quinn@childrens.harvard.edu
Gary Dickinson
EHR Standards Consulting
gary.dickinson@ehr-standards.com
Steven Ward
Eli Lilly and Company
Ward_Steven_T@lilly.com
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Meredith Nahm, PhD
Duke Translational Medicine Institute
meredith.nahm@duke.edu
William Goossen, PhD, RN
Results4Care
williamtfgoossen@cs.com
Yong Choi, RN, MSN
Duke University
Student Intern - TBD
Observers/Periodic Contributors
Craig Parker, MD, MS
Arizona State University
craigparkermd@gmail.com
Davera Gabriel, RN
UC Davis School of Medicine
davera@ucdavis.edu
Kristi Eckerson, MSPH
Emory University/Office of Information Technology
keckers@emory.edu
Jeff James
Cerner
JJAMES@CERNER.COM
Joy Kuhl, MBA
Alliance for Pediatric Quality
joy@kidsquality.org
Joyce Bruno Reitzner, MBA, MIPH
American College of Chest Physicians
jbruno@chestnet.org
Joyce Niland, PhD
City of Hope Comprehensive Cancer Center
jniland@coh.org
Melanie Dragosljvich
Care Communications
MDragosljvich@care-communications.com
Michael Celeste
Pfizer
Michael.Celeste@pfizer.com
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Mitra Rocca
Food and Drug Administration
mitrarocca@aol.com
Steve Bentley
National Health Service (NHS)
steve.bentley@nhs.net
Monica Harry
Canada Health Infoway, Inc.
mharry@infoway-inforoute.ca
Kendra Vehik, PhD
University of South Florida (USF)
Kendra.Vehik@epi.usf.edu
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