C ANCER C ARE E NGINEERING
The Health System
- Systems Engineering
- Health Services Research
- Medical Informatics
- TRIP
- Policy
- Economics
INDIVIDUAL PATIENTS
- Quality Care
- Epidemiology
- Decision Support
- Detection
- Symptoms / QoL
- Access
Biological Sciences
- Blood Biomarkers
- Tumor Biomarkers/Dynamics
- Clinical Trials
- Susceptibility
- Environmental Factors
- Tumor Variations
 mutations
 sessile polyps

Accelerated translation of science to practice
 Screening Rates

Identified best practices to dramatically improve and personalize tx

Better decision tools that aid providers, managers

A knowledge management system incorporating latest research advances
so that every piece of new knowledge does not have to be manually
assimilated by every provider

Methods of implementation and systems engineering to address
systems complexity and speed of response
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Matt Burton, MD

Knowledge of Best and Actual Practices
 Clinical Pathways, Guidelines, Order Sets, Quality Indicators

Clinical Workflow
 Prediction, CDSS, Order Management, Communication, Care Delivery,
and Documentation
 Simulate, Monitor, and Analyze

Clinical Data
 Results Delivery, Quality Measurement and Monitoring,
Feedback/forward (CDSS, Simulation), Knowledge Discovery

Virtual Hospital and Cancer Care Engineering
Matt Burton, MD

Focus on Translation


Goal-Oriented


Drive improvements by using key metrics that summarize system behavior, such as the NIH
statistics cited above;
Global Awareness


Systematic collection, analysis, and dissemination of cancer system data to all participants
for purposes of more effective distributed actions by system participants;
Metric Oriented


Direct researchers towards overcoming barriers likely to result in the greatest care system
improvements;
Knowledge Improvement


Distill research knowledge to useful products that can be easily used by providers,
consumers, and others to overcome the greatest complexities in cancer care
Understand and direct work by considering it within a system wide perspective;
Externalize Knowledge

Reduce knowledge to models that can be widely learned and whose properties can be tested
for improvement.

System Level:
 Indiana Regional Cancer
Care System
Simulate the Indiana
colorectal cancer (CRC) care
system to resemble current
performance
 Using the model:

 Identify areas of potential
improvement in current
system
 Develop and test various
strategies to arrive at an
optimal strategy
6/17/2008
cceHUB

Investigators:
 Selen Aydogan-Cremaschi, PhD,
Purdue Discovery Park
 Brad Doebbeling, MD, MSc; Seza
Orcun, PhD, Purdue Discovery Park,
David Haggstrom, MD, MAS,
Multiple others
S. Cremaschi
7
Interview providers to identify and rank questions
of interest
2. Develop CRC care system models and implement
them
3. Validate the model
4. Run what-if scenarios to answer questions of
interest
1.
6/18/2008
cceHUB
8
9
Survivor
Population
disease free
Cancer
Population
General
Population
negative
cancer or
care complication or
other
not treatable or
do not wish
to be treated
cancer or care complication or other
Death
cancer or care complication or other
Screening
Symptomatic
cancer or
care
complication
or other
Treatment
Staging
&
Evaluation
positive
Diagnosis
false positive
6/18/2008
cceHUB
9
1.
Identify higher impact components of the CRC care system in Indiana:
a) From a healthcare system perspective, what indices or metrics might
be early warning signals for managers or clinical leaders of where to
intervene quickly?
b) If we consider the entire spectrum of care, can we have the greatest
impact on CRC care mortality and cost of care by optimizing one of
the components to perform in a highly reliable fashion? :
--Screening, Screening Follow-up, Diagnosis, Treatment - early stage,
late stage diagnosis, survivorship, palliative care
2.
Determine necessary system resource capacities:
a) If every positive abnormal screening test is followed up with a
colonoscopy, does Indiana have the necessary resources?
b) What should the capacity of the high-volume facilities be in order to
be able to perform the necessary surgical procedures for CRC?
6/17/2008
cceHUB
S. Cremaschi
10

Natural Development and Progression of CRC in
Population Agent
Prob. of
Dev. Polyp
Normal
Invisible
Polyp
Distant
CRC
Regional
CRC
LOP Dist.
between States
6/18/2008
Polyp < 1cm
Polyp > 1cm
Local
CRC
Symptomatic
CRC
cceHUB
In Situ
CRC
LOP Dist.
from asymp.
to symp.
11

Screening & Follow-up
Screening Choice
Compliance Intervals
FOBT
Every 1 year
Sigmoidoscopy
Every 2 years
Colonoscopy
Every 5 years
Never Compliant
One Time Compliant
6/18/2008
cceHUB
12

Treatment
 Type
 Number
 Combinations
 Adherence
 Date
 Result
 Lifestyle
6/18/2008
cceHUB
13

Attributes
 Years in training
 Provider Type, Specialty
 Location/County
 Volume of patients/procedures
 Adherence with “Evidence-Based Medicine”

Treatment maps for CRC stages
 using the interviews of CRC providers/specialists.
6/18/2008
cceHUB
14

Attributes
 Location
 Resources
▪ Screening/Diagnosis/Staging
▪ Surgery
▪ Radiotherapy
▪ Chemotherapy
▪ Hospice
▪ Palliative
 Volume of patients/procedures
6/18/2008
cceHUB
15

System Level:
 Indianapolis Clinic System

Investigators:
 PI – Brad Doebbeling, MD, MSc;
 Co-PI –Jamie Workman-Germann, Purdue University
School of Engineering and Technology at IUPUI

Anticipated Outcome:
 (1) Develop cancer prevention and care process maps and
quality reports for Indianapolis clinics,
 (2) understand barriers to best practice care,
 (3) utilize a Cancer Care – Technical Assistance Program
(TAP) to help implement best practices in one or more
clinics.

CRC Systems Redesign Project
 Aims:
▪ To understand EMR implementation impact of clinical
processes
▪ To optimize patient flow by identifying process barriers to
screening
▪ To increase CRC screening rates by removing clinical barriers
 Methods:
▪ Facilitation of interdisciplinary teams of IUMG and VAMC
primary care clinic staff and area supervisors
▪ Evaluation of existing clinical workflow
▪ Systems engineering, Lean, Positive deviance principles

Scope of Work:
 Assessment of Primary Care CRC processes
 Development of data infrastructure to support sustainability of
initiative
 Develop and administer cultural assessment to determine
organizational readiness for IT and systems redesign initiative
implementation.
 Facilitate IUMG and VAMC project teams in application of systems
engineering tools to:
--Design and perform a pilot test of the process redesign, ensuring
new processes meet clinical and economic objectives, timeline
requirements, and project deliverables
--implement new processes and systems with a robust control
strategy to ensure long term sustainability of improvements

System Level:
 Cross System Information Awareness and Clinic

Investigators:
 PI - Caroline Carney Doebbeling, MD, MSc, Associate Professor of Medicine &
Psychiatry, IUSM; Research Scientist, IU Center for Health Services & Outcomes
Research, Regenstrief Institute, Inc.; Director of Quality and Outcomes, Indiana
Medicaid
 Co-PI – Katherine Schilling, MLS, EdD, AHIP, IU School of Library and
Information Sciences and IU School of Informatics
 Specific Aims:
 (1) Develop a best practice CRC treatment map including recognition and
treatment of cancer-related distress;
 (2) identify opportunities for streamlining processes; and
 (3) pilot implementation of more efficient delivery of psychosocial services
(mental health and social work) concurrent with cancer treatment.
Long-term objective is to implement innovative patient screening and navigation
systems to consistently deliver optimal psychosocial care.
CCE-3 – Literature Matrix



Universal screening in all clinics
Distress Thermometer
Triage to appropriate CL providers
- Moderate to Severe Distress: Mental health, social work, pastoral services
- Mild Distress: Primary Oncology team

Barriers
- Too few providers
- Waiting lists
Recommendation : Can we identify, describe, and better
understand “positive deviant” systems within treatment centers
nationally that are engaged in best practices? How have they
been successful in implementation?
CCE-HSR
C. Carney Doebbeling

System Level:
 System Biology-Oncologist-Patient

Investigators:
 PI – Seza Orcun, PhD, Purdue Discovery Park
 Co-PI – Doraiswami Ramkrishna, PhD, Harry Creighton Peffer Distinguished
Professor of Chemical Engineering, Purdue
 Co-I – Eric Sherer, PhD, Purdue Discovery Park; VA Center of Excellence on
Implementing Evidence-based Practice; Tom Imperiale, MD, Professor of
Medicine, IU School of Medicine and IU Center for Health Services &
Outcomes Research, Regenstrief Institute

Anticipated Outcome:
 (1) Develop a population balanced model to predict efficacy of oncology
treatment,
 (2) validate model with oncologist usage, and
 (3) engineering modeling researchers in clinical settings partnering on joint
projects with oncologists, GI specialists and services researchers.
1.
CRC prevalence model that includes intermediate polyp states and
tumor genetic heterogeneity
1. Already several similar models for incidence
2. None (that we know of) that include polyps or branching
2.
Methodology to extract a minimal set of discrete patient model
parameter sets from CRC & polyp prevalence / incidence data
1. Parameter sets are independent of demographics
=> Bayesian model for predicting likely parameter sets for an individual
patient
1. Certain demographics may be more likely for certain parameter sets
2. Likelihoods adjust to additional patient information
3. Predict incidence, treatment outcome, outcome
05/28/08
CCE-HSR
E. Sherer

System Level:
 Cross System Information Awareness – Assimilation and Integration of
Data From All Projects

Investigators:
 PI – David S. Ebert, PhD, Professor of Electrical and Computer
Engineering, Director of Purdue University Regional Visualization and
Analytics Center, Director of Purdue University Rendering and
Perceptualization Lab, Purdue

Primary Objective:
 Full-fledged, interactive, integrated visual and statistical analysis
capability in a vital analytic environment that brings together massive,
disparate, incomplete and time-evolving -omic data sets.
 Longer term goal---linkage with systems level data—cross projects
with EMR, claims, structure, process, outcomes

“The Dashboard Project”
 Initial pilot, creation of an interactive, integrated dashboard
of facility-level colorectal cancer performance measures to
inform the process of cancer care and systems
management in the VAMC.
 An example of functionality would be the ability to view
CRC-related, facility-wide data output by clinics,
treatment providers or risk-level of patient populations.

Brad Doebbeling, MD, MSc; Selen Aydogan-Cremaschi, PhD,; Matt Burton, MD; Timothy Carney, MPH, MB,;
Jason Saleem, PhD; Darrell Baker, RN; David Haggstrom, MD; Tom Imperiale, MD; Charles Kahi, MD, and Chris Suelzer, MD
Dashboard Report – Corporate View
06/08

Critical, Clinically-Relevant Questions of Interest:

What percentage of patients who have received physician-ordered FOBT
cards, are not returning them? What factors are contributing to noncompliance?

Regarding follow up after a positive FOBT screen, what percentage of
patients is notified within the required 14 days of the results?

What percentage of patients with colonoscopy orders to follow-up for
positive screens isn’t getting colonoscopy completed? What factors are
contributing to this gap?

What percentage of patients with a positive screen get needed
colonoscopy within the required 30 days?
CCE-HSR
B. Doebbeling
-Questions of Interest
-Project kick-off
-Interview VAMC CRC care
providers & administrators
-Identify critical questions of
interest
-Identify CRC performance
measures for visualization
-Conceptual Design of
Dashboard
-Prototype
Implementation
and Testing
-Mock Implementation
Feb
2008
Apr
2008
-Review
-Literature for
dashboards for
healthcare system
-Implemented
healthcare
dashboards
May
2008
Aug
2008
June
2008
-Detailed
Definitions of
CRC
Performance
Measures
INITIATION PHASE
Oct
2008
Sep
2008
June
2009
Dec
2008
-Database &
Software
Selection
-Usability
Testing &
Dashboard
Refinement
PHASE I
-Dissemination
-Proposal
Development
PHASE II

System Level:
 Repository of Data for All CCE Projects, Preparation of Data From All
Projects, and Strategic Statistical Analysis

Investigators:
 PI – Marietta L. Harrison, PhD, Purdue University; Co-I, Laura Jones
Myers, PhD, George Allen, IU School of Medicine & VA COE

Anticipated Outcome:
 (1) Utilitarian project to provide an electronic repository of all CCE
project data,
 (2) Develop a procedure and tools for cleaning and validating CCE
project data, and
 (3) Determine a strategy for combining and analyzing the disparate
data from all projects
Organizational
Social
Subsystem
Structure / Work System Design
Joint
Optimization
External
Technological
Subsystem
Environment
Aim 1: Identify key approaches to CDS development for CRC
screening at two VAMC sites and two nationally recognized nonVA sites, for effective CDS integration into clinical workflow.
Aim 2: Develop and test CDS design alternatives for improved
integration into clinical workflow through a controlled simulation
study and subsequent implementation.
Research Team: Brad Doebbeling, MD, MSc (PI); David Haggstrom, MD,
MAS; Jason Saleem, PhD ; Laura Militello, MA; Heather Hagg, MS; Shawn
Hoke and Lori Losee, and West Haven VA, Columbia, South Carolina VA,
30
Partners Healthcare (Harvard).


Training and research components:
Aim 1: To identify patient-level characteristics
associated with underuse & overuse of surveillance
care among CRC survivors
 colonoscopy, CT scans, CEA tests, history & physical
Aim 2: To determine whether organizational or
physician characteristics are associated with the
quality of CRC surveillance care
 Aim 3: To develop and test a CRC survivor’s personal
health record that promotes high-quality follow-up
care

 Develop methods and tools for effective use of unstructured




data such as narrative text in VA EHR
Improve text processing, text mining and de-identification
capabilities
Applied projects
Seven VAMCs with informatics research capabilities
participating--Salt Lake City, Nashville TVHS, Indianapolis,
Palo Alto, Portland, Tampa, West Haven. Also Boston –
MAVERIC, Pittsburgh-Philadelphia, Mayo Clinic, Carnegie
Mellon.
Ability to extract free (tumor stage, etc) through a web
services model.
[See Len D’Avolio, Mahesh Merchant, Matt Burton]

Innovation, Interdisciplinary Collaboration, Focused on
transforming Healthcare

Positive impact on healthcare

Reduction in cost of healthcare delivery

Increase in the value of healthcare delivery

Translating the project results to the benefit of the
healthcare system

Possibility to leverage the Foundation, DoD, VA and AHRQ
funding

Knowledge
 Patient with given demographic needs a Colonoscopy every 10 yrs

Clinical Workflow
 Pt J. Doe scheduled for routine H&P plan for Colonoscopy in next 3 mos. w/ PCo
 Clinical Reminder: “Dr. Smith, J. Doe needs a Colonoscopy”
 CPOE: “Colonoscopy for J. Doe is ordered and signed”
 Order Mgmt: “Colonoscopy for J. Doe scheduled on 7/12/08”
 Resource/ Supply Mgmt: “Need Colonoscopy suite, resources, and supplies on 07/12/08
at 9:00AM for Pt with give requirements”
 Registration: “J. Doe has arrived for his Colonoscopy”
 Document Preparation: “Populate fields in Procedure Note for Colonoscopy”
 Documentation: “Colonoscopy begun/ completed at 9:03 AM/ 9:37 AM 7/12/08”
 Order: “Path Specimen for Polyp”
 Document Preparation: “Populate fields in Path Report on Joe Doe’s Polyp”

Clinical Data
 Preliminary Procedure Note for Colonoscopy on 7/12/08 is resulted
 Pathology Report on Colonoscopy on 7/12/08 is signed and resulted
Matt Burton, MD

System Level:
 Indiana Regional Cancer Care System

Investigators:
 PI – Selen Aydogan-Cremaschi, PhD, Assistant Research
Scientist, Purdue Discovery Park
 Co-PIs – Bradley N. Doebbeling, MD, MSc; Seza Orcun, PhD,
Associate Research Scientist, Purdue Discovery Park

Anticipated Outcome:
 A model that can explain the existing CRC care system data,
answer “what-if” questions about potential changes to the care
system, and suggest improvements based on analyzing various
options.

Mechanistic modeling of colorectal cancer
(CRC)
 Includes genetic mutations and growth / death
dynamics
 Hypothesize mechanisms
▪ Underlying knowledge
▪ Level of detail determined by measurements
▪ Can be extended to incorporate additional
information as it becomes available

05/28/08
Prediction of likely individual patient CRC
 Temporal likelihoods of CRC
 Likely properties of CRC
CCE-HSR
E. Sherer

System Level:
 Clinical

Project Team:
 PI – Brad Doebbeling, MD, MSc
 Co-Is – Selen Aydogan-Cremaschi, PhD, Assistant Research Scientist, Purdue
Discovery Park, VA HSR&D COE; Matt Burton, MD, Medical Informatics
Fellow, Regenstrief Institute, Inc.; Timothy Carney, MPH, MBA, IU School of
Informatics; Jason Saleem, PhD, Assistant Professor, VA COE and IUPUI
School Engineering & Tech; David Haggstrom, MD, MAS, Assistant Professor,
IU School of Medicine, VA CIEBP and IU CHSOR
 Consultants – Darrell Baker, RN, Clinical Applications Coordinator, VAMC;;
Tom Imperiale, MD, Research Scientist, Regenstrief Institute, Inc. and IU
School of Medicine; Charles Kahi, MD, Roudebush VA Medical Center and
Chris Suelzer, MD, Associate Chief of Staff for Ambulatory Care, Roudebush
VA Medical Center




More effective use of IT is recommended in integrating point of care
access to (e.g., Committee on Quality Health Care in America):
 Health literature and evidence-based guidelines;
 Computerized clinical data;
 Computerized decision support (CDS) systems;
 Automation of decisions to reduce errors;
 Electronic communication among providers and patients into practice.
Computerized CDS can improve clinician decision making and support
adherence to evidence-based guidelines.
Colorectal cancer screening focus: high disease burden, relatively low
screening rates, strong evidence for screening effectiveness
Failure to optimally integrate CDS into workflow has resulted in
inconsistent and incomplete implementation strategies.
 Disparities in hospital selection
 Cultural disparities
 Gender & Age
 Diagnosis at younger age = higher risk for psychosocial





05/28/08
problems related to illness burden
Racial disparities (??)
Literacy and Health Literacy
 Low health literacy associated with less knowledge about
colorectal cancer
 Low health literacy associated with less knowledge about
screening
Practical, life-management issues (insurance, employment)
Lower income, Medicare Part D issues
Ability of providers to recognize distress
CCE-HSR
K. Schilling

System Level:
 Physical CRC Sample and Raw Data Collection

Investigators:
 PI – Stephen D. Williams, MD; Gabi Chiorean, MD, IU
Simon Cancer Center, IU School of Medicine

Anticipated Outcome:
 Augments a DoD-funded project to collect additional
samples and clinical data based on an analysis of initial
results.
 To assess physical samples and laboratory analysis data
from the CPTAC project and other Indiana based cancer
specimens for purposes of understanding Indiana’s
capacity for a total cancer care engineering project.

System Level:
 Opportunistic Project Refinement and Integration of
Regional and National Assets into Indiana CRC CCE
Effort

Investigators:
 PIs – Joe Pekny, PhD, Purdue University;
Brad Doebbeling, MD, MSc, Indiana University

Primary Objective:
 Management of the portfolio of all projects to
maximize impact and to leverage success by further
incremental investment