Cancer Care Engineering

1. Cancer Care Engineering Developing a Hierarchical System Based Approach to Improving Cancer Care:Establishing an Indiana Prototype for Next-Generation Colorectal Cancer CareBrad Doebbeling, MD, MSc, IU School of Medicine, Regenstrief Institute, VA HSR&D Center of Excellence, Regenstrief Center for Healthcare Engineering, Purdue University

2. How do we connect these areas of expertise? The Health System - Systems Engineering - TRIP - Health Services Research - Policy - Medical Informatics - Economics INDIVIDUAL PATIENTS • Quality Care - Detection - Susceptibility • Epidemiology - Symptoms / QoL - Environmental Factors • Decision Support - Access Biological Sciences - Blood Biomarkers - Tumor Variations - Tumor Biomarkers/Dynamics  mutations - Clinical Trials  sessile polyps

3. The current state of CRC care in Indiana highlights our need for • 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

4. 010010010001001001000100110100110100101101101 Knowledge, Workflow,and Data Lifecycle Matt Burton, MD

5. Knowledge, Workflow, & Data • 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

6. CCE >> a methodology to improve cancer care and address the most intractable barriers • Focus on Translation • Distill research knowledge to useful products that can be easily used by providers, consumers, and others to overcome the greatest complexities in cancer care • Goal-Oriented • Direct researchers towards overcoming barriers likely to result in the greatest care system improvements; • Knowledge Improvement • Systematic collection, analysis, and dissemination of cancer system data to all participants for purposes of more effective distributed actions by system participants; • Metric Oriented • Drive improvements by using key metrics that summarize system behavior, such as the NIH statistics cited above; • Global Awareness • 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.

7. 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 CCE1: A Multi-Agent Approach to Modeling of Indiana CRC Care SystemModel Objectives • Investigators: • Selen Aydogan-Cremaschi, PhD, Purdue Discovery Park • Brad Doebbeling, MD, MSc; Seza Orcun, PhD, Purdue Discovery Park, David Haggstrom, MD, MAS, Multiple others cceHUB S. Cremaschi

8. Interview providers to identify and rank questions of interest Develop CRC care system models and implement them Validate the model Run what-if scenarios to answer questions of interest CCE1 Methodology cceHUB

9. Survivor Population disease free cancer or care complication or other Cancer Population not treatable or do not wish to be treated General Population cancer or care complication or other Death cancer or care complication or other negative cancer or care complication or other Screening Symptomatic Treatment positive Diagnosis false positive CRC Care Continuum Map 9 Staging & Evaluation cceHUB

10. Top priority questions to address by the model • Identify higher impact components of the CRC care system in Indiana: • From a healthcare system perspective, what indices or metrics might be early warning signals for managers or clinical leaders of where to intervene quickly? • 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 • Determine necessary system resource capacities: • If every positive abnormal screening test is followed up with a colonoscopy, does Indiana have the necessary resources? • What should the capacity of the high-volume facilities be in order to be able to perform the necessary surgical procedures for CRC? cceHUB S. Cremaschi

11. Population Agent • Natural Development and Progression of CRC in Population Agent Prob. of Dev. Polyp Invisible Polyp Normal Polyp < 1cm Polyp > 1cm Distant CRC Regional CRC Local CRC In Situ CRC LOP Dist. from asymp. to symp. LOP Dist. between States Symptomatic CRC cceHUB

12. Population Agent • Screening & Follow-up Compliance Intervals Screening Choice Every 1 year FOBT Every 2 years Sigmoidoscopy Colonoscopy Every 5 years Never Compliant One Time Compliant cceHUB

13. Population Agent • Treatment • Type • Number • Combinations • Adherence • Date • Result • Lifestyle cceHUB

14. Provider Agents • 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. cceHUB

15. Medical Center Agents • Attributes • Location • Resources • Screening/Diagnosis/Staging • Surgery • Radiotherapy • Chemotherapy • Hospice • Palliative • Volume of patients/procedures cceHUB

16. CCE-2: An Indianapolis CRC Quality Improvement Initiative • 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.

17. CCE-2: An Indianapolis CRC Quality Improvement Initiative • 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

18. CCE-2: An Indianapolis CRC Quality Improvement Initiative • 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

19. CCE-3: Holistic Best Practice Process Flow and Navigation for CRC Patient with Psychosocial Problems • 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.

20. CCE-3 – Literature Matrix

21. “Optimal” Psycho-oncology Practices • 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

22. CCE-4: Bridging Clinical Expertise with Fundamental Cancer Biology Research Using Predictive Computational Cancer Models • 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.

23. Modeling Individualized OutcomeCRC Progression • CRC prevalence model that includes intermediate polyp states and tumor genetic heterogeneity • Already several similar models for incidence • None (that we know of) that include polyps or branching • Methodology to extract a minimal set of discrete patient model parameter sets from CRC & polyp prevalence / incidence data • Parameter sets are independent of demographics => Bayesian model for predicting likely parameter sets for an individual patient • Certain demographics may be more likely for certain parameter sets • Likelihoods adjust to additional patient information • Predict incidence, treatment outcome, outcome 05/28/08 CCE-HSR E. Sherer

24. CCE-5: A Fusion Center for Cancer Care System Information – Visualizing Omics Data • 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

25. CCE-5 HSR/Systems Arm: A Fusion Center for Cancer Care System Information – The Cancer Care Situation Room • “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

26. Dashboard Report – Corporate View

27. CCE-5 HSR: The Dashboard Project • 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 non-compliance? • 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? 06/08 CCE-HSR B. Doebbeling

28. Timeline & Milestones • Questions of Interest • Interview VAMC CRC care providers & administrators • Identify critical questions of interest • Identify CRC performance measures for visualization -Prototype Implementation and Testing • Conceptual Design of Dashboard -Project kick-off -Mock Implementation May 2008 Feb 2008 Oct 2008 June 2009 Aug 2008 June 2008 Sep 2008 Dec 2008 Apr 2008 • Detailed Definitions of CRC Performance Measures • Dissemination • Proposal Development • Database & Software Selection • Usability Testing & Dashboard Refinement • Review • Literature for dashboards for healthcare system • Implemented healthcare dashboards PHASE II INITIATION PHASE PHASE I

29. CCE-6: Information Infrastructure and Raw Data Analysis • 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

30. AHRQ Grant – Improving Integration of CDS into Workflow Aim 1: Identify key approaches to CDS development for CRC screening at two VAMC sites and two nationally recognized non-VA 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, Partners Healthcare (Harvard).

31. VA Research Career Development AwardDavid Haggstrom, MD, MAS • 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

32. VA Consortium for Healthcare Informatics Research • 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]

33. CCE Brainstorming • 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

34. Backup Slides

35. Events, Dependencies,& State Transitions • 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

36. CCE-1: A Multi-Agent Approach to Modeling of Indiana CRC Care System • 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.

37. CCE-4 Overview • Mechanistic modeling of colorectal cancer (CRC) • Includes genetic mutations and growth / death dynamics • Hypothesize mechanisms • Underlyingknowledge • Level of detail determined by measurements • Can be extended to incorporate additional information as it becomes available • Prediction of likely individual patient CRC • Temporal likelihoods of CRC • Likely properties of CRC 05/28/08 CCE-HSR E. Sherer

38. CCE-5 HSR/Systems Arm: A Fusion Center for Cancer Care System Information – The Cancer Care Situation Room • 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

39. Rationale • 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.

40. Emerging Knowledge (Lit Review) on Barriers • Disparities in hospital selection • Cultural disparities • Gender & Age • Diagnosis at younger age = higher risk for psychosocial 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 05/28/08 CCE-HSR K. Schilling

41. CCE-7: Augmenting Physical Sample Collection, Clinical Data Collection, “Omics” Laboratory Analysis, and Conversion to Digital Data • 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.

42. CCE-8: Master Project Management and Development of a Health Services and Systems Engineering Collaborative for Cancer Care Engineering • 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