Using System Dynamics (SD) Methodology for Strategic Planning in VA QUERI Programs

1. Using System Dynamics (SD) Methodology for Strategic Planning in VA QUERI Programs David B. Matchar, MD Kristen Hassmiller Lich, PhD Jack Homer, PhD For the Stroke QUERI

2. Identify problem Collect data Evaluate alternatives Select solutions Implement

3. Difficulties with standard approaches • Challenges to effective, sustainable translation of research into action in the real world (our QUERI mission!): • Limited resources. funding does not cover development and evaluation of policies and clinical interventions. Furthermore, mistakes in strategic direction are costly. • Numerous policy options. It is difficult to develop a single strategic plan from the large and diverse evidence on stroke. • Multiple stakeholders, multiple visions. When dealing with complex problems, stakeholders often operate from conventional and often narrowly focused ‘wisdom’ about how to improve systems of care that all limit their ability to see new ways of operating. • Absence of a forum for integration. Multiple stakeholders are key to successful and sustainable implementation. There is a lack of existing linking structures in which key participants can come together to make change happen.

5. Workshop • (IA) An overview of SD methodology and (IB) its application in a current Stroke QUERI; • (II) Discussion of strategic planning problems in other QUERIs that may be addressed using the SD approach; and • (III) Consider ways the SD approach may be utilized broadly in the QUERI program.

6. (IA) AN OVERVIEW OF SD METHODOLOGY

7. Brief Background on System Dynamics Modeling Compartmental models resting on a general theory of how systems change (or resist change) – often in ways we don’t expect • Developed for corporate policies in the 1950s, and applied to health policies since the 1970s • Concerned with understanding dynamic complexity • Accumulation (stocks and flows) • Feedback (balancing and reinforcing loops) • Used primarily to craft far-sighted, but empirically based, strategies • Anticipate real-world delays and resistance • Identify “high leverage” interventions • Modelers engage stakeholders through interactive workshops Forrester JW. Industrial Dynamics. Cambridge, MA: MIT Press; 1961. Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin/McGraw-Hill; 2000.

8. System Dynamics Health Applications1970s to the Present • Disease epidemiology • Cardiovascular, diabetes, obesity, HIV/AIDS, cervical cancer, chlamydia, dengue fever, drug-resistant infections • Substance abuse epidemiology • Heroin, cocaine, tobacco • Health care patient flows • Acute care, long-term care • Health care capacity and delivery • Managed care, dental care, mental health care, disaster preparedness, community health programs • Health system economics • Interactions of providers, payers, patients, and investors Homer J, Hirsch G. System dynamics modeling for public health: Background and opportunities. American Journal of Public Health 2006;96(3):452-458.

9. Model Uses and Audiences • Set Better Goals (Planners & Evaluators) • Identify what is likely and what is possible • Estimate intervention impact time profiles • Evaluate resource needs for meeting goals • Support Better Action (Policymakers) • Explore ways of combining policies for better results • Evaluate cost-effectiveness over extended time periods • Increase policymakers’ motivation to act differently • Develop Better Theory and Estimates (Researchers) • Integrate and reconcile diverse data sources • Identify causal mechanisms driving system behavior • Improve estimates of hard-to-measure or “hidden” variables

10. Community CV control model

11. Plausible Futures (Policy Experiments) Dynamic Hypothesis (Causal Structure) Obese fraction of Adults (Ages 20-74) 50% 40% 30% Fraction of popn 20-74 20% 10% 0% 1970 1980 1990 2000 2010 2020 2030 2040 2050 Simulations for Learning in Dynamic Systems Multi-stakeholder Dialogue Morecroft JDW, Sterman J. Modeling for learning organizations. Portland, OR: Productivity Press, 2000. Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.

12. Our key challenge: dynamic complexity • System complexity • A moving target

13. Dynamic complexity arises because systems are: • Dynamic • Tightly coupled • Governed by feedback • Nonlinear • History dependent • Self organizing • Adaptive • Evolving

14. “Effective” models of complex systems • Causal (not correlational) • Dynamic (not equilibrium) • Grounded in empirical tests (econometrics, ethnography…) • Broad boundaries (not limited to one disciplinary domain) Engage stakeholders who develop “ownership”

15. (IB) USE OF SD FOR STROKE QUERI STRATEGIC PLANNING

16. Our approach • This project uses System Dynamics (SD) modeling to help key stakeholders of the Stroke QUERI achieve a comprehensive understanding of the complex systems involved in stroke prevention and treatment and provides a tool to support effective stakeholder communication and the establishment of strategic actionable priorities.

17. The process: • Met with key system stakeholders represented on the Stroke QUERI Executive Committee • established a shared conceptual framework of the continuum of stroke in the VA • Identified key classes of interventions under consideration • After several iterations of feedback by stakeholders, the framework was transformed into a stock and flow simulation model.

18. Conceptual framework

19. Stock and flow simulation

20. Technical issues • Simulates veteran enrollees between 2008 and 2028 • Separating enrollees into mutually exclusive states (“stocks”) based on • Event (TIA or stroke, with post-stroke enrollees separated by modified Rankin score) • High or low risk group (>= or < one risk factor: smoking, DM, HTN, AF). • Outcome variables defined by stakeholders (events, DALYs, cost) • Model parameters based on VA data when possible; alternatively, scientific literature and expert opinion. • Programmed using Vensim software (www.vensim.com) • CAVEAT: The current model is preliminary, intended to provide a credible foundation for further improvement, working in close collaboration with a larger group of system stakeholders and content experts.

21. Population stocks & flows (2008) Total population initial 7,630,000 Population entry per year 285,000 Post stroke popn fraction initial 6.30% Post TIA popn fraction initial 6.26% Fractions affecting quality of care Fraction of TIAs going to hospital 20% Fraction of stroke victims eligible for TPA 25% Max fraction of first strokes timely to hospital 50% Max fraction of recurrent strokes timely to hospital 75% Stroke outcomes Fatal fraction of first strokes absent acute care 15% Fraction of first strokes ending at R23 absent care 25% Fraction of first strokes ending at R45 absent care 56% Ability of TPA to mitigate stroke outcomes in those eligible 40% Ability of best rehabilitation to reduce functional loss 20% Key constants

22. Scenario variable definitions • Community awareness: Probability of appropriately responding to stroke sxs. • Fraction of non-event population at higher risk: Fraction of VA population without prior TIA or stroke with a current modifiable risk factor. • Quality of first event (TIA, stroke) prevention: Intensity of efforts to target individuals who have not had a TIA or stroke but have risk factors, with medications and lifestyle change, that could prevent TIA or stroke or other cardiovascular event. (When Quality = 1, prevention interventions are used when appropriate and to their optimal effect.) • Quality of TPA use: Fraction of enrollees experiencing an ischemic stroke who are eligible for and receive tPA correctly in the acute care setting. • Quality of recurrent event (TIA, stroke) prevention: Intensity of post-acute efforts that would prevent a recurrent stroke or TIA (e.g. carotid endarterectomy, discharge planning) and diligence/adherence of long-term caregivers. • Quality of office MD response to non-hospitalized TIA: For patients with TIAs who do not go to hospital, additional intensity of outpatient response. • Quality of stroke rehabilitation: Quality of rehabilitation efforts for first 90 days after stroke intended to prevent permanent loss of functioning.

25. Relative Risk Absolute risks for “No event, lower risk” (# per thousand population per year): TIA 3.47; Stroke 3.71; Non-stroke death: 23.601.

26. Illustrative flow calculation Stroke rate for high risk with prevention is: stroke rate for high risk absent prevention x (1-quality + quality x (1 – ability)) So, if quality = 1, then Stroke rate for high risk with prevention is: stroke rate for high risk absent prevention x (1 – ability) Note, 1 - ability = stroke rate with prevention/stroke rate absent prevention = RR

27. Preliminary results

28. Next Steps • Work with a larger group of system stakeholders and stroke experts to refine and validate model assumptions and parameter estimates. • Analyze the model to identify leverage points for interventions that have the greatest potential to improve stroke care. • Use the model as a flight simulator to “try out” various policy scenarios in an interactive workshop with system stakeholders. • Based on insights/discussion, create an action plan that is feasible (e.g., through leveraging existing resources), and sustainable (e.g., by accounting for barriers and undesirable ripple effects of interventions).

29. Our objective • To achieve a humane, effective, and sustainable health care system • In our lifetime 16 years

30. II and III: Implications for QUERI • Does your QUERI have • a handle on: • The size of the relevant population (current and projected)? • Range of plausible policy and clinical actions? • Potential impact of these actions? • A strategic plan based on the above? • Stakeholder/decision maker participation/buy-in/commitment to action? • Is this relevant to the QUERI broadly?