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

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Using System Dynamics (SD) Methodology for Strategic Planning in VA QUERI Programs David B. Matchar, MD Kristen Hassmiller Lich, PhD Jack Homer, PhD – PowerPoint PPT presentation

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Title: 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.

4
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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 dont 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
200696(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
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 (gt or lt 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
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.

23
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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/co
    mmitment to action?
  • Is this relevant to the QUERI broadly?
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