1. Exploring a New Mechanism Increasing Emergency Department Visits Katrina Hull University at Albany April 17, 2015

2. Contents Background Current Theories Dynamic Hypothesis Model Scenario Model Results Discussion 2

3. Emergency Department Crowding Definition – Wait times – Ambulance diverting Historical trend – Increase in per capita use, twice what would be predicted by population growth – This research focuses on the increased per capita use 3

4. Source: Kaiser Family Foundation 4

5. Research Goal Propose an endogenous dynamic mechanism increasing emergency department visits Develop a model of this hypothesis Use the model to determine useful empirical data to support this hypothesis 5

6. Health of ED Patients Ambulance diversions correlated to poorer outcomes for heart attack patients Overcrowded EDs may result in lower quality of care – Patient boarding – Stressed physicians less effective Stakeholders – ED Patients 6

7. Internal Process Variables Premise: A better structured ED and Hospital could handle the increased load Prior focus of system dynamics work Models examine patient flow through the ED to discharge (appropriate or not) or admission ED as backdoor to admissions Stakeholders – ED staff – Hospitals 7

8. General Population Health Increase ED use as symptom of poorer health in the population Ambulatory sensitive care conditions Failure of system to care for vulnerable populations ED visits are urgent but should be avoidable Stakeholders – Society – Patients 8

9. Literature Summary Lot of exogenous theories Multiple actors with disparate motivations Conflicting policy approaches Perfect space for a model – Unify multiple stakeholder perspectives – Highlight interaction of their activities – Examine outcomes of suggested policies 9

10. Dynamic Hypothesis Where would ED patients come from within the healthcare system? 10

11. The Story Costs of hospital admissions rose due to factors such as improved medical technology Payers became alarmed at the cost of hospital admissions Policies were created to reduce the cost of individual admissions and reduce total admissions The unintended consequence was more emergency department visits 11

12. Reference Mode 12 Physicians Redirecting Patients Increase Outside Model Boundary Historical Data from Avalere Health analysis of American Hospital Association Annual Survey data

13. Feedback Loops 13

14. Payer Sector 14

15. Time Adjusted Costs 15 Based on time series data from 1990, 2000 and 2010 (2015 value extrapolated based on exponential growth)

16. General Practitioner Sector 16

17. Unknown Parameters Normal wait times Actual wait times Percent of patients referred to ED BUT, these don’t matter because the model is normalized, the key unknowns are: – Payer sensitivity to pressure – GP sensitivity to pressure GP and Payer time to adjust expectations also unknown 17

18. Sensitivity Testing 18

19. Effect of Wait Times on GP Redirects 19

20. Final Value of Per Capita ED Visits 20

21. In 2 Dimensions 21

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24. Estimating Sensitive Parameters Next step is find data on these sensitive parameters Where? I don’t know yet. 24