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Taking Maryland‘s Treatment Delivery System to the Efficiency Frontier

Taking Maryland‘s Treatment Delivery System to the Efficiency Frontier. Rafael Corredoira The Wharton School University of Pennsylvania. Agenda. Introduction to DEA. DEA Applications. DEA Addiction MD. DEA Exercise. Conclusion. Agenda. Introduction to DEA DEA applications

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Taking Maryland‘s Treatment Delivery System to the Efficiency Frontier

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  1. Taking Maryland‘s Treatment Delivery System to the Efficiency Frontier Rafael Corredoira The Wharton School University of Pennsylvania

  2. Agenda Introduction to DEA DEA Applications DEA Addiction MD DEA Exercise Conclusion Agenda • Introduction to DEA • DEA applications • DEA and Addiction Treatment in Maryland • Application at the clinic level • Conclusions

  3. Agenda Introduction to DEA DEAApplications DEAAddictionMD DEAExercise Conclusion Data Envelopment Analysis • Novel non-parametric methodology that estimates the relative efficiency of a group of decision making units (Banker, Charnes, & Cooper, 1984) • Used to estimate efficiency in: • Health care sector (hospitals, physicians, clinics) • Education (elementary school systems, colleges, libraries) • Business (bank branches, development teams) • Human Development Index (World Bank)

  4. Agenda IntroductiontoDEA DEAApplications DEAAddiction MD DEAExercise Conclusion Ideas behind DEA • DMU Efficiency is the output-input ratio • Estimation based on multiple inputs and outputs simultaneously • Efficiency estimation: • linear programming optimization • comparisons of similar DMU • maximize output or minimize input • DMU Efficiency Score • =1 when in the “efficient frontier” • 1-efficiency score = distance to “efficient frontier”

  5. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion F Efficient Frontier E D Ox* C B A Hypothetical Example Efficient DMU Inefficient DME O U T P U T Ox X Ix INPUT

  6. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion DEA applications • Best car • What car gives the best price – value ratio? • Improvement needed to reach efficient frontier • Price adjustment to be efficient among the set under consideration • Efficiency of LA Lakers players 04-05 • Who are the most efficient players? • Performance adjustment to be efficient as the most efficient players

  7. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion Car selection (Best Ratio)

  8. Agenda Introduction toDEA DEAApplications DEAAddictionMD DEAExercise Conclusion Car selection (Constant Input)

  9. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion Car selection (Constant Output)

  10. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion LA Lakers Players

  11. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion Target Efficiency (Constant Input)

  12. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion DEA and Addiction Treatment - MD • OBJECTIVES • Identify best practices in the industry • Estimate clinic efficiency in the treatment of addiction • Provide information about areas of improvement to clinics • Identify antecedents of inefficiency

  13. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion OUTPUT INPUT Patients WITH addiction problems Patients WITHOUT addiction problems CLINIC Model • MODEL ASSUMPTIONS: • Constant returns-to-scale • Output maximization • Unit of analysis = clinic

  14. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion Data Source: ADAA-Maryland • Data collected annually by Maryland Alcohol and Drug Abuse Administration • Contains information about every admission to and discharge from the Maryland addiction treatment network • Treatment centers submit information as required by state registration and certification. • 375 programs • Sample: 353 treatment programs (22 dropped - missing data) • Information on 75,000+ patients

  15. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion inputs outputs # patients in 18 categories defined by ASAM level of care (6 levels) and Severity (3 levels) # patients competed treatment # patients not using drugs at discharge CLINIC Models and Variables

  16. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion Results • By making comparisons between clinics with similar patterns of inputs (patient mix), DEA estimates: • Clinic Efficiency score • Clinic Reference set • Clinic Hypothetical Efficient Outcome • Results for Maryland (2005 data) • 41 efficient clinics of a total of 353 • Minimum relative efficiency=0.02

  17. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion Benchmarking Actual Actual Efficient Efficient Clinic Efficiency Reference Group Completion No Use Completion No Use . . . . . . . . . . . . . . . . . . . . . id30 1.000 id30 12 21 12 21 id51 1.000 id51 56 58 56 58 . . . . . . . . . . . . . . . . . . . . . id23 0.993 id8 id77 id124 id222 355 361 355 369.55 Id45 0.952 id30 id200 7 16 9.14 16 . . . . . . . . . . . . . . . . . . . . . id111 0.556 id93 id234 id199 20 21 20 22.78 . . . . . . . . . . . . . . . . . . . . . id32 0.644 id93 id234 id199 45 43 45 62.08 . . . . . . . . . . . . . . . . . . . . . Number of Clinics: 353 Number of Clinics in the efficient frontier: 41

  18. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion Testing Common Assumptions • Examples: • “Our poor performance is caused by the lack of supporting social network of our patients.” • “Our patients have to travel more than usual, that’s why our completion rate is low.” • “State funding is inefficient, market incentives are better in promoting efficiency.” • “Treating more patients facilitates learning and results in more efficient outcomes.” • Test: • regress DEA efficiency score on proxies for supporting social network, travel distance, number of patients treated, and state funding

  19. Agenda IntroductiontoDEA DEAApplications DEAAddictionMD DEAExercise Conclusion Analysis of Parameter Estimates Parameter Estimate Std Err p-value Intercept 0.860 0.756 0.26 Income -0.006 0.011 0.60 Clinic/sqmile 0.265 0.319 0.41 State Funded -0.126 0.266 0.64 Patients -0.001 0.0004 0.02 Results • Clinic inefficiency: • decreases with number of patients treated • is not associated to: • County’s income per capita (supporting social network) • County’s # of clinics per sq. mile (travel distance) • Clinic’s state funding Tobit Regression Dependent variable: Inefficiency

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