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Investigating provider payment mechanisms in the lab

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The behaviour of health care providers is a significant determinant of the quality of health services provided. Improving the performance of individual providers means creating an incentive structure that motivates individuals to exert the maximum level of effort, in order to ensure the optimal level of services provided, in terms of both quantity and quality.

The three traditional methods of reimbursing individual primary care physicians are salary, fee-for-service and capitation. More recently, pay-for-performance (P4P) schemes that give financial incentives to health-care providers for improved performance on measures of quality and efficiency, have been introduced.

This presentation reports the results of two experimental games designed to test the effects of these different payment mechanisms. Both experiments were played with medical students, who were asked to complete a “real effort task”, showing the quality and quantity of effort exerted under different payment conditions.

The presentation formed part of a session on "Investigating Individual Health Providers' Responses to Incentives in Low- and Middle-Income Countries", at the iHEA Congress, July 2013.

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Investigating provider payment mechanisms in the lab

  1. 1. http://resyst.lshtm.ac.uk Investigating provider payment mechanisms in the lab Mylène Lagarde Duane Blaauw
  2. 2. Motivation • Remuneration mechanisms are key to shape the performance of health care systems • Difficult search for optimal incentives – From traditional remuneration schemes (SAL, FFS, CAP) to bundled payments and P4P – Multi-tasking environment: quality and quantity of care – Altruistic providers • Existing evidence is limited – Difficulty to disentangle effects of remuneration schemes from other contextual factors / incentive design characteristics – Observing quality of effort is difficult – Isolating impact of patient’s benefits on doctor’s labour supply decisions is impossible
  3. 3. Experimental Economics • Controlled environment – Actual monetary incentives to elicit decisions – All variables and rules externally manipulated by experimenter • Large body of work on remuneration schemes – Chosen effort experiments  Decisions about virtual levels of effort, associated (real) benefit function  Few applications in health economics to date: Hennig-Schmidt 2011 (JHE), Brosig-Koch et al. 2013, Keser et al. 2013 – Real effort experiments  Performing simple tasks (additions, counting letters, data entry, etc.)  Actual effects of real effort (boredom, intrinsic motivation)
  4. 4. Research questions • What is the relative impact of CAP, SAL and FFS on physicians’ effort (quantity & quality)? • What is the impact of patients’ benefits on physicians’ effort? • What is the relative impact of two quality-enhancing interventions – Pay-for-Performance – Public reporting
  5. 5. A report = a patient - amount of quantity of services provided (number of individual items entered) - amount of quality of services provided (correct entry or not) The medical game: overview • Real effort experiment • Data entry task LABORATORY REPORT REFERENCE NUMBER 421 Patient age: 29 HAEMATOLOGY AND BIOCHEMISTRY RESULTS Test Result Units Reference Range Full Blood Count RED BLOOD CELLS 5.8 x 1012 /L 4.5 - 6.5 HAEMOGLOBIN 15.2 g/dL 13.8 – 18.8 HAEMATOCRIT 47.2 % 40 - 56 MCV 89.8 fL 79 - 100 MCH 27.5 pg 27 - 35 MCHC 35.1 g/dL 29 - 37 WHITE BLOOD CELLS 7.2 x 109 /L 4.0 – 12.0 PLATELETS 317 x 109 /L 150 - 450 U&E SODIUM 142.5 mmol/L 135 - 150 POTASSIUM 3.7 mmol/L 3.5 - 5.1 CHLORIDE 103.2 mmol/L 98 - 107 BICARBONATE 23.5 mmol/L 21 - 29 UREA 6.5 mmol/L 2.1 - 7.1 CREATININE 95.8 μmol/L 80 - 115 Liver Function Test BILIRUBIN - TOTAL 6.2 μmol/L 2 - 26 BILIRUBIN - CONJUGATED 6.0 μmol/L 1 - 7 ALT 10.9 IU/L 0 - 40 AST 16.5 IU/L 15 - 40 ALKALINE PHOSPHATASE 127.6 IU/L 53 - 128 TOTAL PROTEIN 66.1 g/L 60 - 80 ALBUMIN 35.2 g/L 35 - 50 GLOBULIN 21.7 g/L 19 - 35
  6. 6. Payment • 4 consecutive periods of 8min – 15 laboratory reports to enter (9 long, 6 short) • Each period remunerated differently – Fee-for-service: R1 for each number entered (correct or not) – Capitation: R12/R15 for basic/extended report (correct or not) – Salary: R125 for the whole period – Period 4: choice of remuneration scheme • Other experimental procedures – Random order of first 3 remuneration schemes – One period chosen for payment
  7. 7. Outcome variables • Quantity of effort • Quality of effort • Unintended consequences In the game “Equivalent” in real life # of numbers entered # of services provided # of reports completed # of patients seen In the game “Equivalent” in real life % of correct entries Index of quality of care provided % of reports with less than 90% of correct entries Shirking (% of patients receiving poor care) In the game “Equivalent” in real life # of entries unnecessarily made Over-servicing % of numbers entered purposefully incorrectly Gaming
  8. 8. Experimental design • Participants randomly allocated to a treatment • Baseline (N=66) • Social benefit (N=66) – A charity receives R0.50 per correct entry • Pay-for-performance (N=66) – Remuneration partly conditioned to quality: R0.20 for each correct entry • Public reporting (N=58) – Performance (# correct entries made) reported in front of class before payment
  9. 9. http://resyst.lshtm.ac.uk PRELIMINARY RESULTS
  10. 10. (1) Effects of remuneration schemes (baseline treatment) Dependent variable # of acts performed # of patients seen Index of quality of care Shirking rate Over- servicing Gaming (1) (2) (3) (4) (5) (6) SAL -51.636*** -2.045*** 0.095*** -0.033* -2.163** -0.223*** (10.235) (0.619) (0.027) (0.017) (0.763) (0.038) CAP -11.273 -0.318 0.045 -0.005 -2.216*** -0.053 (10.679) (0.468) (0.026) (0.015) (0.551) (0.038) N_acts -0.004*** 0.105*** (0.001) (0.006) N_patients 0.012*** (0.002) Constant 199.364*** 10.682*** 1.186*** -0.118*** (19.885) (0.963) (0.161) (0.032) Controls N N N N N N Observations 198 198 198 198 198 198 Quantity Unint. csqQuality
  11. 11. (2) Impact of social benefit QUANTITY QUALITY UNINT. CSQ # of acts performed # of patients seen Quality index Shirking rate Over- servicing Gaming SB*SAL ns ns 0.097** ns ns -0.161*** SB*FFS ns ns 0.078** ns ns ns SB*CAP ns ns 0.088*** -0.040* ns -0.117**
  12. 12. (3) P4P vs. public reporting QUANTITY QUALITY UNINT. CSQ # of acts performed # of patients seen # of acts well performed Poor care rate Over- servicing Gaming P4P*SAL ns ns ns ns ns ns P4P*FFS ns ns 0.089* ns ns -0.180*** P4P*CAP ns ns ns ns ns -0.139*** PR*SAL ns ns 0.140*** ns -1.643* ns PR*FFS ns ns 0.148*** ns -2.808** -0.153*** PR*CAP ns ns 0.150*** ns -2.402** -0.133***
  13. 13. Summary of preliminary results • Confirm some theoretical predictions – FFS leads to highest quantity of effort  Incentives of CAP not clear enough? – Low-powered incentives (salary) leads to better quality  Quantity-quality trade-off – Over-servicing when high powered incentives linked to quantity • Support models of altruistic physicians • Information (public reporting) as cost-effective alternative to P4P?
  14. 14. Future work • Further analysis – Quantity-quality trade-offs – Determinants of self-selection into remuneration schemes • Experimental economics has a role to play in health economics – Possible to isolate relative effect of different designs more easily – Unpack interactions between remuneration schemes and institutional characteristics
  15. 15. http://resyst.lshtm.ac.uk Thank you Funded by
  16. 16. Capturing over-servicing

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