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http://resyst.lshtm.ac.uk
Investigating provider payment
mechanisms in the lab
Mylène Lagarde
Duane Blaauw
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
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)
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
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
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
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
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
http://resyst.lshtm.ac.uk
PRELIMINARY RESULTS
(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
(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**
(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***
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?
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
http://resyst.lshtm.ac.uk
Thank you
Funded by
Capturing over-servicing

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Investigating provider payment mechanisms in the lab

  • 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. 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. 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. 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. 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. 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. 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
  • 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. (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. (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. 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. 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

Editor's Notes

  1. Rates set to be equivalent for average performance (piloting)