1. Standardization under Group Incentives
Jonathan Ketcham Pierre Leger Claudio Lucarelli
Arizona State University
HEC Montréal
Cornell University
April 27, 2012

2. Introduction
Firms have used firm-wide or group-based incentives
(such as profit sharing) in a variety of settings.
Theoretical work has shown that group-based incentives
are preferred to individual incentives when:
Firms cannot observe individual worker’s contribution to
production, and/or
Workers engage in team production and can more easily
monitor each other’s productivity than the Firm (Alchian and
Demsetz, 1972; Varian, 1990, Che and Yoo, 2001)

3. Introduction
Empirically, group-based incentives such as profit sharing
have been shown to affect individual behavior (Kandel and
Lazear, 1992; Knez and Simester, 2001; Hamilton et al.
(2003); Gaynor et al. (2004)) even in the presence of moral
hazard incentives (Holmstrom, 1982).

4. Introduction
We propose and test a new rationale for team-based
incentives when:
1 agents/workers, through their choice of inputs, influence the
costs borne by the principal/firm, and
2 the principal/firm faces non-linear prices when purchasing
these inputs,
lead to inappropriate input choices under individual
incentives.

5. Introduction
The above situation is very common in the
healthcare-hospital sector.
Physicians are generally not hospital employees and
receive a payment directly from the insurer.
Physicians determine many of the costs (and benefits)
associated with treatment through their choices of drugs
and devices (D&D).
This is especially true in areas with "physician preference
items" (PPI)" like cardiology with stents.
Hospitals are paid a fixed payment for each patient
admitted and must cover all non-MD expenses.
As a result, physician choices of D&D are ultimately borne
by the hospital.

6. Introduction
Hospitals benefit from contract-compliance discounts
(quantity and market share) when purchasing these D&D
(where contracts are endogenous).
So not only do the physician’s choices of D&Ds directly
affect the costs borne by the hospital, they also affect the
prices that the hospital faces for all of its D&D purchases
(through non-linear prices or better bargaining-market
response).
Results in a misalignment of incentives where hospitals
care about the costs associated with different treatment
options (direct and indirect) whereas physicians do not.
What to do about these issues?

7. Introduction
Other researchers have examined alternative ways of
lowering costs in the medical device markets.
Grennan (2011) considers bargaining between hospitals
and suppliers and finds that mergers and GPOs (horizontal
arrangements) do not lead to lower prices .
Pauly and Burns (2008) explore many issues with this
market and advocate a restructuring of the
physician-hospital relationship (i.e., the vertical
relationship).
Our paper examines a specific vertical relationship
(through group-based financial incentives) and finds
important cost savings.

8. Introduction
Other Options
Prospective payments: May make physicians cost
conscious, it provides little incentive for coordination and
standardization.
Command and control: participation constraints and
professional norms limit a hospital’s ability to dictate the
use of certain D&D.
Direct payments: legal prohibitions including ’Stark
self-referral and CMP ’anti-kickback’ restrictions have (in
the past) prevented, hospitals from direct payments to
physicians.

9. Introduction
In this paper, we show theoretically and empirically that
team-based incentives such as profit sharing may provide
incentives to physicians to:
not only consider the costs of the D&Ds that they use
but also standardize with their fellow team members to
benefit from quantity and market share discounts.
That is, profit sharing helps internalize the two externalities
cardiologists cardiologists impose on hospitals.

10. Introduction
We test the predictions of our model of group-based
incentives under non-linear prices using data from a new
program known as gainsharing.
Although forbidden in theory, the Department of HHS’s
OIG has permitted this particular form of profit-sharing in
teams to be implemented.
These ’by permission’ programs with very strict rules were
set up specifically to deal with the rising costs of D&D.

11. Hospital-Physician Gainsharing in Cardiology
Several hospitals in the US have implemented gainsharing
programs with their non-employee cardiologists and
cardiac surgeons.
In these programs, hospitals pay physicians based on cost
reductions in pre-determined areas that are subject to the
physician’s control, such as Bare-Metal Stents (BMS) and
Drug-Eluting Stents (DES) in cardiology (PPI).
The initiatives sought to influence costs by promoting
standardization on manufacturer, limiting use of certain
products to an "as needed" basis, and substituting to
lower-priced items.

12. Hospital-Physician Gainsharing in Cardiology
More specifically, savings to the hospital (and
corresponding payouts to physicians) under gainsharing
can come from:
Moving to lower-priced devices (substitution)
Lower prices paid through: (i) volume and market share
discounts, and (ii) greater bargaining or market response.
Reduce quantity of devices used (although this is
theoretically forbidden).

13. Stents

14. Per-patient total device costs 2000-2007

15. What we do
We first present a theoretical rationale for gainsharing in
the environment presented above.
Generate predictions on how cardiologists respond to
these arrangements through their choice of items (stents).
Also generate predictions on the market response to such
arrangements.

16. What we do
More specifically we test the effect of gainsharing on:
per patient costs (p ∗ q) on stents (both BMS and DES),
quantities of stents (q), prices (p) paid per stent.
within-prices of stent (i.e., did price changes come from
substitution or actual price reductions at the vendor level).

17. What we do
Further test if:
the price effects are coming from standardization (i.e.,
contract compliance)?
whether there was a market response (i.e., did we see
convergence of prices)?
Finally, we examine the effect of team heterogeneity and
team size on choices and costs.

18. Theoretical Rationale for Group-Based Incentives
Recall:
Hospitals receive a fixed (prospective) payment for each
patient and pay all of the device and drug costs used
during treatment.
But treatment decisions about types and quantities of D&D
are made by non-employee cardiologists and internists.
Prices paid for a particular D&Ds are endogenous. They
depend on:
The quantity (and market share) of devices purchased at
the hospital level.
Negotiation on the actual quantity and market share
discounts between the hospitals and
manufacturers/suppliers.

19. Theoretical Rationale for Group-Based Incentives
Hospitals h’s objective function
n 2 Ca n
Πh = DRGi − ( pca qca ) + γ m(θi , q).
i=1 a=1 c=1 i=1
where c = {1, ..., Ca } denotes the device in category
a = {BMS, DES}.
Prices are represented by a hospital-specific function:
T
qca
T
pca = ph qca , T
.
qa

20. Theoretical Rationale for Group-Based Incentives
If Hospital h could choose the actual devices and
quantities, it would maximize its objective function given
above.
When faced with a particular patient, the Hospital must
consider:
the costs and benefits (i.e., health) of each D&D choice on
the patient;
how these choices affect the prices paid for D&D for other
patients (through discounts).
As a result, the Hospital would succesfully internalize all
the costs and benefits associated with D&D choices.

21. Theoretical Rationale for Group-Based Incentives
The physician, however, who treats I patients under the
traditional payment system (i.e., FFS) maximizes:
I I
j
Vk = Revenuei + β m(θi , q),
i=1 i=1
The utility maximizing vector of care is simply:
q ∗ argmax m(θi , q).
Physicians base decisions exclusively on the clinical value
of the care provided (or preferences over devices)
Do not consider the costs of care nor its effect on prices of
D&Ds faced by others.

22. Theoretical Rationale for Group-Based Incentives
Need a mechanism to align physicians’ incentives with
those of the hospital’s.
Need a mechanism whereby cardiologists will:
Consider the costs of the D&D they use;
Consider how their choices affect the prices faced by other
physicians (externality).

23. Theoretical Rationale for Group-Based Incentives
Examine gainsharing for hospital-based coronary
catheterization laboratories ("cath labs").
Under gainsharing hospitals pay physicians based on cost
reductions in pre-determined areas that are subject to
physicians’ control (i.e., BMS, DES...).
Historic baseline for each group for each device category
(BMS and DES).
Cost savings relative to baseline are split 50/50 between
the hospital and the group (and equally between team
members)

24. Hospital-Physician Gainsharing in Cardiology
Worth noting:
Physicians formed partnerships prior and unrelated to
gainsharing.
Therefore, no need to consider endogeneity of teams.

25. Theoretical Model of Gainsharing
Hospitals
n 2 Ca J 2
j
Πh = DRGi − ( pca qca ) − payouta + ...,
i=1 a=1 c=1 j=1 a=1
Payouts
 j
 0, volumet−1 ∗ J Ca j
j 1 total volumet−1 j=1 c=1 pca,t−1 qca,t−1
payouta,t = max
2 − Ca j
c=1 pca,t qca,t


26. Theoretical Model of Gainsharing
Physician Preferences
I 2 I
j 1 j
Vk = Revenuei + payouta + β m(θi,t , q)
Dj
i=1 a=1 i=1
I
− [Λ(q(θi ), q(θi ), Dj )],
i=1
Can substitute altruism for preferences for particular
devices

27. Theoretical Model of Gainsharing
The Multi-Stage Game
1 Stage 1: Hospitals negotiate with manufacturers on the
prices (contingent contracts with the manufacturers based
on the volume and share)
2 Stage 2: Hospitals decide on the “appropriate”
illness-specific treatment vectors q(θ) (treatment guideline
for each illness severity θ to encourage standardization)
3 Stage 3: Physicians treat their patients to maximize their
utility (i.e., decide on q(θ))
4 Stage 4: The physician group monitors the members’
behavior (to decrease the free-riding effect)
5 Stage 5: Payouts are distributed and penalties attributed.

28. Predictions from Gainsharing Model
If physicians have strong preferences for particular
devices (or, equivalently have strong altruism):
Physicians are reluctant to follow guidelines (i.e., reluctant
to switch devices) and thus little standardization.
Manufacturers will exert strong market power and thus
maintain previous levels of price dispersion.
Gainsharing generates few savings from any of the
substitution, bargaining or contract compliance (share
discounts).

29. Predictions from Gainsharing Model
If physicians have weak preferences for particular devices
(or, equivalently, low altruism):
Physicians are willing to follow guidelines (i.e., willing to
switch devices).
Manufacturers will exert weak market power which leads to
convergence of prices across devices (market response).
May or may not lead to strong standardization on devices
→ threat of switching may be enough.
Gainsharing generates savings due to reductions in prices,
but also potentially (but not necessarily) due to substitution
& contract compliance (quantity and market share)

30. Gainsharing Experiment and Data
Comes from Goodroe Healthcare for the 2001 to 2007 →
complete sample of gainsharing programs.
12 Hospitals ran gainsharing programs (from 1 to 5
programs), each lasting one year.
19 one-year programs in all.
In total, 168 physicians from 34 groups treated 73,672
patients under the programs.
Physician group sizes ranged from 1 to 17 physicians.
Also have data on 138 hospitals who did not participate in
gainsharing programs (but did get the software).

31. Gainsharing Experiment and Data
Real time data collected in hospital-based coronary
catheterization laboratories ("cath labs") include:
Patient data (patient characteristics, risk factors ,
procedures performed).
Drugs and devices data (manufacturer, price paid net of
rebates, characteristics) → BMS (8), DES (2).
Identifiers for the diagnostic and interventional cardiologist
and practice affiliations.
Targeting of particular drugs or devices.

32. Gainsharing and Average Costs per Patient
Effect of gainsharing on average costs
We analyze the cost per patient (conditional on PCI) for
BMS and DES separately. We estimate the patient-level
(Tobit) model:
∗
Yight = β0 +β1 Gg +β2 Ag +β3 GQg +β4 AQg +β5 Tt +β6 Hh +β7 θi +εight ,
where the observed cost per patient Yi = 0 if Yi∗ ≤ 0 and
Yi = Yi∗ if Yi > 0.

33. Gainsharing and Average Cost per Patient
TABLE 2. Incremental effects of gainsharing on risk-adjusted costs per patient
Per Patient Cost of
Drug-eluting
stents Bare metal stents
Price target -315.09 -41.83
[26.02]*** [9.88]***
Quantity Target -129.14 128.20
[74.0]** [14.46]***
N 209,734 210,504
Mean of dependent variable 2200.0 505.6
* p<0.05 ** p<0.01 ***p<0.001
Marginal effects from Tobit models that include hospital and year-by-quarter
fixed effects and patient risk adjustment variables.
Analysis is limited to patients who received a percutaneous coronary
intervention.

34. Gainsharing and Average Cost per Patient
With price targets alone, average BMS and DES costs per
patient decreases.
With price + quantity targets (cost targets), per patient
DES costs decrease $444 while per patient BMS increase
by $90 for DES.
Suggests some substitution between DES and BMS.

35. Gainsharing and Average Quantities per patient
Cost reductions could be driven by quantity and-or price
reductions.
Estimate the incremental effect of gainsharing on
risk-adjusted quantities per patient.
Also estimate a similar model (linear) for the incremental
effect of gainsharing on device prices.

36. Gainsharing and Average Quantities per Patient
TABLE 3. Incremental effects of gainsharing on risk-adjusted quantities per
patient
Per Patient Quantity of
Drug-eluting
stents Bare metal stents
Price target -0.09 -0.15
[.010]*** [.030]***
Quantity Target -0.03 0.25
[.019] [.043]***
N 211,800 212,566
Mean of dependent variable 0.86 0.44
* p<0.05 ** p<0.01 ***p<0.001
Marginal effects from Tobit models that include hospital and year-by-quarter
fixed effects and patient risk adjustment variables.
Analysis is limited to patients who received a percutaneous coronary
intervention.
Standard errors in brackets.

37. Gainsharing and Average Quantities per Patient
Price targets lead to some reductions in BMS and in DES.
Price + quantity targets (i.e., cost targets) lead to some
increase in BMS.
So about 10% increase in BMS with corresponding 10%
decrease in DES.

38. Gainsharing and Average Price Paid paid per Stent
TABLE 4. Incremental effects of gainsharing on price per device
Price Per Product for
Drug-eluting stents Bare metal stents
Overall Within Product Overall Within Product
Price target -120.024 -122.495 -106.866 -86.420
[30.725]*** [29.598]*** [34.719]** [22.372]***
Quantity Target 44.359 8.118 53.515 23.376
[35.726] [45.954] [39.291] [15.721]
N 244,219 244,219 208,909 208,909
Mean of dependent variable 2562 2562 1082 1082
* p<0.05 ** p<0.01 ***p<0.001
Marginal effects from individual device-level models that include hospital and year-by-quarter fixed effects. Within-product
results also include product fixed effects.
Standard errors in brackets.

39. Gainsharing and Average Price paid per Stent
Price targets lead to important reductions in BMS and
DES.
For DES, all price reduction comes from within price
reductions.
For BMS, approximately 80% of the reduction in price paid
per stent comes from within price reduction (rest from
substitution).

40. Gainsharing and Standardization
Effect of Gainsharing on Standardization
Within-price reductions can come from (i) contract
compliance, or (ii) better bargaining/market response.
Standardization → contract compliance discounts → within
prices fall.
Threat of Standardization → price competition → within
prices fall
Look at the effect of gainsharing on standardization of
supplier (HHI) and prices (std. dev.)
Look at physician, team and hospital level.

41. Gainsharing and Standardization
Effect of Gainsharing on Standardization
For each D&D category, we estimate (with a fractional
logic):
Yght = β0 +β1 Gg +β2 Ag +β3 GQg +β4 AQg +β5 Tt +β6 Hh +εght ,
where Yght is either the HH1 of CR1.

42. Standardization
TABLE 5. Incremental effects of gainsharing on within-provider standardization
Manufacturer HHI Std Dev of Prices
Drug-eluting Bare metal Drug-eluting Bare metal
stents stents stents stents
Within-physician
Price target 0.030 0.016 -4.67 16.997
[0.013]* [0.017] [4.908] [8.567]*
Quantity Target 0.047 0.151 -179.76 -67.59
[0.053] [0.022]*** [12.100]*** [18.184]***
N 8,375 13,646 8,375 13,646
Mean of dependent variable 0.84 0.78 59.42 94.55
Within-hospital
Price target 0.040 0.001 -7.05 33.00
[0.023] [0.024] [15.133] [13.906]*
Quantity Target -0.003 0.236 -184.40 -42.062
[0.040] [0.037]*** [25.715]*** [35.530]
N 915 1,903 915 1,903
Mean of dependent variable 0.75 0.62 89.35 144.16
Within-group
Price target 0.023 0.036 2.82 21.46
[0.023] [0.025] [9.103] [18.861]
Quantity Target -0.062 0.291 -106.86 -75.80
[0.058] [0.032]*** [10.899]*** [32.119]*
N 313 396 313 396
Mean of dependent variable 0.76 0.72 45.20 92.74
* p<0.05 ** p<0.01 ***p<0.001
HHI is Herfindahl-Hirschman Index.
Robust standard errors in brackets.
All models include provider and year-by-quarter fixed effects. Group-level estimates do not include the
non-gainsharing hospitals. Estimates are weighted by total volume.
Fractional logit models were estimated for HHIs and linear models were estimated for the standard

43. Variation in Gainsharing’s Effects by Group
Composition
Heterogeneity in physician types.
Examine the effect of group composition (heterogeneity in
productivity) (Hamilton, Nickerson and Owen 2003).
We have (i) exogenous formation in team, (ii) a control
group, (iii) more than one treatment group.
Construct individual productivity measures (i.e., w.r.t. to
costs-per-patient per category).
Construct 2 different measures of team heterogeneity
(standard-deviation and highest - lowest).

44. Variation in Gainsharing’s Effects by Group
Composition
Heterogeneity in group size
Examine the effect of group size (Gaynor, Rebitzer and
Taylor, 2004).
Again we have (i) exogenous formation in team (size), (ii) a
control group, (iii) more than one treatment group.

45. Heterogeneity in Costs by Group Characteristics
TABLE 6. Variation in gainsharing's effects on risk-adjusted category cost per patient, by group characteristics
Drug-eluting stents Bare metal stents
Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Cost per patient prior to gainsharing
Gainsharing*Average across MDs in Group -0.261 -0.436 0.045 -0.037
[0.035]*** [0.055]*** [0.030] [0.13]
Gainsharing*Minimum MD's average -0.034 -0.132
[0.040] [.037]**
Gainsharing*Maximum MD's average 0.255 0.147
[0.063]*** [0.11]
Gainsharing*Standard Deviation between MDs 0.327 -0.124
[0.12]** [0.21]
Group size
Gainsharing*Solo 336.694 216.624 -269.347 -71.040 -36.060 -49.876
[132.19]* [155.11] [106.45]* [51.50] [50.22] [47.22]
Gainsharing*Size 2-5 358.417 228.688 -90.759 50.133 23.310 53.106
[118.52]** [132.05] [82.08] [39.56] [34.84] [28.07]
Gainsharing*Size 6-10 235.647 8.965 -204.778 36.344 -44.710 53.690
[95.297]* [119.91] [40.46]*** [32.32] [32.14] [13.68]***
Gainsharing*Size 11+ 401.270 131.547 -67.846 -1.996 -96.258 13.310
[97.016]*** [128.11] [33.85]* [34.10] [32.44]** [11.03]
Observations 28,618 28,626 29,991 33,092 33,394 34,835
Results are the incremental effects from Tobit models.
All models include provider and year-by-quarter fixed effects and patient risk adjustment variables.
Analysis is limited to patients who received a percutaneous coronary intervention and treated by MDs who treated more
than 10 PCI patients in the data prior to gainsharing.
* p<0.05 ** p<0.01 ***p<0.001

46. Heterogeneity in Standardization
TABLE 7. Incremental effects of gainsharing on within-group standardization, by group characteristics
Manufacturer HHI Std Dev of Prices Manufacturer HHI Std Dev of Prices
Drug-eluting Bare metal Drug-eluting Bare metal Drug-eluting Bare metal Drug-eluting Bare metal
stents stents stents stents stents stents stents stents
Model 1 Model 1 Model 1 Model 1 Model 2 Model 2 Model 2 Model 2
Standardization prior to gainsharing
Gainsharing*Prior HHI 0.306 -0.009
[0.251] [0.144]
Gainsharing*Prior Std Dev of Prices -0.544 -0.076
[0.065]*** [0.328]
Group size
Gainsharing*Solo -0.175 0.013 27.238 -19.647 0.051 -0.589 0.007 -25.875
[0.196] [0.143] [12.500]* [43.907] [0.043] [12.034] [0.092] [34.517]
Gainsharing*Size 2-5 -0.195 -0.222 26.403 29.96 0.02 -9.521 -0.227 23.478
[0.185] [0.120] [16.814] [45.744] [0.046] [16.608] [0.057]*** [33.096]
Gainsharing*Size 6-10 -0.133 0.046 38.898 27.031 0.075 -9.553 0.041 19.1
[0.171] [0.096] [16.289]* [41.384] [0.030]* [14.413] [0.040] [26.969]
Gainsharing*Size 11+ -0.198 0.003 40.136 45.563 -0.004 -0.482 -0.001 36.84
[0.156] [0.097] [12.469]** [45.670] [0.022] [11.402] [0.036] [22.720]
N 256 355 256 355 256 256 355 355
Dependent Variable Mean 0.75 0.72 50.67 94.61 0.75 50.67 0.72 94.61
Standard errors in brackets
All models include group and year-by-quarter fixed effects. Fractional logit models are used for HHI and CR1.
* p<0.05 ** p<0.01 ***p<0.001

47. Conclusion
Results suggest that gainsharing led to important
reductions in the average cost per person for DES and
some increase in average costs for BMS.
Quantities of BMS increased and DES decreased under
price+quantity targets but prices paid decreased for both.
Within prices decreased for both DES and BMS → savings
not just coming from substitution to cheaper devices.
Gainsharing increases standardization (3 levels) for BMS
→ savings can come from quantity and market share
discounts or market response)
Gainsharing didn’t lead to standardization for DES →
market response (suggest threat of standardization
sufficient).
Team heterogeneity appears to play some role while team
size doesn’t appear to play much.