LDI Research Seminar_ Standardization under Group Incentives 4_27_12

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LDI Research Seminar_ Standardization under Group Incentives 4_27_12

  1. 1. Standardization under Group IncentivesJonathan Ketcham Pierre Leger Claudio Lucarelli Arizona State University HEC Montréal Cornell University April 27, 2012
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 13. Stents
  14. 14. Per-patient total device costs 2000-2007
  15. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 43. Variation in Gainsharing’s Effects by GroupComposition 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. 44. Variation in Gainsharing’s Effects by GroupComposition 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. 45. Heterogeneity in Costs by Group Characteristics TABLE 6. Variation in gainsharings 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 MDs average -0.034 -0.132 [0.040] [.037]** Gainsharing*Maximum MDs 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. 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. 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.

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