JLEO, V3 S i77 Struturing Inentives within Aountable Care Organizations Brigham Frandsen Brigham Young University James B. Rebitzer* Boston University Shool of Management and ational Bureau of Eonomi Researh and IZA Aountable Care Organizations (ACOs) are new organizations reated by the Affordable Care At to enourage more effiient, integrated are delivery. To promote effiieny, ACOs sign ontrats under whih they keep a fration of the savings from keeping osts below target provided they also maintain uality levels. To promote integration and failitate measurement, ACOs are reuired to have at least 5000 enrollees and so must oordinate aross many providers. We alibrate a model of optimal ACO inentives using proprietary performane measures from a large insurer. Our key finding is that free-riding is a severe problem and auses optimal inentive payments to exeed ost savings unless ACOs simultaneously ahieve extremely large effiieny gains. This implies that suessful ACOs will likely rely on motivational strategies that amplify the effets of underpowered inentives. These motivational strategies raise important uestions about the limits of ACOs as a poliy for promoting more effiient, integrated are (JEL D23, D86, I2, L4, L24, M52). Eonomists and others onerned about ineffiienies in the US health are delivery system freuently worry about the fragmented struture of physiian praties. Fragmented are delivered by physiians working as independent owners of small praties is, so the story goes, inapable of mathing the high uality or low ost of are delivered by large integrated systems. The ineffiient fragmented providers are not driven out of *Boston University Shool of Management, 595 Commonwealth Avenue, Boston, MA 0230, USA. Email: rebitzer@bu.edu. We would like to thank Aetna, a national health benefits ompany for its ollaboration. We also thank the following people for omments and suggestions that greatly improved the artile: David Cutler, Robert Gibbons, Iver Juster, Daniel Kessler, Patrik Warren, Julie Wulf in her apaity as oeditor and three anonymous referees at this journal as well as seminar partiipants at the Haas/Sloan Conferene on the Law and Eonomis of Organizations; the BER onferene on The Organization and Produtivity of Healthare Delivery; The Conferene on Healthare Markets at ortwestern s Kellogg Shool; the International Health Eonomis Assoiation; and the Department of Health Care Poliy at Harvard Medial Shool. J.B.R. also aknowledges support for this researh from Mirosoft. We are, of ourse, responsible for any remaining errors or oversights. The Journal of Law, Eonomis, and Organization, Vol. 3, Supplement doi:0.093/jleo/ewu00 Advane Aess published May 5, 204 ß The Author 204. Published by Oxford University Press on behalf of Yale University. All rights reserved. For Permissions, please email: journals.permissions@oup.om
i78 The Journal of Law, Eonomis, & Organization, V3 S the market, however, beause they operate in a largely feefor-servie payment environment that does not measure or reward ost-effiient delivery of health are servies. The view that fragmented are delivery has both lower uality and higher ost raises two fundamental eonomi uestions. First, what is it about are delivered within an organization that enables superior performane? Seondly, do these advantages arue only to traditional hierarhial organizations that own hospitals and linis and hire physiians as employees? Or might the advantages of integration also arue to hybrid forms that more losely resemble the organizational environment in whih health are is urrently delivered in muh of the United States? We explore these uestions in the ontext of Aountable Care Organizations (ACOs). ACOs are a new model for integrated health are delivery reated by the Obama administration s Patient Protetion and Affordable Care At. ACOs are designed to promote the benefits of integrated are by allowing a network of hospitals and providers to jointly ontrat with the Center for Mediare and Mediaid Servies (CMS) to provide are to a population of Mediare patients in an environment that rewards ost effiieny. The key feature of these ontrats is the use of shared savings to ontain osts ombined with inentives to maintain are uality at aeptable levels. In health are settings, there is a very ompelling reason to aggregate inentives within organizations: uality measures are typially uite noisy and averaging measured performane aross the members of an organization improves preision. One study finds, for example, that primary are physiians had annual median aseloads of 260 Mediare patients (yweide et al. 2009). Of these, 25 were women eligible for mammography and 30 had diabetes. With suh low numbers, individual primary are physiian praties simply do not have a suffiient aseload to reliably detet, say, a 0% improvement in the rate of use of relevant preventive are measures suh as routine breast exams and monitoring hemoglobin levels in blood. If real improvements in uality annot be distinguished from hanges due to random hane, pay-for-performane omes unomfortably lose to pay for luk. On this basis, Fisher et al. (2009) argue that ACOs reuire a minimum of 5000 benefiiaries for performane measures to have suffiient power to reliably identify meaningful performane improvements, a minimum size reuirement that CMS has sine adopted. From an eonomi perspetive, this statistial approah to determining the optimal sale of an organization is inomplete. Improving the preision of performane measures does indeed enhane the effiieny of inentive pay arrangements, but this gain omes at a ost. Inreasing the size of patient populations usually reuires bringing more physiians into the ACO. As the number of physiians grows, the effet of any physiian s ation on the organization s overall performane is diminished and so inentives are diluted. On this basis, one might expet that there exists some optimal ACO size that balanes the marginal osts from free-riding against the marginal benefits of enhaned preision in performane measures. Surprisingly we
Struturing Inentives within Aountable Care Organizations i79 find that this is not the ase. Inreasing the size of ACOs simply makes the inentive problem more severe. We establish this result using a model of physiians operating under ACO-style inentives. Our approah builds upon onventional prinipal-agent models, but is unusual in that it fouses on the sort of nonlinear inentives built into the ACO program and ommonly used elsewhere in health are shared savings relative to ost benhmarks to enourage ost-effiient pratie styles with payouts onditional on ahieving target levels of are uality. We further investigate the nature of the ACO inentive problem by alibrating our inentive model using onfidential laims data and uality measures from a very large sample of hronially ill patients. Here we find that the free-riding problems within ACOs of reuisite size are so severe that pay-for-performane plans aimed at ahieving meaningful ost redutions will typially not be self-funding; that is, the savings they produe will not over the osts of the performane bonuses without extremely large eonomies of sale or produtivity improvements within ACOs. It follows that ACOs ommitted to self-finaned pay-for-performane will likely operate with under-powered inentives. Suessful ACOs will have to find ways to augment their underpowered inentives with motivational strategies that omplement pay-for-performane. The diffiulty of implementing these omplementary motivational strategies determines the ost of transating are delivery within organizations as opposed to markets. Interestingly, some of the omplementary strategies we identify will only be workable within onventional integrated organizations, whereas others might be implemented within hybrid organizational forms that are more ongruent with fragmented pratie patterns. Gibbons (200), in his overview of the field, argues that transation ost eonomis has done a good job identifying the osts of transating within a market, but has not yet provided a satisfatory aount of variations in the ost of transating within an organization. From this perspetive, ACOs provide an interesting and poliy-relevant laboratory for examining integration osts for varying organizational types. The artile proeeds in four setions. Setion briefly introdues relevant institutional bakground on health are fragmentation and the struture of ACOs. Setion 2 develops our model of inentive pay and Setion 3 presents the results of our alibration exerise. Setion 4 onsiders the problem of augmenting underpowered pay-for-performane inentives within ACOs.. Fragmented Care Delivery and the ACO as Poliy Response 2 Health servies researhers have long argued that a entral problem with health are delivery in the United States is fragmentation (Cebul et al. 2008).. Masten et al. (99) identify a similar gap in the literature. 2. Muh of this setion is adapted from Rebitzer and Votruba (20) review of the organizational eonomis of physiian s praties.
i80 The Journal of Law, Eonomis, & Organization, V3 S Individual patients are freuently treated by numerous are providers who have only weak organizational ties with one another and often little expertise in oordinating are. This results in poor information flows, heightened error rates, and inadeuate are oordination problems that are espeially troublesome for the management of patients with ostly hroni diseases. The obvious fix, aording to this view, is for physiians to join large integrated are delivery systems. Yet as late as 200, 60% of physiians worked either in solo pratie or in groups of 2 to 4 physiians and only 7% worked in groups with 50 or more physiians. In that same year, more than 65% of physiians were self-employed and only 35% were employees. Why, given their purported effiieny advantages, do not we see more physiians going to work for large integrated are organizations? Surprisingly little researh has been devoted to this important uestion, but onventional wisdom is that the answer lies in the ways health are servies are finaned and purhased. 3 More speifially, restritions on Mediare s purhasing poliies ombined with oordination failures between nongovernmental buyers and providers prevent the emergene of effiient integrated are delivery organizations. Mediare, the largest single buyer of medial servies, is loked by rules and legislation into a fee-for-servie payment system and annot seletively ontrat with more effiient physiian groups. Compounding the problem is the fat that the Mediare regulatory boards harged with evaluating new tehnologies are onerned primarily with whether new drugs or proedures offer positive benefits rather than whether they are ost-effetive (Baiker and Chandra 20). The failure to onsider osteffetiveness likely has system-wide reperussions beause ommerial health insurane plans are heavily influened by Mediare overage deisions (Baiker and Chandra 20). If Mediare is hamstrung by regulations, the private setor is onstrained by different onsiderations. Many employers who purhase insurane on behalf of their employees are not interested in or apable of evaluating the ost-effetiveness of the are their employees reeive. 4 3. In ontrast, there has been a large literature doumenting the produtive and alloative ineffiienies in our are delivery systems. For an inisive review see Baiker and Chandra (20). 4. In a ase study of Geisinger s Provenare program, Clark and Rosenthal uote the results of onversations between Geisinger and the employers who buy their insurane. We went with the health plan leadership and talked to a number of employers. We told them that we would guarantee delivery of the best are and that we wouldn t submit a bill otherwise. The employers didn t want any of that. Their eyes glazed over. They said, As far as we know, we re already buying best praties. The evidene we really are about is whether or not the patients need the proedure in the first plae. In addition, we don t like all of the unpreditability in osts that you get with eah patient. Give us one prie per proedure and you worry about all the other stuff (Clark and Rosenthal 2008: 8).
Struturing Inentives within Aountable Care Organizations i8 Sophistiated employers (typially large, self-insured ompanies) would like to reward high effiieny providers but are thwarted by a thorny oordination problem. Suppose that the full effiieny gains of integrated are delivery an only be realized under bundled prospetive payment systems (Crosson 2009). In ommunities with highly fragmented are delivery, it is hard to find providers with the apaity to sueed under suh a payment system. As a result, payers do not innovate away from the status uo fee-for-servie payment system and there is little ompetitive advantage for providers to move out of their urrently fragmented delivery organizations. 5 ACOs are designed to overome these impediments to payment reform. 6 First, and perhaps most important, ACOs offer a means by whih Mediare an break away from traditional fee-for-servie reimbursements and reward effiient providers. As a legal entity, ACOs are omposed of a network of hospitals and providers that ontrat with the Center for Mediare and Mediaid Servies (CMS) to provide are to a large blo of Mediare patients (5000 or more). The ontrats, whih last for three years, reate a single risk-bearing entity with inentives to ontrol osts. 7 ACOs that ome in under their speified ost benhmarks earn a fration of the savings. In order to reeive these payments, the ACO must also lear stringent threshold uality levels on a number of indiators that reflet patient and aregiver experiene, are oordination, patient safety, preventive are, and health of at-risk frail and elderly populations (Ginsburg 20). The goal of this inentive system is to reward effiient providers without sarifiing uality. By enouraging the formation of large provider organizations de novo, CMS may also overome the oordination failures that have prevented sophistiated private buyers from reforming their own payment praties. Indeed there is nothing stopping ACOs that ontrat with Mediare from also ontrating with private payers. The prospet of 5. In omments on a previous draft of this artile, Daniel Kessler pointed out another ontributing issue in the private setor. The fat that private insurane expenditures are tax exempt further redues gains from eliminating ineffiient spending. 6. Although ACOs are only a small part of a huge piee of legislation, they have attrated a great deal of attention from poliy-makers, physiians, and managers. As of Otober 202, there were a total of 38 ACOs in 48 states. Mediare ACOs over 2.4 million benefiiaries in 40 states plus Washington DC (Meyer 202). As an indiation of the interest in ACOs, onsider the following inomplete list of relatively reent artiles in suh leading journals as the ew England Journal of Mediine, The Journal of the Amerian Medial Assoiation and Health Affairs: Burns and Pauly (202), Crosson (2009), Crosson (20), Ginsburg (20), Meyer (202), Shields et al. (20), Shortell and Casalino (200), Singer and Shortell (20), and Song et al. (20). 7. The exat nature of the payments to ACOs varies a good deal. All ACOs fae a ost benhmark for taking are of defined groups of patients. If they meet performane standards, they share in any ost savings they ahieve; in some ases, they also may share losses they inur. Mediare ACOs are paid on a fee-for-servie basis rather than on a per member per month basis further louding the piture (Meyer 202).
i82 The Journal of Law, Eonomis, & Organization, V3 S emerging integrated delivery organizations may already be moving savvy insurane ompanies to rethink their payment poliies. Song et al. (20) analyze the effets of a reently introdued global double-sided payment inentive system implemented by Blue Cross Blue Shield of Massahusetts. The ontrat was similar in many respets to the shared savings program for Mediare but instead of Mediare patients it was implemented for Health Maintenane Organizations (HMO) and point of servie ommerial populations. From an organizational perspetive, ACOs have a number of novel features. ACOs annot restrit their members to a speifi network of physiians and there is nothing in the legislation reuiring that ACOs be onstituted as a traditional organization in whih dotors are either employees or owners of a risk-bearing entity that also owns the relevant apital euipment. Indeed advoates who favor ACOs as a means of promoting integrated are systems see them emerging from five different pratie arrangements: integrated delivery systems that ombine insurane, hospitals, and physiians; multispeialty group praties; physiian hospital organizations; independent pratie assoiations; and virtual physiian organizations (Shortell et al. 200). As we disuss below, the transformation of hybrid and virtual physiian organizations into ACOs poses speial problems and opportunities for inentive design. 8 Larson, Van Citters, et al. (202) offer an in-depth look at four reently formed ACOs that gives a tangible sense of the variety of organizations involved. One is an independent pratie assoiation that employs 700 physiians and has 2400 affiliated; another is an integrated hospital delivery system that employs 475 dotors and owns five hospitals. The third is a loose independent pratie assoiation with 40 employed physiians and 2500 affiliated ones that has affiliations with 8 hospitals but owns none of its own. The fourth is a ommunity hospital system that employs 6 physiians and has 800 affiliated and owns two hospitals. In addition to influening primary are pratie, the ACO model may also transform the link between primary are and speialized are. ACOs may be able to improve their bottom line by introduing training and omputer-assisted deision support that failitates generalists substituting their own deisions for those of speialists. It may, for example, be effiient to train primary are physiians to treat rashes and ane rather than sending every ase of rash or ane to a dermatologist. On the other hand, the vast explosion in medial knowledge implies that there are limits to the substitution of generalist for speialist are (Beker and Murphy 992). In this ase, Gariano and Santos (2004) analysis suggests that effiiently managing referrals to speialists will likely entail bringing 8. Meyer (202) reports that of the 4 provider groups in Mediare Shared Savings ACO Program, nearly half are physiian-driven organizations serving fewer than 0,000 benefiiaries. In addition, 32 larger provider groups with experiene in oordinated are started Mediare Pioneer ACOs.
Struturing Inentives within Aountable Care Organizations i83 some speialists into the ACO. Keeping these speialists fully oupied may also exert additional upward pressure on the sale of ACOs. 2. Modeling Inentives in Fee-for-servie and ACO Environments In this setion, we present a simple multitask model of physiian inentives. Physiian effort and attention is divided between finding ways to generate inome and providing uality are to patients. In a fee-for-servie environment there is little tension between these two goals: physiians get reimbursed for all the medially neessary are their patients reuire. Things are different, however, in an ACO environment. Here ost benhmarks make it possible for providers to profit by not providing are to patients. For this reason, ACO ontrats speify that providers an keep some portion of savings below the ost benhmark provided that the organization also lears speifi uality thresholds. 9 More formally, we model an ACO as a team of dotors who aept a ost benhmark for the are of a defined group of patients, and onsider how the prinipal (i.e., CMS) should hoose savings bonuses and uality thresholds in order to indue a desired level of ost savings and are uality. Following the typial ontrat theoretial framework, we first model the physiians best responses to a given inentive sheme, and take those best responses as onstraints in the prinipal s deision problem. Average osts of are for the team depend on eah dotor i s ostontrol efforts, e i, whih are measured in money-metri units, as well as noise, i : C ¼ X i¼ C+ i e i where C is the average baseline ost of are. Quality of are likewise depends on noise and money-metri effort devoted to uality. Average uality for the team is Q ¼ X i¼ e i i 9. The atual inentive ontrats are more varied than this. Meyer (202) briefly desribes a number of different ompensation setups in Mediare s ACO program. In the Shared Savings program, ACOs reeive bonuses if they ahieve ost and uality targets. In the future, Shared Savings ACOs will have to aept two sided risk and pay CMS bak if they exeed spending targets. The Shared Savings program also inludes an Advane Payment ACO model in whih smaller groups reeive their potential savings up front to help them fund infrastruture osts. Pioneer ACOs are formed from large provider groups with more experiene in oordinated are. These ACOs urrently aept two sided risk and they must show that at least half of their revenues in the near future will ome from similar ontrats with other payers.
i84 The Journal of Law, Eonomis, & Organization, V3 S The ost and uality disturbanes are not observable, have mean zero and variane 2 and 2 respetively, and are independent from eah other and aross dotors. ACO members are ompensated based on the entire team s level of osts and uality. The team splits evenly a fration, b, of savings (or losses) relative to baseline, but reeives a positive payout only if average uality exeeds a speified threshold x. Dotors are risk neutral and maximize expeted inome minus effort osts. Dotor i s payoff is therefore 2 U i ¼ EbC ½ ð C Þð ðq < x ÞðC C > 0ÞÞŠ e i +e i : ðþ 2 The first term in the utility funtion in effet assumes that individual physiians are risk-neutral in bonus inome. This assumption may be surprising given the importane of risk pooling in the struturing of ACOs but organizations pool risk beause of liuidity and redit onstraints and the onseuent risk of insolveny should an ACO inur muh higher than expeted osts. Our artile does not analyze these enterpriselevel risk-management onerns. Rather we fous on how organization level inentives influene the ations of individual physiians. In this ontext, risk neutrality most realistially aptures physiians preferenes over the relatively small individual payouts at stake in ACOs. 0 Introduing risk aversion into the model would also greatly ompliate the analysis muh without adding insight. The ore uestion of our artile onerns inentive and moral hazard problems and these are handled entirely through our analysis of the variable omponent of ompensation just as they would be if the model allowed for risk-averse agents. Introduing risk aversion, however, would also reuire us to derive both the variable and fixed omponent of ompensation beause this latter omponent of ompensation provides inome insurane for risk-averse agents. Thus by abstrating from risk aversion, we are greatly simplifying the analysis without altering our onlusions in a substantive way. The last term in the utility funtion reflets the multi-task nature of effort devoted to ost redution and to uality. An inrease in e inreases the marginal ost of providing effort for ost-redution ativities and an inrease in e similarly inreases the marginal ost of uality improving efforts. This speifiation also implies that physiians are intrinsially motivated to supply minimum levels of effort to ost and uality, but additional inentives are reuired to move them beyond these levels. 0. This is onsistent with the onventional eonomi theory of insurane whih finds that rational individuals ought to be approximately risk neutral over gambles that are small relative to individual net worth.. In this setup, the intrinsi levels of effort devoted to uality and ost ontrol are respetively normalized to zero.
Struturing Inentives within Aountable Care Organizations i85 Invoking a entral limit theorem, the utility funtion an be rewritten as: 2 0! U i! b X e p j X p ffiffiffi P e e j + p j j¼ B ffiffiffiffi! 6 j¼ @ j¼ C 4 pffiffiffi P A X j¼ e j j¼ e j ffiffiffiffi!! p x X e j j¼!# 2 e i +e i, 2 where and denote the standard normal df and pdf, whih will be good approximations for physiian teams of suffiient size. The first-order onditions for dotor i 0 s best response to shared savings fration b and uality threshold x are: "!!!# @U @e ¼ b i e i +e i ¼ 0 @U ¼ @e i p b ffiffiffiffi X X j¼ e j X e j j¼ j¼! e p j ffiffiffiffi X j¼ p ffiffiffiffi e j + p ffiffiffiffi! x X j¼ e p j ffiffiffiffi x e i +e i ¼ 0: X e j j¼ Consider a symmetri euilibrium where all dotors hoose the same effort levels, e, e. Then provided the seond-order onditions are satisfied (whih we verify is the ase for the range of parameters we onsider), the euilibrium onditions determining dotors effort are: b p ffiffiffiffi e ðe x Þ ðe +e Þ ¼ 0 b p e ffiffiffiffi e + e ðe x Þ!! ðe +e Þ ¼ 0: For poliy purposes, we treat the Center for Mediaid and Mediare Servies (CMS) as the prinipal but the same logi would apply to any private payer. CMS determines the levels of ost sharing, b, and uality thresholds, x that must be set in order to indue desired levels of ost savings, e, and are uality, e. From the prinipal s point of view, therefore, the first-order onditions determine the reuired hoie of savings bonus, b, and uality threshold, x, for any desired effort levels. With this setup in plae, we an then answer our main theoretial uestion: how does the reuired level of shared savings, b, hange as the team size,, inreases? As we show in an Appendix, it is straightforward to show that db=d > 0:
i86 The Journal of Law, Eonomis, & Organization, V3 S Thus, to ahieve any given level of ost redution and uality level, prinipals must employ higher-powered ost inentives in larger ACOs. The reason for this is that the free-riding problem swamps any gains from improved preision in performane measures. This result is, in turn, due to fundamental properties of group inentives and performane measures. Speifially, it reflets the fat that the free-riding inentive p dilution worsens with =, whereas preision improves with = ffiffiffiffi. 3. Calibrating the Model In this setion, we alibrate our inentive model in order to onsider the onditions under whih the ACO pay-for-performane sheme will be self-finaning. More preisely, we ask under what ombinations of ost targets, uality targets, and group size will the savings generated by the pay-for-performane inentives be enough to pay for the reuisite performane bonuses. Our alibration proeeds in two steps. First, we derive empirial estimates of the two key parameters in the model, and, the standard deviations of the ost and uality measures, respetively, using data on patient health are osts and atual linial uality measures. Plugging these values into our model, we then alulate the maximum ACO size onsistent with a self-finaning pay-for-performane inentive that ahieves a given ost/uality target. 3. Estimating the Standard Deviation of Clinial Cost and Quality Measures Our observed ost and uality measures are derived from onfidential insurane reords on roughly a million hronially ill, ommerial insurane members with health insurane from ommerially insured employers. These data are well suited for this exerise in that the insurer ombined billing reords with data from pharmaies and labs to onstrut an ersatz eletroni medial reord for eah patient that enables the onstrution of detailed ost and uality measures. An important limitation of this data is that they do not inlude information on Mediare patients. This limitation may not be that important for our purposes beause: () this ommerial population suffers from many of the hroni onditions affliting a Mediare population; and (2) our ost and uality regressions inlude uite detailed linial ontrols. In this regard, it is also important to remember that ommerial populations are themselves very relevant to the phenomenon we study beause ACOs are expeted to ontrat with both Mediare and ommerial insurers. Our ost measure is onstruted by applying Mediare reimbursement rates to the diagnosti and proedure odes assoiated with eah laim, and then summing within eah patient. Our ost measure is intended not to reflet the atual amount paid for the are, whih will depend on speifi negotiations between providers and the insurer, but rather the resoure osts of are. Our uality measure is also onstruted from the individual
Struturing Inentives within Aountable Care Organizations i87 laims. The laims reords are passed through a sophistiated artifiial intelligene program to develop a uality measure whih we label Potential Gaps in Care. The adjetive potential emphasizes that these are, in fat, noisy indiators of atual gaps in are. An illustrative issue identified by the system might be that the patient is a good andidate for an ACE inhibitor but there is no evidene that a presription for the drug has been filled (a partial list of targeted issues is provided in an Appendix). 2 This measured outome ould reflet a true gap in are arising from physiian oversight. Alternatively, it might be a data error or it may reflet the patient s failure to fill the issued sript, or an informed deision on the part of the physiian not to offer ACE inhibitors beause of some linial issue not apparent to the software system. The insurer invested substantial resoures in developing these measures of potential gaps in are in order to trak are uality and to ommuniate potential issues to physiians. It is important to note, however, that these measures were not tied to any inentive plan and there were no finanial or other reperussions for physiians whose patients generated potential gaps in are. These uality measures are also useful for our purposes beause they are based upon widely aepted uality indiators and beause they are onstruted from the same sort of billing reords that are available to Mediare. We restrit our sample to patients with a primary are dotor. Patients are defined as having a primary are dotor when a physiian in a primary are speialty (internal mediine, family pratie, pediatris, general pratie) is also the main provider of are as determined from laims information. Using these data we onstrut a dummy variable, Any Potential Gap in Care, whih takes a value of one if any potential gap in are was observed over the period the patient is in the sample. 3 Desriptive statistis for our population are presented in Table. As reported in Table, average patient osts in our sample are $8008, and the mean of Any Potential Gap in Care is 0.29. To alibrate our model, we also reuire an estimate of the noise with whih are ost and uality are measured. We base these on the residual variane from regressions of Costs and Any Potential Gap in Care on variables for age and gender, physiian fixed effets, as well as a vetor of ommonly used risk-adjustor variables known as Hierarhial Clinial Condition (HCC) inditors. The HCC model is used by the Center for Mediare and Mediaid Servies as a risk sore to predit how ostly a Mediare enrollee is likely to be relative to the national average benefiiary. It inludes 70 hierarhial indiators 2. The system used by the insurer identified 246 uniue gaps in are. The most ommon gaps involved well-known preventive are guidelines while some of the rarer ones involved more immediately threatening issues. 3. Costs and Potential gaps in are were identified based on medial laims over a 30-month period. The median elapsed time between the first and last appearane of a patient in our sample is about eight months.
i88 The Journal of Law, Eonomis, & Organization, V3 S Table. Desriptive Statistis Variable Mean Std. Dev. Any potential gap in are 0.29 0.45 Age 45.95 5.5 Fration female 0.57 0.49 Inidene of ommon hroni diseases Fration with diabetes 0.8 0.39 Fration with hypertension 0.45 0.5 Fration with ishemi heart disease 0.3 0.33 Fration with ongestive heart disease 0.03 0.7 Fration with hroni obstrutive pulmonary disease 0.06 0.24 Fration with two or more ommon hroni diseases 0.2 0.4 umber of patients 564,049 umber of primary are physiians 59,087 The patient sample ontains ommerial insurane members whose employers are ommerially insured, and who have evidene of hroni illness. The provider sample onsists of the primary are providers identified as the main providers for these patients on the basis of laims information. that together desribe an enrollee s linial ondition (for a full desription see Pope et al. 2004). The regression we estimate relates the ost or uality measure for patient i, treated by primary are physiian j, to demographi and underlying linial variables X ij and a physiian-speifi effet j : Y ij ¼ X 0 i + j+ ij, where Y ij is either patient osts or the uality indiator. From this exerise, we find that the root mean suared error is $0,976 for the ost regressions and 0.430 for potential gaps in are. Fisher et al. (2009) report that the average physiian group has 260 Mediare patients. Adopting this as the relevant sample size for eah physiian, it follows pthat an empirial measure for the physiian-level ost noise is $0, 976= ffiffiffiffiffiffiffi 260 p ¼ $68, and for the physiian level uality measure noise is ¼ 0:43= ffiffiffiffiffiffiffi 260 ¼ 0:027: 3.2 Converting Empirial Measures to Model Units The final task is to onvert the empirial measures of ost and uality noise to the money-metri units of the model. To do this, we first observe that money-metri effort implies effort and noise units are measured in terms of first-best ost-saving effort. The easiest way to see this is to onsider optimal physiian effort if team size were ¼, uality was at the status uo level e ¼ 0, and physiians kept all ost savings indued by their efforts, b ¼. Under these onditions, euation () implies that firstbest effort level ours when e ¼. Sine both the noise and effort terms are added together in the model, they must be in the same units hene the money metri noise term is measured in terms of first-best physiian effort levels.
Struturing Inentives within Aountable Care Organizations i89 With this as bakground, onverting observed estimates of noise in osts to money-metri noise simply reuires an assumption about how muh osts would fall if physiians operated under first-best ost redution inentives. We assume in what follows that this number is 30%. 4 Remembering that average expenditures in our sample are $8008, it follows that a unit of money metri effort is euivalent to 0.3*8008 ¼ $2402.40. Using this onversion fator, our estimate of ¼ $68 beomes 0.283 in the money-metri units of the model. Converting the empirial measures for the noise in the are uality measure to the money-metri units in the model reuires an additional assumption about the funtional form relating the empirial uality measures to money-metri effort devoted to uality. We posit that the exponential distribution is a reasonable funtional form beause the transformation likely exhibits dereasing returns and a eiling to the observed measure of uality. More formally, we an write the onditional mean of the uality measure as a funtion of effort devoted to uality, e, as EX ½ i je Š ¼ exp ð ð+e Þ=Þ ¼ he ð Þ. The prinipal s money-metri effort is just the inverse of this transformation: x i ¼ h ðx i Þ: This framework implies a loally linear mapping from outome-denominated variane to input-denominated variane at the normalized status uo-input level. Applying a delta-method type approximation to onvert the empirially observed mean and standard deviation of the uality measure, and, to the money metri uality noise parameter needed to alibrate the model, : ¼ X h 0 ðþ: Using the exponential transformation desribed above, the alibration beomes +e ¼ ð Þlnð Þ : Plugging in the values ¼ 0:288 ¼ 0:72 and ¼ 0:027 and normalizing so that e ¼ 0 implying that the target uality level is the status uo the model s uality noise parameter is then ¼ 0.074. With our empirially-based estimates for the model s money-metri parameters in hand, we an proeed with the alibration. We find that for reasonable savings and uality targets, inreasing the number of physiians uikly makes the inentive sheme untenable. Figure plots the prinipal s optimal ost-sharing parameter as the ACO size inreases from physiian to 20 under the assumption that the prinipal is trying to ahieve 20% of the first-best ost redutions. If, for example, first-best inentives yielded a 30% ost savings, the 4. Alternative assumptions of 0% or 50% hange the simulation results only slightly (available upon reuest).
i90 The Journal of Law, Eonomis, & Organization, V3 S 4.5 4 3.5 Fration of savings shared 3 2.5 2.5 0.5 0 0 5 0 5 20 Team size Figure. Optimal Sharing by Team Size. This figure plots the ost-sharing parameter, b, reuired to ahieve a target level of ost savings by group size,. The target level of savings are 20% of the savings ahievable under first-best inentives. The alibration takes as given the number of dotors in the group,, the size of their panel of Mediare enrollees (260), and our estimate of the standard deviation of the noise omponent of the performane measure; ¼ 0:074. Details in text. prinipal will be designing group inentives for the ACO to ahieve a 6% redution in osts. Consistent with the omparative statis results from the previous setion, we see that the fration of savings that are shared inreases with group size. otie, however, that as the size of the ACO exeeds 5, the sharing parameter reuired to ahieve the target exeeds one. This surprising result highlights the primary result of this alibration: even with modest ost and uality targets, it will often be the ase that ACO style pay-for-performane inentives may not be self-finaning. We present this result more fully in Figure 2. This figure plots the maximum team size onsistent with ahieving the presumed ost target and having a self-finaning pay-for-performane system (i.e., the fration of shared savings is less than 00%). Examining the figure we find that a selffunding pay-for-performane inentive aimed at ahieving 5% of the ost redutions obtainable by first-best inentives (i.e., those possible with risk neutral physiians working as residual laimants in solo pratie) annot involve a group larger than 7 physiians. Above this size level, the freeriding problem beomes so severe, that the reuisite ost ontainment bonus exeeds the savings generated under suh a system. If the prinipal s aim was to ahieve 0% of the gains possible under first-best inentives,
Struturing Inentives within Aountable Care Organizations i9 Maximum team size 20 8 6 4 2 0 8 6 4 2 0 0 0.2 0.4 0.6 0.8 Target savings (as fration of first best) Figure 2. Maximum Self-funding Team Size by Target Cost Savings. This figure plots the maximum team size (on the vertial axis) that is onsistent with self-finaning inentives by target ost savings (the horizontal axis). The target level of ost savings are expressed as a perent of the savings ahievable under first-best inentives. Savings are only paid out if a uality threshold is leared. The alibration takes as given the size of eah physiian s panel of Mediare enrollees (260), the uality threshold, and our estimate of the standard deviation of the noise omponent of the performane measure; ¼ 0:074. Details in text. the maximum size of a self-funded pay-for-performane system would be eight physiians. As the desired level of ost savings rise, the maximum ACO size shrinks dramatially. Cost savings goals aimed at ahieving 50% or more of the savings possible under first-best inentives ould only be self-funding under solo praties. The obvious impliation of Figure 2 is that for all but the most trivial ost redution targets, ACOs with self-funding pay-for-performane systems must operate with underpowered finanial inentives. The analysis so far has assumed that team prodution is no different from individual prodution. In all likelihood, however, there may be effiieny gains from ombining physiians into ACOs whih may make self-finaned inentives more feasible. Our alibrated model allows us to uantify how great the effiieny gains from team prodution would have to be in order for the reuired inentives to be self-finaning. We do this by introduing a multiplier, m ð Þ, on ost-savings effort, so that physiians in a team of size who eah exert ost-savings effort e atually realize ost savings eual to m ð Þe. This is a simple way of apturing the possibility that team prodution amplifies the benefits of eah member s efforts.
i92 The Journal of Law, Eonomis, & Organization, V3 S 5 Reuired effiieny multiplier 4 3 2 0 0 5 0 5 20 Team size Figure 3. Reuired Team Prodution Multiplier by Team Size. This figure plots the effiieny multiplier that would have to be ahieved in order for team inentives to be selffinaning, as a funtion of the team size,. The target level of ost savings is set at 20% of the first-best level of savings, and the uality target is set at zero (status uo). Positive savings are only paid out if the uality threshold is leared, but negative savings (losses) are shared regardless. The alibration takes as given the size of eah physiian s panel of Mediare enrollees (260), the uality threshold, and our estimate of the standard deviation of the noise omponent of the performane measure; ¼ 0:074. Details in text. We do not unfortunately have estimates of m ð Þ, but our model allows us to ask how big the multiplier would have to be for eah team size in order for inentives to be self-finaning. Our alibrations show that for the parameters used above, teams of reasonable size would need effiieny multipliers that are very large relative to estimates of the effiieny gains from team prodution in the health are literature. Figure 3 plots the effiieny multiplier, m ð Þ, that would be neessary for the ACO inentive sheme to be self-finaning for team sizes from to 20 physiians. As before, the savings target is set at 20% of the first-best level of savings, with uality being set at the status uo, and noise parameters as alibrated above. For teams of four or fewer physiians, the multiplier ould atually be less than one, sine as we saw in Figure, the reuired payouts for teams this small are already less than the generated savings. For larger teams, however, the reuired effiieny multiplier is greater than one and grows roughly proportionally to the team size. At ¼ 0, the reuired multiplier is greater than 200%, and at ¼ 20 the reuired multiplier is greater than 450%. Are team effiienies of this magnitude realisti? Many studies have ompared health are spending
Struturing Inentives within Aountable Care Organizations i93 in multispeialty and integrated group praties to national averages and have found effiienies up to about 30% (see Tollen 2008 for a summary). More reently, Berwik and Hakbarth (202) estimated that total waste in the health are system inreased total spending by 2% in the low estimate to 47% in the high estimate. These studies do not speak diretly to our model of inentives to redue osts, but they do suggest that the likelihood of ahieving effort effiienies on the order of those in Figure 3 may be uite low. 3.3 Sensitivity of Results to Assumptions Empirial exerises of the sort we have onduted so far depend ritially on key assumptions. It is worth noting that our results are not the result of hoosing a very noisy performane measure or unrealistially small panel sizes. Taking the seond issue first, the median number of Mediare benefiiaries in a pratie in yweide et al. (2009) is 260, suggesting that the median aseload for a physiian would be muh smaller. Also, the aseload for any given uality measure is a small fration of the total aseload (see their Table 2) although to ompare their results with ours, we would need to know the aseload per physiian not per pratie, whih they do not show. Similarly, we were onerned about the sensitivity of our results to sampling variation leading to different estimates of parameters,, and. To examine the role of sampling variation, we bootstrapped our model and found almost no differene at all between alibrations using the minimum and maximum estimates. The unimportane of sampling variation makes sense given the very large samples we use to onstrut these estimates. The model alibrations also reuired an assumption on the fration of osts that ould be saved under first-best inentives, whih we set at 30%. Repeating the analyses for alternative assumptions of 0% and 50% produed only very slight hanges, and no hange at all in the ualitative onlusions. Finally, we were onerned about the sensitivity of our results to the fat that we weighted all potential gaps in are eually regardless of severity. To assess the importane of this assumption, we also experimented with replaing the uality measure desribed in the text with an alternative uality measure that gives greater weight to more severe potential gaps in are. The severity weighted measure produes bonus estimates that are very similar to those desribed in the text. 5 5. The insurer gave eah potential gap a severity ode ranging from one to four. Applying these weights to Potential Gaps in Care, we ran the same regressions desribed in the text and used the mean suare error from that regression to alibrate a new value of ¼ 0.095. This new parameter value produed estimates very lose to those in the text and group prodution multipliers that are, in fat, larger, so the approah desribed in the text was if anything onservative.
i94 The Journal of Law, Eonomis, & Organization, V3 S 4. Mitigating Strategies for Organizations with Underpowered Inentives The finding that self-finaned pay-for-performane inentive shemes for large provider organizations are likely to be underpowered suggests that suessful ACOs will have to make use of omplementary motivators that augment the influene of pay-for-performane finanial inentives. In this setion of the artile, we use our model to desribe how two suh omplementary motivators would work. The first additional inentive instrument we onsider is a performane bond posted by the provider. These bonds would be returned (with interest) to providers along with a payout based on realized savings should the ACO ahieve its uality targets, but they would be forfeited in the event of failure. More formally suppose that physiians are persuaded to post performane bonds of magnitude s with the prinipal or in esrow at the beginning of the period. At the end of the period, after team osts and uality outomes are realized, the bond s is returned to eah physiian if the uality threshold was met, and not otherwise. The prinipal also pays out an amount eual to bc ð C Þ s, unless the uality threshold was not met and C C > 0, in whih ase the payout is zero. Sine the marginal impat of effort on the physiian s net payout is unaffeted by the performane bond, the physiians first-order onditions and the prinipal s hoie of b and x remain the same. Setting the amount of the performane bond at 2 0 3 s ¼ b4e @ e + p ffiffiffiffi e e p A ffiffiffiffi e x e 5 e, where e and e are the ost and uality effort levels the prinipal wishes to indue, ensures that the average euilibrium payout is eual to e ; that is, the payout is exatly finaned by the savings the ACO generates. The advantage of performane bonds is that they an greatly magnify the power of self-finaning pay-for-performane systems. Their great disadvantage is that it might be very diffiult to persuade agents to post them and to trust that they will be returned under the right irumstanes. In the organizational eonomis literature, this diffiulty is often addressed through the devie of deferred ompensation in the ontext of long-term employment relationships. Employees post bonds by aepting pay less than their marginal produt early in their relationship and this is returned later on in the relationship through severane pay, pensions, and other forms of deferred payments. A losely related employment strategy is the effiieny wage strategy under whih employees reeive a salary greater than their next best alternative. The disounted present value of this pay premium, when ombined with a threat to sever relationships should performane targets be missed, would also have the effet of augmenting underpowered pay-for-performane inentives. The future ommitments to handling the bonds and dismissal deisions fairly are presumably
Struturing Inentives within Aountable Care Organizations i95 enfored by the organization s desire to maintain its reputation as a reliable ounterparty for these sorts of agreements. Another possibility for augmenting underpowered inentives is to redue the ost to the physiian of providing effort. In the ase of ACOs, the most important determinant of the ost of effort is likely the opportunity ost of the physiian s time. A dotor, for example, who spends more of the day in meetings devoted to making are proesses more ost-effiient loses the opportunity to see more fee-for-servie ommerial patients. For physiians who are employees, an obvious way to redue this opportunity ost of effort is via job design. The employer an simply mandate that the physiian has to spend ertain hours on proess improvements and annot see patients during that time. This is an illustration of a more general point made by Holmstrom and Milgrom (99). In employment relationships, inentive pay and job design are powerful and omplementary motivational instruments. By narrowing the sope of work, employers an greatly redue the opportunity ost of effort and so ahieve high levels of oordination and motivation with low powered inentives (Roberts 2004). To apture this idea formally in the model, suppose the prinipal an set the sope of a provider s job, denoted by 2ð0, Š. Setting to be very small orresponds to a narrow job sope and therefore a small opportunity ost of time. Setting ¼ gives omplete latitude to the provider and therefore orresponds to a high opportunity ost of time devoted to, say, proess improvements. Introduing the job design parameter, the provider s utility funtion under the ACO inentive sheme beomes 2 0! U i! b X e p j X p ffiffiffi P e e j + p j j¼ B ffiffiffiffi! 6 j¼ @ j¼ C 4 pffiffiffi P A X j¼ e j ffiffiffiffi!! p x e j j¼ X e j j¼!# 2 e i +e i : 2 This utility funtion is the same as before exept the uadrati effort ost term is multiplied by, apturing the notion that job design an redue the opportunity ost of providing effort. We an use this adapted model and the alibrated parameters to see how adjusting the opportunity ost of effort through job design ameliorates the ACO inentive problem. Figure 4 plots the reuired level of ost sharing for a physiian team of size ¼ 20 for values of, the opportunity ost of effort parameter, ranging from zero to one. As before, the simulation assumes a ost savings target eual to 20% of the first-best savings while maintaining uality at the status uo. The figure shows that for values of near one, physiians would have to reeive over 400% of the generated savings in order for inentives to be properly aligned. This result
i96 The Journal of Law, Eonomis, & Organization, V3 S 4.5 4 3.5 Fration of savings shared 3 2.5 2.5 0.5 0 0 0.2 0.4 0.6 0.8 Opportunity ost of effort Figure 4. Optimal Sharing by Opportunity Cost of Effort. This figure plots the sharing parameter, b, reuired to ahieve a target level of ost savings for a team size of ¼ 20, by the opportunity ost of effort parameter,. The target level of savings are 20% of the savings ahievable under first-best inentives. The alibration takes as given the number of dotors in the group,, the size of their panel of Mediare enrollees (260), and our estimate of the standard deviation of the noise omponent of the performane measure; ¼ 0:074. Details in text. was also evident in Figure whih impliitly set ¼. For smaller levels of the opportunity ost of effort, however, the reuired shared savings fration is smaller, and falls below one when is less than 25% or so. These alulations are, of ourse, purely illustrative. They demonstrate how reduing the opportunity ost of effort through job design an allow ACOs to sueed with lower-powered, self-finaning inentives or, if that is infeasible, with smaller performane bonds. To the extent that performane bonds beome more diffiult to implement as they grow in size, this example also illustrates the omplementary nature of the entire bundle of motivators available to employers in onventional employment relationships. Job design makes performane bonds more workable whih in turn enhanes the effetiveness of underpowered pay for performane systems. Performane bonds and job design are, of ourse, not the only omplementary motivational instruments that ACOs an employ to augment their underpowered group inentives. Another possibility, often desribed in the management literature as high-performane human resoure systems, ombines job design with training, sreening, and soialization to motivate employees working under low-powered inentives (Holmstrom and Milgrom 99; Roberts 2004). Related motivational mehanisms