Zirui Song, MD, PhD

 In the move toward value-based payment, new payment models have largely been designed by payers and focused on the role of primary care providers. We examine a new phase of payment reform wherein providers, mostly specialists, are designing alternative payment models (APMs) for their own practices through a task force, called the Physician-Focused Payment Model Technical Advisory Committee, created by the Medicare Access and CHIP Reauthorization Act of 2015. Although it is a potentially notable shift in payment reform, little is known about the content of these proposals to date.

 Qualitative systematic review of physician-focused payment model proposals submitted to CMS.

 We analyzed the first wave of new payment models proposed. For each of the 24 proposals submitted by physicians and physician groups, we assessed the models on their 10 key dimensions and evaluated underlying themes across all or many of the models to gain insights into what providers are looking for in APMs within the constraints of the rules established by the HHS secretary.

 Key features of the models and our analysis include bearing financial risk, a reliance on case management, embrace of new technologies, and consideration of legal barriers.

 We discuss how specialists may help lead in the evolving payment landscape and recommend how these models might be improved. Payers and policy makers could benefit from our findings, which reflect how providers view financial risk in APMs and provide guidance on the types of payment reforms that they may embrace in the journey toward value.

In proposals of new payment models to the Physician-Focused Payment Model Technical Advisory Committee, specialists and other providers have indicated that they would be amenable to assuming financial risk.

Collectively, the payment models that the providers have designed suggest that many providers see investment in new technologies and increased time spent on case management as strategies to succeed in a value-based framework.

Policy makers should critically consider which proposals may slow healthcare spending and should consider how antikickback provisions might hinder the formation of value-based arrangements, as well as how the newly proposed payment models might interact with existing alternative payment models.

Historically, policy efforts to slow healthcare spending centered on changing the way providers are paid—especially those imposed by payers&mdash;have not always been met with enthusiasm by the provider community. Medicare’s mandatory episode-based payment models for cardiac and orthopedic care, for example, met substantial resistance from providers and were ultimately scaled back.

 Physician groups, including the American Medical Association (AMA), have in turn called for voluntary, physician-led payment innovations as an alternative to top-down payment reform. CMS, in a “New Directions” vision statement for its Center for Medicare and Medicaid Innovation, also highlighted voluntary payment models—including for specialty physicians&mdash;as a new focus.

To date, little is known about the appetite of physicians, particularly specialists, to design their own payment models aimed at achieving higher quality at lower cost. Payment innovations by public and private payers have largely focused on the role of primary care physicians and the quality of primary care decisions or outcomes. Specialists thus far have had fewer opportunities to design or implement alternative payment models (APMs) directly pertaining to their scope of practice.

In this paper, we examine the first broad, large-scale effort by providers to design new payment models for their own specialties. We evaluated the payment models submitted to the Physician-Focused Payment Model Technical Advisory Committee (PTAC)—a task force created by the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA). The PTAC reviews payment models submitted by providers and submits recommendations to the secretary of HHS, who may reject, revise, or implement each proposal. The recommendations are structured around 10 PTAC criteria (

As of November 2018, physicians and provider groups submitted 24 of 25 total PTAC proposals,

 which vary in scope, clinical focus, and economic dimensions. These proposals reflect how providers wish to be paid in a manner that generally departs from pure fee-for-service (FFS). Collectively, the submitters are composed of 6 professional societies, 13 provider groups, a state government agency, 2 academic medical centers, a nonprofit association, and an individual physician. In most of these proposals, specialists came forward with new reimbursement models involving financial risk for core services provided by their specialties, which is a stark contrast from the largely primary care—based APMs thus far. Although CMS has not yet launched demonstrations for PTAC proposals, this novel trend and the requests of enterprising providers and their organizations offer insights into possible future directions of payment reform and the challenges that such models may face.

We conducted a systematic review of the 24 physician-focused payment model proposals along 10 dimensions: patient population, population size, physician specialty, specialty size, services covered, payment type, duration of payment coverage, upside financial incentives, downside risk, and technological innovations in care delivery. These were selected to provide a combination of both broad and specialty-specific insights into the types of payment models that physicians are seeking. In addition to proposing novel APMs, the proposals also include requests to change the Medicare fee schedule, along with minor technical changes to payment rules. We excluded 1 proposal submitted by a non—healthcare provider.

Of the 24 proposals, 19 focused on new payment models for specific specialties, including surgery, gastroenterology, pulmonology, nephrology, neurology, oncology, urology, palliative care, interventional cardiology, and emergency medicine. Several key attributes of the proposals are depicted in the 

 A detailed table describing the 24 proposals across all 10 dimensions is included in the 

). Although the proposals differed in the patients, providers, and clinical scenarios that they concerned, we observed important common features across the models.

Given the HHS secretary’s criteria to improve value, most of the proposals chose to demonstrate greater value through an arrangement that required the provider to accept some financial risk for costs of care of their patients. The ways that the proposals structured that risk provide insight into the terms that were considered acceptable. In 21 of 24 proposals, provider entities asked for a shared savings and shared losses (ie, “2-sided”) payment model wherein physicians are rewarded if spending is below a prospective risk-adjusted payment benchmark and penalized if spending exceeds the benchmark. In addition, providers asked for payment adjustments, including varying degrees of bonuses, based on performance on quality measures. The 21 proposals generally capped financial gains and losses with stop-gain and stop-loss limits that varied from 4% to 20% and were frequently about 8% and 9%.

 Most accountable care organizations (ACOs) in Medicare have thus far operated without downside risk. The fact that these specialists are proposing to assume risk suggests a willingness to coordinate care, manage population health, and engage in other activities typically performed by primary care or other allied providers (eg, longitudinal follow-up). Although the submitters are a self-selected group, it is likely that recent experiments with downside risk, through Next Generation ACOs and other advancements in private payer contracts, have provided some level of familiarity with or confidence in such an approach. Another motivator may have been the potential for the providers to achieve “Advanced APM” status under MACRA to earn the 5% bonus while avoiding reporting requirements and payment adjustments under the Merit-based Incentive Payment System (MIPS). Although these models have some degree of risk-bearing, many left important payment details unresolved (eg, how payment adjustments will be calculated or how they will be distributed among providers within the APM), making it difficult to determine how much risk individual providers would bear. A recurring challenge is the trade-off between the technical difficulties of limiting financial risk to disease-specific spending (ie, attributable to the specialist) and the risk of accountability for total costs of care, which is more straightforward to calculate but more difficult to expect a single specialty to manage effectively.

The most frequently proposed mechanism for risk-bearing was bundled payments for episodes of specialty care, ranging from surgeries and dialysis to home hospitalizations and hepatitis C treatment. A common challenge among these bundled payment approaches is identifying the triggering event for an episode. For instance, the end-stage renal disease (ESRD) proposal grappled with whether starting dialysis or declining biomarkers of kidney function is the appropriate trigger for the bundle. This is a key issue, as evidence from Medicare’s voluntary bundled payment program for lower extremity joint replacements highlighted the possibility that savings may be offset by an increase in the number of bundles triggered, especially among healthier patients. (However, it should be noted that this concern has not materialized in evaluations to date of the CMS mandatory bundled payment program for lower extremity joint replacements.

) CMS should monitor how the design of the triggering event might lead to inappropriate initiation of bundles. Mitigating inappropriate triggering is likely important for its intended purpose of slowing spending. Further, although bundled payments have been shown to reduce spending for surgical episodes like joint replacements, a recent evaluation of Medicare’s bundled payment initiatives for medical conditions (including congestive heart failure, chronic obstructive pulmonary disease [COPD], sepsis, and acute myocardial infarction) found no significant changes in Medicare payments, length of stay, emergency department use, hospital readmission, or mortality.

 Policy makers should thus critically assess the cost-saving potential of proposals that bundle services for chronic medical conditions, such as ESRD.

Given the dearth of specialty-based quality measures used by payers today, it is notable that these specialists embraced the HHS secretary’s quality measurement criterion and are willing to subject their payments to quality measures specific to their domain of practice. It is worth noting, however, that many of these specialty-specific quality measures have not been previously implemented on a broad scale. Although expanding quality measurement into specialty care is a meaningful innovation, to the extent that untested measures may be poor markers of improved quality, payment adjustments may not accurately reward specialists for their efforts. Further, given that quality measures play the important role of protecting against underprovision of care (ie, skimping) in the setting of prospective payment contracts, untested quality measures may pose a patient safety risk if they fail to ensure proper care. This concern of skimping is particularly relevant in the case of vulnerable populations, who may be harmed by models that, in tying reimbursement to quality, inadvertently encourage physicians to avoid caring for those patients with the greatest medical and social needs.

 Using evidence from other countries is one approach taken by some proposals to demonstrate the validity of their measures. Experience and learning gained by testing novel quality measures designed by specialists will likely provide valuable evidence for future payment models. Finally, the process of incorporating novel measures might also allow payers to assess the effectiveness of such measures in changing provider behavior.

The proposals also frequently featured case management, often as a key contributor to savings. Twelve of the 24 proposals requested a payment for tasks such as patient monitoring and surveillance. For example, one model

 proposed payments for population health management of elderly patients with chronic conditions, and another

 proposed bundled payments for services that would include home visits and follow-up for patients recovering from acute incidents. Today, patient engagement and monitoring—which are often considered valuable and cost-effective&mdash;are not typically reimbursed by CMS. For instance, the Government Accountability Office showed in a report

 that provider groups frequently cite inadequate payment as a significant barrier to the use of telehealth in Medicare. At least half of the proposals advocated for lowering that barrier.

Most of the new models proposed per-member per-month (PMPM) payments to cover the cost of these services. The COPD and Asthma Monitoring Project (CAMP) proposal,

 for example, requested a $175 PMPM fee for remote monitoring of high-risk patients with COPD and asthma, citing studies whose findings suggest that Medicare will ultimately save money.

 Proposals that featured case management similarly argued for monitoring and surveillance of patients with ESRD,

 as well as in other clinical scenarios, such as palliative care.

includes selected case management features and proposed funding mechanisms.

 These proposals requested that CMS reward them for preventing unnecessary interventions through monitoring and incremental adjustments in care. For the providers, the proposed PMPM payments offset the costs of the time and resources required to engage in these functions. For CMS, these requests present an interesting alternative path toward potential cost control and quality improvement outside the centralized development of APMs. However, PMPM payments involve no risk sharing and are subject to the same FFS-based incentives that policy makers are looking to mitigate.

To enhance the effectiveness of case management, many proposals sought to engage allied professionals, including nurses, social workers, nutritionists, pharmacists, midwives, physical therapists, and others, in care delivery. For instance, the Annual Wellness Visit proposal

 requested that nurses be allowed to perform Medicare-billable annual wellness visits under physician supervision to increase the capability of understaffed rural health clinics. These providers already play critical roles today, but formalizing their contributions within specialty-based APMs at the request of physicians themselves is a notable development. For example, the Patient-Centered Headache Care Payment proposal,

 submitted by the American Academy of Neurology, includes a patient care coordinator as part of the Headache Care Team who is responsible for periodic patient follow-up with questionnaires, headache diaries, and referral tracking and management—important tasks that are not currently paid for by Medicare.

An issue that surfaces in the proposals is whether models based on case management services can be effective in smaller group practices with fewer patients and allied providers. These new payment models may encourage provider consolidation, as larger organizations are likely in a better position to manage financial risk and comply with quality reporting and health information technology (IT) requirements. Nonetheless, most of the proposals attempt to address how small practices could participate in the new APMs. Some, such as Project Sonar,

 propose that small practices form larger purchasing coalitions to contract with shared service providers.

 The contracted services may include quality and outcomes reporting, IT requirements, and care management, allowing small group and solo providers to engage in these important functions. The proposals indicate that this approach is being piloted in various settings across the country. The proposed models could further encourage participation among small practices by facilitating integration with MIPS—a feature that is often overlooked by models that focus on the Advanced APM track of MACRA&mdash;which is otherwise less accessible for small practices. Lastly, proposed models that rely on a specific quality management or accounting system may struggle to engage small practices, as they may face high switching costs for infrastructure and need more flexibility.

Many proposals emphasized care coordination, suggesting that additional revenues through chronic care management codes may not be adequately offsetting practice costs of coordination. However, specialty-based models inherently face challenges in coordinating across a range of specialists caring for a given patient, particularly compared with a primary care—based APM in which an internist or family physician naturally coordinates the care team. This challenge may be exacerbated by the lack of well-accepted standards for what roles specialists should play in cases where specialists and primary care physicians both coordinate care; the potential for disrupting team dynamics and confusing patients is not trivial. Therefore, the proposed models could be improved by more specific delineations of provider roles and responsibilities, particularly with respect to how the patient’s primary care provider fits into the model. Moreover, the models would benefit from clearer guidance on how savings could be shared among members of the care team. Given the lack of precedent, establishing standards for the distribution of responsibilities and incentives across specialties could be a meaningful contribution of PTAC. That said, policy makers should be cautious about the potential of coordination to generate savings, given sparse evidence that care coordination actually lowers spending (although it may improve patient care).

Several groups proposed the implementation of innovative technologies to support care delivery. For example, Project CAMP

 proposed a smartphone application to track patient-reported symptoms, computerized decision support and “smart alarms” for pulmonologists, and Bluetooth-enabled digital peak flow meters that transmit clinical data to providers in real time. The Home Hospitalization proposal

 relies on Bluetooth-enabled medical devices (eg, blood-pressure cuff, pulse oximeter, scale, stethoscope) for biometric data tracking as part of its tablet-based telehealth platform that also features video communication. Selected technological innovations in the proposed models are described in 

Providers view new technologies as a way to improve patient engagement and increase the value of care. However, a central question remains as to who will pay for such technologies. Five of the proposals requested payments for the use of new technologies. However, CMS does not usually fund start-up costs behind new technologies, especially those not approved by the FDA. HHS, in a recent comment on this matter, indicated that payment models testing proprietary technology, such as Project CAMP’s peak flow meter, will not be pursued.

 Thus, whether paying for such new technologies will induce investment in telehealth and whether it can save money remain open questions. The risk of underinvestment in technologies that benefit patients through improved monitoring or decision making also continues to exist. To this end, CMS might consider increasing chronic care management fees to account for the technologies needed to manage patients in the digital age. That aside, the proposed models would benefit by considering more creative ways to support new technologies than simply requesting new billing codes. Innovative strategies may include folding the start-up costs into an enhanced bundled payment or sharing risk with the companies that manufacture the technologies, which are approaches taken by a few proposals.

Five of the proposals requested safe harbor designations from Stark laws, suggesting that these laws—which exist at both the federal and state levels and prohibit physicians from making referrals to those with whom a financial relationship exists&mdash;may act as unintended barriers to improving value in care delivery. The American Hospital Association advocated in Congressional testimony for exceptions under the antikickback statute to advance new APMs, arguing that the statute’s strict liability penalizes providers seeking to collaborate and create efficiencies.

 The AMA currently supports a US Senate bill that would allow such exceptions.

 Most recently, 2 former HHS secretaries called for safe harbor exceptions to Stark laws, which they argue have become roadblocks in the move toward a value-based system by making hospitals reluctant to reward physicians for best practices and causing confusion from inconsistent interpretation.

 Given that APMs rely on more teamwork among providers across specialties, exemptions from antikickback statutes may facilitate contractual agreements among providers. Whether such relief may be generalized in APMs through federal regulation or whether decisions will be made on a case-by-case basis is unknown. A recent Request for Information from CMS calling for ideas on Stark law reform signals momentum on this issue.

 However, although safe harbor designations and other waivers of antikickback provisions may facilitate the formation of APMs that span the care continuum, policy makers should continue to critically assess financial relationships that have the potential to create perverse incentives for overutilization.

The Affordable Care Act commenced a move toward value-based payments. In the last decade, new payment models have largely been designed by payers and policy makers and mostly focus on the role of primary care providers. The payment models submitted to the PTAC, in contrast, were designed by providers themselves with an explicit goal of improving the value of care in specialty care settings. Among these are some potentially innovative ideas, such as longitudinal accountability centered on specialty care, more granular measurement of quality using clinical data, care coordination for chronic illness, and novel technology that aids in patient monitoring and communication. These models propose to increase value through financial risk on the costs of care, albeit with limited downside risk. As of November 2018, 13 of these proposals had been recommended for testing or implementation, 5 had not been recommended, and others were still being considered. More than a dozen additional letters of intent have been submitted to the PTAC, indicating continued momentum in this effort. Most of these future proposals will also focus on specialties, such as radiation oncology, orthopedic surgery, and allergy, asthma, and immunology.

Of note, it is unclear how these proposed models would interact with existing APMs. Many of the proposals overlap clinically with existing models, such as Bundled Payments for Care Improvement models and the Medicare Shared Savings Program (MSSP). This overlap raises 2 issues. First, it may be unclear how to reconcile costs of care when the same patient is included in different models. Second, when participating in parallel programs, evaluation of the impact of each model becomes more challenging methodologically. Some have called for more guidance from CMS on potential interactions and if certain models may take precedence in financial reconciliation or distributions of shared savings or losses.

 Several models considered this overlap in their APM design. For example, the Comprehensive Care Physician Payment Model indicated how its proposals could be added as supplemental payments or penalties on top of MSSP ACOs.

 However, most proposals did not address possible model interactions.

These payment model proposals by healthcare providers offer initial insight into the characteristics of value-based payment arrangements that specialists in the United States may be willing to accept. In addition, they provide guidance on where physicians may see opportunities to improve the delivery of care. They may help inform the future direction of payment reform—toward more risk for specialties, a focus on case management, and new technologies to improve patient engagement. Many of these models face obstacles, from the reorganization of provider organizations to legal barriers, and pose certain risks that need to be mitigated, including safety concerns around untested quality measures and preservation of some FFS incentives. Regardless, these models suggest that some providers, especially specialists, might support payment reform if it is done in a way that incorporates their input. Ultimately, the road from payment reform to delivery system improvement is lengthy&mdash;requiring ingenuity, diligence, and commitment from providers willing to challenge the status quo. This new work by forward-thinking providers to shape payment and accountability for themselves is an important step in this journey.

 Harvard Medical School (SG, TGF, ZS), Boston, MA; Massachusetts General Hospital (TGF, ZS), Boston, MA; Johns Hopkins Medicine (KKP), Baltimore, MD; The Brookings Institution (KKP), Washington, DC.

 Supported by a grant from the Office of the Director, National Institutes of Health (NIH Director’s Early Independence Award, DP5 OD024564, to Dr Song).

 Dr Ferris is a member of the Physician-Focused Payment Model Technical Advisory Committee (PTAC), to which the proposals analyzed in this study were submitted. Dr Patel is a vice president of Johns Hopkins Health System, a member of PTAC, and a board member of Dignity Health, SSM Health, and Paladina Health. Dr Song reports speaking fees from the International Foundation of Employee Benefit Plans, outside the submitted work. Mr Gondi reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (SG, TGF, KKP, ZS); acquisition of data (SG, ZS); analysis and interpretation of data (SG, TGF, KKP, ZS); drafting of the manuscript (SG, KKP); critical revision of the manuscript for important intellectual content (SG, TGF, KKP, ZS); obtaining funding (ZS); administrative, technical, or logistic support (SG); and supervision (TGF, ZS).

 Zirui Song, MD, PhD, Department of Health Care Policy, Harvard Medical School, 180A Longwood Ave, Boston, MA 02115. Email: song@hcp.med.harvard.edu.

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Articles

Physician groups have begun designing alternative payment models for their own specialties, proposing that CMS include financial risk, funding for new technologies, and legal waivers.

Physician-Initiated Payment Reform: A New Path Toward Value

 As US healthcare spending increases, insurers are focusing attention on decreasing potentially avoidable specialist care. Little recent research has assessed whether the design of modern health maintenance organization (HMO) insurance is associated with lower utilization of outpatient specialty care versus less restrictive preferred provider organization (PPO) plans.

 Observational study of Massachusetts residents aged 21 to 64 years with any HMO or PPO insurance coverage from 2010 to 2013.

 We examined rates and patterns of primary care visits, new specialist visits, and specialist spending among HMO versus PPO enrollees. We estimated multivariable regression models for each outcome, adjusting for patient and insurance characteristics.

 From 2010 to 2013, 546,397 and 295,427 individuals had continuous HMO or PPO coverage, respectively. HMO patients had fewer annual new specialist visits per member versus PPO patients (unadjusted, 0.37 vs 0.43), a difference after adjustment of 0.05 annual visits, or a 12% relative decrease among HMO members (

 <.001). These visits were more likely to be with a specialist in the same health system as the patient’s primary care physician (44.9% vs 40.7%; adjusted difference, 2.8 percentage points; 

 <.001). Mean annual spending on new specialist visits and subsequent follow-up per member was lower in HMO versus PPO patients (unadjusted, $104.10 vs $128.10), translating to 12% lower annual spending (adjusted difference, —$16.26; 

 Having HMO insurance was associated with lower rates of new specialist visits and lower spending on specialist visits, and these visits were less likely to occur across multiple health systems. The impact of this change on overall spending and clinical outcomes remains unknown.

Is specialist “gatekeeping” in modern health maintenance organization (HMO) insurance associated with differences in outpatient patterns of care?

Compared with preferred provider organization insurance, HMO insurance was associated with lower rates of new specialist visits.

These visits were less likely to occur across multiple health systems.

HMO insurance was also associated with 12% lower specialist visit spending, which was largely driven by lower use, not lower price.

HMO gatekeeping may meaningfully reduce specialist utilization, although the impact of this change on overall spending and clinical outcomes remains unknown.The majority of private health insurance options in the United States fall into 1 of 2 broad categories: preferred provider organization (PPO) plans and health maintenance organization (HMO) plans. PPO plans generally have broadly inclusive physician networks with little barrier to self-referral, whereas lower-premium HMO plans generally have less inclusive networks and rely on provider-facing managed care strategies, such as requiring primary care physicians (PCPs) to approve referrals to access specialists (ie, “gatekeeping”).

 As of 2014, across the United States, HMO insurance products accounted for 24% of enrollment in the employer-sponsored insurance market, whereas PPO products accounted for nearly 50% of the market.

 In Massachusetts, the setting for this analysis, HMO insurance was 39% of the commercial market, with an additional 19% with point-of-service (POS) coverage and 37% with PPO coverage in 2016.

 One concept underpinning the provider-facing strategies used by HMOs is that PCPs can reduce the use of low-value specialty care and ensuing downstream utilization by either treating a condition in the primary care setting or directing specialty referrals to higher-value providers within their health system. This is in contrast to the consumer-facing cost-control strategies seen in PPO plans that may impose higher cost sharing but retain open provider choice and access at the discretion of the enrollee.

In general, HMO plans require enrollees to identify a PCP to help direct their downstream utilization. Conceptually and empirically, a single PCP with overall responsibility for a patient’s care could lead to better-coordinated care while reducing potentially avoidable outpatient specialist referrals.

 Such gatekeeping arrangements, however, can also create problems for both patients and PCPs when disagreements arise over whether a specialist is needed.

The extent to which such gatekeeping affects specialist utilization or costs has mixed results in studies that are largely more than 15 years old.

 Little recent research has assessed whether the design of modern HMO insurance is associated with lower utilization of outpatient specialty care compared with modern PPO plans. Moreover, broadly understanding the effect of insurance design on specialty utilization across a large heterogeneous payer market is an important policy-relevant question recently made feasible through the availability of statewide all-payer claims databases (APCDs).

We used the Massachusetts APCD to examine the association between insurance design and outpatient care utilization patterns in multiple HMO and PPO arrangements. In Massachusetts, the primary distinction between HMO and PPO plans relates to gatekeeping requirements (ie, a referral requires PCP approval), in contrast to the existence of “closed-model” HMOs like Kaiser Permanente (ie, regardless of PCP approval, referrals outside the internal network are largely unavailable), which do not currently have market share in the state. We hypothesized that HMO enrollment would be associated with more PCP visits and fewer new specialist visits, with a larger share of specialty care received within the PCP’s health system.

The Massachusetts APCD contains detailed data on healthcare utilization, insurance eligibility, and provider credentialing across all commercial payers and public health insurance programs in Massachusetts, representing approximately 90% of the nonelderly population in Massachusetts.

Our study cohort included all Massachusetts residents aged 21 to 64 years who had 4 calendar years of continuous enrollment with any commercial HMO or PPO product from an APCD-participating commercial insurance provider from 2010 to 2013; public insurance was not included. We focused on members with this length of continuous enrollment because we were interested in the long-term association between HMO or PPO membership and specialist use among patients with stable benefit design. Enrollees may change from an HMO to a PPO plan or vice versa because of their plans for specialist use, which we wanted to reduce as a source of bias. We did not include children given the unique characteristics of the pediatric healthcare market in the state. The key exposure of interest was whether an individual was a continuous member of an HMO versus a PPO product, as defined by the insurer submitting data in the APCD member eligibility file. These designations are used by the insurers themselves to distinguish HMO from PPO plans for administering health plan benefits and requirements. We defined an HMO product as any product designated as HMO or POS (9.6% of all members, or 15.2% of HMO members in the final study sample), which have very similar benefit designs among the large insurers in Massachusetts.

 PPO products were defined as those with a designation of PPO or exclusive provider organization (EPO; 1.5% of all members, or 4.3% of PPO members in the final study sample), which have similar flexibility to PPO plans.

 We used diagnoses from all outpatient and inpatient claims in 2010 to measure baseline comorbidities for risk adjustment. We then performed cross-sectional analyses that focused on the period from 2011 to 2013. We defined continuous enrollment as 11 or more months of insurance coverage in a year, such that those with a short period of discontinuous enrollment, often due to an administrative error, were not excluded from the analyses. This project was approved by the Committee on Human Subjects at Harvard Medical School.

To attribute patients to PCPs, we assigned enrollees to the PCP with the plurality of office visits in a calendar year (“empirical” PCP assignment).

 Nonphysician primary care providers, such as nurse practitioners or physician assistants, were not included because they were not consistently identified in provider classifications provided by insurers. We identified visits to primary care based on claim lines with evaluation and management (E&M) Current Procedural Terminology (CPT) codes to physicians classified as general internal medicine, general practice, or family medicine. If there were no primary care visits, we assigned enrollees to the non—primary care office-based specialist (eg, excluding radiology, pathology) with the plurality of visits. Any ties between physicians were broken randomly, and enrollees with no visits were not assigned to a PCP or another physician. We also examined patterns of PCP assignment as recorded by insurers, which we labeled “insurance” PCP assignment.

We also assigned physicians to larger physician organizations or health systems in Massachusetts using a publicly available database of Massachusetts hospital and physician group national provider identifiers,

 grouped into physician organizations using the affiliations defined by the Massachusetts Health Care Delivery System Map, a manually curated listing of hospital and clinic affiliations within larger provider organizations (eg, which satellite clinics are part of Partners HealthCare) published by the Blue Cross Blue Shield of Massachusetts Foundation.

 We assigned 76% of physicians to organizations using this approach, and the remainder were largely unaffiliated independent physicians.

We assessed several dimensions of outpatient care delivery potentially influenced by type of insurance coverage. For primary care utilization, we calculated the average annual number of PCP visits of any kind per member from 2011 to 2013 and the proportion of patients with no PCP visits in a year. For primary care assignment, we assessed the proportion of patients ever assigned a PCP by their insurer and the percentage of all PCP visits occurring with those providers, as well as the consistency of empirically attributed and insurer-defined PCP assignment measured by the number of assigned PCPs per member over the 3-year study period.

We defined a new specialist visit as any claim containing a new visit E&M CPT code with a non-PCP, which can only be billed if a patient has not been seen by any physician within that practice in the previous 3 years.

 We then measured characteristics of new specialist visits that could be associated with higher likelihood of PCP-initiated referral, as opposed to patient self-referral. We did not attempt to identify specialist-to-specialist referrals because HMO referral policy focuses on PCPs as the key provider for approving referrals. We examined whether a new specialist visit’s claim designated a referring physician and whether that referring physician was the patient’s assigned PCP. We also measured the time in days between a new specialist visit and the most recent PCP visit for the patient in the past 365 days. When we had information on organizational affiliation for both the PCP and the specialist (“health system match” was available for 59% of new specialist visits), we assessed whether the PCP for the most recent visit or the assigned PCP was affiliated with the same provider organization as the specialist for the new visit.

We measured the percentage of patients with any new specialist visit from 2011 to 2013 and the average number of new specialist visits per patient in that period. Because HMO gatekeeping may have its most pronounced effect on the highest utilizers, we also examined the proportion of patients seeing 3 or more new specialists or seeing new specialists in 2 or more provider organizations, which we identified as approximately the top decile of utilization for the entire study population.

Lastly, we measured the average cost per new specialist visit and all subsequent visits with that specialist over the subsequent year by summing all costs (including insurer reimbursement and any out-of-pocket spending) from all E&M claims for new specialist visits for members, as well as E&M costs from any downstream established office visits with the same specialist seen in the following year. To test whether differences in spending by insurance type were driven by differences in price versus quantity of specialist visits, we standardized specialist visit prices using the average HMO cost per evaluation and management CPT code as a standard price for all encounters.

To measure comorbidities, we calculated Hierarchical Condition Category (HCC) scores from all diagnosis codes in the baseline year using software available from CMS. The HCC score represents the predicted risk of healthcare spending beyond average in the next year; a score of 1.0 corresponds to a risk profile for a Medicare enrollee with average expected annual spending.

 To derive a proxy measure of socioeconomic status, we linked individuals’ 5-digit zip code, the most granular geographic unit available, to the publicly available area deprivation index (ADI), a measure derived using a weighted combination of 17 Census-level indicators of socioeconomic disadvantage.

 We also included the median co-pays for primary care and specialist physician visits for each separate insurance product in 2010 as a measure of plan generosity. Other variables included member age as of December 31, 2010; sex; fixed effects for insurance carrier (eg, Blue Cross Blue Shield vs UnitedHealthcare); insurance plan risk type (fully insured vs self-insured); and employer size (ranging from individual to ≥500 employees; see 

We compared the characteristics of HMO and PPO patients and the unadjusted rates of the outcomes using χ

 tests. To estimate adjusted comparisons between HMO and PPO patients, we fitted a series of multivariable regression models for each outcome, with the key explanatory variable being an indicator for whether a patient was a member of an HMO versus a PPO insurance product. We fitted models with different link functions depending on the outcome: linear models for visit counts, costs, and visit intervals; Poisson models for counts of unique physicians or health systems; and logistic models for all other outcomes, which were binary or fractional in nature. All models adjusted for patient age, sex, HCC score, ADI, median primary care and specialist co-pays in that plan (with the exception of spending outcomes, which excluded co-pay covariates), and employer size. We also included a fixed effect for insurance carrier to control for unmeasured characteristics of carriers’ networks or policies that might also influence the outcomes of interest. As a sensitivity analysis, we also estimated logistic regression models predicting the propensity to have HMO versus PPO insurance and then used propensity score weighting to more robustly address imbalance on observable variables between HMO and PPO patients and replicated all of our model results (see 

Because of the mix of functional forms (eg, linear, Poisson, logistic), we used bootstrap methods within each model to present the effects of HMO membership as the adjusted difference in each individual outcome between the average HMO member and the average PPO member. For each effect, we drew 1000 simulations of the set of regression coefficients using the asymptotic multivariate normal approximation to the log-likelihood function, and we present the mean and 95% CI from these simulations.

 Analyses were performed in R version 3.1.3 and SAS version 9.4 (SAS Institute; Cary, North Carolina). The 95% CI around reported estimates reflects 0.025 in each tail, or 

The study sample included 546,397 and 295,427 individuals with 4 years of continuous coverage in an HMO or PPO insurance product, respectively, from 2010 to 2013. HMO enrollees were younger than PPO patients (45.7 vs 46.1 years, respectively; 

 <.001), had slightly lower HCC scores (0.34 vs 0.36; 

 = .01), had higher ADI scores (ie, were more socioeconomically disadvantaged; 83.8 vs 80.4; 

 <.001), and were significantly less likely to be employed by an employer with 500 or more employees (51.7% vs 81.1%; 

 <.001; Table 1). Sample characteristics after propensity score weighting were identically balanced (

HMO patients had fewer PCP visits annually than PPO patients (2.31 vs 2.45, respectively), but there was only a marginal difference after adjustment (adjusted difference, —0.02; 

). As expected, HMO patients were significantly more likely to ever have a PCP assigned by their insurer (90.4% vs 39.7%; adjusted difference, 46.2 percentage points; 

 <.001) and had a higher proportion of visits with those PCPs than PPO patients did with their designated PCPs (57.0% vs 19.0%; adjusted difference, 38.3 percentage points; 

Over the 3-year period, 56.2% of HMO patients had any new specialty visit versus 62.4% of PPO patients (adjusted difference, —5.3 percentage points; 

), with HMO patients having 14% fewer annual new specialist visits per member (0.37 vs 0.43; adjusted difference, —0.05; 

 <.001). Consistent with this lower utilization, fewer HMO patients were likely to have seen 3 or more specialists from 2011 to 2013 (13.3% vs 16.2%; adjusted difference, —2.5 percentage points; 

 <.001) or specialists in 2 or more health systems (21.8% vs 24.3%; adjusted difference, —2.2 percentage points; 

The interval between the most recent PCP visit and a new specialist visit was 7.7 fewer days for HMO versus PPO patients (

). A lower proportion of PPO patients had no PCP visits in the 60 days preceding a new specialist visit (55.0% vs 49.4%; adjusted difference, 5.3 percentage points; 

 <.001). HMO patients were more likely to have a referring physician for a new specialty visit (53.3% vs 24.5%; adjusted difference, 36.7 percentage points; 

 <.001) and had a modestly higher rate of health system match (44.9% vs 40.5%; adjusted difference, 2.8 percentage points; 

The average total cost per individual new specialty visit was $12.29 lower for HMO versus PPO patients, a relative difference of 6.1% (mean cost, $189.05 vs $201.34; 

). On average, office visit co-pays for both primary care and specialist visits were higher for HMO patients than PPO patients ($14.83 vs $13.48 for primary care; $20.78 vs $19.55 for specialists; 

 <.001 for both). Mean annual spending on new specialist visits plus subsequent downstream visits per member was also lower in HMO versus PPO patients ($104.06 vs $128.14; adjusted difference, —$16.26; 

 <.001) (Table 5), even after using a standardized price ($104.10 vs $122.98; adjusted difference, —$11.74; 

 <.001) (Table 5), suggesting that most of the difference in spending was related to quantity of new specialist visits and not to price.

There were minimal differences in our estimates for these models using propensity score weighting to balance the observable characteristics of HMO- versus PPO-enrolled individuals (

Using statewide data from Massachusetts, we found that HMO insurance was associated with modestly lower rates of new specialist visits and lower specialist visit costs compared with PPO insurance. New specialty visits within HMOs also were more likely to have closely followed a PCP visit, included a referral from a PCP in the claim, and been within the PCP’s health system. Because this study was observational, we cannot interpret our conclusions as causal, and unobserved differences between the HMO and PPO populations may explain some of the effects we observed. However, these results are consistent with the concept that modest provider-facing requirements of HMO coverage may have the effect of PCPs for HMO patients taking a more active role in managing referrals.

Given the emphasis on gatekeeping within the HMO model design and policies, the relatively modest effect size on specialty utilization is notable. There may be few consequences for PCPs not actively coordinating referrals for HMO patients, such that they might simply have administratively approved all patient-initiated referrals without trying to influence them. Consistent with this fact, we found that the referring physician matched the assigned PCP only 30.8% of the time for HMO patients, despite the fact that 90% of HMO enrollees were assigned a PCP by their insurer. This suggests that current HMO policies may not be implemented consistently to maximize referral coordination. Supporting this observation, we find contrasting patterns of use between patients’ insurance-assigned versus empirically assigned PCPs, with 57% of HMO patients’ PCP visits occurring with insurance-assigned PCPs. It is not clear whether this proportion is desirable from the insurer’s perspective or too low. However, the match between patients’ assigned PCP and the PCP actually seen could be an important determinant of the effectiveness of HMO design.

The modest effect size we find is consistent with the “norms hypothesis,” which postulates that physicians have a relatively uniform approach to care consistent with overall financial incentives as opposed to the payment arrangement for an individual patient they are treating.

 As accountable care organization (ACO) and other risk-based contracts become more prevalent, and as physician organizations take a more active role in managing new specialty use, we will likely see more pressure on PCPs to actively coordinate and approve specialty care (especially within ACO networks to which they belong) in the future.

We also found that reduced spending on new specialist visits was driven largely by differences in utilization, rather than price.

 Although HMOs frequently negotiate lower prices in return for a more restricted network, our results show that the bulk of spending differences was driven by lower utilization of specialty care in HMOs. This strategy, likely influenced by PCP decision making, is a strong contrast to consumer-facing approaches, such as high deductibles, reference pricing, or price transparency, that aim to achieve reduced spending by increasing consumer sensitivity to prices, rather than reducing utilization overall. It is possible that a combination of consumer- and provider-facing approaches could be more effective at reducing medical spending than either alone.

Our findings are consistent with those of some prior research on the association of gatekeeping with specialist utilization. In 1979, a randomized controlled trial of a gatekeeping benefit design with more than 2000 participants found decreased rates of all specialist visits among patients with a gatekeeping arrangement versus those without.

 In contrast, a 1998 natural experiment in a large multispecialty physician group where gatekeeping arrangements were eliminated found no change in total specialist visits, although rates of new specialist visits increased modestly after gatekeeping elimination.

 In that study, however, the group also had constrained the availability of specialists within its system, which might have served to limit new specialist visits. Both of these studies are now decades old, making them less applicable to the current healthcare landscape.

This study has several important limitations. First, this is a cross-sectional and observational analysis, so our results cannot be interpreted as causal. We addressed this limitation in part by using propensity score weighting to address the impact of observable patient characteristics on the outcomes of interest. Second, our sample is restricted to Massachusetts residents, which may not generalize to other states, although HMO and PPO insurance are the most commonly used types of plans throughout the country. In addition, we relied on insurers’ designations of HMO versus PPO plan types to identify exposure to PCP gatekeeping patterns. We also combined similar plan types, like POS and EPOs, into these categorizations. This coarse categorization may misclassify the level of PCP control over referrals in a plan because we can only observe the HMO/PPO designation. Third, we are unable to capture potential mechanisms that providers or patients might use to mitigate the influence of gatekeeping tactics, such as automated approval processes or delegation of referral approvals to nonclinical administrative staff, which could bias our findings toward the null. Fourth, our ability to capture socioeconomic status, an important confounder, is limited by relatively coarse data on 5-digit zip code alone. Fifth, our cross-sectional analysis assumes that any changes in benefit design over time were similar between HMO and PPO plans. Due to missing data in the APCD, we were unable to confirm this assumption, which may bias our results. Sixth, we did not have access to accurate and complete data on some aspects of cost sharing, such as deductibles, which could have influenced our findings. Lastly, we were limited in our ability to capture the appropriateness of a referral using claims data alone, which lack the detailed clinical information necessary to assess a referral’s clinical necessity.

We found that HMO coverage was associated with similar PCP utilization but reduced specialist utilization compared with PPO coverage. To our knowledge, this analysis is among the first examinations of HMO benefit design in the current era across a large multipayer population. Not only was specialist utilization reduced, but the referrals that did happen among HMO patients appeared to be more physician-directed and coordinated within health systems. It is not clear whether this pattern is associated with improved outcomes, patient satisfaction, or population health, a set of questions that deserve further research. However, in light of the growing use of benefit designs such as high-deductible health plans that shift decision making to patients who might be poorly equipped to make treatment choices, modern implementation of HMO methods may present an alternative provider-facing strategy for cost control worth revisiting. However, care must be taken to avoid previous errors in overly onerous gatekeeping designs that were not sensitive to patient needs and appropriate clinical decision making.

 Department of Health Policy and Management, Harvard T.H. Chan School of Public Health (MLB), Boston, MA; Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women’s Hospital (MLB, AB), Boston, MA; Department of Medicine, Massachusetts General Hospital (ZS), Boston, MA; Department of Health Care Policy, Harvard Medical School (ZS, AB, SR, BEL), Boston, MA; Ariadne Labs (AB), Boston, MA; Division of General Medicine and Primary Care, Beth Israel Deaconess Medical Center (BEL), Boston, MA.

 Supported by a grant from the National Institute for Health Care Management.

Is specialist “gatekeeping” in modern health maintenance organization (HMO) insurance associated with differences in outpatient care? The study finds that HMO gatekeeping may meaningfully reduce specialist utilization.

Gatekeeping and Patterns of Outpatient Care Post Healthcare Reform

The United States is simultaneously moving toward value-based payments for populations and precision medicine for individuals. During this evolution, innovations in payment and delivery that enhance tailoring of treatments to individuals while improving the value of care are needed. We propose one such innovation that would allow physician payment and patient cost sharing to better reflect the value of care by allowing the appropriateness of a service for a given patient in a given clinical situation to play a more meaningful role in the design of such incentives. We introduce the idea of a payment modifier, based on indication and appropriateness, and discuss its advantages and challenges to implementation.

As the United States moves toward value-based payments for populations and precision medicine for individuals, innovations in payment and delivery that enhance tailoring of treatments to individuals while improving the value of care are needed.

Incorporating the appropriateness of a service into physician payment and patient cost sharing may encourage the delivery of higher-value care.

Building appropriateness into incentives has advantages, including reliance on evidence-based practice guidelines, coexistence within fee-for-service and prospective payment models, and maintaining physician and patient autonomy.

Incentives for appropriateness could be implemented using a payment modifier, which has advantages and limitations in the current healthcare system.

Alongside a shift toward value-based payments for populations, the US healthcare system is entering an era of precision medicine centered on tailoring treatments to individuals.

 Yet today, although value-based payment and insurance design encourage physicians and patients to think about cost and quality, neither payment systems nor benefit design explicitly take into account the appropriateness of a service for a given patient in a given clinical scenario.

Currently, a physician or hospital that delivers the same service to 2 patients in different clinical situations is reimbursed an identical amount. A coronary stent for a heart attack (on average, higher value) or stable angina (on average, lower value) garners the same payment, as does a magnetic resonance imaging scan for uncomplicated back pain (on average, lower value) or back pain with neurologic signs (on average, higher value). The patient also faces identical cost sharing across higher- and lower-value scenarios. However, the appropriateness of these services clearly varies by patient factors and the clinical situation.

Appropriateness is central to value. The benefit of a given service depends not only on its quality, but also on its clinical indication.

 Payment systems and benefit design could be improved by building appropriateness into physician and patient incentives, allowing them to complement population-level incentives, such as financial risk, that lack individual-level nuance. Within the existing payment and benefit design infrastructure, one potential approach is the incorporation of an “appropriateness modifier” that varies payment and cost sharing based on the clinical appropriateness of a service in a given situation.

For physician payment, an appropriateness modifier would adjust an existing fee upward when a service is delivered in a high-value situation or downward when it is delivered in a low-value situation. The indication can be based on patient characteristics, such as comorbidities or clinical presentation. The modifier could include only upward adjustments (analogous to “1-sided” population-based payment models without financial risk) or only downward adjustments (analogous to lowering fees for “never events”). Alternatively, the appropriateness modifier could include both upside and downside adjustments (analogous to “2-sided” alternative payment models with shared savings and shared risk). Because physicians tend to evaluate payment changes by their impact on income compared with a reference point, such as the prior year's income, 2-sided incentives may be necessary to promote higher-value decisions while encouraging buy-in.

For patients, cost sharing could be adjusted to mirror the incentives in physician payment. Co-payments could be lowered to not blunt demand for certain services in highly appropriate contexts (eg, interventions during urgent situations) or raised to discourage other services in less appropriate situations (eg, elective services that lack proven benefit relative to conservative management). Combining the appropriateness modifier for physician payments with an analogous clinically nuanced modifier for patient cost sharing could be synergistic in encouraging physicians and patients to make higher-value choices.

 Moreover, modifying the existing structure of co-payments using clinical appropriateness would be transparent and familiar to patients, although it would need to be implemented and communicated in a simple manner.

Because the disutility of a loss (lower fee or higher co-payment) is roughly twice the utility of an equal-size gain (higher fee or lower co-payment), the former is likely a more powerful tool for affecting behavior than the latter.

 However, even if total physician payments or patient cost sharing remained unchanged, patients would still benefit if the proportion of services delivered in appropriate scenarios increased. How large of a change in payment optimally balances budgetary effects with behavioral response is an empirical question requiring further evidence to elucidate.

As a starting point, an appropriateness modifier could be implemented among services for which clear differences in appropriateness are observable and supported by guidelines. This initial set of services may be small, given the lack of clinical detail in administrative data. However, the physician-driven Choosing Wisely recommendations offer candidate services.

 Examples of more appropriate and less appropriate situations, respectively, could include imaging for carotid artery stenosis in symptomatic versus asymptomatic patients, stents in unstable versus stable angina, imaging for headache or low back pain with versus without red flag signs (eg, neurologic deficits or fever), and conventional versus hypofractionated irradiation for certain malignancies where they have equal efficacy.

 Given that the value of a service is more likely to be a continuum rather than a binary state of high versus low, distinguishing highly appropriate and less appropriate situations for a particular service might capture a small subset of the situations in which it is delivered, leaving the remainder under status quo incentives.

Advantages of an Appropriateness Modifier

The appropriateness modifier would offer several advantages. First, the definition of “appropriateness” would derive from practice guidelines based on the clinical literature. It builds on the progress of value-based insurance design (which focuses on the average value of a service) by considering the situation-specific value of a service for a given individual. Unlike the blunt overarching nature of population-level financial risk, this clinically nuanced tool that reflects the scientific evidence generated by the physician community itself may more easily garner physician support.

Second, the appropriateness modifier can be built into fee-for-service without requiring downside risk, although it could be similarly implemented in the fee schedule underneath a risk contract. Unlike the new Merit-based Incentive Payment System under the Medicare Access and CHIP Reauthorization Act, which increases or decreases all of a physician’s fees by a certain percentage, the appropriateness modifier impacts only those services for which evidence and guidelines can distinguish higher- from lower-value scenarios.

Third, unlike bundled payments, which have an unintended consequence of rewarding bundles triggered in lower-risk patients or less appropriate situations, thus increasing revenue and raising quality scores that are easier to achieve with lower-risk patients,

 the appropriateness modifier directly focuses on the clinical indication and reduces the incentive to generate volume in less appropriate situations. At the same time, unlike some managed care techniques that seek to directly constrain utilization, the value modifier retains the patient’s and physician’s freedom of choice among treatment options—indeed, even the lower-value options&mdash;while allowing spending to reflect the value of the clinical situation.

Fourth, the appropriateness modifier would enable specialists to have greater control over value through their own decisions rather than relying on primary care physicians to drive value through referral decisions, a process that could create tension between colleagues. Because the modifier can live within and be synergistic with alternative payment models, aligning specialist incentives with primary care physician incentives may enhance behavior change. Furthermore, aligning physician incentives with patient incentives could facilitate shared decision making.

Lastly, incorporating the appropriateness modifier into physician fees could have beneficial spillover effects outside of physician services. These include spending on facility-based services (which generate separate facility fees) and on pharmaceuticals (which often have separate costs, such as the bonus Medicare pays to physicians prescribing chemotherapies).

Challenges of an Appropriateness Modifier

Implementing an appropriateness modifier involves some challenges. Because diagnosis codes help determine appropriateness, an incentive to “upcode” patients into highly appropriate situations (through, for instance, the addition of diagnosis codes) would exist. Although upcoding is similarly a concern under population-based payment models with risk-adjusted spending targets, it could be mitigated by the inclusion of clinical data in determining appropriateness—data that are less susceptible to subjectivity.

The appropriateness modifier may complicate decision making. Physicians frequently find prior authorization burdensome, and any additional documentation required to justify higher-value situations may seem similarly unappealing. However, if implemented as a type of decision support tool (eg, with electronic prompts at the point of order entry), payers or physicians could render this complexity an advantage and provide a compelling alternative to prior authorization or other blunt utilization management techniques.

Finally, the set of services for which appropriateness is clearly distinguishable using existing administrative data today is limited. Yet with greater detail in diagnosis codes through 

International Classification of Diseases, Tenth Revision

 and greater availability of clinical data through electronic health records, payers and physicians could likely expand the set of services for which appropriateness is discernible, potentially influencing clinical areas thus far untouched by value-based insurance design.

Moving American medicine toward value for populations, yet precision for individuals, will require innovations in payment and delivery. Incorporating a clinically nuanced measure of appropriateness into payment and benefit design could offer a meaningful next step.&ensp;

The authors acknowledge seminar participants at Harvard Medical School and the Harvard T.H. Chan School of Public Health for comments and feedback.

Department of Health Care Policy, Harvard Medical School (ZS), Boston, MA; Department of Medicine, Massachusetts General Hospital (ZS), Boston, MA; Department of Medical Ethics & Health Policy, Perelman School of Medicine (ASN, EJE, KGV), and Center for Health Incentives and Behavioral Economics, Leonard Davis Institute (ASN, EJE, KGV), University of Pennsylvania, Philadelphia, PA; Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center (ASN, KGV), Philadelphia, PA.

 Dr Song was supported by the Office of the Director, National Institutes of Health (NIH Director’s Early Independence Award No. 1DP5OD024564-01).

 Dr Navathe reports consultancies for Navvis Healthcare, Lynx Medical, and Indegene; grants received from Hawaii Medical Service Association and Oscar; and lecture fees received from Cleveland Clinic. Dr Emanuel is a board member of VillageMD, Abramson Cancer Center, and IMF External Science and Technology Advisory Group; is a member of the Peterson Center on Healthcare Advisory Board; and is employed by University of Pennsylvania and Oak HC/FT. He reports receiving lecture fees from Leigh Bureau, his speakers bureau, and royalties from book sales through publishers. Dr Volpp reports consultancies for VAL Health and CVS; research grants received from Hawaii Medical Service Association, CVS, Humana, Oscar, and Discovery/Vitality; and stock ownership in VAL Health. The remaining author reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (ZS, ASN, EJE, KGV); acquisition of data (ZS, EJE); analysis and interpretation of data (ZS); drafting of the manuscript (ZS, ASN, EJE); critical revision of the manuscript for important intellectual content (ZS, ASN, EJE, KGV);  obtaining funding (ZS); and administrative, technical, or logistic support (KGV).

 Zirui Song, MD, PhD, Department of Health Care Policy, Harvard Medical School, 180A Longwood Ave, Boston, MA 02115. Email: zirui_song@post.harvard.edu.

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Allowing the appropriateness of services to play a more meaningful role in the design of physician and patient incentives may improve the value of care.

Zirui Song, MD, PhD

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