Timothy G. Ferris, MD, MPH

 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

 Appropriate selection of patients is key to the success of care management programs (CMPs). Hybrid patient selection approaches, in which large data assets are culled to develop a list of patients for more targeted clinical review, are increasingly common. We sought to describe the patient and practice characteristics associated with high-risk patient identification and selection for a CMP during clinical review, and to explore variation across primary care practices. 

Standardized estimates of Medicare beneficiaries identified as high risk for poor outcomes and high medical expense, and appropriate for a CMP within a large Pioneer Accountable Care Organization, were developed using mixed effects logistic models. Study subjects were 2685 Medicare beneficiaries aged over 18 (includes individuals eligible for Medicare due to a disability) aligned to 35 primary care practices in 2013.

 Independent predictors of patient identification as high risk include older age; higher risk score; recent increases in medical conditions; higher numbers of medical hospitalizations, skilled nursing facility days, and primary care physician visits; and shorter relationships with the primary care physician. Older age, and lower income, but no prior hospice use were independently associated with patient selection for a CMP among the subset of patients identified as being high risk. Adjusted predicted percents of high-risk patients varied significantly across practices overall and for 5 of the 6 patient characteristics that were independently associated with identification as high risk. 

Inconsistency in high-risk patient identification and selection for a CMP may reflect differences in practice resources, but also highlight the need for continual training and feedback in order to protect against unintentional biases.

	Clinician review within hybrid case selection approaches includes decisions about which patients are high risk and, among high-risk patients, identification of the subset of patients most appropriate for a care management program (CMP). 

		Primary care physicians were more likely to recommend high-risk patients who are poorer, older, and not using hospice services for CMP. 

		Significant variations in adjusted proportions of patients selected for care management across practices may reflect differences in practice resources, but may also introduce bias into the process.

		Continual training and feedback to practices on their review decisions may help identify missed patient opportunities.

The observation that health expenditures are concentrated within a relatively small subset of the population is well known by now, with 5% of the population accounting for 50% of medical expense in any given year. One-third of the individuals in this group—frequently those with poor health status and multiple chronic conditions&mdash;remain high-cost the following year,

 making them appropriate for primary care—based care management approaches

 However, although effective care management targeted at the right individuals has been shown to moderate costs and utilization,

 inconsistent results have highlighted the need to appropriately select patients.

There are a host of available case-finding tools and algorithms that can be used to identify individuals appropriate for care management; nevertheless, there is also increasing understanding that these tools have limitations.

 Many case-finding tools are proprietary “black boxes” that create a single score to rank patients according to risk.

 Additionally, these tools can vary considerably in terms of their input criteria, and there is no standardization of definitions across tools

—most importantly, the input criteria cannot be modified to meet specific program characteristics and operational realities.

Data timing and completeness are a challenge for all case-finding algorithms, especially those based on paid claims. Delays in data capture can result in the identification of patients who are no longer enrolled within the health plan or practice, as well as identification of patients who no longer require intervention.

 Incomplete data, such as removal of substance use, HIV/AIDS, and in some states, mental health claims,

 paint incomplete pictures of patient needs. In addition, demographic and psychosocial characteristics are often incomplete or missing from readily available administrative data (eg, poverty, education, living situation). The absence of patient psychosocial data is particularly challenging, as these data could help to identify cohorts of patients who would be most likely helped by care management programs (CMPs)

; for example, addressing cost-related medication underuse among low-income patients may improve diabetes control and reduce inappropriate emergency department utilization.

 Although many healthcare systems do collect psychosocial data on patients, system alignment issues often preclude use of more granular and descriptive data for case finding.

Because of these limitations, case finding based on administrative data is typically not the only method used.

 Patient health-risk assessments and clinician referrals are techniques frequently used to identify patients for case management and can provide more detailed and actionable data, but they require time and resources to implement.

 Often, multiple case-finding methodologies, including predicted risk scores, utilization triggers/markers, medication adherence and gaps in care, and clinician referrals, are used in concert.

Case-finding approaches that rely on multiple methodologies are typically termed “hybrid approaches” and can vary widely, but generally refer to the use of multiple methodologies—often in sequence&mdash;to refine case finding. Within this evaluation, we studied a sequential approach to case finding that uses an algorithm to comb through administrative claims and identify a preliminary set of potentially high-risk patients. This preliminary set of patients is then reviewed by physicians and nurse care managers who make the final decision of whether to refer a patient for care management. This sequential hybrid approach is thought to be reliable

Clinician referral, within the boundaries of a pre-selected list, focuses the referral process and reduces the burden on clinicians, while at the same time ensuring primary care practice and physician (PCP) involvement. This involvement may increase buy-in of the program, which is especially important for programs embedded within physician practices. Clinician input can also help to identify patients with more modifiable health risks, including those with sub-optimal health literacy and health insight, social and home environment challenges, and/or inadequate coping skills and financial resources.

 For example, primary care physicians are more likely to describe patients with poorly controlled diabetes, inadequate insurance, or with mental health or substance use disorders, as complex.

However, clinician referral may also introduce unanticipated variations in patient selection.

 Often, there are no specific criteria that physicians are asked to consider when identifying appropriate patients, leaving the door open to unintentional biases.

 Appropriate clinician referral into CMPs requires that clinicians have a clear understanding of the characteristics that place patients at higher risk for poor health outcomes, as well as the benefits the CMP could provide.

In this study, we sought to understand the unique patient characteristics associated with both patient identification as high risk for poor outcomes and subsequent clinician referral to care management. We also sought to describe variations in patient identification and selection for care management across PCPs overall, and for important patient characteristics. We examined the selection decisions during the second year of the CMP to avoid biases associated with the ramp-up of the program.

The Partners Healthcare Pioneer Accountable Care Organization (ACO)—1 of 4 Pioneer ACOs operating in eastern Massachusetts&mdash;is among the largest nationwide, and the only Pioneer ACO that exclusively aligns patients to primary care physicians. One of the signature initiatives within the Partners Pioneer ACO is the CMP. The CMP is a longitudinal program that relies on nurse care managers working in conjunction with primary care physicians to improve care quality and control healthcare utilization for referred patients. After a successful trial within the Medicare High Cost Beneficiary Demonstration Program,

 the CMP was implemented within all Partners Healthcare PCPs under the Medicare Pioneer ACO contract, starting in January 2012. Partners Healthcare uses a hybrid approach for patient selection, combining a claims-based algorithm with clinician review to identify high-risk patients who could benefit from the CMP.

The population for this study included traditional Medicare beneficiaries within the Partners Healthcare Pioneer ACO who were identified using a claims-based algorithm as potentially high risk for poor outcomes in the second year of the program (2013). By the second year, all practices had experience performing the clinical review and had 1 or more ACO patients who were actively engaged in CMP. In total, 7651 (12%) of 63,629 Medicare patients were identified by the Partners algorithm. Among individuals with a predictive risk score for future total medical expense via Optum ImpactPro software version 6.0 (Optum, Inc, Eden Prairie, Minnesota) that exceeds a minimum threshold, the Partners algorithm uses combinations of chronic conditions and patterns of healthcare utilization to identify individuals potentially at high risk and appropriate for subsequent clinical review. The Partners algorithm was developed with extensive input from an advisory committee of operational and clinical experts within the organization. During the clinical review process, PCPs receive detailed information on chronic conditions, utilization patterns, and predictive scores that triggered identification by the algorithm. PCPs had considerable latitude when selecting patients for care management, but were provided additional guidance through reference guides and regional medical directors who received orientation and training on the list review process.

In order to study clinician review decisions during the second year, this analysis excluded 2605 patients who had been identified and reviewed in the prior year, as well as 1267 patients who had moved, died before the review process, were not community-dwelling (eg, residing in long-term care), switched to a Medicare Advantage plan, or did not have a relationship with a Partners primary care clinician. Lists of the remaining 3779 patients (5.9% of 63,629 total Medicare ACO patients) were distributed to practice management organizations and, eventually, to PCPs and clinicians for final identification of individuals appropriate for the primary care—based CMP. We then further excluded 788 patients aligned to 44 PCPs that did not make differential review decisions (eg, identifying none or all patients as high risk for poor outcomes or requiring care management), as well as 306 patients who were not classified by the practice prior to the release of the 2014 list. Thus, the final study population consisted of 2685 patients who received primary care within 35 practices.

The dependent variables are the decisions made by primary care clinicians and their practices regarding whether patients were identified as high risk for poor outcomes and whether patients could benefit from CMP. 

 and include patient demographic characteristics, area-level poverty (American Community Survey 2012 5-Year Estimates

), prospective risk score and changes in the number of conditions from calendar year 2012 to the year directly preceding the first list review (May 1, 2012-April 30, 2013) (CMS-Hierarchical Condition Categories Risk Adjustment Model

), healthcare utilization (ED visits, medical hospitalizations, skilled nursing and hospice service use) in the 12 months prior, and characteristics of the patient’s relationship with their PCP. Practice characteristics include practice size, level of affiliation with the healthcare organization, whether the practice is a community healthcare center, years of experience with care management, CMP capacity, and level of practice readiness for patient-centered medical home transformation. Models were also adjusted for primary care physician characteristics, including primary care physician age, full-/part-time status, and duration of employment within the healthcare organization.

Descriptive analyses of the patient- and practice-level characteristics associated with patient identification as high risk for poor outcomes and selection for care management were explored using bivariate analyses. Standardized estimates of patient identification as high risk and selection for CMP were developed using logistic regression models with robust standard errors clustering at the practice. For regression models, we used the subset of explanatory variables that were independently predictive of either patient identification as high risk for poor outcomes or selection for care management at 

 <.20, and used a similar approach to develop standardized estimates at the practice level.

We explored the extent to which practices consistently identified patients with specific characteristics as high risk for poor outcomes. Specifically, we examined 6 patient characteristics that were independently associated with being considered high risk for poor outcomes and that were present in at least 10% of patients in our sample; similar models were used for each, as described above, to develop standardized practice-level estimates of identification as high risk among patients with and without the characteristic. Full interaction models did not enhance predictive ability and decreased the number of practices with stable estimates; therefore, we only included interactions between PCP and the characteristic of interest in our final models.

Global tests were used to test for differences in overall practice marginal estimates and interaction marginal estimates. Chi-square statistics were used to describe the relationship between overall practice marginal estimates and practice estimates for individuals with specific characteristics.

The study was approved by the Partners Institutional Review Board.

Among Medicare ACO patients identified by the claims-based algorithm and who also passed an administrative review (roughly 5.9% of total Medicare ACO patients), 86.3% were thought to be high risk for poor outcomes by their primary care providers; 77.2% of these patients were referred to a nurse-led CMP within the PCP.

A number of patient characteristics were independently associated with patient identification as high risk during the clinician review. Specifically, independent predictors of high risk include advanced age, higher prospective risk score, a recent increase in diagnosed conditions, and higher numbers of medical hospitalizations and days in skilled nursing facility (SNF) care in the prior year. Patients identified as high risk also were more likely to have more visits with their PCP in the prior 12 months, but shorter relationships with their PCP than patients not identified as high risk. The patient characteristics with the strongest independent association with being identified as high risk include age 85 years or older (93.1%), 2 or more hospitalizations in the prior year (91.5%), and 30 or more SNF days during the prior year (92.7%) (Table 1).

Among high-risk patients, advanced age was also an independent predictor of patient referral to a CMP. However, when determining whether high-risk patients would benefit from care management, clinicians were also more likely to select individuals without prior hospice use and those living in lower-income areas. The adjusted proportions of high-risk patients 85 years or older selected for care management was 80.1%, and between 81.2% and 81.9% among patients residing in areas with more than 10% of individuals living in poverty (Table 1).

Lists of patients identified by the claims-based algorithm were reviewed within 35 practices where patients received their primary care. On average, practices reviewed 76 algorithm-identified Medicare ACO beneficiaries (range = 5-248) in 2013. Adjusted proportions of patients identified as high risk and referred to a CMP varied according to practice characteristics. Specifically, after adjusting for patient characteristics and other practice characteristics, practices with 6 or more physicians (88.8%), non—community health centers (87.3%), and practices without the capacity to provide care management to all patients (93.5%) had the largest adjusted proportions of algorithm-identified ACO patients identified as high risk during clinical review. Among high-risk patients, adjusted proportions selected for care management were highest within practices employed by the health delivery organization (80.8%) and non–community health center practices (78.4%) (

After adjusting for patient and primary care physician characteristics and controlling for the clustering of patients within PCPs, practices varied significantly in terms of both the adjusted proportion of patients identified as high risk (36% to 99%; 

 <.001) and the proportion of patients referred to the CMP (18% to 96% ; 

We further explored the extent to which patients with specific characteristics were consistently categorized as high risk across practices. For this exploratory analysis, we selected the 6 patient characteristics that were both independently associated with patient identification as high risk and were present in at least 10% of the patient population. Practices varied significantly in how they categorized patients for 5 of 6 patient characteristics studied (

Hybrid patient identification approaches, that use algorithms to identify a subset of patients who warrant more careful clinical review, hold promise as a feasible method for incorporating clinician judgment and knowledge of patient psychosocial needs into case findings. However, we know little about the clinical review process, including the criteria used by PCPs to identify patients as high risk, how practices identify which high-risk patients would be appropriate for care management, and how these decisions differ across PCPs. 

Consistent with findings from our interviews of primary care physicians and care managers,

 we found that primary care teams used additional information about their patients’ clinical profiles to decide whether a patient on an algorithm-generated list was high risk. Patients identified as high risk tended to be older, sicker, and with greater healthcare utilization, including primary care visits, than patients not identified as such. Patients with shorter relationships with their primary care physician were more likely to be identified as high risk, suggesting that PCPs might either have different thresholds for identifying patients who are new to their practice as high risk or may not have had the opportunity to establish necessary supports for their newer patients. Lack of a long-term patient—PCP relationship may compromise effective communication with patients and their caregivers, which is particularly important in older, complex patients.

As a second step, primary care teams were asked to identify which high-risk patients were appropriate for care management, and when selecting, patient clinical need was far less of a consideration. In addition to patient age and no prior hospice use, patient area—level poverty was independently associated with selection of high-risk individuals for CMP services. Thus, although predicted risk and prior utilization are important for identifying patients who are high risk, they are not the primary factors associated with the decision to refer patients to a CMP.

Our study extends prior work by clearly differentiating clinical assessments of whether patients are high risk from determinations of whether high-risk patients would be appropriate for CMP services. Additionally, our findings support other studies that have found that one of the benefits of including clinician input is the ability to consider nonclinical factors, such as modifiable health risks, patient health literacy, social and home environment challenges, social support, or inadequate coping skills and financial resources.

 Patients appropriate for care management often have issues with medical decision making and care coordination in addition to poorly controlled disease.

 Physicians may also preferentially select patients who they feel are more willing to participate in a CMP.

We found considerable variation in the overall adjusted proportions of patients identified as high risk across practices, as well as significant variations in practice-level identification rates for specific cohorts of patients, suggesting that practices may not use the same criteria to identify high-risk patients. Although practices were provided with central guidance on how to perform the clinical review, patient-by-patient decisions are left to the practices.  Some of this diversity in identification of high-risk patients may appropriately reflect unmeasured differences in practice resources and access to specialty care and services, such as behavioral health specialists and addiction counselors, for their patient population. Differences could also reflect variations in program implementation, such as nurse skill levels or comfort with different patient populations. Care management, by nature, is a diverse intervention for individuals with complex healthcare needs.

 The multifaceted nature of care management not only complicates the identification of patients, but may bias selection toward patients whose needs can be met by the skills of the care manager available to the practice. Training for care managers and feedback to practices regarding the benefits and limitations of CMP may help minimize unintentional disparities in patient selection.

Hybrid approaches allow for clinician input into case finding for care management, but training and monitoring is required to protect against unintentional biases.

Implementing a Hybrid Approach to Select Patients for Care Management: Variations Across Practices

Hybrid approaches to case finding show promise as a method to increase the success of care management programs (CMPs). A large healthcare system implemented a hybrid approach in which clinicians review algorithm-generated lists of potential high-risk patients within their practice and select the patients most appropriate for the CMP.  We sought to understand the criteria clinicians used when selecting patients.

 We conducted 20 semi-structured interviews with a convenience sample of primary care clinicians and their care managers from a diverse set of practices.

 Two independent reviewers thematically coded interview responses through an iterative process.

 In addition to clinical need (eg, disease severity or multiple comorbidities), interviewees considered a number of nonclinical patient characteristics that they felt placed patients at risk for suboptimal healthcare utilization and poor adherence. These include patients’ predisposing (eg, health literacy or navigation challenges, physical vulnerabilities, insight regarding their health) and enabling characteristics (eg, social and home environment, coping skills, financial resources). Interviewees also considered patients’ existing linkages with the health system and whether other clinicians were already meeting a patient’s care needs.

In selecting patients for a CMP, clinicians considered patient characteristics that are not easily captured in standard clinical and administrative data. A hybrid approach to case finding concentrates clinician review on patients who meet standard clinical and healthcare utilization criteria, and allows clinicians to incorporate knowledge of patients’ predisposing and enabling characteristics that are not readily maintained in clinical data.

	This study identified the factors that primary care clinicians and care managers considered as they reviewed algorithm-generated patient lists to identify a final set of patients appropriate for care management. 

		In addition to the complexity of patients’ medical conditions, clinicians considered patients’ health literacy, physical vulnerabilities, social and home environment, and coping skills when selecting patients for care management. 

		Clinicians also considered the extent to which patients’ care needs were met by strong existing linkages to care providers outside of the primary care practice.

Healthcare delivery systems are increasingly entering into risk contracts which require healthcare providers to manage the utilization of defined patient populations. Care management programs (CMPs), particularly those based in primary care practices, have become a common strategy to meet the cost containment and quality targets in these contracts. Due to the concentration of costs in persons with multiple chronic conditions, these persons are often selected for care management interventions that aim to enhance the coordination of health services and improve patients’ ability to navigate the health system.

 Appropriate case finding, or the identification of patients most likely to benefit from care management, is key to maximizing the cost-effectiveness and sustainability of CMPs.

Many CMPs rely on quantitative approaches to high-risk patient identification, such as risk prediction models based on prior healthcare utilization and clinical conditions. These approaches typically rely on administrative data, such as billing claims, to predict future healthcare utilization, but are often unable to include more nuanced patient characteristics, including socioeconomic and psychosocial circumstances.

 Claims-based approaches often fall short of identifying the patients who are most likely to engage in and benefit from care management because they are unable to detect factors such as patient willingness to participate or barriers to participation.

 Moreover, use of data-driven algorithms often introduces a time lag between when patients need help and when they are identified for the program.

An alternative strategy is to rely on direct referrals from primary care physicians (PCPs) to identify appropriate patients. This strategy is based on the premise that PCPs know their patients best and can consider patient characteristics not routinely available in administrative data or medical records.

 However, PCPs vary widely in their referral patterns for such programs,

 and concerns have been raised about underselection of patients with poor existing linkages with the healthcare system.

 Finally, physicians with a poor understanding of care management goals or approaches may refer inappropriate patients to these programs.

Hybrid approaches that combine predictive modeling and clinician input have been suggested as a way to overcome the biases and limitations of the individual approaches.

 Prior research has explored the extent to which primary care clinician case finding agrees with predictive modeling case finding,

 but has not examined the added benefit of clinical review of a list of potentially high-risk patients generated from a predictive algorithm. We investigated the contribution of clinician input in the selection of high-risk patients within an active primary care-based CMP using semi-structured interviews of PCPs and their nurse care managers (CM). We were particularly interested in the criteria and patient factors clinicians used in practice when determining which potential high-risk patients within their panel should be targeted for care management. Specifically, we examined the extent to which clinicians considered: 1) patient characteristics that are and are not routinely available in clinical and billing data; and 2) existing patient linkages to other providers within the healthcare system when reviewing lists of potential high-risk patients, and identifying patients appropriate for practice-based care management. We hypothesized that clinician involvement enhanced the case selection process by allowing the inclusion of information about the patients not readily available in administrative data.

Description of Care Management Program and Patient Selection

The CMP is a primary care-based, nurse-led longitudinal care program designed to help high-risk patients (ie, chronically ill, medically complex patients) better manage their health and healthcare utilization. Approximately 400,000 Medicare and commercially insured patients 18 years or older were eligible for the CMP in 2013. CMP patient selection starts with the use of a billing claims-based predictive algorithm to identify an initial list of high-risk patients. As a first step, chronic conditions, utilization patterns and a predictive risk score for future total medical expense are generated using the prior year of paid claims using Optum ImpactPro version 6.0 software (Optum Inc, Eden Prairie, Minnesota). Combinations of specific chronic conditions and patterns of healthcare utilization, as well as overall risk score, are then used to identify the initial set of high-risk patients using an algorithm developed with input from an advisory committee of operational and clinical experts within the healthcare organization. Approximately 5% of patients are identified as potentially high risk based on the claims-based algorithm. These patients are then sorted into PCP-specific lists for clinical review. Lists provided to PCPs included the chronic conditions, utilization, and predictive risk score for each patient, as well as patient name and medical number so clinicians could easily recognize their patients. Each PCP, together with their CM, was responsible for reviewing between 1 and 39 potentially high-risk patients for 2 insurance-risk contracts in 2013, the second year of the program. Overall, clinicians selected approximately 61% of patients on the algorithm-generated list for the CMP. Clinicians were asked to classify patients on their list according to whether they were appropriate for the CMP, or they were not appropriate either because they were not considered high-risk or because the patient’s care needs were already being met. PCPs were given considerable latitude in how they classified and selected their patients for the CMP.

Using semi-structured interviews of PCP and CM dyads, we elicited and analyzed the patient characteristics that clinicians considered when selecting patients for CMP. This project was undertaken as a quality improvement initiative, and as such, was not formally supervised by Partners Healthcare’s Institutional Review Board per their policies.

We identified a convenience sample of 20 PCP and CM dyads from 18 ambulatory practices within an integrated healthcare system consisting of 270 primary care practices located in eastern Massachusetts. Physician organization leaders identified participants with the intention of maximizing variation in practice characteristics, such as the level of affiliation with the delivery system, location, and type (

). The CMP director then confirmed willingness to participate.

A physician researcher (CH) and project manager (VH) conducted all interviews, either by phone or in person, at the respective practices. Interviews lasted 45 to 60 minutes and were audio recorded with consent from all participants. All interviews were completed between May and September 2013. A research assistant (LM) transcribed the audiotapes verbatim.

Interviews followed a semi-structured, open-ended interview guide developed through an iterative process by the research team, and designed to obtain general programmatic quality improvement information, as well as information on the characteristics of patients selected for care management. Relevant interview questions included (see the 

] for the complete interview guide): 1) “How do you define a ‘high-risk’ patient and what criteria do you use to determine if a patient is ‘high-risk’?” 2) “What types of patients did you remove because they were ‘not high-risk’?” 3) “What types of patients did you remove because they have their care needs met elsewhere?”

The Andersen Model of Health Services Utilization

 provided a basic framework for organizing interview responses according to whether they reflected patient predisposition to use services (predisposing factors), characteristics that enable or impede healthcare use (enabling factors), and patient need for healthcare (need). We coded and analyzed interview responses using QSR International’s software, NVivo version 9, a qualitative data management and analysis software tool (QSR International Pty Ltd, Doncaster, Australia). In the first level of analysis, an interviewer and a research assistant (VH and LM) independently read and thematically coded all interview transcripts—a process that allows coders to organize and evaluate similar quotations by themes.

 Through an iterative process, content-related minor themes or factors were collapsed into major factors, then minor and major factors were discussed until a mutual agreement was reached. We met with clinician researchers and operational leaders to solicit feedback, present disconfirming evidence, and validate the coding scheme.

 After coding the data into this framework, we retained and summarized the major and minor predisposing, enabling, and need factors mentioned in at least 20% of interviews. Exploratory analyses examined variations in themes by practice affiliation and model.

Although limited to a single large integrated delivery system, our convenience sample of interviewed practices represents a broad range of practice affiliations, locations, and models (Table 1). The major and minor factors that PCPs and CMs considered in deciding which patients would be appropriate for care management within their primary care practice are summarized in 

 and exemplary quotations are provided in 

Most interviewees (85%) considered a patient’s ability to comprehend medical instructions and navigate the healthcare system when determining whether patients were appropriate for the CMP. Specifically, more than half (55%) of interviewed PCPs and CMs reported that they considered patient need for education about their diseases and appropriate management as they selected high-risk patients. Almost half of interviewees suggested that activated patients (ie, patients who are good “advocates for themselves,” “well-versed and knowledgeable about what they need,” and “can take care of themselves”) may not benefit from the additional resources provided by care management. On the other hand, interviewees felt that patients with linguistic or cultural barriers, as well as individuals with impaired executive function, would be more likely to benefit from the services rendered by CMs.

More than half (60%) of interviewees considered whether their patients had physical vulnerabilities, including a high risk of falling, frailty, or advanced age, when determining appropriateness for CMP. Clinicians reported that, in some cases, they would select patients with relatively stable functional status if they had physical vulnerabilities because they felt care management might help facilitate patient care in the event of decompensation.

Patient insight regarding his/her health. 

Just over half (55%) of interviewees considered the extent to which patients accurately perceived their health status and their need for assistance. This included elderly patients who were reluctant to lose their independence, younger patients in denial of their medical conditions, or patients who minimize their health needs. Interviewees felt these types of patients lacked an accurate perception of their limitations and needs, potentially leading them to refuse certain types of medical care or assistance. Interviewees stated that they would include these patients in the program, but that it might be challenging to engage and impact these types of patients.

 All interviews mentioned that they considered their patients’ social and home environment when assessing appropriateness for the CMP. Interviewees observed that relatives often serve as patients’ advocates and caretakers, helping patients navigate the health system, monitoring medication regimens, and noting changes in the patient’s health status. A large majority (90%) of interviews considered the absence of patients’ familial and social supports as reasons to include patients in the CMP. Instability in a patient’s home environment was mentioned as a consideration in a large majority (75%) of interviews, with issues ranging from addiction and verbal abuse to hoarding, as well as spousal illness, hospitalization, or recent death. Some (30%) interviewees mentioned the social isolation of living alone as an added risk factor for elderly and frail chronically ill patients. Finally, lack of access to transportation, home delivery of food, or other community resources were also considerations for CMP inclusion (30%).

Over half (65%) of interviewees noted that they would select patients with underlying mild psychological concerns, such as health anxiety, who often seek reassurance from medical professionals, whether through frequent phone calls or urgent care visits. Such patients may benefit from a CM, who could act as a point-person to help them manage anxiety related to their medical conditions without consuming limited office resources.

A minority (35%) of interviewees mentioned socioeconomic instability (ie, chronic unemployment or unstable housing), and its effect on a patient’s ability to manage his or her chronic illness(es). Medication adherence issues related to financial instability were also considered.

All interviewees considered their patients’ chronic conditions, including the severity or stage of the patient’s disease, and the patient’s complexity or combination of conditions. Interviewees considered whether complicated medication regimens placed their patients at higher risk for decline, complications, and hospitalization. Interviewees specifically highlighted patients with co-occurring medical and psychiatric disorders, such as depression, which they felt further complicated patient care and the patient’s ability to manage his or her medical condition(s).

In identifying patients appropriate for CMP, interviewees considered factors in isolation, but also described the interplay between patient factors, and how the combination of these factors either supported or impeded a patient’s ability to effectively manage his or her health.

Once clinicians determined whether patients were high-risk, interviewees then considered whether the CMP would be the right program for the patient. Interviewees considered whether the patient was already connected with other clinicians, and if the resources available through the CMP would address the patient’s needs. Specifically, they reported considering existing relationships with specialist physicians (eg, psychiatrists, cardiologists) or specialty clinics (eg, transplant, oncology), private CMs, or institutional care providers (eg, group homes, assisted living). Decisions regarding whether care needs were met relied on clinicians’ perceptions of whether there were additional resources or services the CMP could provide, such as transitions to end-of-life care. Moreover, interviewees’ understanding of the CMP also impacted their decisions about whether to include certain subgroups of patients. For instance, some interviewees excluded patients whose primary diagnosis was a psychiatric condition or substance abuse because they felt the CMP did not yet have the resources to meet these patients’ needs.

Our study evaluated the factors clinicians considered important in determining whether potentially high-risk patients within their practice would benefit from a practice-based CMP. Similar to others, we found that clinicians consider patients’ predisposing factors, including health literacy and patient self-efficacy, as well as enabling factors, such as home and family supports, when identifying patients appropriate for care management.

 We found that these factors remained important, even in our hybrid approach to case finding, in which clinician decision making is limited to a set of risk contract patients with prior high-cost care patterns and complex chronic illness. Additionally, clinicians in our investigation considered whether existing medical and community resources were already addressing patient needs and whether CMP would enhance or interfere with existing patient care networks.

Many of the factors clinicians considered in their decision making are not routinely captured in clinical or billing data, and are therefore unavailable for case finding.

 For example, patient home environment and social support, as well as patient health literacy, were factors considered by a large majority of clinicians when identifying patients appropriate for CMP. This information is not contained in billing records and is not routinely captured in electronic health records. 

Previous research found only a small subgroup of patients concordantly selected by PCPs and predictive modeling for care management.

 Compared with PCP-based case selection, a predictive modeling approach tends to identify patients who are at a higher risk of healthcare utilization and have a higher morbidity burden,

 but are less likely to be helped by care management, either because their utilization is not preventable or they are unwilling or unable to participate.

The hybrid approach in this study builds on these findings. Starting with a predictive model to identify a subset of patients that may be appropriate for CMP effectively limits the review burden on clinicians, and focuses the clinical review on the subset of patients who are enrolled in a risk contract and have chronic conditions and patterns of prior healthcare utilization that may be amenable to care management.

More systematic capture of clinical and demographic data may become standard in newer electronic medical records or health information exchanges,

 but many of the nonclinical, nuanced factors clinicians reported considering are unlikely to become standard elements of clinical documentation, either because of privacy concerns or because of their limited relevance to standard clinical care.

Incorporating clinical input in patient selection addresses some of the limitations associated with the use of claims or medical record-based algorithms for case finding, but might also introduce new biases. Physicians and practices may vary in their threshold for CMP referral or how they weigh specific criteria. For example, some practices may have existing programs or supports, such as special programs for patients with heart failure or diabetes, or close ties with cancer centers that might impact their patient selection. Variations in decision making may also be related to differences in knowledge or acceptance of team approaches to patient care. For instance, we found that some clinicians were unsure whether the CMP would have the necessary resources for patients with co-occurring mental health and substance use. More training and education as to the types of patients who would be a good fit for CMP may improve the patient selection process.

Clinicians consider a number of patient predisposing and enabling characteristics not typically available in clinical data systems when selecting high-risk patients for care management.

Clinician Considerations When Selecting High-Risk Patients for Care Management

New payment methods designed to incentivize more efficient care delivery are accelerating the movement of healthcare providers into organized provider groups. More efficient healthcare delivery requires explicit structuring of care delivery processes around teams of clinicians working toward common patient care goals. Provider organizations accepting new payment methods will need to design and implement compensation systems that provide incentives for team-based care. While lessons from studies performed both outside and inside healthcare provide some guidance on designing and implementing team-based incentives, organized delivery systems face several significant barriers to accomplishing this.

		 Future efforts to reduce healthcare spending and improve care quality will likely rely heavily on teamwork.

Team-based performance incentives may improve healthcare team performance, but provider organizations face a number of structural, technical, and cultural barriers to adopting them.

Timothy G. Ferris, MD, MPH

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