Joshua M. Liao, MD, MSc

 As part of its strategy to improve health care value and contain hospital costs, Medicare trialed public reporting for episode-based spending via 6 novel Clinical Episode-Based Payment (CEBP) measures for cellulitis, kidney/urinary tract infection, gastrointestinal hemorrhage, spinal fusion, cholecystectomy, and aortic aneurysm. Because safety-net hospitals may fare more poorly than other hospitals under value-based reforms, we evaluated the relationship between safety-net status and CEBP episode spending.

 We used data from Medicare and the American Hospital Association to identify and describe characteristics of safety-net and non–safety-net hospitals subject to CEBP measures nationwide. Multivariable linear regression, controlled for hospital characteristics, was used to evaluate the association between hospital safety-net status and risk-adjusted, standardized episode spending for each CEBP episode type.

 Of 1771 hospitals eligible for CEBPs, 28% (491) were safety-net and 72% (1280) were non–safety-net hospitals, with the former being larger and more likely to be nonprofit, nonteaching hospitals. The magnitude of episode spending varied by episode type, ranging from the lowest for cellulitis episodes to the highest for aortic aneurysm episodes. Skilled nursing facility care accounted for a considerable proportion of spending variation for procedure-based episodes but not condition-based episodes. In multivariable analysis, safety-net status was not associated with risk-adjusted episode spending for any of the 6 episode types (spending differences ranging from –$111 to $638 by episode; 

 These findings provide the first description of baseline episode spending patterns for safety-net hospitals and suggest that such spending does not vary by safety-net status.

One promising but relatively understudied strategy for containing hospital spending is to publicly report hospital spending for specific episodes of care. Analyzing 6 novel Clinical Episode-Based Payment (CEBP) measures implemented by Medicare in the Hospital Inpatient Quality Reporting program, we found that:

Among hospitals subject to CEBP measures nationwide, safety-net hospitals were larger and were more likely to be nonprofit, nonteaching hospitals than non–safety-net hospitals.

The magnitude of episode spending varied by episode type, ranging from the lowest for cellulitis episodes to the highest for aortic aneurysm episodes.

Episode spending was not associated with hospital safety-net status for any episode type.

Given widespread concerns about the high costs of inpatient care,

 Medicare continues to implement initiatives to contain hospital spending. One potentially promising strategy is to publicly report hospital spending for specific episodes of care.

 By quantifying spending specifically related to given care episodes, such measures provide hospitals, payers, and patients with information that is more clinically relevant and granular than that provided through existing global spending measures. Episode-based spending measures also enable Medicare to identify and track spending for particular episodes, information that could be used to adapt such measures into formal bundled payment or other episode-based payment models. However, because publicly reported hospital episode spending is a novel approach that remains understudied compared with episode-based payment models and other forms of public reporting (eg, quality data), there is a dearth of knowledge about it.

Medicare explored this strategy in 2017 by implementing 6 Clinical Episode-Based Payment (CEBP) measures in the national Hospital Inpatient Quality Reporting Program.

 These measures reflect spending efficiency for hospital care and provide a view of “how expensive a hospital is” by capturing all facility and professional payments for an episode spanning between 3 days prior to hospitalization and 30 days following discharge. The measures also differ from existing spending measures in their specificity and clinical salience: CEBPs capture all facility and professional payments specifically related to a given episode and are subdivided as appropriate to reflect different clinical situations. For instance, the cellulitis CEBP measure captures spending for services related to diagnosing and treating the infection but not for other, unrelated services; there are also measure subtypes to reflect infections in patients with and without major complications or comorbidities or diabetes diagnoses.

Early evidence suggests that CEBP spending differs by specific episode type and spending patterns mirror those observed in episode-based bundled payment models, reinforcing that hospitals may have particular savings opportunities in reducing skilled nursing facility (SNF) care across different episode types.

 However, hospitals may differ in their ability to implement care redesign and perform well on episode spending measures. In particular, safety-net hospitals that care for large proportions of vulnerable patients may have fewer resources than other hospitals to invest in tracking (eg, data analytics) and addressing variation in episodic care (eg, staff to implement clinical pathways or processes to affect discharge disposition and postacute care utilization). The social complexity of vulnerable patients and the potential impact of social factors on health care utilization may also complicate efforts to managing episode spending at safety-net hospitals. Collectively, these factors can potentially increase performance gaps between safety-net and non–safety-net hospitals, as observed in other value-based reforms,

 and lead safety-net hospitals to be unfairly penalized based on publicly reported measures.

These concerns are particularly relevant to reforms focused on episodes of care. For instance, in a large, nationwide episode-based bundled payment program for lower extremity joint replacement, safety-net hospitals were less likely than other hospitals to achieve financial savings, and those that did achieved smaller savings than non–safety-net counterparts.

 To inform future public reporting strategies and potential episode-based payment models, it is critical for policy makers and clinicians to first understand if baseline episode spending patterns differ systematically between safety-net hospitals and other hospitals. Therefore, we examined the national implementation of CEBP measures in 2017 to provide insight on this issue by describing episode spending at safety-net hospitals and evaluating the association between hospitals’ safety-net status and performance on CEBP spending measures.

We used data from the 2016 American Hospital Association (AHA) Annual Survey to identify safety-net hospitals and obtain information about hospitals’ organizational characteristics.

 In line with early work describing CEBP performance,2 data from Hospital Compare were used to obtain information about hospital-level episode spending performance on 3 condition-based measures (cellulitis, kidney/urinary tract infection [UTI], and gastrointestinal hemorrhage) and 3 procedure-based measures (spinal fusion, cholecystectomy and common duct exploration, and aortic aneurysm procedure) tested in 2017.

Medicare calculated and reported CEBP measure performance only for hospitals meeting minimum volume thresholds for each condition-based (40-episode minimum) or procedure-based (25-episode minimum) episode type. Data were published on Hospital Compare in risk-adjusted, payment-standardized form. CEBP performance was reported as (1) average total episode spending and (2) average spending on specific components (hospital, SNF, readmissions, emergency department visits, and home health agency use).

Consistent with prior approaches, we defined safety-net hospitals as those in the top quartile of Medicaid discharges nationwide and all other hospitals as non–safety net.

 Hospital characteristics used in our analysis included hospital size (number of beds), teaching status (nonteaching vs minor teaching vs major teaching), profit status (nonprofit, for profit, government), urban status (urban vs rural), and geographic region (Northeast vs South vs Midwest vs West).

We compared organizational characteristics at safety-net vs non–safety-net hospitals. For each episode type, we also compared CEBP spending amounts by safety-net status. Chi-squared tests were used to compare categorical variables, whereas Kruskal-Wallis tests were used to compare continuous variables.

 we used a residual plot analysis to evaluate how individual episode components contributed to overall episode spending variation. To do so, we first categorized episode components into 3 groups: hospital, SNF, and other (the sum of readmissions, emergency department visits, and home health agency use), evaluating SNF care separately given evidence about well-described savings opportunities in reducing SNF use. We then used linear regression and analysis of variance to generate predicted episode component spending values for each hospital, which were used to calculate the residual (ie, difference) between predicted and actual total episode spending. Variation in episode component residuals were plotted for each CEBP measure type, with lower residual plot variation representing a larger contribution from a spending component to total episode spending variation.

Additionally, we used hospital-level multivariable linear regression, controlling for hospital size, teaching status, profit status, urban/rural status, and geographical region, to evaluate the association between hospital safety-net status (the exposure) and risk-adjusted, standardized average hospital episode spending (the outcome). This was done for each CEBP episode type, and because spending data were reported through Hospital Compare in risk-adjusted and payment-standardized form, we did not perform any additional risk adjustment on CEBP episode spending amounts.

Because episode spending data were available only for hospitals meeting minimum volume thresholds for that episode type, hospital sample sizes varied across our analyses. All analyses were performed using SAS version 9.4 (SAS Institute). Statistical tests were 2-tailed and considered significant at α = .05.

In 2017, 1778 hospitals nationwide met minimum volume thresholds for CEBP performance to be calculated for 1 or more episode type, and 1771 were matched to hospital characteristics from AHA data. Of these, 28% (491) were safety-net and 72% (1280) were non–safety-net hospitals.

Across all 6 CEBP measures, hospital groups differed with respect to organizational characteristics (

). In particular, safety-net hospitals were larger and were more likely to be nonprofit, nonteaching hospitals than non–safety-net hospitals. Safety-net and non–safety-net hospitals also differed with respect to geographic distribution.

The absolute magnitude of episode spending varied by episode type, ranging from the lowest for cellulitis episodes (average spending of $9859 for safety-net hospitals and $9990 for non–safety-net hospitals) to the highest for aortic aneurysm episodes (average spending of $42,457 for safety-net hospitals and $39,260 for non–safety-net hospitals).

Residual plots varied between procedure- and condition-based CEBP measures (

). For the 3 procedure-based episode types, the SNF episode component had lower residual plot variation (ie, contributed the most to total episode spending variation) than the hospital episode component, which had the highest residual plot variation (ie, contributed the least to total episode spending variation). In contrast, for the 3 condition-based episodes, residual plot variation was lower for the hospital episode component compared with the SNF episode component. Across all episodes, the “other” episode component tended to have small plot variation, suggesting greater contribution to overall episode variation. These relationships did not vary by hospital safety-net status. For procedure- and condition-based episodes, the relative contribution of episode components to overall episode spending variation was similar for safety-net and non–safety-net hospitals (Figure).

Association Between Hospital Safety-Net Status and Episode Spending

In univariate comparisons of CEBP spending by safety-net status, safety-net hospitals had higher spending than non–safety-net hospitals for gastrointestinal hemorrhage ($11,319 vs $11,097; 

 = .002) and aortic aneurysm ($42,457 vs $39,260; 

 = .007) episodes. Episode spending on the other 4 episode types did not vary by hospital safety-net status.

In multivariable analysis, safety-net status was not associated with episode spending for any of the 6 CEBP episode types (

). For instance, average spending on cellulitis and kidney/UTI episodes was lower by $111 and $34, respectively, at safety-net vs non–safety-net hospitals, whereas spending on gastrointestinal hemorrhage, aortic aneurysm, cholecystectomy, and spinal fusion episodes was higher by between $85 and $638 at safety-net vs non–safety-net hospitals (

This study used novel CEBP measures for 6 distinct episode types to demonstrate that hospital safety-net status was not associated with differences in episode spending or the relative contribution of episode components to overall episode spending variation.

These findings provide early support that implementing episode-based spending measures is unlikely to disadvantage safety-net vs non–safety-net hospitals. In particular, our results demonstrate that for at least 6 different episode types, baseline spending patterns and drivers of episode spending variation do not appear to demonstrably differ by hospital safety-net status. Such findings suggest that compared with non–safety-net hospitals, safety-net hospitals are not disadvantaged from the perspective of improvement opportunities (ie, opportunities to address episode spending do not appear to vary by safety-net status), improvement areas (ie, areas for spending variation reduction are similar for safety-net and non–safety-net hospitals), or CEBP performance (ie, safety-net status is not associated with higher spending as measured by CEBP measures).

However, findings about baseline episode spending do not imply that improvements will be similarly easy to attain for safety-net vs non–safety-net hospitals over time, particularly given that sicker patients or those with more challenging socioeconomic circumstances may be more likely to seek care at safety-net hospitals. For instance, the fact that we did not observe differences in episode spending for procedure-based episodes does not preclude more differences in the ability to address variation across procedural episodes over time.

SNF care accounted for the most episode spending variation for these procedures, and the combination of more limited financial resources and greater patient social complexity at safety-net hospitals could nonetheless create greater challenges managing transitions of care and utilization between hospital and postacute settings. Future work is needed to evaluate the impact of publicly reported episode spending measures on quality and cost outcomes at safety-net and non–safety-net hospitals over time. If additional studies identify discrepancies in performance over time by hospital safety-net status, policy makers may need to implement measures such as those used in other payment reforms to support safety-net hospitals in their efforts to address episode spending (eg, customized technical assistance

) or ensure that spending data are publicly reported in a more equitable fashion (eg, displayed using “peer groups” that compare performance among safety-net hospitals alone, rather than among all hospitals

Our study has limitations. First, as with all observational studies, it is susceptible to residual confounding and unable to determine causal relationships between hospital safety-net status and episode spending. Although factors such as episode volume may affect CEBP performance, these data were unavailable through CMS. Moreover, the agency designed episode spending measures to compare all hospitals above minimum episode thresholds without additional volume adjustment. Therefore, our findings, which are adjusted for hospital size, reflect CMS policy and are therefore policy salient. Second, future work is needed to complement our findings by evaluating patient-level spending outcomes. Third, although we evaluated all available CEBP measures, findings for these 6 episode types may not generalize to others. Fourth, no episode-specific quality data were available for interpretation alongside CEBP spending information, limiting our ability to draw conclusions about outcomes alongside spending.

Our analysis provides what is to our knowledge the first description of the starting point for safety-net hospitals under novel episode spending measures that are measured and publicly reported for hospitals nationwide. This is critical insight amid the continued emphasis on hospital spending and the shift toward value-based payment.

 Corporal Michael J. Crescenz VA Medical Center (ASN), Philadelphia, PA; Department of Medical Ethics and Health Policy, Perelman School of Medicine (ASN), and Leonard Davis Institute of Health Economics (ASN, JML), University of Pennsylvania, Philadelphia, PA; Department of Medicine (LZ, JML) and Value & Systems Science Lab (LZ, JML), University of Washington School of Medicine, Seattle, WA.

 Dr Navathe reports grants from Hawaii Medical Service Association, Anthem Public Policy Institute, Commonwealth Fund, Oscar Health, Cigna Corporation, Robert Wood Johnson Foundation, Donaghue Foundation, Pennsylvania Department of Health, Ochsner Health System, United Healthcare, Blue Cross Blue Shield of NC, and Blue Shield of CA; personal fees from Navvis Healthcare, Agathos Inc, YNHHSC/CORE, Maine Health Accountable Care Organization, Maine Department of Health and Human Services, National University Health System - Singapore, Ministry of Health - Singapore, Social Security Administration - France, Elsevier Press, Medicare Payment Advisory Commission, and Cleveland Clinic; equity from Embedded Healthcare and Navahealth; and other from Integrated Services Inc, none of which are related to this manuscript. Dr Liao reports an honorarium and textbook royalties from Wolters Kluwer and personal fees from Kaiser Permanente Washington Health Research Institute, none of which are related to this manuscript. Mrs Zhou 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 (ASN, JML); acquisition of data (LZ, JML); analysis and interpretation of data (ASN, LZ, JML); drafting of the manuscript (ASN, JML); critical revision of the manuscript for important intellectual content (ASN, JML); statistical analysis (ASN, LZ); administrative, technical, or logistic support (ASN); and supervision (ASN, JML).

 Joshua M. Liao, MD, MSc, Value & Systems Science Lab, University of Washington School of Medicine, Seattle, WA 98195. Email: joshliao@uw.edu.

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Articles

This study, which provides the first description of episode spending patterns for safety-net hospitals nationwide, demonstrates that episode spending does not vary by safety-net status.

Hospital Safety-Net Status and Performance on Publicly Reported Episode Spending Measures

Transitional care management (TCM) and chronic care management (CCM) fee-for-service billing codes can serve as bridges to help organizations build care management capabilities and effectively transition from volume- to value-based care. TCM codes encourage providers to build capabilities for managing hospital discharge transitions. CCM codes encourage physician and nonphysician staff to build capabilities for longitudinally managing patients with multiple chronic conditions. Implementation challenges include achieving return on investment in health information technology and securing stakeholder commitment and engagement. Nonetheless, policy makers have reinforced their commitment to these codes, offering an encouraging signal for organizations seeking more gradual ways to build competencies and bridge toward value-based payment and care delivery.

For many health care organizations, it remains unclear how to build care management capabilities that enable smooth transitions to value-based payment and care. Transitional care management (TCM) and chronic care management (CCM) billing codes can serve as bridges for this purpose.

TCM codes encourage providers to build capabilities for managing hospital discharge transitions that are emphasized in value-based programs, such as bundled payments.

CCM codes encourage providers to build capabilities for longitudinally managing patients with multiple chronic conditions, which align with capabilities needed in value-based programs, such as accountable care organizations.

Providers must address several challenges to successfully implement these codes.

The optimal approach for transitioning toward value-based payment and care remains unclear for many clinicians and health care organizations. Although a common goal may be to successfully assume financial accountability under value-based payment models that emphasize quality and costs rather than volume of care, success in these reforms can require substantial infrastructure investments, clinician engagement, and the ability to absorb financial losses. Many clinicians and groups may need more incremental steps to practice transformation.

Certain fee-for-service billing codes that emphasize care management—in particular, those related to transitional care management (TCM) and chronic care management (CCM)—may provide a solution. A key feature of value-based payment models is an overarching emphasis on improving quality and reducing unwarranted expenditures through care management (eg, reducing readmissions after hospital discharge, improving referrals between primary and specialty care to manage chronic disease). Therefore, billing codes that promote coordination and management can encourage care delivery changes that align with those needed to succeed in value-based payment models.

Strategically, effective billing codes can serve as guides to help organizations identify how to invest in specific care delivery functions, rather than “boiling the ocean” in search of solutions. Financially, organizations can engage in care management activities and build competencies for succeeding in value-based payment reforms while receiving predictable reimbursements. However, the effectiveness of these codes depends on the degree to which they are adopted, as well as whether the associated activities move organizations toward care delivery structures and processes needed to succeed in a value-based payment environment. In this article, we highlight these dynamics using TCM and CCM billing codes implemented by CMS, a leader in the nationwide shift from volume-based fee-for-service toward value-based payment and care delivery.

CMS implemented TCM codes in 2013 in an effort to improve care management and optimize health outcomes during the potentially vulnerable period following hospital discharge.TCM reimburses clinicians and their organizations for delivering a combination of non–face-to-face communication and in-person care to patients within 7 or 14 calendar days following hospital discharge. In 2015, CMS implemented CCM codes to encourage clinicians to longitudinally manage the care of patients with multiple chronic diseases—individuals who often suffer from care fragmentation while incurring the majority of all Medicare expenditures.

 CCM codes reimburse physicians and other clinical staff for care coordination and chronic disease management outside traditional office visits.

For clinicians and health care organizations, TCM and CCM represent potential bridges to value-based payment arrangements. TCM promotes hospital follow-up by incentivizing clinicians to proactively identify and contact patients who are discharged from the hospital. This often requires a change in practice and clinician workflows—from developing connections and communication channels with hospitals to assigning support staff to reach out to patients in a timely manner. The process of delivering TCM services also prompts outpatient clinicians to review discharge information, medication changes, and recommended follow-up laboratory tests and imaging. These codes have yielded promising results thus far, with early evidence showing that patients receiving TCM after discharge have fewer readmissions, lower health care expenditures, and lower mortality.

Similarly, CCM supports care management capabilities by reimbursing physician and nonphysician staff (eg, nurses, medical assistants) for collaboratively delivering non–visit-based care to patients with multiple chronic conditions. CCM emphasizes the development of patient care plans that incorporate disease self-management and care coordination across specialties (eg, dietitian or endocrinologist referrals for patients with diabetes)—factors that can reduce the impact of care fragmentation and improve disease management. CCM has been shown to reduce health care expenditures among Medicare beneficiaries, likely through decreased use of inpatient hospital and postacute care services,

 while achieving substantial additional revenue for practices.

TCM and CCM codes encourage capabilities that are important for delivering value-based care. For instance, TCM codes can encourage providers to create discharge and care transitions capabilities that are vital to success in value-based programs, such as bundled payments and accountable care organizations.

 CCM codes encourage team-based care and long-term disease management, particularly among individuals with multiple chronic diseases—an emphasis that is aligned with Medicare’s population and primary care value-based payment models, such as Comprehensive Primary Care Plus and forthcoming direct contracting models.

Despite these successes, uptake of TCM and CCM codes has been modest to date,

 possibly due to implementation barriers. First, clinicians and groups must be able to justify the requisite investments (eg, staff, information technology, workflow changes) against reimbursement for TCM and CCM activities. For instance, organizations considering CCM can face obstacles acquiring new electronic health record functionality necessary to support documentation, time tracking, and billing of TCM and CCM.

 Securing commitment and engagement from organizational leaders, physicians, and nonphysician staff poses an additional challenge. Moreover, recouping the investment in staff time to deliver these services may require that practices be large enough to deliver care coordination services to a sufficient number of patients. For example, at least 131 Medicare patients in the initial iteration of CCM are needed to recoup staff salary and overhead costs,

 making the financial value proposition less appealing to smaller practices. From the patient perspective, the inability to afford repeated co-payments can serve as a barrier to accessing these services.

Second, TCM and CCM codes involve site-of-service requirements that may fail to maximize organizations’ ability to use them to coordinate care. For example, TCM can be furnished only to individuals being discharged to community settings, an important limitation given the frequency with which patients are discharged to postacute care facilities, such as skilled nursing facilities. Third, TCM is limited by allowing only 1 payment per patient per hospitalization, an element that may be particularly challenging for complex patients with long hospitalizations. Finally, although the coordination work involved in TCM and CCM may span multiple clinicians or groups (eg, discussions between hospital and primary care teams, coordination between primary care and subspecialty clinicians), the ability to use financial incentives to engage different clinicians is limited by the fact that TCM and CCM payments are made to only 1 entity.

CMS has reinforced its commitment to these codes, in part by addressing both reimbursement and patient population issues in the 2020 Medicare Physician Fee Schedule.

 For example, the agency increased reimbursement amounts for TCM services and allowed these codes to be billed concurrently with CCM, hospice care, home care with care plan oversight, and other services that benefit from provider-to-provider communication during transitions. In addition, CMS introduced a new add-on code to reimburse clinicians for additional time spent on noncomplex CCM and broadened the reimbursement criteria for complex CCM to include disease monitoring without substantial revisions in patient care plans. Finally, the agency implemented new Principal Care Management codes in 2020 to support care management for patients with a single high-risk disease or complex chronic condition.

Other policy changes will be needed to support providers using TCM and CCM to transition to value-based payment. For TCM, refinements that would retain the emphasis on transitions of care while better shifting organizations into an era of value-focused, episode-based payment models may include allowing multiple points of patient contact extended over a 90-day period following discharge and enabling access to TCM services for all patients being discharged from the hospital, including those who transition to postacute care facilities prior to discharge home. For CCM, greater latitude in the frequency that add-on codes can be reported per calendar month would better reflect the time and resources required to manage patients longitudinally across different diseases with varying complexity. Moreover, efforts to increase patient engagement in CCM services could focus on removing out-of-pocket cost barriers. Finally, as uptake of and policy focus on both TCM and CCM gain attention, large-scale evaluations are needed to assess the impact of TCM and CCM services on clinical, quality, and cost outcomes, as well as to elucidate facilitators and barriers to the uptake and use of these codes.

Although these fee-for-service codes neither substitute for nor replace value-based reforms, they nonetheless emphasize the importance of care management activities. Both TCM and CCM services ultimately encourage organizations to adopt broader accountability for patients across the care continuum. Therefore, together with early evidence supporting the success of these codes, emerging policy changes offer an encouraging signal for organizations that are seeking more gradual ways to build competencies and bridge toward value-based payment and care delivery. 


 Department of Medicine, Virginia Mason Medical Center (CSH), Seattle, WA; Value and Systems Science Lab, University of Washington School of Medicine (CSH, AR, JML), Seattle, WA; Department of Medicine, University of Washington School of Medicine (AR, JML), Seattle, WA; Leonard Davis Institute of Health Economics, University of Pennsylvania (JML), Philadelphia, PA.

 The authors report 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 (CSH, AR, JML); drafting of the manuscript (CSH, AR, JML); critical revision of the manuscript for important intellectual content (CSH, AR, JML); and supervision (JML).

 Catherine S. Hwang, MD, MSPH, Virginia Mason Medical Center, 925 Seneca St, H8-GME, Seattle, WA 98101. Email: Catherine.Hwang@virginiamason.org.

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10. CMS, HHS. Medicare program; CY 2020 revisions to payment policies under the physician fee schedule and other changes to Part B payment policies; Medicare Shared Savings Program requirements; Medicaid Promoting Interoperability Program requirements for eligible professionals; establishment of an ambulance data collection system; updates to the Quality Payment Program; Medicare enrollment of opioid treatment programs and enhancements to provider enrollment regulations concerning improper prescribing and patient harm; and amendments to physician self-referral law advisory opinion regulations. 

Fee-for-service billing codes can serve as bridges for organizations to build care management capabilities and transition from volume- to value-based payment and care delivery.

Bridging to Value With Codes That Promote Care Management

 Physician leaders represent diverse career interests, clinical specialties, and leadership types. Despite the perceived value of their clinical perspectives, little is known about how physician leaders balance patient care and leadership responsibilities or how that balance varies by clinical specialty or leadership type.&ensp;We conducted what is to our knowledge the first survey evaluating physician leaders’ perspectives about these issues.

 Web-based survey administered September 2017 to February 2018 among physician leaders from the 22 health systems cited in the 2016-2018 U.S. News & World Report Best Hospitals Honor Rolls.

 Respondents were asked questions regarding their participation in patient care. We described our findings using percentages and χ

 tests to compare categorical variables across leadership type (executive, educational, clinical) and clinical specialty (procedural, nonprocedural, supportive).

 Of 447 leaders contacted, 53% responded to our survey. Of those, 84% reported some degree of participation in patient care (69%, 90%, and 85% of executive, educational, and clinical leaders, respectively; 

&thinsp;=&thinsp;.001); most (74%) spent no more than a quarter of their time on patient care, and they often achieved a balance that they deemed optimal (74%). Proceduralists were more likely to spend over a quarter of their time on patient care and less likely to achieve an optimal balance.

 Although most physician leaders engage in some degree of patient care, the balance that they achieve varies by clinical specialty and leadership type. These findings provide insight for health care organizations seeking to engage and optimize physician leadership amid ongoing health care transformation.

The American Journal of Accountable Care. 2020;8(2):26-31

American health care continues to undergo significant transformation. Changes in health policy

; consolidation among providers, payers, and other stakeholders

 have placed an emphasis on accountability in health care delivery. These changes underscore the potential value of physician leadership,

 which has been associated with improvements in operational efficiency,

 These dynamics have led to increased organizational interest in physicians with leadership skills

 and growing numbers of physicians seeking leadership training.

Importantly, physicians pursue leadership careers for different reasons

: Some seek administrative opportunities (eg, health system medical director) whereas others desire leadership in specific clinical (eg, service line chief) or academic (eg, department chair) areas. Variation also exists in each of these categories. For instance, some medical directors focus on specific projects or topic areas (eg, a certain hospital unit, an infection control program) whereas others focus on topics that cut across teams and units (eg, value-based care, population health).

Different leadership paths require different skill sets.

 For instance, medical directors of clinical units benefit from change management and process improvement skills needed to drive daily operations. Physicians with leadership in cross-cutting topics such as population health must be proficient in policy analysis, data synthesis, and change management.

Despite this diversity, a common thread is that physicians’ first-hand patient care experience lends them unique leadership perspective

 that can be highly valuable to themselves and their organizations. For example, a physician who maintains an active clinical practice may have greater credibility among clinicians whom they lead, or a physician can inform organizational contracting using their clinical perspective to balance financial and operational considerations. Conversely, the time and energy needed to maintain a clinical practice can siphon attention away from leadership work, with impacts on administrative capacity and potential leadership efficacy.

 All physician leaders face the task of balancing clinical and leadership responsibilities.

Despite these dynamics and the possibility that they vary by type of leadership role and clinical specialty, little is known about physician leaders’ perspectives on balancing clinical and leadership responsibilities. Therefore, we conducted what is to our knowledge the first survey capturing these perspectives.

We conducted a web-based survey and followed approaches used in prior work by sampling physician leaders from the 22 hospitals cited at least once in the 

Best Hospitals Honor Roll from 2016 to 2018.

 For each institution, we reviewed online content descriptions of its executive leadership teams and respective departmental websites to identify the names and contact information of the individuals who possessed a medical degree (MD or DO) and filled a position in 1 of the following categories of leadership types: executive (CEOs, vice presidents, and other health system executives), educational (medical school deans and academic department chairs), and clinical (internal medicine division or clinical service line chiefs). Division chiefs were limited to internal medicine division chiefs to promote consistency in use of title and scope of leadership responsibilities. Where online leadership information was limited (1 hospital), we followed an approach used in prior work by initiating email outreach to identify all eligible leaders.

 Individuals filling multiple leadership roles were assigned to 1 category (executive, if relevant, followed by academic and clinical, in that order). Retirees were excluded. This yielded a sample of 447 potential participants (average of 20.3 leaders per institution).

Respondents were asked what amount of time they currently spend on direct patient care, defined as serving as a physician of record or consultant on a clinical service (eAppendix A [

]). They were also asked about the optimal amount of time that they believed someone in their leadership position should spend on direct patient care to be an effective leader; the minimum amount of time someone in their clinical specialty needs to spend on direct patient care to be a safe, effective clinician; and whether they would increase or decrease their participation in patient care. Physicians participating in patient care were asked if it influences their performance in their leadership role and, if so, how. Those not currently involved in direct patient care were asked why they no longer practice. Additional questions confirmed leaders’ leadership type, clinical specialty, and years in clinical practice.

After pretesting among 32 physician leaders (which led to the addition of 1 question about number of years participating in patient care), the survey was sent to eligible respondents between September 19, 2017, and February 5, 2018. Recipients received up to 6 reminders to complete the survey. The protocol was deemed exempt by the University of Pennsylvania Institutional Review Board (protocol No. 827375). Respondents were not financially compensated for their participation.

Leaders’ clinical specialties were categorized according to the nature of their primary patient care responsibilities as procedural (obstetrics-gynecology, ophthalmology, surgery, urology), nonprocedural (emergency medicine, dermatology, family medicine, internal medicine, neurology, pediatrics, physical medicine and rehabilitation, psychiatry), or supportive (anesthesiology, pathology, radiology, radiation oncology).

We used several definitions to describe respondents’ perceptions about balance between clinical and leadership responsibilities. We defined balance as optimal (those spending an amount on patient care equivalent to the amount they deemed optimal from a leadership standpoint) or nonoptimal (all other situations). Among the latter group, we identified individuals for whom clinical burden (spending more time on patient care than was optimal from a leadership standpoint) contributed to nonoptimal balance. Additionally, we defined the feasibility of balancing leadership and clinical responsibilities as infeasible (if the optimal amount of time on patient care from a leadership perspective was less than the amount needed to maintain clinical competence) or feasible (all other situations). We used χ

 tests to compare categorical variables. All tests of significance were 2-tailed and deemed significant at α&thinsp;=&thinsp;.05. All analyses were performed using Stata version 15.1 (StataCorp).

Of 447 eligible respondents, 53% returned complete (n&thinsp;=&thinsp;233) or partial (n = 4) surveys. Nineteen percent of respondents were executive leaders, 58% were educational leaders, and 23% were clinical leaders. Executive leaders were more likely to come from nonprocedural clinical specialties (72% nonprocedural, 12% procedural, 16% supportive) relative to educational leaders (38% nonprocedural, 38% procedural, 24% supportive). Clinical leaders were primarily nonprocedural (90% nonprocedural vs 10% procedural and 0% supportive) (

&thinsp;<&thinsp;.001). Compared with nonrespondents, respondents did not differ with respect to gender, geographic distribution, leadership type, or clinical specialty (eAppendix B).

Overall, 84% of physicians reported participating in direct patient care. The majority (96%) reported that doing so somewhat or greatly improved their performance as leaders. Respondents noted a number of ways in which this occurred, including increased credibility among those they lead (98%) and insight into “frontline” issues (96%) (eAppendix C). Only 3% of physician leaders believed that direct patient care somewhat diminished their performance, citing reasons such as decreased time for leadership responsibilities. Of respondents not participating in direct patient care, the most commonly endorsed reason was “insufficient time or energy to be an effective clinician” (78%). None reported expectations from leadership as a reason for not participating in patient care.

Balancing Clinical and Leadership Responsibilities

Among physicians participating in direct patient care, most reported spending no more than a quarter of their time on it (74%) and that spending less than a quarter of their time was optimal from a leadership standpoint (80%). The majority (74%) reported achieving an optimal balance, with 15% citing clinical burden as contributing to nonoptimal balance. Only 4% reported that balance between clinical and leadership responsibilities was infeasible.

Perspectives varied by leadership type. Overall, executive leaders were less likely to participate in direct patient care (69%) relative to educational (90%) and clinical (85%) leaders (

). Among respondents participating in clinical care, executive leaders were also less likely to spend more than a quarter of their time on patient care or to believe that spending over a quarter of their time on patient care was optimal from a leadership standpoint (

). In contrast, rates of optimal balance or clinical burden and the reported feasibility of balancing clinical and leadership responsibilities did not vary by leadership type.

Participation in direct patient care varied by clinical specialty, ranging from 98% of leaders in procedural specialties to 82% of leaders in nonprocedural specialties and 76% of leaders in supportive specialties (

&thinsp;=&thinsp;.002) (Figure). The amount of and perceptions about time spent on patient care also varied by specialty type (Table). With regard to balance between clinical and leadership responsibilities, leaders in procedural specialties were least likely to achieve optimal balance (61.0% vs 78.4% and 82.1%; 

&thinsp;=&thinsp;.028) and most likely to report clinical burden (28.8% vs 9.9% and 7.1%; 

&thinsp;=&thinsp;.002) relative to leaders from nonprocedural and supportive specialties, respectively.

To our knowledge, this study is the first to document physician leader perspectives about the balance between their clinical and leadership responsibilities. It has 2 key takeaways.

First, our results suggest that many physicians serving a range of leadership roles find it feasible to balance clinical and leadership responsibilities. That many leaders believed patient care aided them in fulfilling their leadership responsibilities is informative to both physician leaders and organizations seeking to foster physician leadership.

 For these groups, it can be instructive to recognize that participation in patient care may be hampered more by insufficient time rather than external pressures or expectations.

Second, our findings suggest that fostering balance among physician leaders’ clinical and leadership responsibilities is not a one-size-fits-all task. In particular, it is relevant that perspectives and features of balance varied across leadership type and clinical specialty.

Poor balance—for instance, through infeasibility or the presence of clinical burden&mdash;can complicate physicians’ abilities to effectively meet either clinical or leadership responsibilities, much less both simultaneously. The fact that the extent and nature of imbalance can vary is exemplified by our finding that leaders from procedural specialties were more likely to report nonoptimal balance and clinical burden compared with leaders from other specialties.

Organizations can use insights about variation in the extent and nature of imbalance to tailor responsibilities and expectations for different types of leaders. For instance, organizations may require greater participation in patient care among clinical leaders, for whom clinical practice has more immediate relevance, than for executive leaders, whose primary responsibilities are often nonclinical (eg, emphasis on finance or operations

) and more removed from those of frontline clinicians. That perceptions about the feasibility of achieving balance did not vary by clinical specialty or leadership type is reassuring and underscores the ability of physician leaders from a range of clinical specialties and leadership types to achieve optimal balance.

First, the generalizability of our findings is limited by the 53% response rate. Second, although our study was descriptive in design and did not evaluate for associations between leadership behaviors and outcomes, the results represent useful early evidence that can be built upon in future work. Third, our sample of leaders may not be representative of the full breadth of physician leadership (eg, it may not capture mid-level leaders or those in other divisions not directly sampled in our study) or representative of physician leaders in all hospitals or health systems. However, we followed prior approaches in sampling leaders across a consistent set of leadership types among a group of institutions recognized for excellence across a set of clinical measures and outcomes.

 Fourth, we were unable to evaluate the presence and extent of institutional requirements, expectations, and minimum standards for physician leaders regarding patient care responsibilities. These factors should be evaluated in future work. Fifth, our study assessed self-reported perspectives among physicians but not other factors that should be studied in future research, including perspectives among those in nonleadership roles or objective measures of success (eg, staff retention, operational performance).

Our study provides important early evidence about physician leaders’ practices and perspectives balancing clinical and leadership responsibilities. Although most engage in some degree of direct patient care, what kind of balance they achieve varies by clinical specialty and leadership type. Collectively, our findings provide context and insight for health care organizations seeking to engage physician leaders from various backgrounds and optimize the value of their clinical perspectives amid ongoing health care transformation.

 Department of Medicine, Perelman School of Medicine, University of Pennsylvania (JWM, JAS), Philadelphia, PA; Institute for Health Policy, Management, and Evaluation, University of Toronto (ASD), Toronto, Ontario, Canada; Department of Medicine, University of Toronto (ASD), Toronto, Ontario, Canada; Department of Medicine, Sinai Health System and University Health Network (ASD), Toronto, Ontario, Canada; Department of Medicine, and Value and Systems Science Lab, University of Washington School of Medicine (JML), Seattle, WA.

 Concept and design (JWM, ASD, JAS, JML); acquisition of data (JWM, ASD); analysis and interpretation of data (JWM, ASD, JAS, JML); drafting of the manuscript (JWM, JML); critical revision of the manuscript for important intellectual content (JWM, ASD, JAS, JML); statistical analysis (JWM); provision of study materials or patients (JWM); administrative, technical, or logistic support (JWM); and supervision (JML).

 John W. Morgan, MD, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Blockley Hall, 13th Floor, 423 Guardian Dr, Philadelphia, PA 19104. Email: john.morgan@pennmedicine.upenn.edu.

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Although most physician leaders from 22 organizations saw patients and felt that it improved their leadership performance, their perceptions of the optimal balance of clinical and leadership responsibilities varied.

Physician Leaders’ Perspectives About Balancing Clinical and Leadership Responsibilities

As a broadly defined care delivery strategy, care management can improve outcomes but has yielded variable results, particularly within value-based payment models.

One contributing factor is that care management encompasses different types of activities, functions, and goals.

A framework categorizing care management as disease management, utilization management, and/or health care navigation interventions can help providers and payers invest in, implement, and reap benefits from care management programs.

Emphasis on care management has become ubiquitous in the era of value-based payment. At the vanguard of the movement, policy makers such as Medicare have emphasized care management via a variety of initiatives, ranging from payment models that encourage longitudinal care management

 to billing codes that reimburse clinicians for coordinating the care of patients who have chronic conditions.

 Hospitals and physician groups increasingly view care management as a key area for investment and as a strategy for succeeding in a value-based environment,

 and there have been isolated reports of success within value-based payment models.

To support effective care management programs in the context of value-based care, we propose a framework categorizing care management as disease management, utilization management, and care navigation interventions.

Joshua M. Liao, MD, MSc

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