Despite years of intense public policy battles, the American healthcare system remains as impenetrably complex and infuriatingly unaffordable for consumers as ever. Americans should expect more of the industry, and the industry should hold itself to a higher standard. Healthcare fundamentally exists as a service to people to improve their health and well-being, yet I am confident that too few consumers and patients feel the system is working in their best interests.

At Blue Cross Blue Shield of North Carolina (Blue Cross NC), our vision statement is to be the model for transforming our health system through unwavering commitment to quality, affordability, and exceptional experience. Each element of that vision is designed around the people we serve so that we can realize a future of world-class care that is both accessible and affordable to everyone.

Here I offer 3 key concepts that are part of driving this ambitious level of change.

Healthcare is astounding in its complexity and in the technological advances it inspires. Unfortunately, fee-for-service payments pit profits against patients in a way that deteriorates trust. We should all be able to have confidence that the decisions made about our health are not tinged with misaligned incentives.

The most powerful tool through which we indicate responsibility is payment. In North Carolina, we have launched a series of initiatives targeting healthcare transformation in which all new contracts include a path to significant shared risk based on cost and quality outcomes. Blue Cross NC already has almost 90% of its payments in value-based arrangements, but we are measuring advanced alternative payment models (APMs) where both the provider and the insurer are at significant risk for cost and quality. Our goal is to have more than 50% of payments in these arrangements by January 1, 2020. Then, as we incorporate new methods of paying for behavioral health, oncology, and other specialties, payment incentives will align across providers in a way that reflects the collaborative, high-quality care we know is possible. Our goal is to have as close to 100% of payments as possible in APMs with joint accountability by 2023.

Successfully transforming healthcare delivery will not occur overnight or without frustrations. Charting this course requires a cultural resilience to react and learn from setbacks so that the question becomes not 

 we will achieve our goals. For employees, we talk about values such as Every Customer Matters and Show Grit and discuss how we need to display Fearless Leadership.

Multiparty dedication to this work is also essential. For instance, we are fortunate that NC DHHS Secretary Mandy Cohen has a similar philosophy to ours and is using the state’s transition to Medicaid managed care—in which we will participate as a health plan&mdash;to ingrain the program with value-based care and novel interventions that address drivers of health.

The health plan of the future will move beyond traditional health plan functions and will help individuals and families navigate the complex healthcare environment and improve their health and well-being. In North Carolina, we are doing this by building close partnerships with providers, community organizations, and technology innovators. Under models of shared accountability, we can find efficient solutions to person-centered goals instead of spending resources on oppositional activities or duplicative functions. The sharing of data and information needs to be frictionless and built for delivery of insights to consumers and providers when and where they need them to make informed decisions.

Americans quite literally cannot afford for us to fail. We will need health plans, providers, employers, policy makers, consumers, and others to work together to drive this change. We must move faster. At Blue Cross NC, we are striving to be a model of health system transformation for the nation.&ensp;

This letter was coauthored by JP Sharp, JD, MPH, of Blue Cross NC.

Articles

From The Editorial Board

From the Editorial Board: Patrick H. Conway, MD, MSc

 To update an ongoing assessment of care pathway development, implementation, and evaluation, and to evaluate the emerging relationship between care pathways and other components of value-based care.

 Targeted literature review followed by an online survey and in-depth interviews.

 The PubMed/Cochrane databases and gray literature were searched for publications on care pathways (January 1, 2014, to March 3, 2017); a supplemental targeted search was completed in October 2017. Qualitative data were collected via an online survey and semistructured, in-depth interviews with payers, providers, pathway vendors, and opinion leaders.

 A total of 112 articles or posters were identified in recently published research. The survey and interviews included 32 and 19 respondents, respectively. Care pathways are increasingly driven by providers and provider networks. Overall, we found increased awareness of and adherence to codified best practices or standards, and prioritization of high-quality evidence during development. Research findings suggest stronger links between outcomes-based measures and both physician reimbursement and care pathway evaluation. Integration with other value-based care initiatives, including alternative payment models, is also gradually emerging.

 This study identified growing use of high standards of evidence and adoption of other best practices in the development, implementation, and evaluation of care pathways. As the influence of care pathways on patient care continues to expand, additional efforts are needed to increase transparency, disclose conflicts of interest, engage with patients, effectively align care pathways with improvements in patient outcomes, and integrate efficiently with other value-based care initiatives.

As the use of care pathways has expanded in the United States, development, implementation, and evaluation processes have evolved.

Research findings support notable increases in process transparency, codification of standards, and prioritization of high-quality evidence.

Provider groups emerged as the driving force behind development and implementation, although physician resistance and administrative burden remain barriers to adoption.

Despite efforts, patient education and engagement remain inadequate.

There are indications of stronger links between outcomes-based measures, rather than traditional compliance measures, and both physician reimbursement and care pathway evaluation.

The movement toward value-based care may significantly affect the development and expansion of care pathways.

In the last decade, the use of care pathways—designed to provide evidence-based treatment options for disease management, improve quality of care, reduce unnecessary variation, and lower costs&mdash;has expanded rapidly in the United States.

 Although pathway use is increasingly prevalent for chronic diseases,

 use in oncology is well known, with 58% of oncology practices reporting use of pathways in 2016,

 Best practices to guide pathway development, implementation, and evaluation have been established in oncology, and the American Society of Clinical Oncology (ASCO) has issued a policy statement and a set of detailed criteria to ensure the integrity and quality of oncology pathways.

At the same time, other value-based healthcare initiatives and alternative payment models (APMs) are being adopted. In 2016, approximately 31% and 28% of physicians across specialties participated in bundled payment programs and medical homes, respectively.

 Overall, 43% of physicians received some form of value-based reimbursement. With the goals of improving quality of care while reducing cost, care pathways not only have objectives similar to those of other value-based initiatives but also may be critical in the transition to value-based care.

A January 2016 landscape assessment of care pathways revealed a wide range of development, implementation, and evaluation methods but a critical absence of transparency.

 In this follow-up study, we assessed changes in development, implementation, and evaluation of pathways and reviewed the latest evidence on integration of pathways with value-based care initiatives in the United States.

Similar to the 2016 study, a targeted review of published literature was followed by an online survey and in-depth interviews. Results were compared with those of the prior study, providing a perspective on the state of care pathways today and direction for the future.

Our search covered the PubMed and Cochrane Reviews databases and selected conferences’ websites. Publications, posters, and abstracts discussing care pathways in US healthcare settings that were in English and published between January 1, 2014, and March 3, 2017, were reviewed. We further searched trade journals and websites of professional organizations, pathway vendors, payers, and major provider groups. A supplemental search was completed in October 2017.

In screening materials identified in our search, we again required that a care pathway have stated objectives defining goals or key elements of care and was limited to specific disease states in defined patient populations, used evidence-based medicine or clinical guidelines to develop treatment recommendations, included a monitoring and evaluation mechanism, and included criteria for the use of pharmacologic therapies.

 Between June and September 2017, we conducted online surveys (n = 32) followed by in-depth interviews (n = 19). Respondents were selected to represent a mix of payers, providers from a range of specialties and practices, commercial care pathway vendors, and opinion leaders. Although all respondents were screened for involvement in pathway development, implementation, evaluation, or use, questions were administered only to those with experience and knowledge relevant to given sections (rated ≥3 on a 5-point scale). Respondents were offered honoraria for survey and interview participation.

 The survey was structurally similar to the 2016 study, with new sections on value frameworks and APMs. Descriptive analyses were conducted in Microsoft Excel.

 Interviewers administered an hour-long, semistructured discussion guide with open-ended questions. Interviews were designed to supplement information from the survey with additional detail on key areas of interest.

We identified 750 unique citations. Full-text review was conducted for 138 publications; 68 full-length articles and 44 abstracts and posters matched the selection criteria. Key themes and gaps in the literature informed the design of the survey and interviews. We found more transparency and codification of the development process, particularly in oncology, where pathway use has matured and more research has been published.

 More studies documented pathway adherence, resource use, and patient outcomes,

 but implementation and evaluation are still inconsistently documented or communicated. Engagement with stakeholders, particularly patients, remains inadequate.

 Efficacy and toxicity still supersede cost as key considerations during pathway development, yet pathways are viewed as a means to provide downward pressure on rising drug prices.

 Nevertheless, closer linkages with features of value-based care have emerged. As payers experiment with linking pathways and APMs,

 barriers to such integration need to be further understood. Interest is also growing in incorporating value frameworks in pathway development.

Twenty-five providers and 7 payers completed the survey. Eleven providers, 4 opinion leaders, 3 payers, and 1 pathway vendor were interviewed (

 Among all respondents with knowledge of pathway development, cost of care was the most frequently identified consideration for therapeutic area selection when care pathways are developed (77% [20/26]), followed by clinical outcomes, variation in treatment patterns, and prevalence rates (

). In 2016, the internal selection process was most cited, followed by cost of care. Pathways for relatively prevalent cancers, such as breast, colon, and lung, remain common; however, interviewees noted that rare cancers with high treatment costs are being identified for pathway development, citing recent FDA approvals of chimeric antigen receptor T-cell therapies and advances in precision medicine. Over the next 5 years, most survey respondents expect pathway use to expand in both oncology (78% [25/32]) and nononcology (84% [27/32]) areas. Pathways have also broadened to cover the care continuum, including symptom management and palliative care. Further, respondents had experience with pathways for common or high-cost chronic diseases, particularly in the areas of rheumatology, diabetes, asthma, and cardiology.

 Nearly all provider respondents (96% [24/25]) had a role in developing, implementing, evaluating, or using 2 or more pathways. Among respondents who addressed pathway development, 92% (24/26) identified clinicians as stakeholders, with fewer indicating a role for health plans (46% [12/26]) or pathway vendors (12% [3/26]). Most providers with development experience (74% [14/19]) indicated that their practice develops or implements pathways separate from those developed by payers or vendors.

Evidence considerations and transparency in care pathway development.

 Treatment guidelines (88% [23/26]) and published systematic literature reviews (73% [19/26]) were identified as key data sources for pathway development (

). Fewer identified retrospective or observational studies than in 2016. Providers were less likely than payers to identify prospective studies (53% [10/19] vs 100% [7/7]) or randomized controlled trials (58% [11/19] vs 86% [6/7]) as influential.

Interviewees identified experts and published or validated data from existing pathways as data sources for pathway development; survey results also confirmed that data from existing pathways informed development (77% [20/26]). Grading evidence quality appears to be less influential than in 2016 (58% [15/26] vs 81% [17/21]).

Efficacy (96% [25/26]), safety/toxicity (88% [23/26]), pharmaceutical costs (88% [23/26]), and medical resource use and costs (85% [22/26]) were the main factors considered when selecting treatments for the clinical algorithm. All (7/7) payers indicated considering medical resource use and costs versus 79% (15/19) of providers. In 2016, respondents identified safety (95% [20/21]), efficacy (86% [18/21]), and medical costs (81% [17/21]) as main factors. In both studies, efficacy was ranked as the most important. Although all interviewees indicated considering costs, particularly pharmaceutical costs, and clinical outcomes during development, less than half described cost as an initial consideration, and many indicated that clinical benefit precedes cost.

More than half (62% [16/26]) reported that their organizations followed best practices as detailed by ASCO’s policy statement; of these, 81% (13/16) noted that best practices, standards, or methodology were available to key stakeholders, including patients and payers. However, access may be restricted through means such as an intranet, patient portal, or membership. Further, awareness of processes for disclosing and managing potential conflicts of interest varied.

 Most (90% [28/31] with knowledge of pathway implementation) reported that pathways typically provide physicians with multiple treatment options for a given diagnosis, and 94% (29/31) reported that physicians have a choice about placing patients on a pathway. Compared with 68% (13/19) in the 2016 study, only 39% (12/31) reported a relationship between pathway compliance and reimbursement (

]). All of these respondents noted financial penalties or disincentives for noncompliance; 83% (10/12) indicated significant or extremely significant penalties. Interviewees expected incentives for performance, patient satisfaction, and other quality metrics captured in value-based care to receive traction in the near future. Process-oriented indicators, like compliance, were viewed as intermediate, surrogate measures. All payers (7/7) indicated that prior approval is required compared with 58% (14/24) of providers.

 Quality metrics (78% [18/23] with knowledge of pathway evaluation) were most commonly used to evaluate pathway performance, followed closely by readmission rates, compliance, and hospitalization rates (

). Quality metrics, readmission rates, and adverse event rates were each identified by 43% (10/23) as top 3 metrics. In the 2016 study, compliance was top-ranked and identified by most as a top 3 metric.

Interviewees observed that compliance remains an important measure. Target compliance rates of 75% to 80% were reported in 2016; however, only 43% (10/23) of respondents in the current research experienced 70% or greater compliance. Respondents reported challenges collecting or analyzing pathway data, including 75% (9/12) who encountered data collection issues.

 Some respondents (35% [11/31] with knowledge of pathway implementation) recognized changes in patient engagement over the last 3 years, among whom 91% (10/11) indicated that patients increasingly sought information on care pathways and 64% (7/11) observed increasing involvement of patient advocacy groups. Less than half of respondents (42% [13/31]) reported that on-pathway patients are provided disclosure or education about pathways; this included 46% (11/24) of providers, compared with 20% (1/5) of providers in 2016 (

). Interviewees also suggested that treatments may not be described as being part of a pathway. Financial incentives may not be transparent; 38% (5/13) of respondents who reported patient disclosure believed that patients were aware of financial incentives provided to physicians. Moreover, 26% (8/31) indicated that details on financial incentives are publicly available to all stakeholders, including patients.

Patient feedback on pathways is not generally mandated, with 30% (7/23), 43% (10/23), and 26% (6/23) reporting always, sometimes requested, and not formally engaged, respectively, among those with knowledge of pathway evaluation. Increased patient involvement is expected (78% [25/32]) in the next 5 years, particularly in evaluation (50% [16/32]) compared with development and implementation.

 The majority of survey respondents (93% [26/28] with knowledge of APMs [accountable care organizations, medical homes, and bundled payments]) and interviewees noted that care pathways are very or somewhat likely to be used in APMs; in APMs using pathways, 43% (12/28) estimated that greater than 50% of patients are on-pathway. Some interviewees noted that pathways could be key to value-based contracting and outcomes-based payments but also cautioned that costs could drive clinical care as a result. Even so, 57% (16/28) reported barriers to pathway use in APMs, including administrative burden (81% [13/16]) and lack of technological integration (75% [12/16]).

 Most survey respondents (74% [14/19] with knowledge of prominent US value frameworks) reported the use of value assessment frameworks in pathway development. Sixty-four percent (9/14), 57% (8/14), 50% (7/14), and 43% (6/14) noted value frameworks developed by the National Comprehensive Cancer Network (NCCN), American College of Cardiology and American Heart Association, ASCO, and Institute for Clinical and Economic Review (ICER), respectively. Interviewees often mentioned NCCN, ASCO, and ICER and generally advised considering value frameworks during development but not in isolation.

 Increased cost containment efforts (75% [24/32]), use of bundled payments (66% [21/32]), and applicability beyond oncology (47% [15/32]) may advance care pathway expansion and uptake. Other catalysts included the popularity of value frameworks (44% [14/32]), integration of clinical trials (34% [11/32]) and biomarker testing (25% [8/32]), and proliferation of medical homes (25% [8/32]). Interviewees additionally highlighted the value of standardization. In general, more providers reported the use of value frameworks in development; however, more payers felt that the popularity of value frameworks may expand the use of care pathways.

Physician resistance (pushback) remains the largest potential barrier to expansion (84% [27/32]), followed by administrative burden (

). Payers were more likely than providers to believe that administrative burden (71% [5/7] vs 56% [14/25]) and perceived implementation costs (71% [5/7] vs 32% [8/25]) are greater potential barriers, but more providers than payers found that the failure to demonstrate cost savings was a greater potential barrier (40% [10/25] vs 29% [2/7]). Reports of insufficient information technology (IT)/tracking systems and failure to demonstrate patient outcomes and cost savings have declined.

Care pathway development, implementation, and evaluation continue to play a significant role in utilization management in the United States, with cost considerations serving as a core motivation for these activities, collectively. During the development process, however, efficacy and safety continue to be prioritized overall. In large part, this trend continues to reflect efforts to manage care in the treatment of patients with cancer. Although pathways continue to be used in prevalent cancer types, primarily, we found a wider range of therapy areas for which pathways have been implemented, from rare cancers to chronic diseases.

We observed a higher bar for evidence during development, including greater emphasis on prospective studies and clinical guidelines, compared with the lack of codified standards and transparency in development we noted in our 2016 research. Although transparency has improved, access to specific development methodology remains limited to internal stakeholders, and disclosure of potential conflicts of interest is inconsistent. To some extent, challenges with transparency contribute to the difficulty in presenting a more definitive picture of conflicts or tension between pathways that guide medical care versus initiatives motivated by the imperative to manage expenditures, particularly in oncology.

Efforts to improve patient engagement with care pathways were among the most notable recent trends. About one-third of respondents observed changes over the past 3 years, noting increases in patient requests and patient advocacy group involvement, and more providers reported informing patients about pathway use. Most respondents anticipated future increases, with respondents also suggesting that patient engagement is becoming a feature of pathway evaluation, although this was not formally assessed in the prior study. Despite these efforts, a fundamental lack of patient awareness remains regarding the use of, and financial incentives associated with, pathways.

 We observed trends toward provider-driven pathways, the use of value frameworks during pathway development, and the growing relationship between pathways and APMs. Providers are increasingly central to the development and implementation of care pathways.

 In the 2016 study, providers were consistently identified as critical stakeholders, but the role of payers was often dominant. Pathway vendors still play an important role—particularly in serving payers that use pathways to manage plan member benefits&mdash;but providers are becoming the key drivers. Although we observed some trends distinguishing payer and provider behavior (data sources for pathway development, consideration of medical resource use and costs, requirement of prior approval, views on value frameworks, and barriers to expansion), further research in this area must be conducted.

Continued pressure to reduce costs has the potential to further expand the scope and development of care pathways. Value frameworks are often integral in the development process, ranging from being the primary approach for pathway construction to a reference for a component of treatment selection. The influence of value-based care was also noted in pathway implementation, with some respondents pointing to a shift in incentive structures. Now, fewer respondents indicated physician reimbursement being tied to pathway compliance; instead, reimbursement may become more directly linked to patient outcomes. Additionally, respondents believed that patient outcomes and other quality measures may eventually become standard pathway evaluation metrics. Pathways can be an effective tool in the delivery of value-based care, particularly in oncology.

 There is potential for more integration between pathways and APMs, which have similar objectives.

 Moreover, catalysts for pathway uptake include use of bundled payments and medical homes.

However, care pathways continue to face barriers to adoption. Although prior concerns with IT/tracking systems and the demonstration of patient outcomes and cost savings have decreased, concerns with physician pushback and administrative burden persist. Such concerns could be mitigated with provider-driven pathways and availability of evidence on patient outcomes, long-term efficiency, and reduced practice costs.

We utilized a hybrid approach with a targeted literature review followed by qualitative primary research, entailing survey and interview components, similar to the 2016 study. The survey and interview findings aligned with those of the literature review; however, the literature review findings are not exhaustive and are subject to the limitations of keyword-based searches of publication databases and ad hoc searches of the gray literature. Although survey and interview respondents had oncology and nononcology experience, the samples are not necessarily representative, and findings may not be generalizable to the wide diversity of care pathways. Several factors also limited our ability to cross-validate responses between the current and prior studies and perform statistical tests. The samples’ size and composition differed, and both the survey structure and responses to questions were updated to adapt to changes in the pathway landscape.

METHODS

The authors examined the latest trends in development, implementation, and evaluation of care pathways and the impact of the movement toward value-based care.

US Care Pathways: Continued Focus on Oncology and Outstanding Challenges

 To assess the association of a clinical decision support (CDS) algorithm for hospital discharge disposition with spending, readmissions, and postdischarge emergency department (ED) use.

 A retrospective study in a cohort of fee-for-service Medicare patients 65 years or older linked to a database of patients receiving CDS.

We evaluated (1) patients whose discharge disposition was concordant with the CDS recommendation versus those whose disposition was not and (2) patients receiving CDS for discharge disposition versus those not receiving CDS, regardless of concordance. Outcomes were spending over a 90-day episode, 90-day readmissions, and postdischarge ED utilization not associated with a readmission.

 Analysis of concordant versus discordant cases showed decreased spending for concordant cases ($860 savings; 95% CI, $162-$1558; 

 = .016), a decrease in readmissions (adjusted odds ratio [OR], 0.920; 95% CI, 0.850-0.995; 

 = .038), and no change in rate of postdischarge ED use (adjusted OR, 0.990; 95% CI, 0.882-1.110; 

 = .858). Analysis of patients receiving CDS versus not receiving CDS showed no significant difference in spending ($221 savings; 95% CI, —$115 to $557; 

 = .198), ED use (adjusted OR, 0.959; 95% CI, 0.908-1.012; 

 = .128), or readmission rate (adjusted OR, 1.004; 95% CI, 0.966-1.043; 

 Following the recommendation of a CDS algorithm for hospital discharge disposition was associated with lower spending, fewer readmissions, and no change in ED use over a 90-day episode of care.

Following the recommendation of a clinical decision support (CDS) algorithm for hospital discharge disposition was associated with lower spending and reduced readmissions with no change in emergency department (ED) use.

A CDS algorithm incorporating cognition, ambulation, activities of daily living, capable caregiver availability, skilled therapy needs, and skilled nursing needs was evaluated in 15,887 patients participating in Medicare’s bundled payment program.

Following the algorithm’s recommended level of care (home vs home with home health agency vs postacute facility) was associated with an $860 decrease in spending, fewer readmissions, and unchanged postdischarge ED use over a 90-day episode compared with those patients for whom the recommended level of care was not followed.

The judicious use of postacute care resources by the hospital discharge team can be enhanced by using a CDS algorithm.

Spending on postacute care accounts for a substantial portion of overall healthcare costs and is growing faster than other spending categories.

 For conditions like pneumonia, chronic obstructive pulmonary disease, heart failure, and joint replacement, Medicare spends nearly as much in the 30 days after discharge as it does during hospitalization.

 Moreover, postacute costs are associated with large geographic variation across the United States, with three-fourths of all regional variation in Medicare spending attributable to postacute care spending.

Achieving the judicious, appropriate use of resources following hospitalization can be a critical success factor for organizations participating in full risk capitation and in payment models such as accountable care organizations and bundled payments. Additionally, Medicare spending per beneficiary—a measure of spending encompassing the 30 days subsequent to hospitalization&mdash;is publicly reported and tied to financial incentives for all US Inpatient Prospective Payment System hospitals as part of Hospital Value-Based Purchasing and is a component of the physician payment formula under the Merit-based Incentive Payment System.

Despite growing pressure for hospitals to develop a systematic approach to using postacute care for all patients, most published studies examining such approaches address a single diagnosis, such as stroke or joint replacement; describe a method that may be too complex and time-consuming for widespread use; or rely on information that is unavailable early in the hospitalization, when factors influencing discharge destination may be modified.

We sought to build a clinical decision support (CDS) algorithm to assist hospital discharge planning teams in identifying the most appropriate discharge care level while avoiding untoward effects such as increases in readmissions, emergency department (ED) use, and overall spending. To assess the algorithm, as a convener in CMS’ Bundled Payments for Care Improvement initiative (BPCI),

 we accessed Medicare claims data for acute hospitalizations and the subsequent 90-day period.

We evaluated the effect of the algorithm on spending, 90-day readmissions, and postdischarge 90-day ED use in cases in which discharge disposition was concordant or discordant with the algorithm’s recommendation and in which the algorithm was or was not used (regardless of concordance).

The setting of this study was acute hospitalization and the 90-day period following discharge, encompassing patient care in the home, home with a home health agency (hereafter written simply as home health agency), and postacute facility (including skilled nursing facilities, inpatient rehabilitation facilities, and long-term acute care hospitals).

We developed a proprietary CDS tool incorporating an algorithm to help teams determine an appropriate level of care following hospital discharge. Inputs to the algorithm were ambulatory status; ability to perform activities of daily living; cognitive status; availability of a capable caregiver; postacute physical, occupational, and speech therapy needs; and postacute skilled nursing needs. In developing the CDS tool, we reviewed the literature to identify patient-level factors to serve as the basis of decision support.

 Caregiver support was also recognized as critical to effective discharge planning.

 We convened experts in home health, postacute facility care, and hospital care in a series of working sessions to finalize factors driving a decision to discharge patients to 1 of 3 options. Other potentially informative factors, including comorbidities, polypharmacy, environmental factors (eg, stairs, home modifications), and social determinants, were intentionally omitted to increase the tool’s ease of use. After assessing the tool for user requirements and reproducibility of results across users, we created a scoring system based on the identified inputs that yielded a recommendation for 1 of 3 postdischarge care intensity levels: home, home health agency, or postacute facility.

A pilot was then conducted, using an analysis of 1537 BPCI patients to whom the CDS tool was retrospectively applied. This pilot yielded a proposal that made recommendations as follows (results shown as pilot vs controls): More patients go home or to a home health agency (home, 50.6% [95% CI, 47.7%-53.6%] vs 36.1% [95% CI, 32.8%-39.5%]; home health agency, 32.5% [95% CI, 29.1%-35.1%] vs 26.0% [95% CI, 16.8%-24.4%]), and fewer go to a postacute facility (16.9% [95% CI, 13.1%-20.8%] vs 43.3% [95% CI, 40.2%-46.5%]). The tool’s performance was then evaluated using risk-adjusted regression models created to predict the rate of 90-day readmissions for patients discharged to home, a home health agency, and a postacute facility. These rates were compared with observed 90-day readmissions and found to be not statistically different across the 3 discharge dispositions: home (32.4% [95% CI, 30.4%-34.4%] vs 32.8% [95% CI, 29.5%-36.0%]), home health agency (33.1% [95% CI, 31.1%-35.1%] vs 32.0% [95% CI, 27.5%-36.4%]), and postacute facility (33.3% [95% CI, 31.4%-35.3%] vs 31.7% [95% CI, 28.7%-34.7%]).

 Subsequently, the algorithm was embedded in the convener’s proprietary software platform used by bundled payment program operating personnel to identify and manage patients during the bundle episode. These users—personnel at the convener’s BPCI episode—initiating hospitals&mdash;were trained in the use of the tool, which involved a webinar, a user’s manual, and targeted face-to-face and remote individualized education and support. Training addressed how and when to populate the components of the algorithm and also the importance of creating a process to discuss the tool’s inputs and recommendation with the interdisciplinary discharge team, including the patient/surrogate decision maker. Although all episode-initiating hospitals were encouraged to use the tool, only a subset did so, which was at the discretion of the organizations’ leadership.

All Part A and Part B claims for the convener’s population of fee-for-service Medicare BPCI patients encompassing acute hospitalization and the subsequent 90-day period occurring between January 1, 2016, and March 31, 2017, were accessed. After deleting claims with incomplete information, there were 148,385 episodes available for analysis. During the same time period, a subset of 15,887 patients in this population were tested using the CDS tool. Our analytic data set consists of Medicare claims data on 132,498 episodes that did not receive the CDS tool and Medicare claims data plus CDS testing results for the 15,887 episodes that did receive the CDS tool.

For each episode, we examined the following outcomes: allowed payment amounts (spending), discharge disposition (home, home health agency, or postacute facility), readmission within 90 days, and postdischarge ED visits not associated with a readmission within 90 days of hospital discharge.

We used propensity modeling with inverse probability weighting (IPW) for each analysis

; propensity to follow the CDS recommendation was used in the concordant versus discordant analysis, and propensity to use the test was used in the intention-to-treat (ITT) analysis (tested vs untested). We used logistic regression for both propensity models. The following variables were included as independent variables: length of stay, race, sex, dual-enrolled status (binary), prior system utilization (the number of days spent as a hospital inpatient in the year prior to the index hospitalization), entered through the ED, clinical episode name, Medicare Severity Diagnosis Related Group tier (no comorbid complication, comorbid complication, major comorbid complication), and primary diagnosis code. In all analyses, we used IPW to compute average treatment effect among the treated (ATT) estimates. Although these variables were all selected a priori without first examining their relationship to the probability of testing, most of them are significantly associated with it.

We performed 2 analyses of the effect of the CDS tool’s use on patient outcomes: concordant versus discordant and tested versus untested.

 was defined as discharge disposition agreeing with the CDS tool’s recommendation, whereas 

 was defined as discharge disposition disagreeing with the recommendation. We used IPW regression (spending) and logistic regression (ED use and readmissions) to test for the effect of agreeing with the CDS tool recommendation on outcomes.

Within this analysis, we also examined the impact of more intense and less intense levels of care. In an attempt to control for hospital-level differences, we performed a post hoc analysis of CDS-concordant versus CDS-discordant cases, showing results for when the hospital is controlled for as a main effect.

 level of care is defined as either (1) the CDS tool recommends home and the actual disposition was either home health agency or postacute facility or (2) the CDS tool recommends home health agency and the actual disposition was postacute facility. A 

 level of care is defined as either (1) the CDS tool recommends postacute facility and the actual disposition was either home health agency or home or (2) the CDS recommends home health agency and the actual disposition was home. In these models we included the impact of more or less intense levels of care on the outcomes.

The second analysis consisted of tested versus untested cases. Tested cases received the CDS tool, whereas untested cases did not receive the CDS tool. This was an ITT analysis, examining outcomes independent of whether the CDS tool recommendation was followed. This analysis reflects the expectation that the decision of discharge disposition is based on a merging of clinical expertise and the CDS tool recommendation, with responsibility for the final decision resting with the discharge planning team and patient/caregiver.

We acknowledge that in actual use, providers may not apply the CDS tool to all patients, nor do we expect that everyone in the population will receive the test. Therefore, we used a propensity model to estimate the ATT, or the effect of the test on the outcome of those who received it.

 provides an exemption from institutional review board requirements when the purpose is to study, evaluate, or otherwise examine a public benefit or a service program—in this case, CMS’ BPCI program. The contractor signed a data use agreement stating that all data were securely and solely used for the purposes of this study.

In general, compared with patients found to be discordant, those who were concordant were about 1 year younger, had shorter lengths of stay, were less likely to be women, were less likely to be dual enrolled, and had lower rates of major complications. Concordant patients were also less likely to be sent to home health agencies and postacute facilities and were more likely to go home (

). Adjustment for differences in concordant versus discordant patients using IPW resulted in the 2 groups being similar in all categories (

There were 148,385 patients in the sample in total. Of the 15,887 who received CDS, 10,218 were CDS concordant and 5669 were CDS discordant. Of the latter group, 2066 were discharged to a less intense level of care than what the CDS proposed and 3603 were discharged to a more intense level of care (

Episode spending was $860 less (95% CI, $162-$1558; 

 = .016), 90-day readmissions were lower (adjusted odds ratio [OR], 0.920; 95% CI, 0.850-0.995; 

 = .038), and ED use was unchanged (adjusted OR, 0.990; 95% CI, 0.882-1.110; 

 = .858) for concordant compared with discordant cases (

). A post hoc analysis, controlling for the hospital as a main effect, showed episode spending to be $934 less (95% CI, $247-$1621; 

 = .008), 90-day readmissions lower (adjusted OR, 0.916; 95% CI, 0.846-0.992; 

 = .031), and ED use unchanged (adjusted OR, 0.997; 95% CI, 0.887-1.120; 

 = .957) for concordant versus discordant cases.

More Intense and Less Intense Levels of Care

When concordant cases were compared with discordant cases discharged to more intense levels of care, concordance was associated with decreased spending ($4802; 95% CI, $3896-$5709; 

 <.001), decreased readmission rates (adjusted OR, 0.834; 95% CI, 0.757-0.920; 

 <.001), and unchanged ED use (adjusted OR, 0.956; 95% CI, 0.832-1.096; 

 = .517). Results of the adjusted analysis of concordant versus discordant cases discharged to less intense care suggest that concordance was more expensive ($6417; 95% CI, $5551-$7283; 

 <.001), with no changes in readmission rates (adjusted OR, 1.086; 95% CI, 0.951-1.240; 

 = .222) or ED use (adjusted OR, 1.086; 95% CI, 0.893-1.319; 

Disposition to Home, Home Health Agency, and Postacute Facility

Recommended rates for disposition to home, home health agency, and postacute facility were 41.5%, 29.6%, and 28.9%, respectively, whereas actual disposition rates among the tested population were 40.8%, 20.6%, and 38.7% (

). Compared with actual, the CDS tool recommended fewer patients be sent to a postacute facility and more patients be sent to a home health agency than was observed. Approximately the same proportion of patients were sent home (40.8%) as were recommended (41.5%). Conversely, more patients received a recommendation to go to a home health agency (29.6%) than was observed (20.6%), and fewer received a postacute facility recommendation (28.9%) than was observed (38.7%). The overall rate of concordance with the CDS recommendation was 64%. Table 2 shows the numbers of patients for each combination of disposition and recommendation.

Tested Versus Untested Populations and Outcomes

Overall, there was no difference in age between the tested and untested populations. However, the tested were less likely to be white, were more likely to be female, had longer lengths of stay, and had higher rates of dual enrollment. See 

 for the rate of testing split across a selection of demographic variables.

The IPW-adjusted ITT analysis showed decreased spending among those tested, but it was not statistically significant ($221 savings; 95% CI, —$115 to $557; 

 = .198). There was also no difference among those tested in ED use (adjusted OR, 0.959; 95% CI, 0.908-1.012; 

 = .128) or readmission rates (adjusted OR, 1.004; 95% CI, 0.966-1.043; 

We tested a CDS tool for post—hospital discharge destination and found that following its recommendation was associated with reduced spending and readmissions, with no change in ED use, over the course of an episode encompassing a hospitalization and the ensuing 90-day postdischarge period for patients participating in the Medicare bundled payment program. For cases that were discharged to a more intense care level than the recommendation, associated spending and readmissions were greater, whereas ED use was unchanged. For cases discharged to less intense care than recommended, spending was reduced, whereas readmissions and ED use were unchanged.

Because of the observational nature of the study, it is difficult to definitively state the reasons for lower spending or readmission reductions when following the tool’s recommendation. It is possible that the recommendation-concordant group—who went home more often and to postacute facilities less often (Table 1)&mdash;recovered more successfully because they were in the home environment. The hazards of postacute facilities (eg, falls, delirium, infection, poor nutrition, decreased mobility, deconditioning

) may play a role in increased readmissions and ED use, although the evidence on the impact of home care versus alternative locations on health outcomes is inconclusive.

 Alternatively, in select patients, such as those undergoing elective total joint replacement, a home discharge has been associated with lower readmissions.

Similarly, the reasons for findings associated with less intense and more intense discharge decisions are speculative. It is conceivable that for many patients, receiving less intense posthospital care than the tool recommends is in actuality the appropriate care level, thus explaining why spending is lower and readmissions and ED use were no different from those of the recommendation-concordant group. For patients receiving more intense care, which may in part be driven by patient/caregiver preferences, one may argue that higher spending and readmissions are explained by patient factors; however, the propensity model was designed to adjust for such factors.

For patients discharged to a less intense level of care than recommended, because readmissions and ED use were unchanged in that group, it is likely that if the discharge team’s judgment supports the decision to discharge to a less intense level, such a decision is safe and appropriate. Conversely, if a more intense level of care is felt to be required, the team should consider this study’s findings of higher readmissions and ED use and carefully consider the purported benefits of the decision.

MANAGERIAL

The use of clinical decision support for hospital discharge disposition was associated with a reduction in spending and readmissions without negatively affecting emergency department use.

Association of Decision Support for Hospital Discharge Disposition With Outcomes

 To compare cancer care spending and utilization by site of provider-administered chemotherapy in Medicare.

 A retrospective analysis using 2010-2013 Medicare claims.

The study population was a random sample of Medicare fee-for-service beneficiaries with cancer who initiated provider-administered chemotherapy in a hospital outpatient department (HOPD) or physician office (PO). We assessed the following outcomes during the 6-month follow-up period: (1) spending on cancer-related outpatient services excluding chemotherapy, (2) spending on cancer-related inpatient services, (3) utilization of select cancer-related outpatient services (evaluation and management, commonly used expensive billing codes, and radiation therapy sessions), and (4) the number of cancer-related hospitalizations. We used regression analyses to adjust for patient health risk factors and market characteristics.

 During the 6-month follow-up period, risk-adjusted spending on nonchemotherapy outpatient services was slightly lower among patients receiving chemotherapy in HOPDs than in POs ($12,183 [95% CI, $12,008-$12,358] vs $12,444 [95% CI, $12,313-$12,575]; 

 <.05). Risk-adjusted cancer-related inpatient spending was higher in the HOPD group than in the PO group ($3996 [95% CI, $3837-$4156] vs $3168 [95% CI, $3067-$3268]; 

 <.01). The HOPD group had fewer visits in all select outpatient services but had a higher number of hospitalizations than the PO group.

 Differences in cancer care spending by site of chemotherapy (HOPDs vs POs) vary by service type. Those differences are partially driven by utilization differences. As the site of chemotherapy shifts from POs to HOPDs, spending and utilization patterns in both settings need to be monitored.

Our study examined risk-adjusted differences in cancer care spending and utilization for the 6-month follow-up period after initiation of provider-administered chemotherapy by site of chemotherapy in Medicare. We found that:

Risk-adjusted spending on outpatient cancer services other than chemotherapy was slightly lower for patients who received chemotherapy in hospital outpatient departments (HOPDs) versus physician offices (POs).

Risk-adjusted inpatient cancer care spending was higher for patients who received chemo&shy;therapy in HOPDs versus POs.

Although utilization of select outpatient services was higher among patients who received chemotherapy in POs, the number of hospitalizations was higher in the HOPD group.

Medicare shoulders a large burden of the costs for cancer care delivered in the United States. In 2014, cancer care costs exceeded $87 billion,

 with costs projected to increase to more than $173 billion in 2020.

 Medicare’s share is roughly one-third.

 A main treatment modality for cancer is chemotherapy,

 which is mostly administered by providers.

Provider-administered chemotherapy is usually covered by medical benefits. In Medicare, Part B (coverage for outpatient medical services) pays for provider-administered chemotherapy. In recent years, there has been a shift in the site of provider-administered chemotherapy from physician offices (POs) to hospital outpatient departments (HOPDs).

 In 2016, nearly 50% of Part B chemotherapy administration claims occurred in HOPDs, a rise from less than 25% in 2008.

 A concern has been raised that this trend may lead to increased cancer care spending because of potential differences in spending patterns between HOPDs and POs.

Spending differences by site of chemotherapy have been extensively studied among commercially insured patients.

 Prior studies using commercial claims consistently found cancer-related spending on outpatient services among patients receiving chemotherapy in HOPDs to be substantially higher than that in POs.

 However, patients receiving chemotherapy in HOPDs were found to have only a slightly smaller number of office visits and outpatient services than those in POs.

 Thus, prior discussions of spending differentials in outpatient cancer spending by site of chemotherapy focused on differences in payments for outpatient cancer services between HOPDs and POs, especially in the commercially insured population.

 However, in Medicare, differences in payments for outpatient chemotherapy services by site of care are not profound.

 For example, Medicare pays the same for chemotherapy drugs between HOPDs and POs,

 whereas among commercial insurers, the average cost per chemotherapy drug claim was more than $2000 higher in HOPDs than in POs ($3799 vs $1466) after controlling for different distributions of chemotherapy drugs between HOPDs and POs.

 Thus, in Medicare, part of the difference in outpatient cancer care spending between HOPDs and POs may come from differences in utilization.

Two prior studies compared Medicare spending on provider-administered chemotherapy in HOPDs and POs and reached conflicting conclusions.

 A report by The Moran Company found that average per-patient spending on chemotherapy drugs was higher in HOPDs than in POs.

 However, Kalidindi et al found that spending per beneficiary was higher among patients receiving chemotherapy in POs, driven by higher chemotherapy utilization.

 It is important to note that Kalidindi et al used more rigorous risk adjustments than The Moran Company. For example, Kalidindi et al accounted for differences in the distribution of cancer type between the 2 care settings, whereas The Moran Company did not. However, both studies were limited to analyzing chemotherapy claims only. Neither study considered other services used by patients receiving chemotherapy. Patients who visit physicians to receive provider-administered chemotherapy often use additional services (eg, radiation therapy, computed tomography scans). Thus, analyzing only chemotherapy costs paints an incomplete picture of cost differences by site of care.

One report attempted to examine total care spending by site of chemotherapy in Medicare. The Milliman group analyzed 2006-2009 claims and found that patients receiving chemotherapy in HOPDs had approximately $6500 higher total annual costs than those receiving chemotherapy in POs.

 However, the Milliman study had the following limitations and did not allow us to identify sources of its main finding. First, the Milliman report calculated total costs by cancer type but did not adjust for other risk factors, such as metastasis or other chronic conditions. Second, it analyzed all care spending and not just the costs associated with chemotherapy and cancer care. Finally, it did not look at utilization differences between care settings. Thus, its estimates of cost differentials could result from (1) differences in Medicare payments for outpatient services between care settings, (2) differences in utilization of outpatient services, (3) differences in utilization of inpatient services, or (4) a combination of any of the aforementioned factors. Thus, drivers of cancer care cost differentials by site of chemotherapy in Medicare remain to be examined.

Our study fills this gap by expanding the study by Kalidindi et al of chemotherapy services to include other cancer services.

 For outpatient cancer care, we complement their study by focusing on spending on services other than chemotherapy. We also compare utilization of select outpatient cancer services by care site to understand whether site-specific cancer care spending in Medicare is entirely driven by payment differences or partially due to utilization differences. Further, because differences in utilization could affect patient outcomes, which can lead to hospitalizations, we examine cancer-related inpatient care spending and use. These analyses are important because they help us identify drivers of differential cancer care spending by site of chemotherapy in Medicare and thereby offer implications for patient care.

The study was conducted from the Medicare payer perspective. We used Medicare data including 2010-2013 MedPAR files (inpatient and skilled nursing facility claims), Carrier files (PO claims), Hospital Outpatient files (HOPD claims), and Medicare Master Beneficiary Summary files. We acquired these Medicare data from CMS. We also used publicly available American Community Survey data for zip code—level income, education, and unemployment rates.

The study population was a random sample of the national cohort of Medicare beneficiaries with cancer between 2010 and 2013, which was created as follows. CMS first identified all patients with cancer using 100% Medicare claims based on the standard algorithm used to create cancer indicators in the Medicare Chronic Condition Warehouse file: (1) having at least 1 inpatient or skilled nursing facility cancer claim or (2) having at least 2 Carrier or Outpatient cancer claims in a given year.

 Then, CMS selected a 10% random sample and provided us with the data for that sample.

We selected beneficiaries who initiated provider-administered chemotherapy between January 1, 2010, and June 30, 2013, using the Healthcare Common Procedure Coding System Level II (J-code) in the Carrier or Outpatient file (

]). We used a 1-year washout to identify new chemotherapy users and designated the date of the first claim for chemotherapy after the washout as the index date.

Patients were required to have continuous Part A and Part B coverage for 12 months. We excluded those who died within 3 months of the index date and Medicare Advantage enrollees. Patients were categorized into 2 groups by the location of chemotherapy (HOPD vs PO). We excluded patients who used both settings. The eAppendix presents sample sizes associated with each exclusion.

We measured outcomes for a 6-month period following the index date. Spending measures were (1) spending on nonchemotherapy cancer-related outpatient services (these include all cancer-related outpatient services, such as supportive medications/premedications for chemotherapy, but they exclude chemotherapy services themselves) and (2) spending on cancer-related inpatient services (including chemotherapy services). We measured spending by Medicare allowed payments, which include both patient out-of-pocket spending and Medicare reimbursements. Spending was adjusted to 2013 dollars.

We measured 3 utilization outcomes for outpatient cancer-related services: (1) number of evaluation and management (E&M) visits, (2) number of most commonly used expensive billing codes, and (3) number of radiation therapy sessions. Additionally, we assessed the number of cancer-related hospitalizations. Further details of these measures are included in the eAppendix.

Differences in cancer care spending and utilization between Medicare beneficiaries receiving chemotherapy in hospital outpatient departments versus physician offices vary by service type.

June 2019

From the Editorial Board: Patrick H. Conway, MD, MSc

US Care Pathways: Continued Focus on Oncology and Outstanding Challenges

Association of Decision Support for Hospital Discharge Disposition With Outcomes

Cancer Care Spending and Use by Site of Provider-Administered Chemotherapy in Medicare

Understanding Price Growth in the Market for Targeted Oncology Therapies

Reports of the Demise of Chemotherapy Have Been Greatly Exaggerated

Health Insurance Design and Conservative Therapy for Low Back Pain

Improving Quality Measure Maintenance: Navigating the Complexities of Evolving Evidence

Changing Electronic Formats Is Associated With Changes in Number of Laboratory Tests Ordered

Enhanced Care Coordination Improves HIV Viral Load Suppression Rates

Will 2019 Kick Off a New Era in Person-Centered Care?

Impact of Care Coordination Based on Insurance and Zip Code