Karen E. Joynt, MD, MPH

 Although we know that healthcare costs are concentrated among a small number of patients, we know much less about the concentration of these costs among providers or markets. This is important because it could help us to understand why some patients are higher-cost compared with others and enable us to develop interventions to reduce costs for these patients.

We used a 20% sample of Medicare fee-for-service claims data from 2011 and 2012, and defined high-cost patients as those in the top 10% of standardized costs. We then characterized high-concentration hospitals as those with the highest proportion of high-cost patient claims, and high-concentration markets as the Hospital Referral Regions (HRRs) with the highest proportion of high-cost patients. We compared the characteristics and outcomes of each.

 High-concentration hospitals had 69% of their inpatient Medicare claims from high-cost Medicare beneficiaries compared with 51% for the remaining 90% of hospitals. These hospitals were more likely to be for-profit and major teaching hospitals, located in urban settings, and have higher readmission rates. High-concentration HRRs had 13% high-cost patients compared with 9.5% for the remaining 90% of HRRs. These HRRs had a smaller supply of total physicians, a greater supply of cardiologists, higher rates of emergency department visits, and significantly higher expenditures on care in the last 6 months of life.

High-cost beneficiaries are only modestly concentrated in specific hospitals and healthcare markets.

		High-cost patients were only modestly concentrated in hospitals; those that disproportionately cared for high-cost beneficiaries were generally larger, academic teaching institutions with better outcomes on mortality but higher readmission rates. 

		High-cost patients were only modestly concentrated in markets. We saw differences between the markets that were more concentrated versus not; concentrated markets had much higher proportions of racial and ethnic minorities and individuals in poverty. 

		Efforts to lower spending among high-cost patients should remain broad. Policy efforts need to target the special needs of organizations and communities disproportionately serving high-cost beneficiaries.

High and rising healthcare costs are the single biggest challenge facing federal and state governments, many businesses, and families. These healthcare costs are highly concentrated among a small number of patients; for example, just 10% of Medicare patients account for more than half of all Medicare spending.

 There is broad consensus that we will need to improve care for this population of highly expensive patients in order to manage spending and improve outcomes.

Although we know that healthcare costs are concentrated among a small number of patients, we know much less about the concentration of healthcare costs among providers or markets. This is important information as it could help us to understand why some patients are higher-cost compared with others and help us begin to develop interventions to reduce costs for these patients. For example, if differences in practice patterns between providers are a major driver of costs, we would expect to see high-cost patients clustered within a subset of providers. Such clustering would suggest that interventions might be more effective if they targeted physicians with a high proportion of high-cost patients. Similarly, it is possible that geographic variation in practice patterns is the dominant feature; if this were true, we would expect to see clustering of high-cost patients within communities and we would need to design interventions that address the practice patterns or underlying medical needs of these communities. However, we know very little about whether care for high-cost patients is concentrated and, if so, whether the characteristics of the providers and communities where these patients are disproportionately situated differ from those providers or communities with a lower concentration of high-cost beneficiaries.

Hospitals are the setting where most expenditures for high-cost patients are incurred. Therefore, in this study, we set out to answer 2 sets of questions: 1) Are high-cost patients concentrated among certain hospitals? If so, how do hospitals that disproportionately care for high-cost patients differ from other hospitals? 2) Are high-cost patients concentrated within certain communities? If so, how do communities with a high proportion of high-cost patients differ from those communities with lower proportions of high-cost patients?

We used a 20% sample of Medicare fee-for-service (FFS) claims data from 2011 and 2012. Patients enrolled in Medicare Advantage and those not continuously enrolled in Parts A and B during the study period were excluded from our analysis because we do not have complete data on annual healthcare costs in these subpopulations. We excluded those younger than 65 years because they are a highly heterogeneous population, achieving eligibility for Medicare based on clinical conditions (eg, end-stage renal disease) that are highly correlated with spending.

Patient race was categorized in the Medicare data based on self-report. We assigned comorbidities using CMS Hierarchical Condition Categories and based on diagnoses in inpatient, outpatient, and carrier claims. Cost information was aggregated from all Medicare claims files, including inpatient, outpatient, carrier, skilled nursing facility, home health, hospice, and durable medical equipment. 

International Classification of Diseases, Ninth Revision, Clinical Modification 

) codes were used to identify comorbidities.

We focused on general acute care nonfederal hospitals and only included those with a minimum of 17 beneficiaries in 2012 (from our 20% sample), thus eliminating the very smallest of hospitals. Failure to exclude these hospitals would have led to the identification of very small hospitals as disproportionately caring for the highest-cost patients; for example, in a hospital with only 3 Medicare claims, 1 from a high-cost patient would be reported as having 33% of its patients being high-cost. We used the American Hospital Association Annual Survey to obtain data on hospital structural characteristics and the Hospital Compare database to obtain data on hospital quality and patient experience.

 We calculated both mortality and readmissions using standard methods with adjustment via the Elixhauser comorbidity adjustment scheme, which is commonly used with administrative data,

 and which we have used in prior work in this area.

 We used Hospital Referral Regions (HRRs), as defined by the Dartmouth Atlas for Health Care,

 to analyze healthcare markets. We obtained data on HRR Medicare spending from the Dartmouth Atlas. Regional demographic characteristics and data on physician and hospital bed supply were obtained from the Area Resource File. We used the Commonwealth Scorecard Health System Data Center to obtain data on population health measures.

n order to identify high-cost beneficiaries, we first calculated total standardized costs of care for each Medicare beneficiary in our sample using CMS methodology.

 By standardizing costs of care, we can identify patients who use a comparable amount of medical care across differing regions in which actual costs of care may vary. For example, although Medicare may pay more for a chest radiograph at a teaching hospital in New York City than at a community hospital in Phoenix, based on differences in wage index and other factors, standardized cost using the Medicare Wage Index assigns the same value to the service in both places. We then defined high-cost patients as those whose spending was in the top decile nationally in 2012.

To define high-concentration hospitals, we first calculated the percentage of the hospital’s claims attributable to high-cost patients. We then calculated the distribution of this percentage among our sample of hospitals and categorized hospitals as high-concentration if their percentage of claims from high-cost patients was in the top decile nationally.

We next calculated the concentration of high-cost patients within markets and then compared high-concentration and non—high-concentration HRRs. We used HRRs as our geographical unit of observation for healthcare markets. For each market, we computed the percentage of beneficiaries in our sample residing in each HRR who were designated as high-cost patients. We then computed the distribution of this percentage and labeled HRRs as high-concentration markets if their percentage of beneficiaries who were high-cost was in the top decile nationally. We computed statistics on the distribution of annual patient costs, the percentage of beneficiaries in a region who were high-cost patients, and the percentage of a hospital’s claims attributable to high-cost patients. Because costs of end-of-life care constitute a substantial portion of overall Medicare costs, decedents were overrepresented among the group of highest-cost patients; thus, we conducted our analyses with and without those patients who died during the period January 1, 2012, through June 30, 2013. The results were qualitatively similar and we present our primary results based on including decedents.

After categorizing high-cost and non—high-cost patients, we first compared characteristics between these 2 groups of patients. We examined the following beneficiary-level predictors of high-cost status: age, race, gender, Medicaid eligibility, and comorbidities. We also investigated the relationship between a number of hospital characteristics and high-cost concentration status. We examined the relationship between high-cost concentration and the following market-level variables: percentages of blacks, Hispanics/Latinos, and the population in poverty; total physician supply; cardiologist supply; number of short- and long-term general hospital beds; and the rate of spending in the last 6 months of life. We also examined the relationship between high-concentration HRRs and health system performance on quality metrics, including potentially avoidable emergency department visits among Medicare beneficiaries, potentially preventable mortality, and percentage of adults 50 years or older receiving recommended screening and preventive care. For all bivariate analyses we used 

 tests to assess the statistical significance of differences based on high-cost and

For our multivariate analyses, we estimated logistic regression models of the likelihood of being a high-cost concentration hospital or high-cost concentration HRR as a function of hospital and market characteristics. In the hospital-level regression, we clustered our standard errors within market.

All analyses were performed using SAS version 9.4 (SAS Institute, Cary, North Carolina).

Characteristics of High-Cost and Non—High-Cost Beneficiaries

There were 4,937,361 Medicare beneficiaries in our 20% sample file, 493,736 of which were categorized as high-cost. As expected, a large percentage of Medicare costs (55%) were concentrated in a small percentage of beneficiaries (10%). The average cost for the high-cost cohort was more than 10-fold higher than that of the non—high-cost cohort. Compared with non–high-cost patients, high-cost patients were more likely to be black (9.84% vs 7.2%; 

<.0001) and nearly twice as likely to be eligible for Medicaid (22.5% vs 12.7%; 

). As expected, high-cost patients also had higher rates of chronic disease, including mental health conditions, congestive heart failure, renal failure, chronic obstructive pulmonary disease, and vascular disease (Table 1).

Concentration of High-Cost Beneficiaries by Hospitals

High-cost beneficiaries accounted for a disproportionate share of the Medicare claims at every hospital. The highest decile of &shy;hospitals had 69% of their inpatient Medicare claims from high-cost Medicare beneficiaries compared with 51% for the remaining 90% of hospitals. The hospitals in the lowest decile of concentration, by contrast, still had 32.6% of their inpatient claims come from high-cost beneficiaries.

If high-cost beneficiary hospital claims and inpatient costs were evenly distributed nationally, we would expect high-cost hospitals to account for 10% of all inpatient claims and costs. We found that high-cost patients and their inpatient expenditures were only slightly more concentrated: just 12.54% of hospital claims attributable to high-cost beneficiaries and 14% of inpatient costs were for care delivered at hospitals in the highest decile of concentration of high-cost patients (

). The median cost per claim in high-concentration hospitals was 15% greater than the median cost per claim in other hospitals (

Concentration of High-Cost Beneficiaries in the Markets

Similar to the hospital analysis, we found only a modest concentration of high-cost patients in the markets. The percentage of Medicare beneficiaries in each HRR considered high-cost varied modestly across communities (

). In the 10% of markets with the highest proportion of high-cost patients, on average, 13% of beneficiaries were high-cost compared with 7% of beneficiaries in the lowest decile of concentration. The 30 HRRs with the highest concentration of high-cost patients—representing 10% of all HRRs in the United States&mdash;accounted for 9.7% of Medicare costs. The median annual per-beneficiary total standardized cost in these HRRs was $14,193 per year in high-concentration markets compared with $11,077 in all other markets (

Comparing High-Concentration Hospitals Versus Other Hospitals

We found substantial differences in the characteristics of hospitals that disproportionately care for high-cost patients (ie, the high-concentration hospitals) versus other institutions in bivariate (eAppendix Table 1) and multivariate (

) analyses. The hospitals with the greatest proportion of claims from high-cost patients were more likely to be for-profit hospitals and teaching hospitals. Possibly reflecting resource constraints, high-concentration hospitals were more likely to receive disproportionate share payments and to have lower nurse-to-patient staffing ratios (although the latter was found only in bivariate analyses). High-concentration and lower-concentration hospitals scored similarly on many quality metrics; however, high-concentration hospitals were more likely to have higher 30-day readmission rates and slightly lower 30-day mortality rates (Table 4 and 

Differences Between Markets With More Versus Fewer High-Cost Patients

We found significant differences between the 30 HRRs with the highest concentration of high-cost patients and the 276 other HRRS with lower concentrations of high-cost patients. In bivariate analyses, high-concentration HRRs were more likely to have a higher percentage of beneficiaries who were black (18.5% vs 10.1%; 

= .0009), living in poverty (19.1% vs 15.5%), and living in urban areas (82.2% vs 71.3%) (

). High-concentration HRRs also had a greater supply of specialists—but an equal supply of total physicians&mdash;and a lower supply of long-term hospital beds despite a higher population density.

High-concentration HRRs have both high costs of care and high rates of care utilization. In high-concentration HRRs, the Dartmouth Hospital Care Intensity Index (1.39 for high-concentration hospitals and 0.94 for non—high-concentration hospitals) and the Medicare end-of-life spending measure ($79,446 for

concentrated HRRs and $65,538 for nonconcentrated HRRs) were both significantly higher in high-concentration HRRs than in lower-concentration HRRs. Although the general pattern of these findings held in multivariate analyses (

), supply-side factors and end-of-life spending of the market were more influential in the likelihood of being high-concentration than sociodemographic characteristics of the market.

We examined the concentration of high-cost patients in hospitals and markets and found only a modest degree of concentration. However, the hospitals and markets that disproportionately care for high-cost beneficiaries were markedly different than those that cared for fewer such patients: in general, these hospitals were either academic teaching or for-profit institutions operating in urban settings and serving a greater proportion of low-income patients. Similarly, we saw differences between the markets that were more concentrated versus not, with concentrated markets having a greater supply of specialists and a lower supply of long-term care beds. Spending in the last 6 months of life was also significantly higher in high-cost concentration HRRs. Taken together, these findings suggest that although high-cost patients are widely distributed, particular provider and delivery system characteristics are associated with a higher degree of concentration.

These findings have important implications for our approach to caring for high-cost beneficiaries. Although there must be a broad approach to identifying and caring for these patients, our findings suggest that we must pay special attention to the providers where these patients receive their care. The high-concentration hospitals were more likely to be teaching hospitals, which may reflect the more specialized and costly types of care provided in these institutions. However, high-concentration hospitals were also more likely to be for-profit; whether this reflects differences in practice patterns or patient mix is unclear and warrants further examination. Additionally, given that the high-concentration hospitals tended to care for a poorer population, it is possible that the higher costs were reflective of a greater degree of medical and social need. Initiatives like the Disproportionate Share Hospital (DSH) program were designed for this reason and provide additional payments to help safety net providers care for the poor. Whether DSH payment cuts under the Affordable Care Act, in conjunction with insurance expansion, will ultimately hamper or help these facilities that care for high-cost beneficiaries is unclear.

There are important limitations to our study. First, our analyses are based on data for the traditional FFS Medicare population; the patterns of high-cost concentration may differ in other populations. Second, our measure of patient costs did not include prescription drug expenditures. However, given that drug costs constitute approximately 10% of total healthcare spending, small differences for this kind of expenditure are unlikely to explain our findings. Third, we used the most recent Medicare data available at the time of our analysis (calendar year 2012), but patterns may have changed in more recent years. Fourth, we used the 10th percentile as our cutoff for being considered high-cost and high-concentration. This cutoff has been used by our group and others in previous research

 and was chosen to allow a population large enough to be nationally representative, but small enough to represent potential targets for intervention to reduce costs; nevertheless, cost is a continuous variable. The use of alternative cutoffs (eg, the top 1% or 5% of spenders, as has also been examined previously)

 might yield different patterns in cost concentration. Finally, our multivariate analyses reflect cross-sectional associations and the relationships identified cannot be interpreted as causal. Furthermore, we could not address the endogeneity of hospitals’ location and strategy decisions.

High-cost beneficiaries are only modestly concentrated in specific hospitals and healthcare markets; as such, efforts to efficiently manage costs and care among the high-cost cohort should remain broad. However, the providers and communities where these patients disproportionately receive their care are meaningfully different, suggesting that additional research on the mechanisms underlying these differences might benefit policy efforts to improve care for these high-cost beneficiaries. 

 Department of Health Care Policy, Harvard Medical School (NDB, RCW), Boston, MA; Department of Health Policy and Management, Harvard T.H. Chan School of Public Health (KEJ, AKJ), Boston, MA.

 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 (NDB, AKJ, KEJ); acquisition of data (NDB); analysis and interpretation of data (NDB, KEJ, RCW); drafting of the manuscript (NDB, AKJ, KEJ, RCW); critical revision of the manuscript for important intellectual content (NDB, AKJ, KEJ); statistical analysis (NDB, RCW); obtaining funding (AKJ); administrative, technical, or logistic support (NDB); and supervision (NDB, AKJ).

Ashish K. Jha, MD, MPH, Harvard T.H. Chan School of Public Health, 42 Church St, Cambridge, MA 02138. E-mail: ajha@hsph.harvard.edu.

Articles

High-cost patients are only modestly concentrated in specific hospitals and healthcare markets.

Concentration of High-Cost Patients in Hospitals and Markets

 To determine the opinions of US hospital leadership on the Hospital Readmissions Reduction Program (HRRP), a national mandatory penalty-for-performance program.

 We developed a survey about federal readmission policies. We used a stratified sampling design to oversample hospitals in the highest and lowest quintile of performance on readmissions, and hospitals serving a high proportion of minority patients. 

 We surveyed leadership at 1600 US acute care hospitals that were subject to the HRRP, and achieved a 62% response rate. Results were stratified by the size of the HRRP penalty that hospitals received in 2013, and adjusted for nonresponse and sampling strategy.

 Compared with 36.1% for public reporting of readmission rates and 23.7% for public reporting of discharge processes, 65.8% of respondents reported that the HRRP had a “great impact” on efforts to reduce readmissions. The most common critique of the HRRP penalty was that it did not adequately account for differences in socioeconomic status between hospitals (75.8% “agree” or “agree strongly”); other concerns included that the penalties were “much too large” (67.7%), and hospitals’ inability to impact patient adherence (64.1%). These sentiments were each more common in leaders of hospitals with higher HRRP penalties.

 The HRRP has had a major impact on hospital leaders’ efforts to reduce readmission rates, which has implications for the design of future quality improvement programs. However, leaders are concerned about the size of the penalties, lack of adjustment for socioeconomic and clinical factors, and hospitals’ inability to impact patient adherence and postacute care. These concerns may have implications as policy makers consider changes to the HRRP, as well as to other Medicare value-based payment programs that contain similar readmission metrics.

	Despite the fact that the Hospital Readmissions Reduction Program (HRRP) has increased efforts to reduce readmissions, hospital leaders identified important issues with the program. Our findings from a national survey of hospital leaders indicate that: 

		Leaders are concerned about the size of the penalties and the lack of adjustment for socioeconomic and clinical factors. 

		Currently, the HRRP remains a lower priority for leaders than other areas of quality improvement, such as patient safety and adherence to guidelines. 

		Federal policy makers may want to address these issues as they consider future changes to the program and seek to maximize its impact.

Reducing hospital readmissions has the potential to simultaneously improve patient outcomes and reduce healthcare spending and, as such, has become a major target for US policy makers. In an effort to spur a reduction in readmissions, Medicare began publicly reporting on hospitals’ discharge planning in 2007 and, in 2009, added public reporting on readmission rates for acute myocardial infarction (AMI), heart failure (HF), and pneumonia. Despite these efforts, 30-day readmission rates remained stable near 20% during this time frame.

 Consequently, with the passage of the Affordable Care Act in 2010, Congress included legislation establishing the Hospital Readmissions Reduction Program (HRRP).

 Under the HRRP, CMS penalizes hospitals with higher than expected readmission rates for Medicare patients; it has been in effect since the beginning of fiscal year (FY) 2013.

 In the HRRP’s third year, hospitals performing poorly may lose up to 3% of their base Medicare diagnosis-related group (DRG) payments—a substantial amount given that many hospitals have negative Medicare inpatient margins at baseline.

However, the HRRP has been controversial. Initial reports suggested that the program was more likely to penalize large, teaching, and safety net hospitals.

 Multiple organizations have argued that the program’s methodology should take sociodemographic factors into account and exclude readmissions unrelated to the initial reason for hospitalization,

 and at least 2 bills have been proposed in Congress to address these concerns and others.

 On the other hand, early data show that readmission rates have fallen by 1% to 2% since the implementation of the HRRP, suggesting that this program may have had a positive impact on this outcome, although causality cannot be established.

The HRRP is one of a number of value-based payment models within Medicare, and the US Secretary of HHS recently announced a goal to have 85% of Medicare fee-for-service payments tied to quality or value by 2016.

 Many of these new payment programs are closely related to the HRRP; for example, the forthcoming Skilled Nursing Facility Value-Based Payment program is similarly based on a single readmission measure: 30-day readmission following a hospitalization.

 Readmissions metrics similar to the one used in the HRRP are also now included in quality measures for the Medicare Shared Savings Program

 and the Physician Value-Based Modifier Program,

 and will be included in payment programs in additional settings, such as dialysis facilities, in future years.

Given the importance of the HRRP as a model for future value-based payment programs, its controversy, and its initial success, it is crucial to understand how hospital leaders have responded to the program and closely examine their concerns about its methodology. Therefore, we surveyed hospital leadership—including chief executive officers (CEOs), chief medical officers (CMOs), and chief quality officers (CQOs)&mdash;at approximately 1600 hospitals, stratified by whether their hospitals received a penalty under the HRRP. We aimed to answer 3 key questions: first, how has the HRRP impacted hospitals’ readmission reduction efforts, particularly compared with prior readmissions policies such as public reporting? Second, how have leaders prioritized the HRRP in the context of multiple other federal quality improvement initiatives that they face simultaneously? Third, what are the opinions of hospital leaders on the program’s methodology and implementation?

Our first step in survey development was to conduct a set of case studies examining hospitals’ efforts to reduce readmission rates; this work has been described previously.

 Based on this work, we developed a survey instrument that was tested with hospital leaders, hospital personnel, and survey experts, and revised accordingly.

We began in mid-2012 with a list of all acute care hospitals that were eligible for the HRRP, excluding Critical Access Hospitals and other facilities not paid under the Inpatient Prospective Payment System and, thus, ineligible for participation. 

Based on calculations performed prior to survey administration, we anticipated needing 1000 survey responses to have adequate power to address our hypotheses. We anticipated a response rate of 60% to 65%; thus, our final sample consisted of 1600 hospitals. We also designed our survey sample to enable us to pursue secondary analyses that focused on differences between hospitals that care for a large proportion of black patients (who have previously been shown to have particularly high readmission rates

 and are also more likely to face unique challenges

) versus other hospitals; and differences between hospitals that had high, average, or low 30-day readmission rates. Thus, we calculated the overall proportion of Medicare patients at each hospital that self-identified as black. We then calculated 30-day risk-adjusted readmission rates for AMI, HF, and pneumonia, from 2008 to 2010 (the years used to assign hospital penalties during the first year of the HRRP) for each hospital, using methods that have been described previously.

 We selected all of the top 900 hospitals in terms of the highest proportion of black patients hospitalized with either AMI, HF, or pneumonia for inclusion in our sample. We divided the remaining hospitals into 3 groups based on performance on readmissions from 2008 to 2010, which was determined by ranking hospitals with the mean risk-adjusted readmission rates for the 3 target conditions into quintiles: top (best) quintile, middle 3 quintiles, and bottom quintile. We selected 266 hospitals from each of these groups using random number generation. There were a small number of hospitals in our sample that had closed, merged with other hospitals, or become Critical Access Hospitals or long-term care facilities; we replaced these using random selection from the same group.

To identify clinical leaders, we obtained the hospital leadership list of CMOs from the American Hospital Association. Study staff called each hospital leader to verify contact information, and once a recipient was verified, his or her hospital was moved into the active fielding stage. The survey was then fielded in 2 phases. The first phase (June 2013 to June 2014) was conducted by DataStat Inc, of Ann Arbor, MI. A hard copy of the survey was mailed to hospitals, along with a cover letter explaining the intent of the survey and the consent process. This was followed by follow-up phone calls and a second mailing. If requested, recipients were sent a version of the survey as a PDF file. The second phase (June to December 2014) was conducted by research staff at the Harvard T.H. Chan  School of Public Health, and followed a similar protocol—a second mailing and follow-up phone calls&mdash;but also gave hospital leaders the option of completing a Web-based version of the survey instrument. The second phase was instituted to ensure an adequately high response rate. Throughout the survey, although the initial point of contact at the hospitals was the office of the CMO, we encouraged that individual to reach out to other leaders within the hospital who were best equipped to help to either provide assistance or actually complete the survey.

We computed summary statistics both overall and stratified by HRRP penalty amount. We stratified the hospitals into 3 groups based on their penalty in FY2013, the main time frame in which the survey was in the field. Penalty statuses included a) no penalty, b) minor penalty (greater than 0 but less than the median penalty of 0.32% of base DRG payments), and c) major penalty (equal to or greater than the median penalty). Responses were tabulated for each question. For multiple choice or Likert scale questions, responses were summed within groups as they were defined on the survey (ie, “not important,” “somewhat important,” “very important,” and “extremely important”; or “disagree strongly,” “disagree,” “neither agree nor disagree,” “agree,” or “agree strongly”). For open-ended questions, we created a taxonomy based on the frequency of similar responses, and grouped responses accordingly.

Survey responses were adjusted for both sampling strategy and nonresponse to better reflect a national representation of US hospitals. To adjust for sampling strategy, we assigned sample weights to each group. To adjust for nonresponse, we constructed a logistic regression model, in which, returning the survey was the primary outcome, and hospital characteristics—including size, teaching status, ownership, and urban location&mdash;were predictors, as has been done previously.

 Each hospital received a likelihood of response based on this model; responses were then weighted with the inverse of this likelihood. Finally, we conducted additional regression analyses, in which, we further adjusted responses for the hospital characteristics listed above, as well as for the safety net status of the hospitals—those hospitals in the top quintile of Disproportionate Share Hospital Index were considered to be in the safety net

—and the proportion of black patients at each hospital.

All responses were de-identified before analysis. Informed consent was obtained within the survey itself and the introductory page to the survey included detailed information about privacy and data de-identification, as well as consent, stating, “Completion of this survey implies informed consent.” The study was approved by the Office of Human Research Administration at the Harvard T.H. Chan School of Public Health.

Of the 1600 hospitals contacted, we received completed surveys from 992 (62% response rate). Of that group, 951 were eligible for HRRP penalties in FY2013 and, thus, comprise our analytic sample. The other hospitals, mainly those in Maryland, or hospitals that no longer had enough cases to qualify for the program, were not eligible for HRRP. Hospital characteristics differed significantly by penalty receipt, with nonteaching, public, and Northeastern hospitals more likely to be in the “major penalty” group, and higher proportions of black and Medicaid patients in the highly penalized hospitals. Readmission rates were, as expected, higher in the highly penalized hospitals (

Compared with nonrespondents, respondents were more often leaders from large, nonprofit, or teaching hospitals; respondents were also more likely to represent urban hospitals and those located in the Northeast and Midwest (eAppendix Table 1 [

Of the respondents, 29.6% identified themselves as directors of case management or equivalent, 27.1% as CQOs or equivalent, 26.3% as CMOs or chiefs of staff, 4.6% as chief nursing officers, 2.5% as CEOs, and 9.8% as “other,” including vice president for medical affairs and chief operating officer.

Impact of the HRRP on Efforts to Reduce Readmissions

Nearly two-thirds (65.8%) of hospital leaders reported that the HRRP had a “significant” or “great” impact on increasing their hospital’s efforts to reduce readmissions compared with the 2 readmissions policies that preceded the HRRP: public reporting of readmission rates (36.1%) and public reporting of discharge planning (23.7%). When we examined these opinions, stratified by receipt of a penalty, we found that leaders at hospitals receiving a penalty were much more likely to report that each of the federal policies had impacted their efforts to reduce admissions than at those hospitals not receiving a penalty (

). Adjusting these results for hospital characteristics and socioeconomic status (SES) factors yielded similar results (eAppendix Figure 1).

In terms of potential responses to the HRRP, 26.6% of leaders reported that it was more than moderately likely that hospitals would increase the use of observation status to improve their perceived performance on readmissions, and 15.1% felt it was more than moderately likely that hospitals would increasingly avoid high-risk patients. These responses were similar across penalty strata (

 = .14 for differences in response, respectively).

Prioritization of Readmissions Reduction in the Context of Other Federal Programs

When asked to prioritize readmissions reduction among other current federal quality improvement initiatives, only 44.1% of leaders reported that it was of “highest priority” compared with 79.2% for improving patient safety, 76.6% for improving patient experience, 75.2% for reducing hospital-acquired infections, 65% for meeting Meaningful Use requirements, and 44.4% for improving compliance with guideline-based care. The biggest gap in prioritization between leaders at hospitals with readmission penalties versus without readmission penalties was in prioritizing readmissions. However, leaders at highly penalized hospitals still rated all 6 of the competing priorities more highly than those at nonpenalized hospitals (

). Results adjusted for hospital characteristics and SES factors were similar (eAppendix Figure 2).

Opinions on the Methodology and Impact of the HRRP

A majority (67.5%) of leaders felt that the HRRP penalties were “much too large”; this was more common among leaders at hospitals receiving major penalties (74.7%) than those hospitals without penalties (65.2%: 

 <.001) but was still a majority in all groups. The most commonly endorsed critique of the HRRP penalty was that it did not adequately account for differences in SES between hospitals (76.2% “agree” or “agree strongly”). Other common concerns included an inadequate account of medical complexity by the penalty (75.9%), and hospitals' limited ability to impact patient adherence (64.1%) (

). Each concern was expressed more often among leaders of hospitals receiving major or minor penalties than among leaders of hospitals without penalties (Table 2); results adjusted for hospital characteristics and SES factors were similar, although the differences between groups narrowed somewhat (eAppendix Table 2).

Only a minority of study hospitals were participating in bundled payment programs or accountable care organizations (ACOs), and just over half of hospitals were participating in private pay-for-performance programs (

, top panels). When asked whether these value-based payment programs were likely to improve quality, 42.5% of leaders responded affirmatively about the HRRP compared with 32% for bundled payment programs, 45.6% for ACOs, and 52.6% for pay-for-performance (Table 3, bottom panels).

Response patterns were generally similar when stratified by receipt of a penalty, but leaders at hospitals receiving penalties were less likely to respond that the HRRP was likely to improve care (35.7% for hospitals with major penalties vs 45% for minor penalties vs 48.4% for no penalties [Table 3]) and response patterns were also similar when adjusting for hospital characteristics and SES factors (eAppendix Table 3). The highest proportion of respondents (54.8%) felt that the HRRP was likely to reduce costs compared with the other programs (Table 3); responses were similar across penalty strata and after adjustment (eAppendix Table 3).

In a large, national survey, hospital leaders reported that the HRRP has had a sizable impact on their hospitals’ efforts to reduce readmissions. However, despite paying more attention to readmissions than previously done, hospital leaders continue to prioritize other quality improvement efforts, such as improving patient safety, improving patent experience, and adhering to clinical guidelines. Hospital leaders also reported critiques of the policy—largely centered around risk adjustment for SES and clinical factors&mdash;and the ability of hospitals to impact patient adherence, as well as postacute, ambulatory, and institutional care. Leaders at hospitals that were receiving penalties under the HRRP tended to have more negative opinions about the program than leaders at hospitals without penalties.

According to hospital leaders, the HRRP has had a significantly greater impact on their own efforts to reduce readmission rates than its policy predecessors—namely, public reporting of discharge planning and public reporting of readmission rates. This observation, that financial incentives alter behavior to a greater degree than public reporting alone, is consistent with prior observations

 and may explain why the HRRP has been associated with improvements in readmission rates

 whereas public reporting was not. This experience may suggest that policy makers should move more rapidly to financially reward or penalize hospitals for desired outcomes rather than merely reporting them publicly.

 Of course, the fact that over one-fourth of respondents suggested that hospital leaders might increase the use of observation status to improve performance on readmissions, and 15% thought hospitals might avoid high-risk patients, could serve as cautionary counterpoints to the enthusiasm for financial incentives.

Nevertheless, despite the reported impact of the HRRP, nearly every other mandatory federal quality improvement program was rated higher in terms of its importance in our survey, although the study design did not allow us to determine why this was the case. One possibility is that readmissions were seen by many as being outside the control of the hospital; in the setting of competing priorities, perceiving a lack of ability to change an outcome could cause hospital leadership to focus on other areas for intervention. As programs, such as ACOs, increasingly bridge the inpatient and outpatient settings, it is possible that increasing integration could alter these perceptions. Additionally, as the number of Medicare programs that reward readmissions as components of performance continues to grow—not only in ACOs,

 but also including the Physician Value-Based Modifier

 and the coming pay-for-performance programs in the dialysis

—it is feasible that inpatient facilities may prioritize readmission reduction more highly.

In terms of methodology, the frequently cited critiques of the HRRP included its lack of adjustment for SES or patient adherence and concern about its adjustment for medical complexity. Although these findings were not necessarily surprising given prior publications

 our survey allows us, for the first time, to quantify the degree to which these are concerns for hospital leaders. Given that more than 3 of 4 hospital leaders reported SES as a critical issue, it is clear that the concerns are not just among those who disproportionately care for the poor. On the other hand, a sizable proportion of leaders did not feel that SES adjustment was necessary, suggesting that support is not unanimous among the hospital community. There is a great deal of current activity around SES and readmission policy: the National Quality Forum recently released recommendations in this area,

 and is currently undertaking analyses to determine if adjustment for SES is appropriate for certain measures, including many having to do with readmissions.

 Two bills that were recently proposed in Congress aimed to incorporate measures of poverty, income, and education into risk adjustment for the HRRP,

  but neither legislation has moved forward. The Medicare Payment Advisory Commission has argued that the HRRP should stratify hospitals into groups based on SES,

 which is one promising strategy. Congress also passed the Improving Medicare Post-Acute Care Transformation (IMPACT) Act in October 2014, which calls on the HHS to study the relationship between SES and performance in Medicare’s incentive-based programs, and to suggest changes in these programs that might be warranted.

As is the case with all surveys, it is possible that nonrespondents were different than those who responded to our survey. Although we used appropriate techniques to deal with nonresponse, these statistical techniques are imperfect. Further, we believe that hospital leaders answered these questions to the best of their ability, but, nevertheless, there may be differing opinions within hospitals, such that our results reflect only the individual who filled out the instrument. Therefore, it is possible that sampling different individuals within these hospitals would have yielded different results. Finally, we surveyed hospitals during the first 2 years of the HRRP, and leaders’ opinions may change over time.

In a national survey of hospital leaders, we found that the HRRP has had a major impact on hospital leaders’ efforts to reduce readmissions; however, the HRRP currently remains a lower priority for leaders than other areas of quality improvement, such as patient safety, patient experience, and adherence to guidelines. Further, concerns remain about its manner of accounting for social and medical risk factors and whether hospitals, by themselves, can impact patient adherence or the transitional and postacute care that helps determine whether a patient is readmitted. These findings may be useful for policy makers contemplating future iterations of the HRRP and other programs using readmissions as a quality metric that may have a synergistic effect on improving patient care. 

 Department of Health Policy and Management (KEJ, JFF, AKJ) and Department of Biostatistics (EJO), Harvard T.H. Chan School of Public Health, Boston, MA; Division of General Internal Medicine (JFF, EJO, AKJ) and Division of Cardiovascular Medicine (KEJ), Department of Medicine, Brigham and Women’s Hospital, Boston, MA.

 This research was funded by NHLBI grant number 1R01HL113567-01.

 Dr Joynt is currently employed in the United States Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, where Dr Orav also serves as an advisor. The work described here was conducted when the authors were employees of Harvard University. The views expressed herein are those of the authors alone, and do not represent the official position of the federal government. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

The Hospital Readmissions Reduction Program has had a major impact on hospital leaders’ efforts to reduce readmission rates; however, important concerns about the program remain.

Opinions on the Hospital Readmission Reduction Program: Results of a National Survey of Hospital Leaders

To assess the relationship between care fragmentation and both quality and costs of care for commercially insured, chronically ill patients.

We used claims data from 2004 to 2008 for 506,376 chronically ill, privately insured enrollees of a large commercial insurance company to construct measures of fragmentation. We included patients in the sample if they had chronic conditions in any of the following categories: cardiovascular disease, diabetes, asthma, arthritis, or migraine.

We assigned each patient a fragmentation index based on the patterns of care of their primary care provider (PCP), with care patterns spread across a higher number of providers considered to be more fragmented. We used regression analysis to examine the relationship between fragmentation and both quality and cost outcomes.

Patients of PCPs in the highest quartile of fragmentation had a higher chance of having a departure from clinical best practice (32.8%, vs 25.9% among patients of PCPs in the lowest quartile of fragmentation; 

<.001). Similarly, patients of PCPs with high fragmentation had higher rates of preventable hospitalizations (9.1% in highest quartile vs 7.1% in lowest quartile; 

<.001). High fragmentation was associated with $4542 higher healthcare spending ($10,396 in the highest quartile vs $5854 in the lowest quartile; 

<.001). We found similar or larger effects on quality and costs among patients when we examined the most frequently occurring disease groups individually.

Chronically ill patients whose primary care providers offer highly fragmented care more often experience lapses in care quality and incur greater healthcare costs.

	Despite widespread consensus that fragmented care leads to higher costs and lower quality, there is little empirical evidence on the relationship between care fragmentation, quality, and costs. Our findings indicate that:

		Fragmentation is associated with increased costs of care, a higher chance of having a departure from clinical best practice, and higher rates of preventable hospitalizations.

		Even among patients with the same chronic condition, quality was lower and costs were higher in patients who received more fragmented care.

		Policy makers and clinical leaders may be able to reduce costs and improve quality by reducing fragmentation.

The US healthcare system suffers from high costs that do not yield commensurately high levels of quality. Although there are many competing explanations for this inefficiency, one area of relatively broad consensus is care fragmentation. According to the fragmentation hypothesis, care delivery too often involves multiple providers and organizations with no single entity effectively coordinating different aspects of care.

 Poor coordination across providers may lead to suboptimal care, including important healthcare issues being inadequately addressed, poor patient outcomes, and unnecessary or even harmful services that ultimately both raise costs and degrade quality. It is precisely this hypothesis that has spurred policy makers to make investments in care models that emphasize care coordination, such as the patient-centered medical home model and accountable care organizations.

However, there is surprisingly little empirical evidence to either support or refute the fragmentation hypothesis. A key challenge to assessing the validity of the fragmentation hypothesis is determining whether higher costs and poorer outcomes are the result of fragmentation itself or simply a reflection of the fact that sicker patients see more providers—thus looking more “fragmented”&mdash;and have worse health outcomes at higher costs. Understanding the relationship between fragmentation and quality, as well as costs of care, is critical for policy makers and clinical leaders struggling to find ways to improve the value of healthcare, especially for chronically ill patients.

Given the central importance of understanding the role of fragmentation in healthcare delivery, and given the paucity of national data that directly address these issues, we sought to answer 3 key questions. First, is there a relationship between the degree to which a patient’s care is fragmented and the quality of care he or she receives? Second, what is the relationship between the degree to which a patient’s care is fragmented and their total costs of care? Finally, are the quality and cost consequences of fragmentation apparent in prespecified groups of patients with the most common chronic diseases?

Our patient sample consisted of 506,376 chronically ill enrollees in a major nationwide private health plan. We only included individuals who met the following criteria: received their insurance through a fully insured employer participating in this plan, had at least 1 insurance claim associated with a primary care provider (PCP), and had claims between 2004 and 2007 with a primary diagnostic code corresponding to any of 15 major common chronic conditions. The most frequent of these conditions included diabetes, hypertension, ischemic heart disease (IHD), congestive heart failure (CHF), and chronic obstructive pulmonary disease (COPD). The complete list of inclusion criteria, including the full list of conditions (and their associated 

codes), is included in the online supplement 

For research purposes, the insurance company agreed to make available the complete insurance claims history of these individuals—anonymized to preserve confidentiality&mdash;as well as a broad set of internally generated quality measures associated with their care. The claims data set contains standard diagnostic and procedure codes as well as an anonymized, unique provider identifier associated with each claim. For providers, the data set contains specialty and a unique billing address and practice identifier. We assigned all patients to the PCP associated with their claims. PCPs were defined as physicians in 1 of the following specialties: family practice, internal medicine, general practice, and pediatrics. For patients with claims associated with more than 1 PCP, we assigned each patient to the PCP associated with the plurality of their healthcare costs.

There is no standard operational measure of fragmentation. Previous studies of fragmentation in a Medicare setting have added up the number of providers that a patient sees during the course of treatment for a single health episode

 While the counting approach is helpful in assessing the degree to which a patient's care is fragmented, it is limited in its utility when assessing the degree to which fragmentation is associated with quality and costs of care.

An important limitation of the counting approach is that it does not reflect differences in the concentration of care. For example, a patient whose care is equally divided between 2 providers would be labeled as having the same level of fragmentation as a patient whose care is almost exclusively handled by 1 provider, but who briefly interacts with another. To address this problem, we measured care fragmentation in terms of a Herfindahl-Hirschman concentration index (HHI). The HHI is commonly used in economic studies of industrial structure and is usually a measure of the degree to which a market is concentrated among a small number of companies. We used the HHI to measure the degree to which a patient’s care is concentrated among a set of providers; we constructed an HHI for each patient by first calculating each provider’s share of the total costs associated with that patient’s claims. We then summed the squares of the cost shares across all providers that a patient sees. A patient’s care would be considered to be the least fragmented when all care was from a single provider (and corresponded to an HHI of 1). A patient’s care would be considered maximally fragmented if their care was equally divided across a large number of providers (and corresponded to an HHI approaching zero as the number

To address the challenge that measured fragmentation may be higher among patients who are sicker or clinically more complex in ways that are not captured by our observed controls, we focused on the style of care of each patient’s PCP as our marker for the patient’s care fragmentation. Specifically, for each patient we calculated the fragmentation of their PCP’s 

patients, excluding that particular patient. That is, we defined a patient’s fragmentation score to be the concentration of care for all the other patients in their PCP’s panel, reflecting that PCP’s practice style and not that patient’s severity of illness directly. Of course, it may be that sicker patients cluster among certain PCPs—a possibility that we address below in our modeling approach. We define the fragmentation score as 1 minus this average HHI so that the score is increasing as fragmentation increases. As a sensitivity analysis, we also estimate specifications with the unadjusted (individual-level) fragmentation measure.

Our principal quality measure was derived from a proprietary algorithm the health plan uses to detect potential gaps in care. The algorithm analyzes patients’ medical and drug claims, diagnostic history, and laboratory results for indications of departures from best clinical practice, and also triggers an alert that is sent to the provider. We refer to these alerts as potential gaps in care (PGCs). The algorithm generating the PGCs targets over 500 different potential clinical issues, the most frequent 20 of which are listed in 

—these account for about two-thirds of the total alerts issued. These are very closely aligned to national quality measures such as those in the Healthcare Effectiveness Data and Information Set. The algorithm also generates measures of whether the detected issue was eventually resolved. Our 4 main quality outcomes are patient-level indicators for: 1) whether any PGC was generated; 2) number of PGCs generated; and

3) whether any PGC was left unresolved. Finally, 4) we examined hospitalizations that resulted from ambulatory care—sensitive conditions—conditions in which effective ambulatory care should prevent or reduce the need for hospitalization.

 These potentially preventable hospitalizations are thus generally accepted as indicators of poor quality of ambulatory care. The measures were developed by the Agency for Healthcare Research and Quality as Prevention Quality Indicators (PQIs), and we used their definitions to construct our measures.

 Most of the conditions underlying PQIs are targets of the PGC algorithms as well. We chose a priori to focus on whether any PGCs were generated as our primary quality measure of interest, but we examined all 4.

Finally, we examined total costs of care by assigning each claim a standardized Medicare payment rate—we did this both because we did not have the proprietary reimbursement amounts from the health plan and because we wanted the results to be broadly generalizable. The standard rate was using the Medicare allowable payments, adjusted for the geographical pricing cost index to standardize across locations.

Because we were concerned that sicker patients might cluster together among certain PCPs, we included covariates to account for underlying patient characteristics that might otherwise confound the relationship between fragmentation and our outcomes of interest. We used the hierarchical condition categories (HCCs) created and used by CMS

 as a risk-adjustment tool in our analyses to account for differences in patient severity.

We divided our population of patients by quartile of the fragmentation measure and compared key demographic and clinical characteristics of patients across these 4 groups. Next, we estimated the relationship between the fragmentation measure and our outcomes using regression models that accounted for age, gender, and the HCC risk-adjustment variables, clustering to account for correlation among patients assigned to a given provider. For each outcome we estimated a model specifying a linear effect of fragmentation scaled in units of a standard deviation. We also estimated a more flexible nonlinear specification using indicators for fragmentation quartile.

In our subgroup analyses we estimated the same set of models separately for patients in each of the 5 disease categories we chose a priori: diabetes, hypertension, IHD, CHF, and COPD. Using the results of the regression analysis, we calculated regression-adjusted means for each of our cost and quality measures for each quartile of the fragmentation measure.

This study was approved by the Office of Human Research Administration at the Harvard T.H. Chan School of Public Health, the Harvard University Committee on the Use of Human Subjects in Research, and the Boston University Institutional Review Board.

Patients whose PCPs exhibited a more fragmented style were, on average, older, more likely to be female, and more likely to suffer from diabetes, IHD, hypertension, CHF, or COPD than patients whose PCPs practiced in a less fragmented style (

). The patients of PCPs with a more fragmented style of practice had a greater number of primary care visits in a given year (24 in the highest quartile vs 10 in the lowest; 

<.001 for trend) as well as more specialist visits (24 in the highest quartile vs 4 in the lowest; 

<.001 for trend). Further, we found that patients of PCPs with the most fragmented practice style saw, on average, more PCPs (4.0 in highest quartile vs 2.3 in lowest quartile) as well as common types of specialists (Table 1) than patients whose PCPs practiced in a less fragmented style.

We found that a higher degree of fragmentation was associated with a higher number of PGCs generated (

). A standard deviation increase in fragmentation was associated with a 3.9% absolute increase in the likelihood of having at least 1 PGC. Across quartiles, this relationship held: 25.9% of patients in the lowest quartile of fragmentation had a PGC, compared with 32.8% of patients in the highest quartile (

). The patterns were similar for the number of PGCs (higher among patients in more fragmented practices), the likelihood of having any unresolved PGC, and the total number of unresolved PGCs (Table 2).

We found a similar relationship between fragmentation and rates of preventable hospitalizations: a standard deviation increase in fragmentation was associated with a 1.4% absolute increase in the likelihood of having a preventable hospitalization (Table 2). The analyses examining fragmentation in quartiles gave similar results: approximately 7.1% of patients in the lowest fragmentation quartile had a preventable hospitalization in a given year compared with approximately 9.1% in the highest quartile (

<.001 for difference across quartiles) (Figure).

Finally, we found that fragmentation was associated with substantial increases in costs of care (

). An increase of 1 standard deviation in the fragmentation measure was associated with a $2642 increase in costs over a median period of 35 months. In examining quartiles, we found that patients in the most fragmented quartile had an average total cost of $10,396, compared with just $5854 among those in the least fragmented quartile (Figure).

Fragmentation, Costs, and Quality by Individual 

When we examined each of the 5 prespecified chronic disease groups independently, we found relatively similar effects across each. In each of the 5 conditions, the likelihood of having any PGC (our measure of a quality gap) increased with increasing fragmentation, and as did the likelihood of having a preventable hospitalization. Finally, we found that costs were, in each of the 5 conditions, highest in the 2 quartiles with the most fragmentation and substantially lower in the quartiles with the least fragmentation (Table 3).

We repeated the analyses above using the unadjusted, individual-level fragmentation score. The results are reported in 

, and they show that the results are qualitatively and quantitatively similar to the main results achieved with the adjusted fragmentation score.

We examined the relationship between fragmentation and both quality and costs of care among a chronically ill, commercially insured population and found that greater fragmentation was consistently associated with worse quality and higher costs. Even among select subgroups of patients with common chronic diseases, receiving care from a primary care physician who exhibits a more fragmented style of practice was associated with greater gaps in quality, more preventable hospitalizations, and higher healthcare spending. Taken together, these findings offer new evidence that national policy efforts may benefit from a greater effort toward reducing the fragmentation of care that chronically ill patients often experience. Methodologically, our study introduces a new measure of care fragmentation, and analyzes commercially insured patients, a relatively under-studied population.

We could not directly examine why fragmentation was associated with worse quality and higher costs, although there are several potential explanations. One possibility is that with multiple providers each heavily involved in a patient’s care, no single provider is able to ensure that the entirety of a patient’s clinical needs are taken into account, leading to gaps in care as important issues go unaddressed.

 The substantial coordination costs of managing input from specialists drives another possible explanation. Among PCPs with a fragmented style of care delivery, the time spent managing multiple specialists may be crowding out primary care physicians’ direct efforts to provide optimal care to their patients.

The higher costs associated with fragmentation may be driven by unnecessary duplication of services, or additional testing that results as patients see more and more providers, consistent with the findings here that patients with higher fragmentation saw a greater number of different providers of a given specialty type. Given the relatively poor exchange of clinical data among providers,

 it is possible that each additional visit with a new provider led to more tests, especially as patients saw more specialists. Finally, it is possible that that differences in costs may have been driven by poor care coordination leading to more preventable hospitalizations.

Our study adds to prior literature on the issue of fragmentation in medical care. Pham and colleagues demonstrated that the average Medicare patient sees a median of 2 primary care physicians and 5 specialists over a 2-year time period.

 Schrag found that 17% of patients in New York experienced fragmented inpatient care, and that this was particularly common among Medicaid recipients.

 Liu and colleagues showed that in a population of patients with diabetes and chronic kidney disease, increasing fragmentation was associated with higher rates of emergency department (ED) use.

 Others have focused on the opposite phenomenon—that is, continuity of care&mdash;and have demonstrated that high levels of continuity are associated with better preventive care, lower likelihood of hospitalization, and better patient experience,

 though others have still failed to find the same association.

 Most recently, Hussey and colleagues found that chronically ill Medicare beneficiaries who had more continuous care were less likely to experience complications, visit the ED, or be admitted to the hospital.

Increased care fragmentation among chronically ill, commercially insured patients is associated with higher costs and lower quality of care.

Karen E. Joynt, MD, MPH

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