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The American Journal of Managed Care May 2015
Comparison of Provider and Plan-Based Targeting Strategies for Disease Management
Ann M. Annis, MPH, RN; Jodi Summers Holtrop, PhD, MCHES; Min Tao, PhD; Hsiu-Ching Chang, PhD; and Zhehui Luo, PhD
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Christine K. Cassel, MD, President and CEO, National Quality Forum
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Care Fragmentation, Quality, and Costs Among Chronically Ill Patients
Brigham R. Frandsen, PhD; Karen E. Joynt, MD, MPH; James B. Rebitzer, PhD; and Ashish K. Jha, MD, MPH
Association Between the Patient-Centered Medical Home and Healthcare Utilization
Rainu Kaushal, MD, MPH; Alison Edwards, MStat; and Lisa M. Kern, MD, MPH
Transforming Oncology Care: Payment and Delivery Reform for Person-Centered Care
Kavita Patel, MD, MS; Andrea Thoumi, MSc; Jeffrey Nadel, BA; John O'Shea, MD, MPA; and Mark McClellan, MD, PhD
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Heather Black, PhD; Rodalyn Gonzalez, BA; Chantel Priolo, MPH; Marilyn M. Schapira, MD, MPH; Seema S. Sonnad, PhD; C. William Hanson III, MD; Curtis P. Langlotz, MD, PhD; John T. Howell, MD; and Andrea J. Apter, MD, MSc
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Karen Davis, PhD, APN; Christine Buttorff, PhD; Bruce Leff, MD; Quincy M. Samus, PhD; Sarah Szanton, PhD, APN; Jennifer L. Wolff, PhD; and Farhan Bandeali, MSPH
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Elham Hatef, MD, MPH; Bruce G. Vanderver, MD, MPH; Peter Fagan, PhD; Michael Albert, MD; and Miriam Alexander, MD, MPH
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Oluwatobi Awele Ogbechie, MD, MBA; and John Hsu, MD, MBA, MSCE
Changing Trends in Type 2 Diabetes Mellitus Treatment Intensification, 2002-2010
Rozalina G. McCoy, MD; Yuanhui Zhang, PhD; Jeph Herrin, PhD; Brian T. Denton, PhD; Jennifer E. Mason, PhD; Victor M. Montori, MD; Steven A. Smith, MD; Nilay D. Shah, PhD
Medicaid-Insured and Uninsured Were More Likely to Have Diabetes Emergency/Urgent Admissions
Monica A. Fisher, PhD, DDS, MPH, MS; and Zhen-qiang Ma, MD, MPH, MS
Roles of Prices, Poverty, and Health in Medicare and Private Spending in Texas
Chapin White, PhD; Suthira Taychakhoonavudh, PhD; Rohan Parikh, MS; and Luisa Franzini, PhD
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Ann-Marie Rosland, MD, MS; Sarah L. Krein, PhD, RN; Hyungjin Myra Kim, ScD; Clinton L. Greenstone, MD; Adam Tremblay, MD; David Ratz, MS; Darcy Saffar, MPH; and Eve A. Kerr, MD, MPH

Care Fragmentation, Quality, and Costs Among Chronically Ill Patients

Brigham R. Frandsen, PhD; Karen E. Joynt, MD, MPH; James B. Rebitzer, PhD; and Ashish K. Jha, MD, MPH
Increased care fragmentation among chronically ill, commercially insured patients is associated with higher costs and lower quality of care.
To assess the relationship between care fragmentation and both quality and costs of care for commercially insured, chronically ill patients.

Study Design: 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.

Methods: 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.

Results: 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; P <.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; P <.001). High fragmentation was associated with $4542 higher healthcare spending ($10,396 in the highest quartile vs $5854 in the lowest quartile; P <.001). We found similar or larger effects on quality and costs among patients when we examined the most frequently occurring disease groups individually.

Conclusions: Chronically ill patients whose primary care providers offer highly fragmented care more often experience lapses in care quality and incur greater healthcare costs.
Am J Manag Care. 2015;21(5):355-362
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.1,2 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.3

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”—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?


Study Population and Data

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 International Classification of Diseases, Ninth Revision, Clinical Modification codes), is included in the online supplement eAppendix Methods (available at

For research purposes, the insurance company agreed to make available the complete insurance claims history of these individuals—anonymized to preserve confidentiality—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.

Fragmentation Measure

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 episode4,5 or during a year.6,7 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

of providers increases).

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 other 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.

Quality and Cost Outcomes

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 eAppendix Table 2—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.8,9 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.10 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.

Covariates of Interest

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 CMS11 as a risk-adjustment tool in our analyses to account for differences in patient severity.

Statistical Analysis

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.


Patient and Provider Characteristics

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