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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
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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
Increased care fragmentation among chronically ill, commercially insured patients is associated with higher costs and lower quality of care.
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 (Table 1). 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; P <.001 for trend) as well as more specialist visits (24 in the highest quartile vs 4 in the lowest; P <.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.

Quality and Patient Outcomes

We found that a higher degree of fragmentation was associated with a higher number of PGCs generated (Table 2). 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 (P <.001 across quartiles) (Figure). 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 (P <.001 for difference across quartiles) (Figure).

Finally, we found that fragmentation was associated with substantial increases in costs of care (Table 3). 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 Diseases

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).

Sensitivity Analysis

We repeated the analyses above using the unadjusted, individual-level fragmentation score. The results are reported in Table 4, and they show that the results are qualitatively and quantitatively similar to the main results achieved with the adjusted fragmentation score.

DISCUSSION

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.12 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,13 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.6 Schrag found that 17% of patients in New York experienced fragmented inpatient care, and that this was particularly common among Medicaid recipients.14 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.15 Others have focused on the opposite phenomenon—that is, continuity of care—and have demonstrated that high levels of continuity are associated with better preventive care, lower likelihood of hospitalization, and better patient experience,16,17 though others have still failed to find the same association.18 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.19

Limitations

First, some patients may have unobserved underlying health issues that make care more complex and that require more specialized services. It may be that it is the need for specialized services, rather than fragmentation per se, that leads to higher costs and lower quality. We attempted to address this in 3 ways: first, we used a fragmentation measure that is based on the other patients a physician sees, which removes a patient’s own clinical conditions from the fragmentation measure; second, we included detailed covariates in the regression models; and finally, we used 5 relatively homogeneous populations (those with specific chronic diseases). However, none of these techniques is perfect, and residual confounding remains a possibility.

Another potential limitation is that our sample comes from a single large health plan, and for this reason likely includes only a subset of any provider’s panel of patients. This feature of our data introduces measurement error into our fragmentation measure and this, in turn, likely reduces the magnitude and precision of our estimates. Thus, our results may represent a conservative estimate of the relationship between fragmentation and care and quality outcomes. Next, to the extent that the alerts triggered by the identified PGCs altered providers’ behavior, the impacts we measured on other quality and cost outcomes are net of the potentially ameliorating effect of the PGC alerts, which would decrease the magnitude of any relationship we find between increasing fragmentation and worse quality. Because our data are limited to a commercially insured population, whether other patients, such as the elderly on Medicare, experience similar effects is unclear.

Finally, our fragmentation measure focuses on a specific dimension of fragmentation: the dispersion of care across multiple providers. Another important dimension of fragmentation captures information flow disruptions among the providers involved in a patient’s care,14 as measured perhaps by the presence of a cohesive information system linking the providers. Our data could not directly examine information flows among physicians, but this dimension of fragmentation is likely to be highly correlated with our notion of care dispersion, and their effects are likely to be complementary: a pattern of care that is dispersed over several physicians is likely to be particularly susceptible to the consequences of information discontinuities, and vice versa.

CONCLUSIONS

In summary, we found that more fragmented care is associated with lower quality and higher costs among nonelderly, chronically ill patients. The effects were sizable, and suggest that policy makers and clinical leaders may need to pay greater attention to reducing fragmentation in order to improve care and reduce healthcare spending.

Acknowledgments

The authors would like to thank Aetna for its collaboration. They also thank Iver Juster for comments and suggestions that improved the paper. Rebitzer also acknowledges support for this research from Microsoft. The authors are responsible for any remaining errors or oversights.

Author Affiliations: Department of Economics, Brigham Young University (BRF), Provo, UT; Department of Health Policy and Management, Harvard T.H. Chan School of Public Health (KEJ, AKJ), Boston, MA; Department of Markets, Public Policy, and Law, Boston University School of Management (JBR), Boston, MA.
 
Source of Funding: This research was supported in part by a gift to Dr Rebitzer from Microsoft Corporation.
 
Author Disclosures: 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.
 
Authorship Information: Concept and design (AKJ, JBR); acquisition of data (BRF, JBR); analysis and interpretation of data (BRF, AKJ, KEJ, JBR); drafting of the manuscript (BRF, AKJ, KEJ, JBR); critical revision of the manuscript for important intellectual content (BRF, AKJ, KEJ, JBR); statistical analysis (BRF, JBR); obtaining funding (JBR); administrative, technical, or logistic support (AKJ); and supervision (AKJ, JBR).
 
Address correspondence to: Ashish K. Jha, MD, MPH, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115. E-mail: ajha@hsph.harvard.edu.
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