Primary Care Practices Providing a Broader Range of Services Have Lower Medicare Expenditures and Emergency Department Utilization

Rich, Eugene C.; O’Malley, Ann S.; Burkhart, Claire; Shang, Lisa; Ghosh, Arkadipta; Niedzwiecki, Matthew J.

doi:10.1007/s11606-021-06728-2

Cited by 9 publications

(14 citation statements)

References 13 publications

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“…Furthermore, patients of practices in communities with a higher ratio of primary care practitioner to total practitioners had lower risk of highly fragmented care, which aligns with prior studies showing the benefits of greater local infrastructure for primary care 37. That all the 3 measures of physician comprehensiveness were independently linked to lower fragmentation is consistent with these measures’ intended purpose to account for various aspects of the breadth and depth of care provided by primary care physicians 38,39…”

Section: Discussionsupporting

confidence: 81%

“…37 That all the 3 measures of physician comprehensiveness were independently linked to lower fragmentation is consistent with these measures' intended purpose to account for various aspects of the breadth and depth of care provided by primary care physicians. 38,39 This work adds to the existing literature because no previous study attempted to determine how much fragmentation could be explained by different levels of the health system. That most of the variation in fragmentation is unexplained is a novel finding with important policy implications.…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…We used Medicare Data on Provider Practice and Specialty to measure physician demographics and Medicare claims to construct 3 measures of physician comprehensiveness of care in 2017: the range of services provided, new problem management, and involvement in patient conditions (Table 2). 32,33 We studied multiple practice characteristics, such as the number of practitioners, whether the site is multispecialty, and whether a hospital or health system owns the site.…”

Section: Explanatory Variablesmentioning

confidence: 99%

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Predicting Fragmented Care

Timmins¹,

Kern²,

Ghosh³

et al. 2022

Medical Care

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Background: Understanding what drives fragmented ambulatory care (care spread across multiple providers without a dominant provider) can inform the design of future interventions to reduce unnecessary fragmentation.Objectives: To identify the characteristics of beneficiaries, primary care physicians, primary care practice sites, and geographic markets that predict highly fragmented ambulatory care in the United States.Research Design: Cross-sectional analysis of Medicare claims data for beneficiaries attributed to primary care physicians and practices in 2018. We used hierarchical linear models with random intercepts and an extensive list of explanatory variables to predict the likelihood of high fragmentation.Subjects: A total of 3,540,310 Medicare fee-for-service beneficiaries met the inclusion criteria, attributed to 26,344 primary care physicians in 9300 practice sites, and 788 geographic markets. Measures:We defined high care fragmentation as a reversed Bice-Boxerman Index score above 0.85.Results: Explanatory variables explained only 6% of the variation in highly fragmented care. Unobserved differences between primary care physicians, between practice sites, and between markets together accounted for 4%. Instead, 90% of the variation in high fragmentation was unobserved residual variance. We identified the characteristics of beneficiaries (age, reason for original Medicare entitlement, and dually eligible for Medicaid insurance), physicians (comprehensiveness of care), and practices (size, being part of a system/hospital) that had small associations with high fragmentation.Conclusions: Variation in fragmentation was not explained by observed beneficiary, primary care provider, practice site, or market characteristics. Instead, the aggregate behavior of diverse health care providers beyond primary care, along with unmeasured patient preferences and behaviors, seem to be important predictors.

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Section: Discussionsupporting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Explanatory Variablesmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Fragmented Care

Timmins¹,

Kern²,

Ghosh³

et al. 2022

Medical Care

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“…We hypothesise that physician characteristics—for example, age (as a proxy for the level of experience), sex, and specialty—can influence their treatment or referral decisions 18 . We also expect physician comprehensiveness to affect LVS use, with greater PCP involvement in care reducing overall spending and LVS utilisation 23 …”

Section: Methodsmentioning

confidence: 99%

Assessing patient, physician, and practice characteristics predicting the use of low‐value services

Shin

Fleming

Ghosh

et al. 2022

Health Services Research

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Objective: To examine characteristics of beneficiaries, physicians, and their practice sites associated with greater use of low-value services (LVS) using LVS measures that reflect current care practices.Data Sources: This study was conducted in the context of a large, nationwide primary care redesign initiative (Comprehensive Primary Care Plus), using Medicare claims data in 2018. Study Design:We examined beneficiary-level total counts of LVS based on the existing 31 claims-based measures updated by excluding three services provided with diminishing frequency to Medicare beneficiaries and by replacing these with more recently identified LVS. We estimated hierarchical linear models with an extensive list of beneficiary, physician, and practice site characteristics to examine the contribution of characteristics at each level in predicting greater use of LVS. We also examined the proportion of variation in LVS use attributable to the set of characteristics at each level.Data Collection/Extraction Methods: The study included 5,074,642 Medicare feefor-service beneficiaries attributed to 32,406 primary care physicians in 11,009 primary care practice sites.Principal Findings: Patients with disabilities, end-stage renal disease, and those in regions with higher poverty rates receive 10 (standard error [SE] = 3.0), 80 (SE = 14.0), and 10 (SE = 1.0) more LVS per 1000 beneficiaries across all 31 measures combined than patients without such attributes, respectively. Greater physician comprehensiveness and an increase in the number of primary care practitioners at a practice were associated with 40 (SE = 20.0) and 20 (SE = 6.0) fewer LVS per 1000 beneficiaries, respectively. Yet, the explanatory variables we examined only account for 11 percent of the variation in LVS use, with most of the variation (87 percent) being due to unobserved differences at the beneficiary level.Conclusions: Unexplained residual variation, from underlying patient preferences and behavior of non-primary care providers, could be important determinants of LVS use.

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