Team Formation and Performance: Evidence from Healthcare Referral Networks

Agha, Leila; Ericson, Keith M. Marzilli; Geissler, Kimberley H.; Rebitzer, James B.

doi:10.3386/w24338

Cited by 20 publications

(34 citation statements)

References 39 publications

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“…However, this study demonstrates with a population of colorectal cancer patients and providers in North Carolina that shared patient networks may differ in important ways depending on the data source. Very few studies have combined multiple payer sources into a single network (e.g., Agha et al 26 ).…”

Section: Resultsmentioning

confidence: 99%

Comparing Shared Patient Networks Across Payers

et al. 2019

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Section: Resultsmentioning

confidence: 99%

Comparing Shared Patient Networks Across Payers

et al. 2019

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“…One solution may come from further adoption of the medical home model, in which the primary care physician serves as coordinator for all the specialists that an individual patient sees. However, it is also important to consider the role of specialist collaboration in diagnosis and treatment of complex or rare medical disorders-as Agha et al (2018) point out, there is a trade-off between the optimal strategy of the PCP seeking to reduce the effort of care coordination from referrals with quality derived from a patient having more options of specialists to see in order to find an ideal patient-specialist match. Further research is needed to determine optimal network structure in cases where a large number of specialists are needed for effective care.…”

Section: Specialty/knowledge Complementaritymentioning

confidence: 99%

“…One aspect of network structure that is related to health care outcomes is care fragmentation. Fragmented service delivery occurs when a patient is seen by a large number of physicians, introducing risk for lapses in coordination of care, with growing evidence that network structures with large numbers of specialist providers (i.e., higher levels of fragmented care) are associated with higher health care utilization and higher costs (Agha, Marzilli Ericson, Geissler, & Rebitzer, ; Agha, Frandsen, & Rebitzer, ). However, despite the advances in connecting physician patient sharing networks with outcome measures, less is known about the factors that give rise to these physician networks in the first place.…”

Section: Introductionmentioning

confidence: 99%

The formation of physician patient sharing networks in medicare: Exploring the effect of hospital affiliation

Linde

2019

Health Economics

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This study explores the forces that drive the formation of physician patient sharing networks. In particular, I examine the degree to which hospital affiliation drives physicians' sharing of Medicare patients. Using a revealed preference framework where observed network links are taken to be pairwise stable, I estimate the physicians' pair‐specific values using a tetrad maximum score estimator that is robust to the presence of unobserved physician specific characteristics. I also control for a number of potentially confounding patient sharing channels, such as (a) common physician group or hospital system affiliation, (b) physician homophily, (c) knowledge complementarity, (d) patient side considerations related to both geographic proximity and insurance network participation, and (e) spillover from other collaborations. Focusing on the Chicago hospital referral region, I find that shared hospital affiliation accounts for 36.5% of the average pair‐specific utility from a link. Implications for reducing care fragmentation are discussed.

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“…This is the same threshold that has been used in other settings to define relationships among providers. [8][9][10][11][12] To be considered a "sharing" community oncologist, we further required that a community oncologist share at least 10% of their patients with MM with an NCICCC MM specialist. The following 3 mutually exclusive categories based on type of primary oncologist were included in this model: (1) NCICCC MM specialists, (2) community oncologists with a history of sharing patients with MM with NCICCC MM specialists, and (3) community oncologists with no history of sharing.…”

Section: Exposure and Outcome Measurementmentioning

confidence: 99%

Influence of Treating Facility, Provider Volume, and Patient-Sharing on Survival of Patients With Multiple Myeloma

Freeman

Kuo

Zhou

et al. 2019

Journal of the National Comprehensive Cancer Network

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Background: Population-based studies suggest that patients with multiple myeloma (MM) have better outcomes when treated at high-volume facilities, but the relative contribution of provider expertise and hospital resources to improved outcomes is unknown. This study explored how treating facility, individual provider volume, and patient-sharing between MM specialists and community providers influenced outcomes for patients with MM. Patients and Methods: A state cancer registry linked to public and private insurance claims was used to identify a cohort of patients diagnosed with MM in 2006 through 2012. Three multivariable Cox models were used to examine how the following factors impacted overall survival: (1) evaluation at an NCI-designated Comprehensive Cancer Center (NCICCC), (2) the primary oncologist’s volume of patients with MM, and (3) patient-sharing between MM specialists and community oncologists. Results: A total of 1,029 patients diagnosed with MM in 2006 through 2012 were identified. Patients who were not evaluated at an NCICCC had an increased risk of mortality compared with those evaluated at an NCICCC (hazard ratio [HR], 1.50; 95% CI, 1.21–1.86; P<.001). Compared with patients treated by NCICCC MM specialists, those treated by both low-volume community providers (HR, 1.47; 95% CI, 1.14–1.90; P<.01) and high-volume community providers (HR, 1.29; 95% CI, 1.04–1.61; P<.05) had a higher risk of mortality. No difference in mortality was seen between patients treated by NCICCC MM specialists and those treated by the highest-volume community oncologists in the ninth and tenth deciles (HR, 1.08; 95% CI, 0.84–1.37; P=.5591). Patients treated by community oncologists had a higher risk of mortality regardless of patient-sharing compared with patients treated by MM specialists (eg, community oncologist with a history of sharing vs NCICCC MM specialist: HR, 1.49; 95% CI, 1.10–2.02; P<.05). Conclusions: Findings of this study add to the accumulating evidence showing that patients with MM benefit from care at high-volume facilities, and suggest that similar outcomes can be achieved by the highest-volume providers in the community.

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Team Formation and Performance: Evidence from Healthcare Referral Networks

Cited by 20 publications

References 39 publications

Comparing Shared Patient Networks Across Payers

Comparing Shared Patient Networks Across Payers

The formation of physician patient sharing networks in medicare: Exploring the effect of hospital affiliation

Influence of Treating Facility, Provider Volume, and Patient-Sharing on Survival of Patients With Multiple Myeloma

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