BACKGROUND: There is growing recognition that health care providers are embedded in networks formed by the movement of patients between providers. However, the structure of such networks and its impact on health care are poorly understood. PURPOSE: We examined the level of dispersion of patient-sharing networks across U.S. hospitals and its association with three measures of care delivered by hospitals that were likely to relate to coordination. METHODOLOGY/APPROACH: We used data derived from 2016 Medicare Fee-for-Service claims to measure the volume of patients that hospitals treated in common. We then calculated a measure of dispersion for each hospital based on how those patients were concentrated in outside hospitals. Using this measure, we created multivariate regression models to estimate the relationship between network dispersion, Medicare spending per beneficiary, readmission rates, and emergency department (ED) throughput rates. RESULTS: In multivariate analysis, we found that hospitals with more dispersed networks (those with many low-volume patient-sharing relationships) had higher spending but not greater readmission rates or slower ED throughput. Among hospitals with fewer resources, greater dispersion related to greater readmission rates and slower ED throughput. Holding an individual hospital's dispersion constant, the level of dispersion of other hospitals in the hospital's network was also related to these outcomes. CONCLUSION: Dispersed interhospital networks pose a challenge to coordination for patients who are treated at multiple hospitals. These findings indicate that the patient-sharing network structure may be an overlooked factor that shapes how health care organizations deliver care. PRACTICE IMPLICATIONS: Hospital leaders and hospital-based clinicians should consider how the structure of relationships with other hospitals influences the coordination of patient care. Effective management of this broad network may lead to important strategic partnerships.
BACKGROUND: There is growing recognition that health care providers are embedded in networks formed by the movement of patients between providers. However, the structure of such networks and its impact on health care are poorly understood. PURPOSE: We examined the level of dispersion of patient-sharing networks across U.S. hospitals and its association with three measures of care delivered by hospitals that were likely to relate to coordination. METHODOLOGY/APPROACH: We used data derived from 2016 Medicare Fee-for-Service claims to measure the volume of patients that hospitals treated in common. We then calculated a measure of dispersion for each hospital based on how those patients were concentrated in outside hospitals. Using this measure, we created multivariate regression models to estimate the relationship between network dispersion, Medicare spending per beneficiary, readmission rates, and emergency department (ED) throughput rates. RESULTS: In multivariate analysis, we found that hospitals with more dispersed networks (those with many low-volume patient-sharing relationships) had higher spending but not greater readmission rates or slower ED throughput. Among hospitals with fewer resources, greater dispersion related to greater readmission rates and slower ED throughput. Holding an individual hospital's dispersion constant, the level of dispersion of other hospitals in the hospital's network was also related to these outcomes. CONCLUSION: Dispersed interhospital networks pose a challenge to coordination for patients who are treated at multiple hospitals. These findings indicate that the patient-sharing network structure may be an overlooked factor that shapes how health care organizations deliver care. PRACTICE IMPLICATIONS: Hospital leaders and hospital-based clinicians should consider how the structure of relationships with other hospitals influences the coordination of patient care. Effective management of this broad network may lead to important strategic partnerships.
Authors: Bruce E Landon; Nancy L Keating; Jukka-Pekka Onnela; Alan M Zaslavsky; Nicholas A Christakis; A James O'Malley Journal: JAMA Intern Med Date: 2018-01-01 Impact factor: 21.873
Authors: Bruce Y Lee; Sarah M McGlone; Yeohan Song; Taliser R Avery; Stephen Eubank; Chung-Chou Chang; Rachel R Bailey; Diane K Wagener; Donald S Burke; Richard Platt; Susan S Huang Journal: Am J Public Health Date: 2011-02-17 Impact factor: 9.308
Authors: Laura A Dummit; Daver Kahvecioglu; Grecia Marrufo; Rahul Rajkumar; Jaclyn Marshall; Eleonora Tan; Matthew J Press; Shannon Flood; L Daniel Muldoon; Qian Gu; Andrea Hassol; David M Bott; Amy Bassano; Patrick H Conway Journal: JAMA Date: 2016-09-27 Impact factor: 56.272
Authors: Hoangmai H Pham; Ann S O'Malley; Peter B Bach; Cynthia Saiontz-Martinez; Deborah Schrag Journal: Ann Intern Med Date: 2009-02-17 Impact factor: 25.391