Abstract
ObjectiveWe assess healthcare provider collaboration and the impact on patient outcomes using social network analysis, a multi-scale community detection algorithm, and generalized estimating equations.Material and methodsA longitudinal analysis of health claims data of a large employer over a 3 year period was performed to measure how provider relationships impact patient outcomes. The study cohort included 4,230 patients with 167 providers. Social network analysis with a multi-scale community detection algorithm was used to identify groups of healthcare providers more closely working together. Resulting measures of provider collaboration were: 1) degree, 2) betweenness, and 3) closeness centrality. The three patient outcome measures were 1) emergency department visit, 2) inpatient hospitalization, and 3) unplanned hospitalization. Relationships between provider collaboration and patient outcomes were assessed using generalized estimating equations. General practitioner, family practice, and internal medicine were labeled as primary care. Cardiovascular, endocrinologists, etc. were labeled as specialists, and providers such as radiology and social workers were labeled as others.ResultsHigher connectedness (degree) and higher access (closeness) to other providers in the community were significant for reducing inpatient hospitalization and emergency department visits. Patients of specialists (e.g. cardiovascular) and providers specified as others (e.g. social worker) had higher rate of hospitalization and emergency department visits compared to patients of primary care providers.ConclusionApplication of social network analysis for developing healthcare provider networks can be leveraged by community detection algorithms and predictive modeling to identify providers’ network characteristics and their impacts on patient outcomes. The proposed framework presents multi-scale measures to assess characteristics of healthcare providers and their impact on patient outcomes. This approach can be used by implementation experts for informed decision-making regarding the design of insurance coverage plans, and wellness promotion programs. Health services researchers can use the study approach for assessment of provider collaboration and impacts on patient outcomes.
Highlights
Chronic conditions are the leading causes of death and disability, resulting in annual healthcare costs of $3.3 trillion in the United States [1]
Application of social network analysis for developing healthcare provider networks can be leveraged by community detection algorithms and predictive modeling to identify providers’ network characteristics and their impacts on patient outcomes
Collaborative approaches for management of chronic conditions are the key to ensure effective healthcare delivery, to prevent health deterioration, and to reduce adverse outcomes for the patients [7, 8], and the objective of this work is to assess how measures of healthcare provider collaboration impact on patient outcomes
Summary
Chronic conditions are the leading causes of death and disability, resulting in annual healthcare costs of $3.3 trillion in the United States [1]. Collaborative approaches for management of chronic conditions are the key to ensure effective healthcare delivery, to prevent health deterioration, and to reduce adverse outcomes for the patients [7, 8], and the objective of this work is to assess how measures of healthcare provider collaboration impact on patient outcomes. Collaboration encompasses themes such as coordination, cooperation, teams, shared-decision making, and partnerships [9]. To better assess impacts of providers collaboration on patient care and outcomes, more objective approaches are needed to study healthcare providers’ relations, team structures, and the associated impacts on patients
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