Impact of care provider network characteristics on patient outcomes: Usage of social network analysis and a multi-scale community detection

English version

Autoři: Mina Ostovari ^aff001; Denny Yu ^aff002
Působiště autorů: Value Institute, Christiana Care Health System, Newark, Delaware, United States of America ^aff001; School of Industrial Engineering, Purdue University, West Lafayette, Indiana, United States of America ^aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0222016

Souhrn

Objective

We 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 methods

A 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.

Results

Higher 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.

Conclusion

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. 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.

Klíčová slova:

Physical sciences – Research and analysis methods – Computer and information sciences – Network analysis – Mathematics – Simulation and modeling – Medicine and health sciences – Critical care and emergency medicine – Health care – Health care facilities – Hospitals – Health care providers – Patients – Applied mathematics – Algorithms – Inpatients – Centrality – Hospitalizations – Primary care

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