Dataset Information

The association between patient sharing network structure and healthcare costs.

ABSTRACT:

Study question

While physician relationships (measured through shared patients) are associated with clinical and utilization outcomes, the extent to which this is driven by local or global network characteristics is not well established. The objective of this research is to examine the association between local and global network statistics with total medical spending and utilization.

Data source

Data used are the 2011 Massachusetts All Payer Claims Database.

Study design

The association between network statistics and total medical spending and utilization (using standardized prices) is estimated using multivariate regression analysis controlling for patient demographics and health status.

Data collection

We limit the sample to continuously enrolled commercially insured patients in Massachusetts in 2011.

Principal findings

Mean patient age was 45 years, and 56.3% of patients were female. 73.4% were covered by a health maintenance organization. Average number of visits was 5.43, with average total medical spending of $4,911 and total medical utilization of $4,252. Spending was lower for patients treated by physicians with higher degree (p<0.001), eigenvector centrality (p<0.001), clustering coefficient (p<0.001), and measures reflecting the normalized degree (p<0.001) and eigenvector centrality (p<0.001) of specialists connected to a patient's PCP. Spending was higher for patients treated by physicians with higher normalized degree, which accounts for physician specialty and patient panel size (p<0.001). Results were similar for utilization outcomes, although magnitudes differed indicating patients may see different priced physicians.

Conclusions

Generally, higher values of network statistics reflecting local connectivity adjusted for physician characteristics are associated with increased costs and utilization, while higher values of network statistics reflecting global connectivity are associated with decreased costs and utilization. As changes in the financing and delivery system advance through policy changes and healthcare consolidation, future research should examine mechanisms through which this structure impacts outcomes and potential policy responses to determine ways to reduce costs while maintaining quality and coordination of care.

What this study adds

It is unknown whether local and global measures of physician network connectivity associated with spending and utilization for commercially insured patients?In this social network analysis, we found generally higher values of network statistics reflecting local connectivity are associated with increased costs and utilization, while higher values of network statistics reflecting global connectivity are associated with decreased costs and utilization.Understanding how to influence local and global physician network characteristics may be important for reducing costs while maintaining quality.

SUBMITTER: Geissler KH

PROVIDER: S-EPMC7307780 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

The association between patient sharing network structure and healthcare costs.

Geissler Kimberley H KH Lubin Benjamin B Ericson Keith M Marzilli KMM

PloS one 20200622 6

<h4>Study question</h4>While physician relationships (measured through shared patients) are associated with clinical and utilization outcomes, the extent to which this is driven by local or global network characteristics is not well established. The objective of this research is to examine the association between local and global network statistics with total medical spending and utilization.<h4>Data source</h4>Data used are the 2011 Massachusetts All Payer Claims Database.<h4>Study design</h4>T ...[more]

PMID: 32569294

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Dataset Information

The association between patient sharing network structure and healthcare costs.

Study question

Data source

Study design

Data collection

Principal findings

Conclusions

What this study adds

Publications

The association between patient sharing network structure and healthcare costs.

Similar Datasets

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