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Healthcare Network Analysis of Patients With Diabetes and Their Physicians

James Davis, PhD; Eunjung Lim, PhD; Deborah A. Taira, ScD; and John Chen, PhD
Network analyses of patients with diabetes in Hawaii illustrate structures and links that health plans could leverage to strengthen quality improvement and disease management programs.

Our results show that network analysis can uncover the underlying structures of healthcare networks, which are often invisible to those managing care. In Hawaii, the healthcare plan consists of highly connected networks both of patients seeing the same doctors and of doctors caring for the same patients. Network algorithms uncover subgroups that potentially could be managed together, ranging from clusters of directly connected patients or doctors to larger communities that include indirect connections. Healthcare interventions might be stratified based on the composition and quality of network structures.

Links between patients can result from sharing the same physicians, as we demonstrated for the patients sharing the physicians they saw most often. The strongest associations were by ethnicity: Native Hawaiian, Filipino, Japanese, and white patients all tended to share physicians with other patients of the same ethnicity. Similarities in older age, sex, and having CAD or CKD also helped identify patients seeing the same primary physicians. Patients seeing a higher number of primary care physicians were less likely to be connected. Health plans might use such information to understand how to best coordinate care or deliver culturally appropriate interventions.

Another study examined the similarity of patients among physician panels using national Medicare data.19 Physician panels were similar by race/ethnicity for white, black, and Hispanic patients and alike in the mean health status of the patients treated. Physician sharing was greater when the distance between offices was shorter.

We observed in Hawaii that the structure of healthcare networks varied geographically. On the more rural neighboring islands, communities of patients and physicians were smaller and the degree (ie, the number of links) in the networks was fewer. For the physician networks, centrality was appreciably greater on Oahu than on neighboring islands. Leveraging the central individuals in networks by using them as opinion leaders to promote behaviors to change social norms has proven success for healthcare interventions.20,21 Physicians with the highest centrality can be prominent in their networks and might be recruited to spearhead physician interventions.

The methods we applied for network analysis have been used in other healthcare studies. The most frequent approach, as we employed, analyzes the networks of patients sharing physicians and physicians sharing patients as separate networks.6,7,9,10,19,22-24 This approach simplifies the analyses and interpretation of results and provides insight into both patient and physician relationships. Other healthcare studies have also employed ERGMs, the regression method used here.10 ERGMs are like other regression models in that they can include multiple predictors of an outcome; however, with ERGMs, the outcome is being connected in network rather than incurring an outcome such as having an adverse event. The goal of ERGMs is to explain the network structure.


Our study should be interpreted with respect to its limitations. The results are for the members of 1 large insurer in a single state that has a distinct, multiethnic population. The study is exploratory in nature; the cross-sectional design limits the breadth of the conclusions. Information on ethnicity is limited to members who returned member satisfaction surveys. The study methods, however, are applicable to the investigation of other populations and other geographic regions. The results illustrate the potential value to health plans of conducting network analyses.


The results have implications for managed care. Health plans might take advantage of the pre-existing structures to plan programs and encourage collaboration. Patients missing quality indicators might be reached through the multiple providers identified in networks, especially patients without a regular primary physician. Structures identified by network algorithms find patients and doctors with a high density of connections. These structures offer natural targets for interventions to show clinical or cost benefit. High-cost clusters, as an example, might be identified to provide coordinated or enhanced care. In these various ways, results of network analysis might aid health plans to reduce costs and improve clinical outcomes.

Health plans have detailed network information in their administrative claims data. The analysis software is free and open source, easily available to health plan analysts. Network analyses offer a distinct approach to understand the structure of healthcare and the relationships that are critical to managed care. Analyzing the structure of local networks can lead to enhanced strategies for disease management to improve health quality and outcomes and offer more patient-centered care. Network analyses reveal structures and links that healthcare plans might leverage to strengthen quality improvement and disease management programs.

Author Affiliations: John A. Burns School of Medicine, University of Hawaii (JD, EL, JC), Honolulu, HI; Daniel K. Inouye College of Pharmacy, University of Hawaii (DAT), Hilo, HI.

Source of Funding: The research was supported by grants U54MD007584 and U54MD007601 from National Institutes of Health/National Institute on Minority Health and Health Disparities and U54GM104944 from National Institutes of Health/National Institute of General Medical Sciences.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (JD, EL, DAT, JC); acquisition of data (JD, DAT); analysis and interpretation of data (JD, EL, DAT, JC); drafting of the manuscript (JD, EL, DAT); critical revision of the manuscript for important intellectual content (JD, DAT, JC); and statistical analysis (JD).

Address Correspondence to: James Davis, PhD, John A. Burns School of Medicine, University of Hawaii, 651 Ilalo St, Honolulu, HI 96813. Email:

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