Currently Viewing:
The American Journal of Managed Care July 2019
Changing Demographics Among Populations Prescribed HCV Treatment, 2013-2017
Naoky Tsai, MD; Bruce Bacon, MD; Michael Curry, MD; Steven L. Flamm, MD; Scott Milligan, PhD; Nicole Wick, AS; Zobair Younossi, MD; and Nezam Afdhal, MD
Precision Medicines Need Precision Patient Assistance Programs
A. Mark Fendrick, MD; and Jason D. Buxbaum, MHSA
From the Editorial Board: Robert W. Dubois, MD, PhD
Robert W. Dubois, MD, PhD
Real-Time Video Detection of Falls in Dementia Care Facility and Reduced Emergency Care
Glen L. Xiong, MD; Eleonore Bayen, MD, PhD; Shirley Nickels, BS; Raghav Subramaniam, MS, BS; Pulkit Agrawal, PhD; Julien Jacquemot, MSc, BSc; Alexandre M. Bayen, PhD; Bruce Miller, MD; and George Netscher, MS, BS
Impact of a Co-pay Accumulator Adjustment Program on Specialty Drug Adherence
Bruce W. Sherman, MD; Andrew J. Epstein, PhD; Brian Meissner, PharmD, PhD; and Manish Mittal, PhD
Heroin and Healthcare: Patient Characteristics and Healthcare Prior to Overdose
Michele K. Bohm, MPH; Lindsey Bridwell, MPH; Jon E. Zibbell, PhD; and Kun Zhang, PhD
Medicare’s Bundled Payment Model Did Not Change Skilled Nursing Facility Discharge Patterns
Jane M. Zhu, MD, MPP; Amol Navathe, MD, PhD; Yihao Yuan, MSc; Sarah Dykstra, BA; and Rachel M. Werner, MD, PhD
Number of Manufacturers and Generic Drug Pricing From 2005 to 2017
Inmaculada Hernandez, PharmD, PhD; Chester B. Good, MD, MPH; Walid F. Gellad, MD, MPH; Natasha Parekh, MD, MS; Meiqi He, MS; and William H. Shrank, MD, MSHS
Insurers’ Perspectives on MA Value-Based Insurance Design Model
Dmitry Khodyakov, PhD; Christine Buttorff, PhD; Kathryn Bouskill, PhD; Courtney Armstrong, MPH; Sai Ma, PhD; Erin Audrey Taylor, PhD; and Christine Eibner, PhD
Currently Reading
Healthcare Network Analysis of Patients With Diabetes and Their Physicians
James Davis, PhD; Eunjung Lim, PhD; Deborah A. Taira, ScD; and John Chen, PhD
Physician Satisfaction With Health Plans: Results From a National Survey
Natasha Parekh, MD, MS; Sheryl Savage; Amy Helwig, MD, MS; Patrick Alger, BS; Ilinca D. Metes, BS; Sandra McAnallen, MA, BSN; and William H. Shrank, MD, MSHS
Evaluation of Interdisciplinary Geriatric Transitions of Care on Readmission Rates
Nada M. Farhat, PharmD; Sarah E. Vordenberg, PharmD, MPH; Vincent D. Marshall, MS; Theodore T. Suh, MD, PhD, MHS; and Tami L. Remington, PharmD

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.
Network Analysis

The network of patients and providers is bipartite, meaning that it consists of 2 separate sets of connections: patients who see the same physicians and physicians who care for the same patients. There are no connections between individuals within a set (patients to other patients or physicians to other physicians). This paper analyzes both sets of relationships. The network structures are summarized using 5 metrics: components, degree, cliques, communities, and betweenness centrality.

Components are fully connected structures; everyone in a component has 1 or more direct or indirect paths to everyone else. Components are often large. Components are not connected to one another.

An individual’s degree is their number of direct connections to others in the network. As an example, for a physician, degree is the number of patients they treat. Patients have 2 sets of degrees: 1 from their connections to all other patients seeing the same physicians and 1 based on connections to patients having the same primary physician.

Cliques are based on direct links between individuals; they are maximal subgraphs in the sense that a clique is not a subset of a larger clique and every pair in a clique is connected. Cliques are typically smaller than components because they require direct connections within the clique. A person can belong to multiple cliques. For the analyses, we selected the largest clique to which a person belonged.

Communities are identified by algorithms that divide a network into groups that are more densely connected internally than to other groups. Communities can range widely in size but are mutually exclusive: A person belongs to a single community. We used an algorithm called “fast greedy” to find patient and physician communities.15 The fast greedy algorithm maximizes a measure called modularity that assesses the strength of the community networks. Modularity ranges from –0.5 to 1; if positive, connections within communities exceed that expected by chance.

Betweenness centrality is a measure of how central an individual is in a network, or to what extent the shortest connections between others in a network pass through them.

Network analyses were performed with the igraph and statnet packages within R version 3.4.2.16,17

Statistical Analysis

Characteristics of the patients were summarized by descriptive statistics. The distributions of the network metrics were skewed and so are summarized by medians with 25th and 75th percentiles. Differences between Oahu and its neighboring islands were compared using the Wilcoxon rank sum test. Regression analyses employed an exponential random graph model (ERGM).18 The outcome for the ERGMs was patients seeing the same primary physician, defined as the physician seen the most often or, in case of ties, most recently. ERGMs offer flexibility in incorporating regression predictors. Terms can be entered as comparisons (eg, are patients with CAD connected more often than those without CAD?) or as homophily, meaning comparisons based on similarities (eg, are Japanese patients more often linked to other Japanese patients?). Results are presented as odds ratios (ORs) with 95% CIs.

The University of Hawaii Institutional Review Board granted the study exempt status.

RESULTS

The study includes 41,941 patients with diabetes and the 1003 doctors who treated them. A little less than half of patients were 65 years or older, and 71.4% resided on Oahu, the most urban island in Hawaii (Table 1). The percentages of men and women were about equal. The most common ethnicity was Japanese (46.7%), followed by Native Hawaiian (16.3%), Filipino (14.2%), other ethnicities (11.6%), and white (11.2%). In terms of comorbidities, 22.2% of the patients had CAD, 9.2% had CHF, and 5.7% had CKD. The mean (SD) numbers of visits to primary care physicians and specialists were 1.27 (0.87) and 0.27 (0.76), respectively.

A primary aim of our study was to illustrate structures that can be identified in healthcare networks by examining networks in Hawaii; several network structures are illustrated in Figures 1 and 2. Both patient and physician networks were highly interconnected on the major Hawaiian islands, forming “giant” components with more than 95% of the respective populations. Figure 1 presents the component of patients living on the island of Kauai. The percentages of patients in the largest components were 99.8%, 98.9%, 98.7%, and 98.2% for the islands of Oahu, Maui, Kauai, and Hawaii, respectively. For physicians, the percentages in the largest components were 94.2%, 80.8%, 78.2%, and 77.7%, respectively.

Figure 2 provides examples of a clique and a community, which are smaller structures based on links between patients. The left panel illustrates that a clique is a maximally connected subgraph. The people in the clique are all linked to one another. The community on the right provides a contrast to the clique. Communities are identified based on the similarity of connections; not everyone in a community is necessarily connected to all the others. Modularity offers a measure of the extent to which the populations form communities on the Hawaii islands. Modularity was 0.61, 0.46, 0.59, and 0.56 for patients on the islands of Oahu, Maui, Kauai, and Hawaii, respectively. For physicians, modularity was 0.47, 0.34, 0.36, and 0.48, respectively.

Oahu and the neighboring islands were also compared on network measures based on individual patient and physician connections (Table 2). Communities of patients included a median of 150 to 177 patients, and communities of physicians included 3 to 8 physicians. Communities of patients and physicians were larger, on average, than the respective cliques. The median degree (ie, the number of links) between patients seeing the same physicians was greater on Oahu than on neighboring islands (195 vs 175), as was the median number of patients seeing the same primary physician (143 vs 126). The centrality of physicians was highly skewed toward high values and substantially greater on Oahu than on neighboring islands.

Patients seeing the same primary physicians were analyzed in greater detail using an ERGM (Table 3). Patients of the same ethnicity were more likely to share primary physicians. The ORs for sharing a primary physician with patients of the same ethnicity versus patients of other ethnicities ranged from 1.26 for Native Hawaiians to 1.82 for Japanese. Other tests for network links based on having similar characteristics were statistically significant, but the ORs were smaller. Patients 65 years or older were more likely to see the same primary physicians, and men and women tended to share physicians with others of the same sex. The more physicians the patients saw, the less often they shared the same primary physician. Patients with CAD and CKD were more likely to see the same physicians than those without the conditions. Residents of neighboring islands less often shared the same primary care physicians than residents of Oahu.


 
Copyright AJMC 2006-2019 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
x
Welcome the the new and improved AJMC.com, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up