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Healthcare Network Analysis of Patients With Diabetes and Their Physicians
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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.
ABSTRACT

Objectives: To illustrate methods using administrative data on patients with diabetes that can offer a foundation for using network analyses in managed care.

Study Design: The study used an administrative claims database to analyze patients with diabetes in a large health plan in Hawaii in 2010.

Methods: The networks were explored graphically and analyzed at several levels of complexity. Levels ranged from major components comprising the majority in the networks to smaller, highly connected cliques to communities of patients and physicians grouped by a network algorithm. The attributes of patients linked by seeing the same primary physicians were evaluated using an exponential random graph model that predicted links in the network.

Results: The study included 41,941 patients with diabetes of Native Hawaiian (16.3%), Filipino (14.2%), Japanese (46.7%), white (11.2%), and other (11.6%) ethnicity. About half were 65 years or older. When examined by Hawaiian island of residence, at least 95% of patients and at least 78% of physicians belonged to loosely connected major components within a network. Smaller communities of patients, identified by being closely linked together, averaged 150 to 177 patients; communities of physicians averaged 3 to 8 physicians. The average numbers of patients sharing physicians and physicians sharing patients were greater on the island of Oahu than on the rural neighboring islands. Patients of the same ethnicity were significantly more likely to share the same primary physician.

Conclusions: Network analyses reveal structures and links that health plans could leverage to strengthen quality improvement and disease management programs.

Am J Manag Care. 2019;25(7):e192-e197
Takeaway Points

Network analysis of administrative data can reveal hidden structures—clusters of patients and physicians—that offer targets for interventions. Our study of patients with diabetes in Hawaii highlights the strengths and flexibility of network analysis for managed care. Analyzing the structure of local networks can lead to enhanced strategies for disease management to improve health quality and outcomes.
  • Network analyses can identify patients sharing doctors and doctors sharing patients, and they can uncover factors associated with network ties.
  • Network analysis can be done with free, open-source software.
  • Understanding patient links in administrative data could lead to more patient-centered care.
Network analyses examine the structure of human connections, such as those between friends at school, workers in jobs, and individuals on the internet, as well as inanimate connections, such as proteins at the cellular level. Healthcare networks have also attracted network analysts. Researchers have studied physicians sharing patients, patient satisfaction, healthcare teams, and networks of physicians providing hospital care.1-5 Network analysis offers a method to understand and manage healthcare. The analyses can reveal hidden structures that are distinct from formal structures, such as physician groups. The analyses can identify patients who might be best managed together and physicians who might lead in healthcare interventions. The results of network analyses can complement and extend more traditional healthcare analyses.

Administrative claims are a ready source of network analysis data. Patient links to physicians they share, as well as links between physicians caring for the same patients, define the networks. Physicians acknowledge that they share the patients found in administrative data, although recognition is higher among primary care physicians than among specialists.6 A national study of Medicare patients compared physician sharing across the networks of 528 hospitals7 and found that the higher the median number of links a physician had with other physicians, the higher the total costs and the number of hospital days. By contrast, the more centralized the network of primary care physicians, the fewer the specialist visits and the lower the spending on imaging and tests. A more recent study of physicians sharing patients for distinct episodes of care confirmed the findings.8 These studies revealed that healthcare networks can influence health outcomes in both positive and negative ways.

A network analysis of 85 hospitals caring for patients having hip replacement placed the physicians into distinct groups called communities based on strong interconnections.9 Hospitals averaged 4.25 communities in each physician collaboration network, and hospitals with more communities had lower readmission rates. Another study compared 2 hospital referral regions varying in the evidence-based use of cardioverter defibrillators (86% and 66%).10 Differences in network structure helped explain the differences in adherence to the clinical guidelines between the referral regions.

Other studies have analyzed claims data from private insurers or using electronic health records. A study of patients with congestive heart failure and diabetes developed a metric called “care density” that measures how often providers share patients with one another.11 For both chronic conditions, patients in the highest tertile of care density had significantly lower costs and reduced rates of hospitalizations compared with patients in the lowest tertile. A second study created a criterion called the “shared positive outcome ratio”12 and found that the patients of pairs of providers with greater ratios reported higher satisfaction with their care. A third study, however, provides a cautionary note: The study reported that among providers sharing patients, 54% shared only a single patient and just 19% shared 2.13 Patient sharing in healthcare may often not occur.

In this paper, we illustrate methods of network analysis by examining connections among patients with diabetes in Hawaii, the physicians they shared, and the physicians caring for the same patients. We describe how identifying structures of differing complexity can help a health plan understand the networks it manages. The analyses investigate direct links and broader communities, as well as examine demographic and other influences that help explain the connections. This article illustrates methods using administrative data on patients with diabetes that can offer a foundation for using network analyses in managed care.

METHODS

Study Population

The study population was 41,941 patients with diabetes who belonged to a large insurer in Hawaii in 2010.14 The diagnosis was based on criteria from the Healthcare Effectiveness Data and Information Set.

Study Variables

Patient characteristics included demographic variables, chronic diseases, and island of residence. Age was categorized as being either younger than 65 years or aged at least 65 years, and sex as male or female. Ethnicities were Native Hawaiian, Filipino, Japanese, white, and other ethnicity as self-reported on member satisfaction surveys.14 Residence was examined by island of residence (Oahu, Kauai, Maui, or Hawaii) and by comparing the most populous island of Oahu with the other, more rural neighboring islands. The major chronic diseases comorbid with diabetes were coronary artery disease (CAD), congestive heart failure (CHF), and chronic kidney disease (CKD). Physician visits were defined as visits to primary care providers (ie, internal medicine, general practice, family practice physicians) and to specialists (ie, cardiologists, endocrinologists).


 
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