Data-driven modeling of diabetes care teams using social network analysis.

OBJECTIVE: We assess working relationships and collaborations within and between diabetes health care provider teams using social network analysis and a multi-scale community detection. MATERIALS AND METHODS: Retrospective analysis of claims data from a large employer over 2 years was performed. The study cohort contained 827 patients diagnosed with diabetes. The cohort received care from 2567 and 2541 health care providers in the first and second year, respectively. Social network analysis was used to identify networks of health care providers involved in the care of patients with diabetes. A multi-scale community detection was applied to the network to identify groups of health care providers more densely connected. Social network analysis metrics identified influential providers for the overall network and for each community of providers. RESULTS: Centrality measures identified medical laboratories and mail-order pharmacies as the central providers for the 2 years. Seventy-six percent of the detected communities included primary care physicians, and 97% of the communities included specialists. Pharmacists were detected as central providers in 24% of the communities. DISCUSSION: Social network analysis measures identified the central providers in the network of diabetes health care providers. These providers could be considered as influencers in the network that could enhance the implication of promotion programs through their access to a large number of patients and providers. CONCLUSION: The proposed framework provides multi-scale metrics for assessing care team relationships. These metrics can be used by implementation experts to identify influential providers for care interventions and by health service researchers to determine impact of team relationships on patient outcomes.

Identifying Key Players in the Care Process of Patients with Diabetes Using Social Network Analysis and Administrative Data.

Determining networks of healthcare providers quantitatively can identify impactful care processes that improve health outcomes for a high-risk populations such as elderly people with multiple chronic conditions. By applying social network analysis to health claim data of a large university in the Midwest, we measured healthcare provider networks of patients with diabetes for two consecutive years. Networks were built based on the assumption that having common patients may indicate potential working relationships between providers. Measures of the social network analysis including degree and betweenness centrality were utilized to identify healthcare providers with an important role in the care process. Both degree and betweenness centrality measures identified a supply center and three laboratories as the central providers of the network for both years. This study can positively impact informed decision-making of policymakers and insurance companies to better design their insurance coverage plans based on the collaboration patterns of the healthcare providers.

Impact of an Onsite Clinic on Utilization of Preventive Services.

OBJECTIVE: To assess impact of an onsite clinic on healthcare utilization of preventive services for employees of a public university and their dependents. METHOD: Descriptive statistics, logistic regression and classification tree techniques were used to assess health claim data to identify changes in patterns of healthcare utilization and factors impacting usage of onsite clinic. RESULT: Utilization of preventive services significantly increased for women and men employees by 9% and 14% one year after implementation of the onsite clinic. Hourly-paid employees, employees without diabetes, employees with spouse opt out or no coverage were more likely to go to the onsite clinic. CONCLUSION: Adapted framework for assessing performance of onsite clinics based on usage of health informatics would help to identify health utilization patterns and interaction between onsite clinic and offsite health providers.