Patient-sharing Network Research Articles

Exploring the impact of different multi-level measures of physician communities in patient-centric care networks on healthcare outcomes: A multi-level regression approach.

A patient-centric care network can be defined as a network among a group of healthcare professionals who provide treatments to common patients. Various multi-level attributes of the members of this network have substantial influence to its perceived level of performance. In order to assess the impact different multi-level attributes of patient-centric care networks on healthcare outcomes, this study first captured patient-centric care networks for 85 hospitals using health insurance claim dataset. From these networks, this study then constructed physician collaboration networks based on the concept of patient-sharing network among physicians. A multi-level regression model was then developed to explore the impact of different attributes that are organised at two levels on hospitalisation cost and hospital length of stay. For Level-1 model, the average visit per physician significantly predicted both hospitalisation cost and hospital length of stay. The number of different physicians significantly predicted only the hospitalisation cost, which has significantly been moderated by age, gender and Comorbidity score of patients. All Level-1 findings showed significance variance across physician collaboration networks having different community structure and density. These findings could be utilised as a reflective measure by healthcare decision makers. Moreover, healthcare managers could consider them in developing effective healthcare environments.

Provider Patient-Sharing Networks and Multiple-Provider Prescribing of Benzodiazepines

Prescription benzodiazepine overdose continues to cause significant morbidity and mortality in the US. Multiple-provider prescribing, due to either fragmented care or "doctor-shopping," contributes to the problem. To elucidate the effect of provider professional relationships on multiple-provider prescribing of benzodiazepines, using social network analytics. A retrospective analysis of commercial healthcare claims spanning the years 2008 through 2011. Provider patient-sharing networks were modelled using social network analytics. Care team cohesion was measured using care density, defined as the ratio between the total number of patients shared by provider pairs within a patient's care team and the total number of provider pairs in the care team. Relationships within provider pairs were further quantified using a range of network metrics, including the number and proportion of patients or collaborators shared. The relationship between patient-sharing network metrics and the likelihood of multiple prescribing of benzodiazepines. Patients between the ages of 18 and 64 years who received two or more benzodiazepine prescriptions from multiple providers, with overlapping coverage of more than 14days. A total of 5659 patients and 1448 provider pairs were included in our study. Among these, 1028 patients (18.2%) received multiple prescriptions of benzodiazepines, involving 445 provider pairs (30.7%). Patients whose providers rarely shared patients had a higher risk of being prescribed overlapping benzodiazepines; the median care density was 8.1 for patients who were prescribed overlapping benzodiazepines and 10.1 for those who were not (p < 0.0001). Provider pairs who shared a greater number of patients and collaborators were less likely to co-prescribe overlapping benzodiazepines. Our findings demonstrate the importance of care team cohesion in addressing multiple-provider prescribing of controlled substances. Furthermore, we illustrate the potential of the provider network as a surveillance tool to detect and prevent adverse events that could arise due to fragmentation of care.

Social Network Analysis of Patient Sharing Among Hospitals in Orange County, California

We applied social network analyses to determine how hospitals within Orange County, California, are interconnected by patient sharing, a system which may have numerous public health implications. Our analyses considered 2 general patient-sharing networks: uninterrupted patient sharing (UPS; i.e., direct interhospital transfers) and total patient sharing (TPS; i.e., all interhospital patient sharing, including patients with intervening nonhospital stays). We considered these networks at 3 thresholds of patient sharing: at least 1, at least 10, and at least 100 patients shared. Geographically proximate hospitals were somewhat more likely to share patients, but many hospitals shared patients with distant hospitals. Number of patient admissions and percentage of cancer patients were associated with greater connectivity across the system. The TPS network revealed numerous connections not seen in the UPS network, meaning that direct transfers only accounted for a fraction of total patient sharing. Our analysis demonstrated that Orange County's 32 hospitals were highly and heterogeneously interconnected by patient sharing. Different hospital populations had different levels of influence over the patient-sharing network.