Referral paths in the U.S. physician network

Optimal Physician Shared-Patient Networks and the Diffusion of Medical Technologies

Social network analysis has created a productive framework for the analysis of the histories of patient-physician interactions and physician collaboration. Notable is the construction of networks based on the data of "referral paths" - sequences of patient-specific temporally linked physician visits - in this case, culled from a large set of Medicare claims data in the United States. Network constructions depend on a range of choices regarding the underlying data. In this paper we introduce the use of a five-factor experiment that produces 80 distinct projections of the bipartite patient-physician mixing matrix to a unipartite physician network derived from the referral path data, which is further analyzed at the level of the 2,219 hospitals in the final analytic sample. We summarize the networks of physicians within a given hospital using a range of directed and undirected network features (quantities that summarize structural properties of the network such as its size, density, and reciprocity). The different projections and their underlying factors are evaluated in terms of the heterogeneity of the network features across the hospitals. We also evaluate the projections relative to their ability to improve the predictive accuracy of a model estimating a hospital's adoption of implantable cardiac defibrillators, a novel cardiac intervention. Because it optimizes the knowledge learned about the overall and interactive effects of the factors, we anticipate that the factorial design setting for network analysis may be useful more generally as a methodological advance in network analysis.

Patterns of Upper Extremity Reconstruction for Patients With Tetraplegia Across the United States: A Retrospective Study

PURPOSE

The rates of upper extremity reconstruction for patients with tetraplegia remain low. We performed a retrospective study to assess recent reconstruction rates and delineate factors associated with the occurrence of reconstruction.

METHODS

We examined the National Inpatient Sample database (2012-2017) for the rate of reconstruction for patients with tetraplegia. The details of provider distribution characteristics and neighborhood attributes were obtained from the American Medical Association Physician Masterfile and based on the area deprivation index, respectively. We calculated the mean reconstruction rate per year and generated multivariable logistic regression models to examine the influence of patient factors, hospital characteristics, and provider distribution on the odds of undergoing functional reconstruction for tetraplegia patients.

RESULTS

Among 404,660 encounters with patients with tetraplegia, only 1,430 (0.4%) patients underwent upper extremity reconstruction from 2012 to 2017, with a mean rate of 238 procedures per year. We identified 5,450 hand surgeons, 12,751 physiatrists, and 444 spinal cord injury specialists, with variation in their national distribution. A greater number of surgeons near SCIS was associated with increased probability of reconstruction (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.03-1.12). The odds of surgery were greater for patients receiving care at urban teaching (OR 5.00, 95% CI 3.35-7.47) or urban nonteaching (OR 1.71, 95% CI 1.11-2.63) hospitals, whereas those at private nonprofit (OR 0.67, 95% CI 0.58-0.78) or investor-owned (OR 0.65, 95% CI 0.52-0.82) hospitals had lower odds. Although most patients had insurance coverage, patients with a higher income or those who received subsidized care had greater odds of undergoing reconstruction.

CONCLUSIONS

Reconstruction rates remain low and are correlated with the environment of care, financial factors, and provider availability. Policies that focus on reducing these factors in addition to increasing interspecialty collaboration could improve access to surgery for patients with tetraplegia.

TYPE OF STUDY/LEVEL OF EVIDENCE

Prognostic I.

A Social Network Analysis Approach for Contact Tracing in the Hospital Setting

Since the beginning of the COVID-19 pandemic, the State of Delaware has implemented various strategies including a stay-at-home order, mask-wearing requirements in public places, and community-based testing to control the spread of the disease. Health systems across the U.S. have taken actions including symptom monitoring and screening for visitors and healthcare workers, providing personal protection equipment (PPE), and contact tracing of confirmed infected individuals to provide maximum possible protection for healthcare workers. Despite such efforts, there remains a significant risk of intra-hospital transmission of COVID-19. Healthcare workers who contact patients with COVID-19 or were exposed to the disease in the community may transmit the infection to coworkers in the inpatient setting. In addition to universal and case-based precautions to prevent exposure and disease transmission, contact tracing is essential to minimizing the impact of outbreaks among healthcare workers and the community. A rapid increase in cases can quickly diminish hospital infection control and prevention program capacity to perform high-quality contact tracing. This article will describe an approach using the application of social network analysis (SNA) and Electronic Medical Records (EMR) to enhance the current efforts in COVID-19 contact tracings.

Towards intelligent complex networks: the space and prediction of information walks

In this paper we study the problem of walk-specific information spread in directed complex social networks. Classical models usually analyze the "explosive" spread of information on social networks (e.g., Twitter) - a broadcast or epidemiological model focusing on the dynamics of a given source node "infecting" multiple targets. Less studied, but of equal importance is the case of single-track information flow, wherein the focus is on the node-by-node (and not necessarily a newly visited node) trajectory of information transfer. An important and motivating example is the sequence of physicians visited by a given patient over a presumed course of treatment or health event. This is the so-called a referral sequence which manifests as a path in a network of physicians. In this case the patient (and her health record) is a source of "information" from one physician to the next. With this motivation in mind we build a Bayesian Personalized Ranking (BPR) model to predict the next node on a walk of a given network navigator using network science features. The problem is related to but different from the well-investigated link prediction problem. We present experiments on a dataset of several million nodes derived from several years of U.S. patient referral records, showing that the application of network science measures in the BPR framework boosts hit-rate and mean percentile rank for the task of next-node prediction. We then move beyond the simple information walk to consider the derived network space of all information walks within a period, in which a node represents an information walk and two information walks are connected if have nodes in common from the original (social) network. To evaluate the utility of such a network of information walks, we simulate outliers of information walks and distinguish them with the other normal information walks, using five distance metrics for the derived feature vectors between two information walks. The experimental results of such a proof-of-concept application shows the utility of the derived information walk network for the outlier monitoring of information flow on an intelligent network.