Comparison of physician networks constructed from thresholded ties versus shared clinical episodes

Identifying opioid agonist treatment prescriber networks from health administrative data: A validation study

BACKGROUND

Given the growth of collaborative care strategies for people with opioid use disorder and the changing composition of the illicit drug supply, there is a need to identify and analyze clinic-level outcomes for centers prescribing opioid agonist treatment (OAT). We aimed to determine and validate whether prescriber networks, constructed with administrative data, can successfully identify distinct clinical practice facilities in Ontario, Canada.

METHODS

We executed a retrospective population-based cohort study using OAT prescription records from the Canadian Addiction Treatment Centres in Ontario, Canada between 01/01/2013 and 12/31/2020. Social network analysis was utilized to create networks with connections between physicians based on their shared OAT clients. We defined connections two different ways, by including the number of clients shared or a relative threshold on the percentage of shared OAT clients per physician. Clinics were identified using modularity maximization, with sensitivity analyses applying Louvain, Walktrap, and Label Propagation algorithms. Concordance between network-identified facilities and the (gold standard) de-identified facility-level IDs was assessed using overall, positive and negative agreement, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

RESULTS

From 144 physicians at 105 clinics with 32,842 OAT clients, we assessed 250 different versions of the created networks. The three different detection algorithms had wide variation in concordance, with ranges on sensitivity from 0.02 to 0.88 and PPV from 0.06 to 0.97. The optimal result, derived from the modularity maximization method, achieved high specificity (0.98, 95% CI: 0.98, 0.98) and NPV (0.98, 95% CI: 0.97, 0.98) and moderate PPV (0.54, 95% CI: 0.52, 0.57) and sensitivity (0.45, 95% CI: 0.43, 0.47). This scenario had an overall agreement of 0.96, negative agreement of 0.98, and positive agreement of 0.49.

CONCLUSIONS

Social network analysis can be used to identify clinics prescribing OAT in the absence of clinic-level identifiers, thus facilitating construction and comparison of clinic-level caseloads and treatment outcomes.

Considerations for Social Networks and Health Data Sharing: An Overview

The use of network analysis as a tool has increased exponentially as more clinical researchers see the benefits of network data for modeling of infectious disease transmission or translational activities in a variety of areas, including patient-caregiving teams, provider networks, patient-support networks, and adoption of health behaviors or treatments, to name a few. Yet, relational data such as network data carry a higher risk of deductive disclosure. Cases of reidentification have occurred and this is expected to become more common as computational ability increases. Recent data sharing policies aim to promote reproducibility, support replicability, and protect federal investment in the effort to collect these research data by making them available for secondary analyses. However, typical practices to protect individual-level clinical research data may not be sufficiently protective of participant privacy in the case of network data, nor in some cases do they permit secondary data analysis. When sharing data, researchers must balance security, accessibility, reproducibility, and adaptability (suitability for secondary analyses). Here, we provide background about applying network analysis to health and clinical research, describe the pros and cons of applying typical practices for sharing clinical data to network data, and provide recommendations for sharing network data.

Can peer effects explain prescribing appropriateness? a social network analysis

BACKGROUND

Optimizing prescribing practices is important due to the substantial clinical and financial costs of polypharmacy and an increasingly aging population. Prior research shows the importance of social relationships in driving prescribing behaviour. Using social network analysis, we examine the relationship between a physician practices' connectedness to peers and their prescribing performance in two German regions.

METHODS

We first mapped physician practice networks using links established between two practices that share 8 or more patients; we calculated network-level (density, average path length) and node-level measures (degree, betweenness, eigenvector). We defined prescribing performance as the total number of inappropriate medications prescribed or appropriate medications not prescribed (PIMs) to senior patients (over the age of 65) during the calendar year 2016. We used FORTA (Fit fOR The Aged) algorithm to classify medication appropriateness. Negative binomial regression models estimate the association between node-level measures and prescribing performance of physician practices controlling for patient comorbidity, provider specialization, percentage of seniors in practice, and region. We conducted two sensitivity analyses to test the robustness of our findings - i) limiting the network mapping to patients younger than 65; ii) limiting the network ties to practices that share more than 25 patients.

RESULTS

We mapped two patient-sharing networks including 436 and 270 physician practices involving 28,508 and 20,935 patients and consisting of 217,126 and 154,274 claims in the two regions respectively. Regression analyses showed a practice's network connectedness as represented by degree, betweenness, and eigenvector centrality, is significantly negatively associated with prescribing performance (degree-bottom vs. top quartile aRR = 0.04, 95%CI: 0.035,0.045; betweenness-bottom vs. top quartile aRR = 0.063 95%CI: 0.052,0.077; eigenvector-bottom vs. top quartile aRR = 0.039, 95%CI: 0.034,0.044).

CONCLUSIONS

Our study provides evidence that physician practice prescribing performance is associated with their peer connections and position within their network. We conclude that practices occupying strategic positions at the edge of networks with advantageous access to novel information are associated with better prescribing outcomes, whereas highly connected practices embedded in insulated information environments are associated with poor prescribing performance.

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.

Care fragmentation predicts 90-day durable ventricular assist device outcomes

OBJECTIVES

To examine whether fragmentation of care is associated with worse in-hospital and 90-day outcomes following durable ventricular assist device (VAD) implant.

STUDY DESIGN

Cohort study.

METHODS

This study was conducted using Medicare claims linked to the Society of Thoracic Surgeons (STS) Interagency Registry for Mechanically Assisted Circulatory Support (Intermacs) among patients undergoing VAD implant between July 2009 and April 2017. Medicare data were used to measure fragmentation of the multidisciplinary care delivery network for the treating hospital, based on providers' history of shared patients within the previous year. STS Intermacs data were used for risk adjustment and outcomes ascertainment. Hospitals were sorted into terciles based on the degree of network fragmentation, measured as the mean number of links separating providers in the network. Multivariable regression was used to associate network fragmentation with 90-day death or infection risk.

RESULTS

The cohort included 5159 patients who underwent VAD implant, with 11.2% dying and 27.6% experiencing an infection within 90 days after implant. After adjustment, a 1-unit increase in network fragmentation was associated with an increase of 0.179 in the probability of in-hospital infection and an increase of 0.183 in the probability of 90-day infection (both P < .05). Similar results were observed in models of the numbers of in-hospital and 90-day infections. Network fragmentation was predictive of the probability of 90-day mortality, although this relationship was not significant after adjustment.

CONCLUSIONS

Care delivery network fragmentation is associated with higher in-hospital and 90-day infection rates following durable VAD implant. These networks may serve as novel targets for enhancing outcomes for patients undergoing VAD implant.

Beyond patient-sharing: Comparing physician- and patient-induced networks

The sharing of patients reflects collaborative relationships between various healthcare providers. Patient-sharing in the outpatient sector is influenced by both physicians' activities and patients' preferences. Consequently, a patient-sharing network arises from two distinct mechanisms: the initiative of the physicians on the one hand, and that of the patients on the other. We draw upon medical claims data to study the structure of one patient-sharing network by differentiating between these two mechanisms. Owing to the institutional requirements of certain healthcare systems rather following the Bismarck model, we explore different triadic patterns between general practitioners and medical specialists by applying exponential random graph models. Our findings imply deviation from institutional expectations and reveal structural realities visible in both networks.

Embracing the Complexity of Primary Health Care: System-Based Tools and Strategies for Researching the Case Management Process

The provision of health care is frequently a complex process, and favourable clinical outcomes are dependent on the effective management of this complexity. Contemporary medicine and health care practices that are biomedically aligned have been informed by a reductionist paradigm, potentially creating a misalignment between health care and the human organism as a complex adaptive system. Complexity science is increasingly gaining momentum within the academic literature and is being employed across a wide range of scientific disciplines, although this is less evident in medicine. Limited evidence was found within the literature of a complexity science framework being used to explore and inform individual health care practices; in this paper, this gap will be explored through consideration of the use of strategies and tools (specifically mind maps, computer-generated network mappings, exploratory data analysis, and computer-derived network analysis) which are congruent with a complexity science framework. This information may be useful to researchers investigating health care provision and to clinicians wishing to incorporate a complexity sensibility within their practice.

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Tie Decay and Dissolution: Contentious Prescribing Practices in the Prescription Drug Epidemic

Although a substantial body of work has investigated drivers of tie formation, there is growing interest in understanding why relationships decay or dissolve altogether. The networks literature has tended to conceptualize tie decay as driven by processes similar to those underlying tie formation. Yet information that is revealed through ongoing interactions can exert different effects on tie formation and tie decay. This paper investigates how tie decay and tie formation processes differ by focusing on contentious practices. To the extent that information about dissimilarities in contentious practices is learned through ongoing interactions, it can exert diverging effects on tie formation and tie decay. Using a longitudinal data set of 141,543 physician dyads, we find that differences in contentious prescribing led ties to weaken or dissolve altogether but did not affect tie formation. The more contentious the practice and the more information available about the practice, the stronger the effect on tie decay and dissolution. Collectively, these findings contribute to a more nuanced understanding of relationship evolution as an unfolding process through which deeper-level differences are revealed and shape the outcome of the tie.

The Role of Physician Professional Networks in Physicians' Receipt of Pharmaceutical and Medical Device Industries' Payments

BACKGROUND

Financial relationships between physicians and the pharmaceutical and medical device industries are common, but the factors associated with physicians receiving payments are unknown.

OBJECTIVE

The objective of this study is to evaluate the influence of physicians' professional networks' characteristics on the receipt of payments among physicians.

DESIGN

Network analysis of cross-sectional data PARTICIPANTS: US physicians who shared Medicare patients with other physicians in 2015 (N=357,813).

EXPOSURE (INTERVENTION)

Proportion of a physician's professional network that received industry payments and other network characteristics including number of physician connections, how central the physician is within the network, and the tightness of the referral network in which a physician is located.

MAIN OUTCOME MEASURES

Relative risk of receiving industry payments. We used modified Poisson regression to control for confounding by gender, time since graduation, practice size, and practice setting (teaching hospital vs. not). We included dummy variables for specialty and hospital referral region level.

KEY RESULTS

The proportion of a physician's peers in their professional network that received payments was strongly associated with receipt of pharmaceutical or device industry payments by the physician (top vs bottom quartile aRR=1.28, 95%CI=1.25-1.31). Physician's centrality within a network had a small positive effect on receiving payment (top vs bottom quartile aRR=1.02, 95%CI=1.01-1.04). Network density also had a small negative association with receipt of payment (top vs bottom quartile aRR=0.97, 95%CI=0.96-0.98).

CONCLUSIONS

Network characteristics, particularly the receipt of payments among physicians one shares patients with, are associated with whether a physician receives payments. This finding has implications for institutional regulation of industry payments to physicians and demonstrates how institutional policy may impact not only the physicians within the institution but also physicians outside of the institution.

Assessment of Racial Disparities in Primary Care Physician Specialty Referrals

IMPORTANCE

Disparities in quality of care according to patient race and socioeconomic status persist in the US. Differential referral patterns to specialist physicians might be associated with observed disparities.

OBJECTIVE

To examine whether differences exist between Black and White Medicare beneficiaries in the observed patterns of patient sharing between primary care physicians (PCPs) and physicians in the 6 specialties to which patients were most frequently referred.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional observational study of Black and White Medicare beneficiaries used claims data from 2009 to 2010 on 100% of traditional Medicare beneficiaries who were seen by PCPs and selected high-volume specialists in 12 health care markets with at least 10% of the population being Black. Statistical analyses were conducted from December 20, 2017, to September 30, 2020.

EXPOSURES

Differences in patterns of patient sharing among Black and White patients.

MAIN OUTCOMES AND MEASURES

Primary care physician and specialist degree (the number of other PCPs or specialists to whom each physician is connected) and strength (the number of shared patients per connection, overall, for Black patients and White patients and after equalizing the numbers of Black and White patients per PCP), as well as distance between PCP and patient and specialist zip code centroids.

RESULTS

The 12 selected markets ranged in size from Manhattan, New York (187 054 Black or White beneficiaries seen by at least 2 physicians within an episode of care; 9794 total physicians), to Tallahassee, Florida (44 644 Black or White beneficiaries seen by at least 2 physicians within an episode of care; 847 total physicians). The percentage of Black beneficiaries ranged from 11.5% (Huntsville, Alabama) to 46.8% (Chicago, Illinois). The mean PCP-specialist degree (number of specialists with whom a PCP shares patients) was lower for Black patients than for White patients. For instance, the mean PCP-cardiologist degree across all markets for White patients was 17.5 compared with 8.8 for Black patients. After sampling White patients to equalize the numbers of patients seen, the degree differences narrowed but were still not equivalent in many markets (eg, for all specialties in Baton Rouge, Louisiana: 4.5 for Black patients vs 5.7 for White patients). Specialist networks among White patients were much larger than those constructed based just on Black patients (eg, for cardiology across all markets: 135 for Black patients vs 330 for White patients), even after equalizing the numbers of patients seen per PCP (123 for Black patients vs 211 for White patients). The overall test for differences in referral patterns was statistically significant for all 6 specialties examined in 7 of the 12 markets and in 5 specialties for another 3.

CONCLUSIONS AND RELEVANCE

This study suggests that differences exist in specialist referral patterns by race among Medicare beneficiaries. This is an observational study, and thus some differences might have resulted from patient-initiated visits to specialists.

The impact of sampling patients on measuring physician patient-sharing networks using Medicare data

OBJECTIVE

To investigate the impact of sampling patients on descriptive characteristics of physician patient-sharing networks.

DATA SOURCES

Medicare claims data from 10 hospital referral regions (HRRs) in the United States in 2010.

STUDY DESIGN

We form a sampling frame consisting of the full cohort of patients (Medicare enrollees) with claims in the 2010 calendar year from the selected HRRs. For each sampling fraction, we form samples of patients from which a physician ("patient-sharing") network is constructed in which an edge between two physicians depicts that at least one patient in the sample encountered both of those physicians. The network is summarized using 18 network measures. For each network measure and sampling fraction, we compare the values determined from the sample and the full cohort of patients. Finally, we assess the sampling fraction that is needed to measure each network measure to specified levels of accuracy.

DATA COLLECTION/EXTRACTION METHODS

We utilized administrative claims from the traditional (fee-for-service) Medicare.

PRINCIPAL FINDINGS

We found that measures of physician degree (the number of ties to other physicians) in the network and physician centrality (importance or prominence in the network) are learned quickly in the sense that a small sampling fraction suffices to accurately compute the measure. At the network level, network density (the proportion of possible edges that are present) was learned quickly while measures based on more complex configurations (subnetworks involving multiple actors) are learned relatively slowly with relative rates of learning depending on network size (the number of nodes).

CONCLUSIONS

The sampling fraction applied to Medicare patients has a highly heterogeneous effect across different network measures on the extent to which sample-based network measures resemble those evaluated using the full cohort. Even random sampling of patients may yield physician networks that distort descriptive features of the network based on the full cohort, potentially resulting in biased results.

Association of Physician Peer Influence With Subsequent Physician Adoption and Use of Bevacizumab

IMPORTANCE

Understanding adoption of new cancer therapies may help identify opportunities to increase use for high-value indications.

OBJECTIVE

To determine whether use of bevacizumab in 2005 to 2006 by oncologists' peers was associated with greater bevacizumab use among oncologists in 2007 to 2010.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study of physicians and their patients took place in 51 randomly selected hospital referral regions in the United States. Participants were 44 012 fee-for-service Medicare beneficiaries aged 65 years or older with cancers of the colorectum, lung, breast, kidney, brain, or ovary treated by 3261 oncologists in 2005 to 2010 and assigned to one of 252 communities. Data were analyzed in 2017 to 2018.

EXPOSURES

Among patients treated with chemotherapy during 2007 to 2010 by an oncologist who had not treated patients with bevacizumab in 2005 to 2006, models assessed the association of bevacizumab use with rates of bevacizumab use in their physician's community of connected physicians in 2005 to 2006. Models adjusted for patient and physician characteristics and physician, practice, and community random effects.

MAIN OUTCOMES AND MEASURES

Receipt of bevacizumab.

RESULTS

A total of 34 750 patients (14 126 [40.6%] aged ≥75 years; 21 321 [61.4%] female) with cancers of the colorectum, lung, breast, kidney, brain, and ovary were treated with chemotherapy in 2005 to 2006 in the 51 hospital referral regions. Among 9262 patients treated in 2007 to 2010 by 829 physicians whose patients did not use bevacizumab in 2005 to 2006, 3654 (39.5%) were aged 75 years or older and 6227 (67.2%) were female. The rate of bevacizumab use relative to other chemotherapy in 2007 to 2010 by tertile of use (bevacizumab for <4.4%, 4.4%-6.2%, and >6.2% of all patients receiving chemotherapy) among their physician's peers in 2005 to 2006 was 10.0%, 9.5%, and 13.6%, respectively. After adjustment, use of bevacizumab in 2007 to 2010 was greater among physicians in communities with the highest rates of bevacizumab use in 2005 to 2006 compared with those whose peers were in the lowest tertile of bevacizumab use in 2005 to 2006 (adjusted odds ratio, 1.64; 95% CI, 1.20-2.25).

CONCLUSIONS AND RELEVANCE

This study found that an increase in oncologists' adoption and use of bevacizumab in the years after its approval was associated with their peer physicians being earlier adopters. As organizations seek to provide better care at lower costs, interventions that leverage physician ties may help to promote adoption of high-value use of new cancer treatments and deimplementation of low-value therapies.

Investigating Coordination of Hospital Departments in Delivering Healthcare for Acute Coronary Syndrome Patients Using Data-Driven Network Analysis

Healthcare systems are challenged to deliver high-quality and efficient care. Studying patient flow in a hospital is particularly fundamental as it demonstrates effectiveness and efficiency of a hospital. Since hospital is a collection of physically nearby services under one administration, its performance and outcome are shaped by the interaction of its discrete components. Coordination of processes at different levels of organizational structure of a hospital can be studied using network analysis. Hence, this article presents a data-driven static and temporal network of departments. Both networks are directed and weighted and constructed using seven years’ (2010–2016) empirical data of 24902 Acute Coronary Syndrome (ACS) patients. The ties reflect an episode-based transfer of ACS patients from department to department in a hospital. The weight represents the number of patients transferred among departments. As a result, the underlying structure of network of departments that deliver healthcare for ACS patients is described, the main departments and their role in the diagnosis and treatment process of ACS patients are identified, the role of departments over seven years is analyzed and communities of departments are discovered. The results of this study may help hospital administration to effectively organize and manage the coordination of departments based on their significance, strategic positioning and role in the diagnosis and treatment process which, in-turn, nurtures value-based and precision healthcare.

Influence of Peer Physicians on Intensity of End-of-Life Care for Cancer Decedents

BACKGROUND

The intensity of end-of-life care varies substantially both within and between areas. Differing practice patterns of individual physicians are likely influenced by their peers.

OBJECTIVE

To assess whether intensity of end-of-life care previously provided by a physician's peers influences patterns of care at the end-of-life for that physician's patients.

RESEARCH DESIGN

Observational study.

SUBJECTS

A total of 185,947 fee-for-service Medicare enrollees with cancer who died during 2006-2010 who were treated by 26,383 physicians.

MEASURES

Spending in the last month of life, >1 emergency room visit, >1 hospitalization, intensive care unit admission in the last month of life, chemotherapy within 2 weeks of death, no/late hospice, terminal hospitalization.

RESULTS

Mean (SD) spending in the last month of life was $16,237 ($17,124). For each additional $1000 of spending for a peer physician's patients in the prior year, spending for the ego physician's patients was $83 higher (P<0.001). Among physicians with peers both in and out of their practice, more of the peer effect was explained by physicians outside of the practice ($72 increase for each $1000 increase by peer physicians' patients, P<0.001) than peer physicians in the practice ($27 for each $1000 increase by within-practice peer physicians' patients, P=0.01). Results were similar across the other measures of end-of-life care intensity.

CONCLUSIONS

Physician's peers exert influence on the intensity of care delivered to that physician's patients at the end-of-life. Physician education efforts led by influential providers and provider organizations may have potential to improve the delivery of high-value end-of-life care.