Physician patient-sharing networks and the cost and intensity of care in US hospitals

A Scoping Review of Multilevel Patient-Sharing Network Measures in Health Services Research

Social network analysis is the study of the structure of relationships between social entities. Access to health care administrative datasets has facilitated use of "patient-sharing networks" to infer relationships between health care providers based on the extent to which they have encounters with common patients. The structure and nature of patient-sharing relationships can reflect observed or latent aspects of health care delivery systems, such as collaboration and influence. We conducted a scoping review of peer-reviewed studies that derived patient-sharing network measure(s) in the analyses. There were 134 papers included in the full-text review. We identified and created a centralized resource of 118 measures and uncovered three major themes captured by them: Influential and Key Players, Care Coordination and Teamwork, and Network Structure and Access to Care. Researchers may use this review to inform their use of patient-sharing network measures and to guide the development of novel measures.

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.

Rural-Urban Differences in Patient-Sharing by Clinicians Caring for Children with Medical Complexity: Network Analysis of the Pediatric Workforce in Three States

OBJECTIVE

To examine rural-urban differences in the clinician workforce caring for children with medical complexity (CMC) in ambulatory settings and to determine how measures of clinician patient-sharing differ for rural- and urban-residing CMC.

STUDY DESIGN

We analyzed Massachusetts, New Hampshire, and Colorado all-payer claims data from 2012 through 2017 to identify CMC and their clinicians. We assembled patient-sharing networks in which clinicians were connected on the basis of having encounters with common pediatric patients. We evaluated rural-urban differences in CMC care team size, composition (ie, which specialists were included), and care density (ie, extent to which a CMC's care team shared patients) and network measures of primary care clinician (PCC) centrality (eg, number of patient-sharing relationships).

RESULTS

Analysis included 107 692 CMC, of whom 7065 (7.0%) were rural-residing and 100 627 (93.0%) were urban-residing. Rural-residing CMC had lower relative risk of having a PCC specialized in pediatrics (relative risk 0.81, 95% CI 0.73-0.89) compared with urban-residing CMC. Despite having similarly sized care teams, rural-residing CMC had substantially lower care density (median [IQR] = 21.6 [7.9, 72.8]) compared with urban-residing CMC (median [IQR] = 48.3 [13.0, 158.7]). Rural PCC were less central in the networks and had a greater percentage of connections with advanced practice providers compared with their urban counterparts (median [IQR] = 19.2 [14.3, 24.5] vs 14.3 [7.5, 23.4]).

CONCLUSIONS

Our study provides new insight into the clinicians and teams caring for CMC. Rural-residing CMC were more likely to receive care from clinicians with fewer connections and lower care density, reflecting fewer shared patients within the team. Programs supporting care of CMC may benefit from recognizing rural-urban differences in team composition and relationships between clinicians.

Analysis of the patient-sharing network in hypertension management: a retrospective study in China

OBJECTIVE

To explore the robustness of the patient-sharing network and validate the association between strength and persistence of physicians' relationships in China.

DESIGN, SETTING AND PARTICIPANTS

We conducted a patient-sharing network analysis to describe the persistence of patient-sharing relationships and logistic regression to analyse factors associating with the persistence of patient-sharing relationships in the Yinzhou Health Information System from 1 January 2010 to 31 December 2018; all outpatient records that had a hypertension diagnosis were included in this study.

OUTCOME MEASURES

The persistence ratio was defined as the proportion of the patient-sharing relationships in a given year that continued to exist in the following year, the 1-, 2- and 3-year persistence to test the robustness of the findings.

RESULTS

This study included 3916 physicians from 42 public healthcare facilities in Yinzhou. The 1-year persistence ratio fluctuated around 80%, and the 3-year persistence ratio was around 60% over the study period. The strength of the relationship, tie characteristics and physician specialty were important factors associating with the persistence of the relationships. The persistence of the relationships increased significantly as the strength of the relationships increased (for relationships with strength ∈ [3, 5), OR=3.987, 95% CI 3.896 to 4.08; for relationships with strength ∈ [5, 7), OR=6.379, 95% CI 6.147 to 6.626; and for relationships with strength ∈ [7, 9), OR=8.373, 95% CI 7.941 to 8.829). Physicians from the same healthcare institution were more likely to form ties that persisted for at least 1 year compared with physicians from different institutions (OR=1.510, 95% CI 1.480 to 1.540).

CONCLUSIONS

Our study showed that physicians frequently formed relationships with other physicians through sharing patients in Yinzhou, China, and these relationships had similar rates of persistence to studies conducted in developed countries, which indicated that findings of social network analyses conducted in developed countries still hold value in developing countries.

Patient-sharing networks among Finnish primary healthcare professionals taking care of patients with mental health or substance use problems: a register study

OBJECTIVES

Patient-sharing networks based on administrative data are used to understand the organisation of healthcare. We examined the patient-sharing networks between different professionals taking care of patients with mental health or substance use problems.

DESIGN

Register study based on the Register of Primary Health Care visits (Avohilmo) that covers all outpatient primary health care visits in Finland.

SETTING

We used the register data covering the visits for the service providers of seven municipalities, adult patients with at least one visit to a health and social service centre within one of the municipalities and visits during the year 2021.

PARTICIPANTS

We first selected patients with mental health or substance use problems based on psychiatric diagnoses and information on service type and then identified the professionals (N=1566) visited. A patient-sharing relationship was defined between two professionals if a same patient had visited both of them at least once.

PRIMARY OUTCOME MEASURES

We analysed the potential associations of the network structure and the nodal attributes (municipality, belonging to a certain occupational group and the service type) with nodal formation using Exponential Random Graph Models.

RESULTS

The main findings showed that two professionals were more likely to share patient(s) when they belonged to the same occupational group, provided similar types of services or worked in the same municipality. Being a physician was associated with having more connections to other professionals than belonging to other occupational groups (OR for nurses 0.70, 95% CI 0.69 to 0.7 and for other occupations 0.83, 95% CI 0.81 to 0.84). Shared patients among different professionals were also more probable when the patients were shared with the professionals working within mental health or substance use services compared with outpatient healthcare services (OR 1.64, 95% CI 1.61 to 1.67).

CONCLUSIONS

Patient-sharing contacts were mainly homogenous, supporting the tendency of people to have connections with similar people. The results also highlight the role of the physicians as important partners in the patient-sharing networks.

Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships

Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or "homophily" is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and would suggest strategies for interventions seeking to reduce risky-prescribing (e.g., strategies to directly reduce risky prescribing might be most effective if applied as group interventions to risky prescribing physicians connected through the network and the connections between these physicians could be targeted by tie dissolution interventions as an indirect way of reducing risky prescribing). Furthermore, if such effects varied depending on the structural features of a physician's position in the network (e.g., by whether or not they are involved in cliques-groups of actors that are fully connected to each other-such as closed triangles in the case of three actors), this would further strengthen the case for targeting groups of physicians involved in risky prescribing and the network connections between them for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician's risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology may be applied, adapted or generalized to study homophily and its generalizations on other network and attribute combinations involving analogous shared-patient networks and more generally using other kinds of network data underlying other kinds of social phenomena.

The Association Between Oncology Outreach and Timely Treatment for Rural Patients with Breast Cancer: A Claims-Based Approach

BACKGROUND

Oncology outreach is a common strategy for increasing rural access to cancer care, where traveling oncologists commute across healthcare settings to extend specialized care. Examining the extent to which physician outreach is associated with timely treatment for rural patients is critical for informing outreach strategies.

METHODS

We identified a 100% fee-for-service sample of incident breast cancer patients from 2015 to 2020 Medicare claims and apportioned them into surgery and adjuvant therapy cohorts based on treatment history. We defined an outreach visit as the provision of care by a traveling oncologist at a clinic outside of their primary hospital service area. We used hierarchical logistic regression to examine the associations between patient receipt of preoperative care at an outreach visit (preoperative outreach) and > 60-day surgical delay, and patient receipt of postoperative care at an outreach visit (postoperative outreach) and > 60-day adjuvant delay.

RESULTS

We identified 30,337 rural-residing patients who received breast cancer surgery, of whom 4071 (13.4%) experienced surgical delay. Among surgical patients, 14,501 received adjuvant therapy, of whom 2943 (20.3%) experienced adjuvant delay. In adjusted analysis, we found that patient receipt of preoperative outreach was associated with reduced odds of surgical delay (odds ratio [OR] 0.75, 95% confidence interval [CI] 0.61-0.91); however, we found no association between patient receipt of postoperative outreach and adjuvant delay (OR 1.04, 95% CI 0.85-1.25).

CONCLUSIONS

Our findings indicate that preoperative outreach is protective against surgical delay. The traveling oncologists who enable such outreach may play an integral role in catalyzing the coordination and timeliness of patient-centered care.

Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships

Social network analysis and shared-patient physician networks have become effective ways of studying physician collaborations. Assortative mixing or "homophily" is the network phenomenon whereby the propensity for similar individuals to form ties is greater than for dissimilar individuals. Motivated by the public health concern of risky-prescribing among older patients in the United States, we develop network models and tests involving novel network measures to study whether there is evidence of geographic homophily in prescribing and deprescribing in the specific shared-patient network of physicians linked to the US state of Ohio in 2014. Evidence of homophily in risky-prescribing would imply that prescribing behaviors help shape physician networks and could inform interventions to reduce risky-prescribing (e.g., should interventions target groups of physicians or select physicians at random). Furthermore, if such effects varied depending on the structural features of a physician's position in the network (e.g., by whether or not they are involved in cliques - groups of actors that are fully connected to each other - such as closed triangles in the case of three actors), this would further strengthen the case for targeting of select physicians for interventions. Using accompanying Medicare Part D data, we converted patient longitudinal prescription receipts into novel measures of the intensity of each physician's risky-prescribing. Exponential random graph models were used to simultaneously estimate the importance of homophily in prescribing and deprescribing in the network beyond the characteristics of physician specialty (or other metadata) and network-derived features. In addition, novel network measures were introduced to allow homophily to be characterized in relation to specific triadic (three-actor) structural configurations in the network with associated non-parametric randomization tests to evaluate their statistical significance in the network against the null hypothesis of no such phenomena. We found physician homophily in prescribing and deprescribing in both the state-wide and multiple HRR sub-networks, and that the level of homophily varied across HRRs. We also found that physicians exhibited within-triad homophily in risky-prescribing, with the prevalence of homophilic triads significantly higher than expected by chance absent homophily. These results may explain why communities of prescribers emerge and evolve, helping to justify group-level prescriber interventions. The methodology could be applied to arbitrary shared-patient networks and even more generally to other kinds of network data that underlies other kinds of social phenomena.

Detecting Variation in Clinical Practice Patterns for Geriatric Trauma Care Using Social Network Analysis

OBJECTIVE

To characterize hospital-level professional networks of physicians caring for older trauma patients as a function of trauma patient age distribution.

BACKGROUND

The causal factors associated with between-hospital variation in geriatric trauma outcomes are poorly understood. Variation in physician practice patterns reflected by differences in professional networks might contribute to hospital-level differences in outcomes for older trauma patients.

METHODS

This is a population-based, cross-sectional study of injured older adults (age 65 or above) and their physicians from January 1, 2014, to December 31, 2015, using Health Care Cost and Utilization Project inpatient data and Medicare claims from 158 hospitals in Florida. We used social network analyses to characterize the hospitals in terms of network density, cohesion, small-worldness, and heterogeneity, then used bivariate statistics to assess the relationship between network characteristics and hospital-level proportion of trauma patients who were aged 65 or above.

RESULTS

We identified 107,713 older trauma patients and 169,282 patient-physician dyads. The hospital-level proportion of trauma patients who were aged 65 or above ranged from 21.5% to 89.1%. Network density, cohesion, and small-worldness in physician networks were positively correlated with hospital geriatric trauma proportions ( R =0.29, P <0.001; R =0.16, P =0.048; and R =0.19, P <0.001, respectively). Network heterogeneity was negatively correlated with geriatric trauma proportion ( R =0.40, P <0.001).

CONCLUSIONS

Characteristics of professional networks among physicians caring for injured older adults are associated with the hospital-level proportion of trauma patients who are older, indicating differences in practice patterns at hospitals with older trauma populations. Associations between interspecialty collaboration and patient outcomes should be explored as an opportunity to improve the treatment of injured older adults.

Return to sender: Unraveling the role of structural and social network ties in patient sharing networks

Healthcare is increasingly delivered through networks of organizations. Well-structured patient sharing networks are known to have positive associations with the quality of delivered services. However, the drivers of patient sharing relations are rarely studied explicitly. In line with recent developments in network and integration theorizing, we hypothesize that structural and social network ties between organizations are uniquely associated with a higher number of shared patients. We test these hypotheses using a Bayesian zero-dispersed Poisson regression model within the Additive and Multiplicative Effects Framework based on administrative claims data from 732,122 dermatological patients from the Netherlands in 2017. Our results indicate that 2.6% of all dermatological patients are shared and that the amount of shared patients is significantly associated with structural (i.e. emergency contracts) and social (i.e. shared physicians) ties between organizations, confirming our hypotheses. We also find some evidence that patients are shared with more capable organizations. Our findings highlight the role of relational ties in the way health services are delivered. At the same time, they also raise some potential anti-trust concerns.

Within-Physician Differences in Patient Sharing Between Primary Care Physicians and Cardiologists Who Treat White and Black Patients With Heart Disease

BACKGROUND

Black-White disparities in heart disease treatment may be attributable to differences in physician referral networks. We mapped physician networks for Medicare patients and examined within-physician Black-White differences in patient sharing between primary care physicians and cardiologists.

METHODS AND RESULTS

Using Medicare fee-for-service files for 2016 to 2017, we identified a cohort of Black and White patients with heart disease and the primary care physicians and cardiologists treating them. To ensure the robustness of within-physician comparisons, we restricted the sample to regional health care markets (ie, hospital referral regions) with at least 10 physicians sharing ≥3 Black and White patients. We used claims to construct 2 race-specific physician network measures: degree (number of cardiologists with whom a primary care physician shares patients) and transitivity (network tightness). Measures were adjusted for Black-White differences in physician panel size and calculated for all settings (hospital and office) and for office settings only. Of 306 US hospital referral regions, 226 and 145 met study criteria for all settings and office setting analyses, respectively. Black patients had more cardiology encounters overall (6.9 versus 6.6; P<0.001) and with unique cardiologists (3.0 versus 2.6; P<0.001), but fewer office encounters (31.7% versus 41.1%; P<0.001). Primary care physicians shared Black patients with more cardiologists than White patients (mean differential degree 23.4 for all settings and 3.6 for office analyses; P<0.001 for both). Black patient-sharing networks were less tightly connected in all but office settings (mean differential transitivity -0.2 for all settings [P<0.001] and near 0 for office analyses [P=0.74]).

CONCLUSIONS

Within-physician Black-White differences in patient sharing exist and may contribute to disparities in cardiac care.

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.

Physician patient sharing relationships within insurance plan networks

OBJECTIVE

To quantify shared patient relationships between primary care physicians (PCPs) and cardiologists and oncologists and the degree to which those relationships were captured within insurance networks.

DATA SOURCES

Secondary analysis of Vericred data on physician networks, CareSet data on physicians' shared Medicare patients, and insurance plan attributes from Health Insurance Compare. Data validation exercises used data from Physician Compare and IQVIA.

STUDY DESIGN

Cross-sectional study of the PCP-to-specialist in-network shared patient percentage (primary outcome). We also categorized networks by insurance market segment (Medicare Advantage [MA], Medicaid managed care, small-group or individually purchased), insurance plan type, and network breadth.

DATA EXTRACTION

We analyzed data on 219,982 PCPs, 29,400 cardiologists, and 22,745 oncologists who, in 2021, accepted MA (n = 941 networks), Medicaid managed care (n = 293), and individually-purchased (n = 332) and small-group (n = 501) plans.

PRINCIPAL FINDINGS

Networks captured, on average, 64.6% of PCP-cardiology shared patient ties, and 61.8% of PCP-oncologist ties. Less than half of in-network ties (44.5% and 38.9%, respectively) were among physicians with a common organizational affiliation. After adjustment for network breadth, we found no evidence of differences in the shared patient percentage across insurance market segments or networks of different types (p-value >0.05 for all comparisons). An exception was among national versus local and regional networks, where we found that national plans captured fewer shared patient ties, particularly among the narrowest networks (58.4% for national networksvs. 64.7% for local and regional networks for PCP-cardiology).

CONCLUSIONS

Given recent trends toward narrower networks, our findings underscore the importance of incorporating additional and nuanced measures of network composition to aid plan selection (for patients) and to guide regulatory oversight.

Comparing professional communities: Opioid prescriber networks and Public Health Preparedness Districts

Problem opioid use and opioid-related drug overdoses remain a major public health concern despite attempts to reduce and monitor opioid prescriptions and increase access to office-based opioid treatment. Current provider-focused interventions are implemented at the federal, state, regional, and local levels but have not slowed the epidemic. Certain targeted interventions aimed at opioid prescribers rely on populations defined along geographic, political, or administrative boundaries; however, those boundaries may not align well with actual provider-patient communities or with the geographic distribution of high-risk opioid use. Instead of relying exclusively on commonly used geographic and administrative boundaries, we suggest augmenting existing strategies with a social network-based approach to identify communities (or clusters) of providers that prescribe to the same set of patients as another mechanism for targeting certain interventions. To test this approach, we analyze 1 year of prescription data from a commercially insured population in the state of Indiana. The composition of inferred clusters is compared to Indiana's Public Health Preparedness Districts (PHPDs). We find that in some cases the correspondence between provider networks and PHPDs is very high, while in other cases the overlap is low. This has implications for whether an intervention is reaching its intended provider targets efficiently and effectively. Assessing the best intervention targeting strategy for a particular outcome could facilitate more effective interventions to tackle the ongoing opioid use epidemic.

The diffusion of health care fraud: A bipartite network analysis

Many studies have examined the diffusion of health care innovation but less is known about the diffusion of health care fraud. In this paper, we consider the diffusion of potentially fraudulent Medicare home health care billing in the United States during 2002-16, with a focus on the 21 hospital referral regions (HRRs) covered by local Department of Justice (DOJ) anti-fraud "strike force" offices. We hypothesize that patient-sharing across home health care agencies (HHAs) provides a mechanism for the rapid diffusion of fraudulent strategies. We measure such activity using a novel bipartite mixture (or BMIX) network index, which captures patient sharing across multiple agencies and thus conveys more information about the diffusion process than conventional unipartite network measures. Using a complete population of fee-for-service Medicare claims data, we first find a remarkable increase in home health care activity between 2002 and 2009 in many regions targeted by the DOJ; average billing per Medicare enrollee in McAllen TX and Miami increased by $2127 and $2422 compared to just an average $289 increase in other HRRs not targeted by the DOJ. Second, we establish that the HRR-level BMIX (but not other network measures) was a strong predictor of above-average home health care expenditures across HRRs. Third, within HRRs, agencies sharing more patients with other agencies were predicted to increase billing. Finally, the initial 2002 BMIX index was a strong predictor of subsequent changes in HRR-level home health billing during 2002-9. These results highlight the importance of bipartite network structure in diffusion and in infection and contagion models more generally.

A Study of Healthcare Team Communication Networks using Visual Analytics

Cooperation among teams or individuals of healthcare professionals (HCPs) is one of the crucial factors towards patients' survival outcome. However, it is challenging to uncover and understand such factors in the complex Multiteam System (MTS) communication networks representing daily HCP cooperation. In this paper, we present a study on MTS communication networks constructed with real-world cancer patients' Electronic Health Record (EHR) access logs. We adopt a visual analytics workflow to extract associations between semantic characteristics of MTS communication networks and the patients' survival outcomes. The workflow consists of a neural network learning phase to classify the data based on the chosen input and output attributes, a dimensionality reduction and optimization phase to produce a simplified set of results for examination, and finally an interpreting phase conducted by the user through an interactive visualization interface. We provide the insights found using this workflow with two case studies and an expert interview.

Characteristics of Specialty Mental Health Provider Networks in Oregon Medicaid

OBJECTIVE

Provider networks for mental health are narrower than for other medical specialties. Providers' influence on access to care is potentially greater in Medicaid because enrollees are generally limited to contracted providers, without out-of-network options for nonemergency mental health care. The authors used claims-based metrics to examine variation in specialty mental health provider networks.

METHODS

In a cross-sectional analysis of 2018 Oregon Medicaid claims data, claims from adults ages 18-64 years (N=100,515) with a psychiatric diagnosis were identified. In-network providers were identified as those associated with any medical claims filed for at least five unique Medicaid beneficiaries enrolled in a health plan (coordinated care organization [CCO]) during the study period. Specialty mental health providers were categorized as prescribers (psychiatrists and mental health nurse practitioners) and nonprescribers (therapists, counselors, clinical nurse specialists, psychologists, and social workers). Measures of network composition, provider-to-population ratio, continuity, and concentration of care were calculated at the CCO level; the correlation between these measures was estimated to describe the degree to which they capture unique dimensions of provider networks.

RESULTS

Across 15 CCOs, the number of prescribing providers per 1,000 patients was relatively stable. CCOs that expanded their networks did so by increasing the number of nonprescribing providers. Moderately negative correlations were found between the nonprescriber provider-to-population ratio and proportions of visits with prescribers as well as with usual provider continuity.

CONCLUSIONS

This analysis advances future research and policy applications by offering a more nuanced view of provider network measurement and describing empirical variation across networks.

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.

An observational study of health care provider collaboration networks and heterogenous hospital cost efficiency and quality outcomes

Provider network structure has been linked to hospital cost, utilization, and to a lesser degree quality, outcomes; however, it remains unknown whether these relationships are heterogeneous across different acute care hospital characteristics and US states. The objective of this study is to evaluate whether there are heterogeneous relationships between hospital provider network structure and hospital outcomes (cost efficiency and quality); and to assess the sources of measured heterogeneous effects. We use recent causal random forest techniques to estimate (hospital specific) heterogeneous treatment effects between hospitals' provider network structures and their performance (across cost efficiency and quality). Using Medicare cost report, hospital quality and provider patient sharing data, we study a population of 3061 acute care hospitals in 2016. Our results show that provider networks are significantly associated with costs efficiency (P < .001 for 7/8 network measures), patient rating of their care (P < .1 in 5/8 network measures), heart failure readmissions (P < .01 for 3/8 network measures), and mortality rates (P < .02 in 5/8 cases). We find that fragmented provider structures are associated with higher costs efficiency and patient satisfaction, but also with higher heart failure readmission and mortality rates. These effects are further found to vary systematically with hospital characteristics such as capacity, case mix, ownership, and teaching status. This study used an observational design. In summary, we find that hospital treatment responses to different network structures vary systematically with hospital characteristics..

Association Between Care Fragmentation and Total Spending After Durable Left Ventricular Device Implant: A Mediation Analysis of Health Care-Associated Infections Within a National Medicare-Society of Thoracic Surgeons Intermacs Linked Dataset

BACKGROUND

Care fragmentation is associated with higher rates of infection after durable left ventricular assist device (LVAD) implant. Less is known about the relationship between care fragmentation and total spending, and whether this relationship is mediated by infections.

METHODS

Total payments were captured from admission to 180 days post-discharge. Drawing on network theory, a measure of care fragmentation was developed based on the number of shared patients among providers (ie, anesthesiologists, cardiac surgeons, cardiologists, critical care specialists, nurse practitioners, physician assistants) caring for 4,987 Medicare beneficiaries undergoing LVAD implantation between July 2009 - April 2017. Care fragmentation was measured using average path length, which describes how efficiently information flows among network members; longer path length indicates greater fragmentation. Terciles based on the level of care fragmentation and multivariable regression were used to analyze the relationship between care fragmentation and LVAD payments and mediation analysis was used to evaluate the role of post-implant infections.

RESULTS

The patient cohort was 81% male, 73% white, 11% Intermacs Profile 1 with mean (SD) age of 63.1 years (11.1). The mean (SD) level of care fragmentation in provider networks was 1.7 (0.2) and mean (SD) payment from admission to 180 days post-discharge was $246,905 ($109,872). Mean (SD) total payments at the lower, middle, and upper terciles of care fragmentation were $250,135 ($111,924), $243,288 ($109,376), and $247,290 ($108,241), respectively. In mediation analysis, the indirect effect of care fragmentation on total payments, through infections, was positive and statistically significant (β=16032.5, p=0.008).

CONCLUSIONS

Greater care fragmentation in the delivery of care surrounding durable LVAD implantation is associated with a higher incidence of infections, and consequently, higher payments for Medicare beneficiaries. Interventions to reduce care fragmentation may reduce the incidence of infections and in turn enhance the value of care for patients undergoing durable LVAD implantation.

Density of Patient-Sharing Networks: Impact on the Value of Parkinson Care

BACKGROUND

Optimal care for Parkinson's disease (PD) requires coordination and collaboration between providers within a complex care network. Individual patients have personalised networks of their own providers, creating a unique informal network of providers who treat ('share') the same patient. These 'patient-sharing networks' differ in density, ie, the number of identical patients they share. Denser patient-sharing networks might reflect better care provision, since providers who share many patients might have made efforts to improve their mutual care delivery. We evaluated whether the density of these patient-sharing networks affects patient outcomes and costs.

METHODS

We analysed medical claims data from all PD patients in the Netherlands between 2012 and 2016. We focused on seven professional disciplines that are commonly involved in Parkinson care. We calculated for each patient the density score: the average number of patients that each patient's providers shared. Density scores could range from 1.00 (which might reflect poor collaboration) to 83.00 (which might reflect better collaboration). This score was also calculated at the hospital level by averaging the scores for all patients belonging to a specific hospital. Using logistic and linear regression analyses we estimated the relationship between density scores and health outcomes, healthcare utilization, and healthcare costs.

RESULTS

The average density score varied considerably (average 6.7, SD 8.2). Adjusted for confounders, higher density scores were associated with a lower risk of PD-related complications (odds ratio [OR]: 0.901; P<.001) and with lower healthcare costs (coefficients: -0.018, P=.005). Higher density scores were associated with more frequent involvement of neurologists (coefficient 0.068), physiotherapists (coefficient 0.052) and occupational therapists (coefficient 0.048) (P values all <.001).

CONCLUSION

Patient sharing networks showed large variations in density, which appears unwanted as denser networks are associated with better outcomes and lower costs.

Identifying drivers of health care value: a scoping review of the literature

BACKGROUND

As health care spending reaches unsustainable levels, improving value has become an increasingly important policy priority. Relatively little research has explored factors driving value. As a first step towards filling this gap, we performed a scoping review of the literature to identify potential drivers of health care value.

METHODS

Searches of PubMed, Embase, Google Scholar, Policy File, and SCOPUS were conducted between February and March 2020. Empirical studies that explored associations between any range of factors and value (loosely defined as quality or outcomes relative to cost) were eligible for inclusion. We created a template in Microsoft Excel for data extraction and evaluated the quality of included articles using the Critical Appraisal Skills Programme (CASP) quality appraisal tool. Data was synthesized using narrative methods.

RESULTS

Twenty-two studies were included in analyses, of which 20 focused on low value service utilization. Independent variables represented a range of system-, hospital-, provider-, and patient-level characteristics. Although results were mixed, several consistent findings emerged. First, insurance incentive structures may affect value. For example, patients in Accountable Care Organizations had reduced rates of low value care utilization compared to patients in traditionally structured insurance plans. Second, higher intensity of care was associated with higher rates of low value care. Third, culture is likely to contribute to value. This was suggested by findings that recent medical school graduation and allopathic training were associated with reduced low value service utilization and that provider organizations had larger effects on value than did individual physicians.

CONCLUSIONS

System, hospital, provider, and community characteristics influence low value care provision. To improve health care value, strategies aiming to reduce utilization of low value services and promote high value care across various levels will be essential.

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.

Retrospective observational study of the robustness of provider network structures to the systemic shock of COVID-19: a county level analysis of COVID-19 outcomes

OBJECTIVE

To evaluate whether certain healthcare provider network structures are more robust to systemic shocks such as those presented by the current COVID-19 pandemic.

DESIGN

Using multivariable regression analysis, we measure the effect that provider network structure, derived from Medicare patient sharing data, has on county level COVID-19 outcomes (across mortality and case rates). Our adjusted analysis includes county level socioeconomic and demographic controls, state fixed effects, and uses lagged network measures in order to address concerns of reverse causality.

SETTING

US county level COVID-19 population outcomes by 3 September 2020.

PARTICIPANTS

Healthcare provider patient sharing network statistics were measured at the county level (with n=2541-2573 counties, depending on the network measure used).

PRIMARY AND SECONDARY OUTCOME MEASURES

COVID-19 mortality rate at the population level, COVID-19 mortality rate at the case level and the COVID-19 positive case rate.

RESULTS

We find that provider network structures where primary care physicians (PCPs) are relatively central, or that have greater betweenness or eigenvector centralisation, are associated with lower county level COVID-19 death rates. For the adjusted analysis, our results show that increasing either the relative centrality of PCPs (p value<0.05), or the network centralisation (p value<0.05 or p value<0.01), by 1 SD is associated with a COVID-19 death reduction of 1.0-1.8 per 100 000 individuals (or a death rate reduction of 2.7%-5.0%). We also find some suggestive evidence of an association between provider network structure and COVID-19 case rates.

CONCLUSIONS

Provider network structures with greater relative centrality for PCPs when compared with other providers appear more robust to the systemic shock of COVID-19, as do network structures with greater betweenness and eigenvector centralisation. These findings suggest that how we organise our health systems may affect our ability to respond to systemic shocks such as the COVID-19 pandemic.

Provider Network Structure and Black-to-White Disparity Gaps for Medicare Patients with Diabetes: County-Level Analysis of Cost, Utilization, and Clinical Care

BACKGROUND

Prior work has shown that provider network structures correlate with outcomes such as patient costs, utilization, and care. However, it remains unknown whether certain provider networks are associated with reduced disparity gaps.

METHODS

We study the population of Medicare beneficiaries with diabetes who were continuously enrolled in Medicare FFS in 2016. Using multivariable regression analysis of county-level risk adjusted cost, hospitalization, emergency department visits, A1c testing, and preventable diabetes-related hospitalizations, we measure the effect that the relative network connectivity of primary care providers (PCPs) in relation to medical and surgical specialists (PCP/Specialist degree centrality ratio), derived from Medicare patient sharing data, has on non-Hispanic black-to-white disparity gaps controlling for county-level socioeconomic and demographic variables and state fixed effects.

RESULTS

Relative to non-Hispanic white, our adjusted results show that non-Hispanic black beneficiaries have $1673 (p<0.001) higher risk adjusted total costs, 2.6 (p<0.001) more hospitalizations (per 1000 beneficiaries), 11.6 (p<0.001) more ED visits (per 1000 beneficiaries), receive 2.2% (p<0.001) less A1c testing, and have 69.4 (p<0.01) more (per 100,000) avoidable diabetes-related hospital admissions. Our main results show that increasing the PCP/Specialist degree centrality ratio by one standard deviation is associated with a disparity gap decrease of 25.3% (p<0.01) in hospitalizations, 8.3% (p<0.05) in ED visits, 2.8% (p<0.01) in A1c testing, and 26.9% (p<0.1) in the volume of preventable diabetes-related hospital admissions.

CONCLUSIONS

Network structures where PCPs are more central relative to medical and surgical specialists are associated with reduced non-Hispanic black-to-white disparity gaps, suggesting that how we organize and structure our health systems has implications for disparity gaps between non-Hispanic black and white Medicare beneficiaries with diabetes.

Assessing the Association Between Network-based Provider Communities and Patient Mortality in the Medicare Population with Multiple Chronic Conditions

Understanding the complexity of care delivery and care coordination for patients with multiple chronic conditions is challenging. Network analysis can model the relationship between providers and patients to find factors associated with patient mortality. We constructed a network by connecting the providers through shared patients, which was then partitioned into tightly connected communities using a community detection algorithm. After adjusting for patient characteristics, the odds ratio of death for one standard deviation increase in degree centrality ratio between primary care providers (PCPs) and non-PCPs was 0.95 (0.92-0.98). Our result suggest that the centrality of PCPs may be a modifiable factor for improving care delivery. We demonstrated that network analysis can be used to find higher order features associated with health outcomes in addition to patient-level features.

Health workers' social networks and their influence in the adoption of strategies to address the stillbirth burden at a subnational level health system in Uganda

Health workers' peer networks are known to influence members' behaviours and practices while translating policies into service delivery. However, little remains known about the extent to which this remains true within interventions aimed at addressing the stillbirth burden in low-resource settings like Uganda. The objective of this study was to examine the health workers' social networks and their influence on the adoption of strategies to address the stillbirth burden at a subnational level health system in Uganda. A qualitative exploratory design was adopted on a purposively selected sample of 16 key informants. The study was conducted in Mukono district among sub-national health systems, managers, health facility in-charges, and frontline health workers. Data was collected using semi-structured interview guides in a face-to-face interview with respondents. The analysis adopted a thematic approach utilising Atlas. ti software for data management. Participants acknowledged that workplace social networks were influential during the implementation of policies to address stillbirth. The influence exerted was in form of linkage with other services, caution, and advice regarding strict adherence to policy recommendations perhaps reflective of the level of trust in providers' ability to adhere to policy provisions. At the district health management level and among non-state actors, support in perceived areas of weak performance in policy implementation was observed. In addition, timely initiation of contact and subsequent referral was another aspect where health workers exerted influence while translating policies to address the stillbirth burden. While the level of support from among network peers was observed to influence health workers' adoption and implementation of strategies to address the stillbirth burden, different mechanisms triggered subsequent response and level of adherence to recommended policy aspects. Drawing from the elicited responses, we infer that health workers' social networks influence the direction and extent of success in policy implementation to address the stillbirth burden at the subnational level.

Characteristics and Network Influence of Providers Involved in the Treatment of Patients With Chronic Back, Neck or Joint Pain in Arkansas

Increasing emphasis on guidelines and prescription drug monitoring programs highlight the role of healthcare providers in pain treatment. Objectives of this study were to identify characteristics of key players and influence of opioid prescribers through construction of a referral network of patients with chronic pain. A retrospective cohort study was performed and patients with commercial or Medicaid coverage with chronic back, neck, or joint pain were identified using the Arkansas All-Payer Claims-Database. A social network comprised of providers connected by patient referrals based on 12-months of healthcare utilization following chronic pain was constructed. Network measures evaluated were indegree and eigen (referrals obtained), betweenness (involvement), and closeness centrality (reach). Outcomes included influence of providers, opioid prescribers, and brokerage status. Exposures included provider demographics, specialties and network characteristics. There were 51,941 chronic pain patients who visited 8,110 healthcare providers. Primary care providers showed higher betweenness and closeness whereas specialists had higher indegree. Opioid providers showed higher centrality compared to non-opioid providers, which decreased with increasing volume of opioid prescribing. Non-pharmacologic providers showed significant brokerage scores. Findings from this study such as primary care providers having better reach, non-central positions of high-volume prescribers and non-pharmacologic providers having higher brokerage can aid interventional physician detailing. PERSPECTIVE: Opioid providers held central positions in the network aiding provider-directed interventions. However, high-volume opioid providers were at the borders making them difficult targets for interventions. Primary care providers had the highest reach, specialists received the most referrals and non-pharmacological providers and specialists acted as brokers between non-opioid and opioid prescribers.

Trends in Outpatient Care for Medicare Beneficiaries and Implications for Primary Care, 2000 to 2019

BACKGROUND

Despite the central role of primary care in improving health system performance, there are little recent data on how use of primary care and specialists has evolved over time and its implications for the range of care coordination needed in primary care.

OBJECTIVE

To describe trends in outpatient care delivery and the implications for primary care provider (PCP) care coordination.

DESIGN

Descriptive, repeated, cross-sectional study using Medicare claims from 2000 to 2019, with direct standardization used to control for changes in beneficiary characteristics over time.

SETTING

Traditional fee-for-service Medicare.

PATIENTS

20% sample of Medicare beneficiaries.

MEASUREMENTS

Annual counts of outpatient visits and procedures, the number of distinct physicians seen, and the number of other physicians seen by a PCP's assigned Medicare patients.

RESULTS

The proportion of Medicare beneficiaries with any PCP visit annually only slightly increased from 61.2% in 2000 to 65.7% in 2019. The mean annual number of primary care office visits per beneficiary also changed little from 2000 to 2019 (2.99 to 3.00), although the mean number of PCPs seen increased from 0.89 to 1.21 (36.0% increase). In contrast, the mean annual number of visits to specialists increased 20% from 4.05 to 4.87, whereas the mean number of unique specialists seen increased 34.2% from 1.63 to 2.18. The proportion of beneficiaries seeing 5 or more physicians annually increased from 17.5% to 30.1%. In 2000, a PCP's Medicare patient panel saw a median of 52 other physicians (interquartile range, 23 to 87), increasing to 95 (interquartile range, 40 to 164) in 2019.

LIMITATION

Data were limited to Medicare beneficiaries and, because of the use of a 20% sample, may underestimate the number of other physicians seen across a PCP's entire panel.

CONCLUSION

Outpatient care for Medicare beneficiaries has shifted toward more specialist care received from more physicians without increased primary care contact. This represents a substantial expansion of the coordination burden faced by PCPs.

PRIMARY FUNDING SOURCE

National Institute on Aging.

Variation in Provider Connectedness Associates With Outcomes of Inflammatory Bowel Diseases in an Analysis of Data From a National Health System

BACKGROUND & AIMS

Inflammatory bowel diseases (IBD) often require multidisciplinary care with tight coordination among providers. Provider connectedness, a measure of the relationship among providers, is an important aspect of care coordination that has been linked to higher quality care. We aimed to assess variation in provider connectedness among medical centers, and to understand the association between this established measure of care coordination and outcomes of patients with IBD.

METHODS

We conducted a national cohort study of 32,949 IBD patients with IBD from 2005 to 2014. We used network analysis to examine provider connectedness, defined using network properties that measure the strength of the collaborative relationship, team cohesiveness, and between-facility collaborations. We used multilevel modeling to examine variations in provider connectedness and association with patient outcomes.

RESULTS

There was wide variation in provider connectedness among facilities in complexity, rural designation, and volume of patients with IBD. In a multivariable model, patients followed in a facility with team cohesiveness (odds ratio, 0.38; 95% CI, 0.16-0.88) and where providers often collaborated with providers outside their facility (odds ratio, 0.48; 95% CI, 0.31-0.75) were less likely to have clinically active disease, defined by a composite of outpatient flare, inpatient flare, and IBD-related surgery.

CONCLUSIONS

A national study found evidence for heterogeneity in patient-sharing among IBD care teams. Patients with IBD seen at health centers with higher provider connectedness appear to have better outcomes. Understanding provider connectedness is a step toward designing network-based interventions to improve coordination and quality of care.

Physician Network Connections Associated With Faster De-Adoption of Dronedarone for Permanent Atrial Fibrillation

BACKGROUND

Physicians' professional networks are an important source of new medical information and have been shown to influence the adoption of new treatments, but it is unknown how physician networks impact the de-adoption of harmful practices.

METHODS

We analyzed changes in physicians' use of dronedarone after the PALLAS trial (Palbociclib Collaborative Adjuvant Study; November 2011) showed that dronedarone increased the risk of death from cardiovascular events among patients with permanent atrial fibrillation. Deidentified administrative claims from the OptumLabs Data Warehouse were combined with physicians' demographic information from the Doximity database and publicly available data on physicians' patient-sharing relationships compiled by the Centers for Medicare and Medicaid Services. We used a linear probability model with an interrupted linear time trend specification to model the impact of the PALLAS trial on physicians' dronedarone usage between 2009 and 2014.

RESULTS

Before the PALLAS trial, the use of dronedarone was increasing by 0.22 percentage points per quarter (95% CI, 0.19-0.25) in our Medicare Advantage sample (N=343 429 patient-quarter observations) and 0.63 percentage points per quarter (95% CI, 0.52-0.75) in our commercially insured sample (N=44 402 patient-quarter observations). After the PALLAS trial and subsequent United States Food and Drug Administration black box warning, physicians in the Medicare Advantage sample with an above-median number of network connections to other physicians decreased their quarterly usage of dronedarone by 0.12 percentage points more per quarter (95% CI, -0.20 to -0.04; P=0.031) than physicians with equal to or below the median number of network connections. Similar patterns existed in the commercially insured sample (P=0.0318).

CONCLUSIONS

After controlling for a wide range of patient, physician, and geographic characteristics, physicians with a greater number of network connections were faster de-adopters of dronedarone for patients with permanent atrial fibrillation after the PALLAS trial and subsequent United States Food and Drug Administration black box warning detailed the harmfulness of dronedarone for these patients. Policies for improving physicians' responsiveness to new medical information should consider utilizing the influence of these important professional network relationships.

Improving the continuity and coordination of ambulatory care through feedback and facilitated dialogue-a study protocol for a cluster-randomised trial to evaluate the ACD study (Accountable Care in Germany)

Background

Patients in Germany are free to seek care from any office-based physician and can always ask for multiple opinions on a diagnosis or treatment. The high density of physicians and the freedom to choose among them without referrals have led to a need for better coordination between the multiple health professionals treating any given patient. The objectives of this study are to (1) identify informal networks of physicians who treat the same patient population, (2) provide these physicians with feedback on their network and patients, using routine data and (3) give the physicians the opportunity to meet one another in facilitated network meetings.

Methods

The Accountable Care Deutschland (ACD) study is a prospective, non-blinded, cluster-randomised trial comprising a process and economic evaluation of informal networks among 12,525 GPs and office-based specialists and their 1.9 million patients. The units of allocation are the informal networks, which will be randomised either to the intervention (feedback and facilitated meetings) or control group (usual care). The informal networks will be generated by identifying connections between office-based physicians using complete datasets from the Regional Associations of Statutory Health Insurance (SHI) Physicians in Hamburg, Schleswig Holstein, North Rhine and Westphalia Lip, as well as data from three large statutory health insurers in Germany. The physicians will (a) receive feedback on selected indicators of their own treatment activity and that of the colleagues in their network and (b) will be invited to voluntary, facilitated network meetings by their Regional Association of SHI physicians. The primary outcome will be ambulatory-care-sensitive hospitalisations at baseline, at the end of the 2-year intervention period, and at six months and at 12 months after the end of the intervention period. Data will be analysed using the intention-to-treat principle. A pilot study preceded the ACD study.

Discussion

Cochrane reviews show that feedback can improve everyday medical practice by shedding light on previously unknown relationships. Providing physicians with information on how they are connected with their colleagues and what the outcomes are of care delivered within their informal networks can help them make these improvements, as well as strengthen their awareness of possible discontinuities in the care they provide.

Trial registration

German Clinical Trials Register DRKS00020884. Registered on 25 March 2020—retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-021-05584-z.

A measure of local uniqueness to identify linchpins in a social network with node attributes

Network centrality measures assign importance to influential or key nodes in a network based on the topological structure of the underlying adjacency matrix. In this work, we define the importance of a node in a network as being dependent on whether it is the only one of its kind among its neighbors' ties. We introduce linchpin score, a measure of local uniqueness used to identify important nodes by assessing both network structure and a node attribute. We explore linchpin score by attribute type and examine relationships between linchpin score and other established network centrality measures (degree, betweenness, closeness, and eigenvector centrality). To assess the utility of this measure in a real-world application, we measured the linchpin score of physicians in patient-sharing networks to identify and characterize important physicians based on being locally unique for their specialty. We hypothesized that linchpin score would identify indispensable physicians who would not be easily replaced by another physician of their specialty type if they were to be removed from the network. We explored differences in rural and urban physicians by linchpin score compared with other network centrality measures in patient-sharing networks representing the 306 hospital referral regions in the United States. We show that linchpin score is uniquely able to make the distinction that rural specialists, but not rural general practitioners, are indispensable for rural patient care. Linchpin score reveals a novel aspect of network importance that can provide important insight into the vulnerability of health care provider networks. More broadly, applications of linchpin score may be relevant for the analysis of social networks where interdisciplinary collaboration is important.

Practice and market factors associated with provider volume of health information exchange

OBJECTIVE

To assess the practice- and market-level factors associated with the amount of provider health information exchange (HIE) use.

MATERIALS AND METHODS

Provider and practice-level data was drawn from the Meaningful Use Stage 2 Public Use Files from the Centers for Medicare and Medicaid Services, the Physician Compare National Downloadable File, and the Compendium of US Health Systems, among other sources. We analyzed the relationship between provider HIE use and practice and market factors using multivariable linear regression and compared primary care providers (PCPs) to non-PCPs. Provider volume of HIE use is measured as the percentage of referrals sent with electronic summaries of care (eSCR) reported by eligible providers attesting to the Meaningful Use electronic health record (EHR) incentive program in 2016.

RESULTS

Providers used HIE in 49% of referrals; PCPs used HIE in fewer referrals (43%) than non-PCPs (57%). Provider use of products from EHR vendors was negatively related to HIE use, while use of Athenahealth and Greenway Health products were positively related to HIE use. Providers treating, on average, older patients and greater proportions of patients with diabetes used HIE for more referrals. Health system membership, market concentration, and state HIE consent policy were unrelated to provider HIE use.

DISCUSSION

HIE use during referrals is low among office-based providers with the capability for exchange, especially PCPs. Practice-level factors were more commonly associated with greater levels of HIE use than market-level factors.

CONCLUSION

This furthers the understanding that market forces, like competition, may be related to HIE adoption decisions but are less important for use once adoption has occurred.

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.

Physician patient-sharing relationships and healthcare costs and utilization in China: social network analysis based on health insurance data

OBJECTIVES

Evidence on physician patient-sharing relationships from developing countries is limited. This study aimed to identify patient-sharing networks among physicians in China and explore the effect of attributes of physician networks on healthcare utilization and costs.

METHODS

Retrospective analysis was undertaken based on healthcare claims from Urban Employee Basic Medical Insurance Data spanning the years 2015 to 2018. We identified patients with hypertension and modeled physician patient-sharing networks using social network analysis. Relationships among physicians were further quantified using network measures. We fitted a log-linear model to examine the association between networks and healthcare at the physician level.

RESULTS

29,321 patients, seen by 3,429 physicians from 57 hospitals in one eastern city of China were included. Physicians were connected to 21 other physicians (threshold = 1 shared patients) or 7 other physicians (threshold = 4, 6, or 8 shared patients). Degree and centrality measures of physicians at primary care facilities were significantly lower than those at secondary or tertiary hospitals (p < 0.001). The links between physicians at different hospital grades were weak and patients tended to flow among physicians at the same hospital grade. Compared with a low closeness centrality, a medium level was associated with fewer hospitalization costs and days, and high closeness centrality was accompanied by a sharper decrease (all P < 0.001).

CONCLUSIONS

Primary care physicians were located in peripheral positions in China, and the links between primary care facilities and higher-grade hospitals were still weak. Characteristics of physicians' networks and the position of physicians in the network were associated with spending and utilization of services, but not all associations were in the same direction.

Investigation of patient-sharing networks using a Bayesian network model selection approach for congruence class models

A Bayesian approach to conduct network model selection is presented for a general class of network models referred to as the congruence class models (CCMs). CCMs form a broad class that includes as special cases several common network models, such as the Erdős-Rényi-Gilbert model, stochastic block model, and many exponential random graph models. Due to the range of models that can be specified as CCMs, our proposed method is better able to select models consistent with generative mechanisms associated with observed networks than are current approaches. In addition, our approach allows for incorporation of prior information. We illustrate the use of this approach to select among several different proposed mechanisms for the structure of patient-sharing networks; such networks have been found to be associated with the cost and quality of medical care. We found evidence in support of heterogeneity in sociality but not selective mixing by provider type or degree.

The association of insurance plan characteristics with physician patient-sharing network structure

Professional and social connections among physicians impact patient outcomes, but little is known about how characteristics of insurance plans are associated with physician patient-sharing network structure. We use information from commercially insured enrollees in the 2011 Massachusetts All Payer Claims Database to construct and examine the structure of the physician patient-sharing network using standard and novel social network measures. Using regression analysis, we examine the association of physician patient-sharing network measures with an indicator of whether a patient is enrolled in a health maintenance organization (HMO) or preferred provider organization (PPO), controlling for patient and insurer characteristics and observed health status. We find patients enrolled in HMOs see physicians who are more central and densely embedded in the patient-sharing network. We find HMO patients see PCPs who refer to specialists who are less globally central, even as these specialists are more locally central. Our analysis shows there are small but significant differences in physician patient-sharing network as experienced by patients with HMO versus PPO insurance. Understanding connections between physicians is essential and, similar to previous findings, our results suggest policy choices in the insurance and delivery system that change physician connectivity may have important implications for healthcare delivery, utilization and costs.

Physician network connections to specialists and HIV quality of care

OBJECTIVE

To estimate novel measures of generalist physicians' network connectedness to HIV specialists and their associations with two dimensions of HIV quality of care.

DATA SOURCES

Medicare and Medicaid claims and the American Medical Association Masterfile data on people living with HIV (PLWH) and the physicians providing their HIV care in California between 2007 and 2010.

STUDY DESIGN

I construct regional patient-sharing physician networks from the shared treatment of PLWH and calculate (a) measures of network connectedness to all physician types and (b) specialty-weighted measures to describe connectedness to HIV specialists. Two HIV quality of care outcomes are then evaluated: medication quality (prescribing antiretroviral drugs from at least two drug classes) and monitoring quality (at least two annual HIV virus monitoring scans). Linear probability models estimate the associations between network statistics and the two dimensions of HIV quality of care, and a policy simulation demonstrates the importance of these statistical relationships. These analyses include 16 124 PLWH, 3240 generalists, and 1031 HIV specialists.

DATA COLLECTION/EXTRACTION METHODS

PLWH are identified from claims for patients with any indication of HIV using an existing algorithm from the literature.

PRINCIPAL FINDINGS

Generalists' network connectedness to HIV specialists is positively related with their own HIV medication quality; one additional HIV specialist connection is associated with a 1.46 percentage point (SE 0.42, P < .01) increase in generalist's medication quality. Based on the estimated associations, a simulated policy that increases connectedness between generalists and HIV specialists reduces the annual rate of HIV infections by up to 6%, roughly 290 fewer infections per year. Only network connectedness to all physician types is associated with improved monitoring quality.

CONCLUSIONS

Network connectedness to HIV specialists is positively associated with generalists' HIV medication quality, which suggests that specialists provide clinical support through patient-sharing for complex treatment protocol.

Dispersion in the hospital network of shared patients is associated with less efficient care

BACKGROUND

There is growing recognition that health care providers are embedded in networks formed by the movement of patients between providers. However, the structure of such networks and its impact on health care are poorly understood.

PURPOSE

We examined the level of dispersion of patient-sharing networks across U.S. hospitals and its association with three measures of care delivered by hospitals that were likely to relate to coordination.

METHODOLOGY/APPROACH

We used data derived from 2016 Medicare Fee-for-Service claims to measure the volume of patients that hospitals treated in common. We then calculated a measure of dispersion for each hospital based on how those patients were concentrated in outside hospitals. Using this measure, we created multivariate regression models to estimate the relationship between network dispersion, Medicare spending per beneficiary, readmission rates, and emergency department (ED) throughput rates.

RESULTS

In multivariate analysis, we found that hospitals with more dispersed networks (those with many low-volume patient-sharing relationships) had higher spending but not greater readmission rates or slower ED throughput. Among hospitals with fewer resources, greater dispersion related to greater readmission rates and slower ED throughput. Holding an individual hospital's dispersion constant, the level of dispersion of other hospitals in the hospital's network was also related to these outcomes.

CONCLUSION

Dispersed interhospital networks pose a challenge to coordination for patients who are treated at multiple hospitals. These findings indicate that the patient-sharing network structure may be an overlooked factor that shapes how health care organizations deliver care.

PRACTICE IMPLICATIONS

Hospital leaders and hospital-based clinicians should consider how the structure of relationships with other hospitals influences the coordination of patient care. Effective management of this broad network may lead to important strategic partnerships.

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.

How Specialist Aftercare Impacts Long-Term Readmission Risks in Elderly Patients With Metabolic, Cardiac, and Chronic Obstructive Pulmonary Diseases: Cohort Study Using Administrative Data

BACKGROUND

The health state of elderly patients is typically characterized by multiple co-occurring diseases requiring the involvement of several types of health care providers.

OBJECTIVE

We aimed to quantify the benefit for multimorbid patients from seeking specialist care in terms of long-term readmission risks.

METHODS

From an administrative database, we identified 225,238 elderly patients with 97 different diagnosis (ICD-10 codes) from hospital stays and contact with 13 medical specialties. For each diagnosis associated with the first hospital stay, we used multiple logistic regression analysis to quantify the sex-specific and age-adjusted long-term all-cause readmission risk (hospitalizations occurring between 3 months and 3 years after the first admission) and how specialist contact impacts these risks.

RESULTS

Men have a higher readmission risk than women (mean difference over all first diagnoses 1.9%, P<.001), but similar reduction in readmission risk after receiving specialist care. Specialist care can reduce readmission risk by almost 50%. We found the greatest reductions in risk when the first hospital stay was associated with diagnoses corresponding to complex chronic diseases such as acute myocardial infarction (57.6% reduction in readmission risk, SE 7.6% for men [m]; 55.9% reduction, SE 9.8% for women [w]), diabetic and other retinopathies (m: 62.3%, SE 8.0; w: 60.1%, SE 8.4%), chronic obstructive pulmonary disease (m: 63.9%, SE 7.8%; w: 58.1%, SE 7.5%), disorders of lipoprotein metabolism (m: 64.7%, SE 3.7%; w: 63.8%, SE 4.0%), and chronic ischemic heart diseases (m: 63.6%, SE 3.1%; w: 65.4%, SE 3.0%).

CONCLUSIONS

Specialist care can greatly reduce long-term readmission risk for patients with chronic and multimorbid diseases. Further research is needed to identify the specific reasons for these findings and to understand the detected sex-specific differences.

Network analysis of medical care services

Medical care services can be organized into a network. Understanding the structure of this network cannot only help analyze common clinical protocols but can also help reveal previously unknown patterns of care. The objective of this research is to introduce the concept and methods for constructing and analyzing the network of medical care services. We start by demonstrating how to build the network itself and then develop algorithms, based on principal component analysis and social network analysis, to detect communities of services. Finally, we propose novel graphical techniques for representing and assessing patterns of care. We demonstrate the application of our algorithms using data from an Emergency Department in New York State. One of the implications of our research is that clinical experts could use our algorithms to detect deviations from either existing protocols of care or administrative norms.

Relation of the Networks Formed by Diabetic Patients Sharing Physicians With Emergency Department Visits and Hospitalizations

OBJECTIVE

The objective of this study was to evaluate if the networks of diabetic patients sharing physicians are associated with emergency department (ED) visits and hospitalizations.

STUDY DESIGN

This is a retrospective cohort study.

METHODS

We used administrative data from a large insurer in Hawaii in 2010. Three types of networks were defined based on patient visits: (1) the total number of links from one patient to other patients sharing a physician; (2) the number of other patients connected by sharing the physician seen the most often; and (3) the number of other patients connected by seeing all the same physicians during the year. The networks were characterized into thirds based on their complexity and analyzed using zero-inflated negative binomial regression models on ED visits and hospitalizations.

RESULTS

The study included 38,767 diabetes patients with a mean age of 64 years. Patients sharing the most physicians had double the risks of ED visits and hospitalizations. Patients linked by belonging to the largest primary care practices had a 28% reduced odds of ED visits. Patients linked by seeing all of the same physicians during the year had the fewest primary care providers and specialists visits and 25%-50% reductions in ED visits and hospitalizations.

CONCLUSIONS

Networks of diabetic patients sharing all the same physicians were associated with decreased ED visits and hospitalizations. Encouraging diabetic patients to find a provider they like and trust and to stay in the provider's care may help reduce the risks of adverse events. Physicians building loyalty among their patients may reduce their patients' risks.

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.

Therapeutic Duplication in Taiwan Hospitals for Patients With High Blood Pressure, Sugar, and Lipids: Evaluation With a Mobile Health Mapping Tool

BACKGROUND

Cardiovascular disease causes approximately half of all deaths in patients with type 2 diabetes. Duplicative prescriptions of medication in patients with high blood pressure (hypertension), high blood sugar (hyperglycemia), and high blood lipids (hyperlipidemia) have attracted substantial attention regarding the abuse of health care resources and to implement preventive measures for such abuse. Duplicative prescriptions may occur by patients receiving redundant medications for the same condition from two or more sources such as doctors, hospitals, and multiple providers, or as a result of the patient's wandering among hospitals.

OBJECTIVE

We evaluated the degree of duplicative prescriptions in Taiwanese hospitals for outpatients with three types of medications (antihypertension, antihyperglycemia, and antihyperlipidemia), and then used an online dashboard based on mobile health (mHealth) on a map to determine whether the situation has improved in the recent 25 fiscal quarters.

METHODS

Data on duplicate prescription rates of drugs for the three conditions were downloaded from the website of Taiwan's National Health Insurance Administration (TNHIA) from the third quarter of 2010 to the third quarter of 2016. Complete data on antihypertension, antihyperglycemia, and antihyperlipidemia prescriptions were obtained from 408, 414, and 359 hospitals, respectively. We used scale quality indicators to assess the attributes of the study data, created a dashboard that can be traced using mHealth, and selected the hospital type with the best performance regarding improvement on duplicate prescriptions for the three types of drugs using the weighted scores on an online dashboard. Kendall coefficient of concordance (W) was used to evaluate whether the performance rankings were unanimous.

RESULTS

The data quality was found to be acceptable and showed good reliability and construct validity. The online dashboard using mHealth on Google Maps allowed for easy and clear interpretation of duplicative prescriptions regarding hospital performance using multidisciplinary functionalities, and showed significant improvement in the reduction of duplicative prescriptions among all types of hospitals. Medical centers and regional hospitals showed better performance with improvement in the three types of duplicative prescriptions compared with the district hospitals. Kendall W was 0.78, indicating that the performance rankings were not unanimous (Chi square₂=4.67, P=.10).

CONCLUSIONS

This demonstration of a dashboard using mHealth on a map can inspire using the 42 other quality indicators of the TNHIA by hospitals in the future.

The association between patient sharing network structure and healthcare costs

STUDY QUESTION

While physician relationships (measured through shared patients) are associated with clinical and utilization outcomes, the extent to which this is driven by local or global network characteristics is not well established. The objective of this research is to examine the association between local and global network statistics with total medical spending and utilization.

DATA SOURCE

Data used are the 2011 Massachusetts All Payer Claims Database.

STUDY DESIGN

The association between network statistics and total medical spending and utilization (using standardized prices) is estimated using multivariate regression analysis controlling for patient demographics and health status.

DATA COLLECTION

We limit the sample to continuously enrolled commercially insured patients in Massachusetts in 2011.

PRINCIPAL FINDINGS

Mean patient age was 45 years, and 56.3% of patients were female. 73.4% were covered by a health maintenance organization. Average number of visits was 5.43, with average total medical spending of $4,911 and total medical utilization of $4,252. Spending was lower for patients treated by physicians with higher degree (p<0.001), eigenvector centrality (p<0.001), clustering coefficient (p<0.001), and measures reflecting the normalized degree (p<0.001) and eigenvector centrality (p<0.001) of specialists connected to a patient's PCP. Spending was higher for patients treated by physicians with higher normalized degree, which accounts for physician specialty and patient panel size (p<0.001). Results were similar for utilization outcomes, although magnitudes differed indicating patients may see different priced physicians.

CONCLUSIONS

Generally, higher values of network statistics reflecting local connectivity adjusted for physician characteristics are associated with increased costs and utilization, while higher values of network statistics reflecting global connectivity are associated with decreased costs and utilization. As changes in the financing and delivery system advance through policy changes and healthcare consolidation, future research should examine mechanisms through which this structure impacts outcomes and potential policy responses to determine ways to reduce costs while maintaining quality and coordination of care.

WHAT THIS STUDY ADDS

It is unknown whether local and global measures of physician network connectivity associated with spending and utilization for commercially insured patients?In this social network analysis, we found generally higher values of network statistics reflecting local connectivity are associated with increased costs and utilization, while higher values of network statistics reflecting global connectivity are associated with decreased costs and utilization.Understanding how to influence local and global physician network characteristics may be important for reducing costs while maintaining quality.

Care coordination for severe mental health disorders: an analysis of healthcare provider patient-sharing networks and their association with quality of care in a French region

BACKGROUND

For patients with multiple and complex health needs, such as those suffering from mental health disorders, outcomes are determined by the combined actions of the care providers they visit and their interactions. Care coordination is therefore essential. However, little is known on links between hospitals providing psychiatric care and community-based care providers which could serve as a basis for the creation of formal mental care networks supported by recent policies. In this context, we first aimed to identify and characterize existing types of healthcare provider patient-sharing networks for severe mental health disorders in one French region. Second, we aimed to analyse the association between their characteristics and the quality of the care they provide.

METHODS

Patient flows among healthcare providers involved in treating severe mental health disorders in the Provence-Alpes-Côte-d'Azur region were extracted from the French national health data system, which contains all billing records from the social health insurance. Healthcare provider networks that have developed around public and private non-profit hospitals were identified based on shared patients with other providers (hospitals, community-based psychiatrists, general practitioners and nurses). Hierarchical clustering was conducted to create a typology of the networks. Indicators of quality of care, encompassing multiple complementary dimensions, were calculated across these networks and linked to their characteristics using multivariable methods.

RESULTS

Three main types of existing healthcare provider networks were identified. They were either networks strongly organized around the main hospital providing psychiatric care; scattered networks involving numerous and diverse healthcare providers; or medically-oriented networks involving mainly physician providers. Few significant associations between the structure and composition of healthcare provider networks and indicators of quality of care were found.

CONCLUSIONS

Our findings provide a basis to develop explicit structuring of mental care based on pre-existing working relationships but suggest that healthcare providers' patient-sharing patterns were not the main driver of optimal care provision in the context explored. The shift towards a stronger integration of health and social care in the mental health field might impact these results but is currently not observable in the administrative data available for research purpose which should evolve to include social care.

Physician networks and potentially inappropriate opioid prescriptions

Background: Opioid overdose is a national health priority and curbing inappropriate prescribing is critical. In 2016, the Centers for Disease Control and Prevention (CDC) issued appropriate prescribing guidelines.Objectives: Examine associations between care networks defined by shared patients and problematic opioid prescribing.Methods: Analysis was at the provider-year level. Social network analysis (SNA) applied to the Medicaid MarketScan^® Research Database for the years 2010-2015 identified care communities, each community's level of integration (centralization), and each provider's integration (centrality). Nested multivariable logistic regressions controlling for patient mix and provider specialty simultaneously examined the risk of any (incident) and repeated (prevalent) inappropriate prescribing.Outcomes: Four behaviors defined by the CDC guidelines were examined: (1) more than 90 days continuous supply of high-dose opioid analgesics for chronic pain, (2) overlapping opioid supplies, (3) overlapping opioid and benzodiazepine prescriptions, and (4) prescribing an extended release opioid for an acute pain diagnosis.Results: Provider centrality was associated with reduced incidence of outcome (2) (OR: 0.95) and decreased prevalence of outcomes (1), (2), and (3). However, higher incidence (OR: 1.32) and prevalence (OR: 1.027) of outcome (4) were observed. Conversely, centralization associated with decreased incidence of (1) and (2) and lower prevalence of (1), (2), and (3).Conclusions: Greater provider integration is associated with a lower risk of a provider's patients repeatedly having potentially inappropriate prescription fills; however, the association with a provider having any potentially problematic prescription is more ambiguous.