Variation in patient-sharing networks of physicians across the United States

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.

Representation of the hierarchical and functional structure of an ambulatory network of medical consultations through Social Network Analysis, with an emphasis on the role of medical specialties

BACKGROUND

Ambulatory Health Care Networks (Amb-HCN) are circuits of patient referral and counter-referral that emerge, explicitly or spontaneously, between doctors who provide care in their offices. Finding a meaningful analytical representation for the organic and hierarchical functioning of an Amb-HCN may have managerial and health policymaking implications. We aimed to characterize the structural and functional topology of an Amb-HCN of a private health insurance provider (PHIP) using objective metrics from graph theory.

METHODS

This is a cross-sectional quantitative study with a secondary data analysis study design. A Social Network Analysis (SNA) was conducted using office visits performed between April 1, 2021 and May 15, 2022, retrieved from secondary administrative claim databases from a PHIP in Belo Horizonte, Southeastern Brazil. Included were beneficiaries of a healthcare plan not restricting the location or physician caring for the patient. A directional and weighted network was constructed, where doctors were the vertices and patient referrals between doctors, within 7-45 days, were the network edges. Vertex-level SNA measures were calculated and grouped into three theoretical constructs: patient follow-up (aimed at assessing the doctor's pattern of patient follow-up); relationship with authorities (which assessed whether the doctor is an authority or contributes to his or her colleague's authority status); and centrality (aimed at positioning the doctor relative to the network graph). To characterize physician profiles within each dimension based on SNA metrics results, a K-means cluster analysis was conducted. The resulting physician clusters were assigned labels that sought to be representative of the observed values of the vertex metrics within the clusters.

FINDINGS

Overall, 666,263 individuals performed 3,863,222 office visits with 4,554 physicians. A total of 577 physicians (12.7%) had very low consultation productivity and contributed very little to the network (i.e., about 1.1% of all referrals made or received), being excluded from subsequent doctor profiles analysis. Cluster analysis found 951 (23.9%) doctors to be central in the graph and 1,258 (31.6%) to be peripheral; 883 (22.2%) to be authorities and 266 (6.7%) as seeking authorities; 3,684 (92.6%) mostly shared patients with colleagues, with patient follow-up intensities ranging from weak to strong. Wide profile dispersion was observed among specialties and, more interestingly, within specialties. Non-primary-care medical specialties (e.g., cardiology, endocrinology etc.) were associated with central profile in the graph, while surgical specialties predominated in the periphery, along with pediatrics. Only pediatrics was associated with strong and prevalent (i.e., low patient sharing pattern) follow-up. Many doctors from internal medicine and family medicine had unexpectedly weak and shared patient follow-up profiles. Doctor profiles exhibited pairwise relationships with each other and with the number of chronic comorbidities of the patients they treated. For example, physicians identified as authorities were frequently central and treated patients with more comorbidities. Ten medical communities were identified with clear territorial and specialty segregation.

CONCLUSIONS

Viewing the Amb-HCN as a social network provided a topological and functional representation with potentially meaningful and actionable emerging insights into the most influential actors and specialties, functional hierarchies, factors that lead to self-constituted medical communities, and dispersion from expected patterns within medical specialties.

A flexible approach to measure care coordination based on patient-sharing networks

BACKGROUND

Effective care coordination may increase clinical efficiency, but its measurement remains difficult. The established metric "care density" (CD) measures care coordination based on patient-sharing among physicians, but it may be too rigid to generalize across disorders and countries. Therefore, we propose an extension called fragmented care density (FCD), which allows varying weights for connections between different types of providers. We compare both metrics in their ability to predict hospitalizations due to schizophrenia.

METHODS

We conducted a longitudinal cohort study based on German claims data from 2014 through 2017 to predict quarterly hospital admissions. 21,016 patients with schizophrenia from the federal state Baden-Württemberg were included. CD and FCD were calculated based on patient-sharing networks. The weights of FCD were optimized to predict hospital admissions during the first year of a 24-month follow-up. Subsequently, we employed likelihood ratio tests to assess whether adding either CD or FCD improved a baseline model with control variables for the second follow-up year.

RESULTS

The inclusion of FCD significantly improved the baseline model, Χ2(1) = 53.30, p < 0.001. We found that patients with lower percentiles in FCD had an up to 21% lower hospitalization risk than those with median or higher values, whereas CD did not affect the risk.

CONCLUSIONS

FCD is an adaptive metric that can weight provider relationships based on their relevance for predicting any outcome. We used it to better understand which medical specialties need to be involved to reduce hospitalization risk for patients with schizophrenia. As FCD can be modified for different health conditions and systems, it is broadly applicable and might help to identify barriers and promoting factors for effective collaboration.

Longitudinal Trends in Insurer Participation in Multisector Population Health Activities

Healthcare organizations increasingly engage in activities to identify and address social determinants of health (SDOH) among their patients to improve health outcomes and reduce costs. While several studies to date have focused on the evolving role of hospitals and physicians in these types of population health activities, much less is known about the role health insurers may play. We used data from the National Longitudinal Survey of Public Health Systems for the period 2006 to 2018 to examine trends in health insurer participation in population health activities and in the multi-sector collaborative networks that support these activities. We also used a difference-in-differences approach to examine the impact of Medicaid expansion on insurer participation in population health networks. Insurer participation increased in our study period both in the delivery of population health activities and in the integration into collaborative networks that support these activities. Insurers were most likely to participate in activities focusing on community health assessment and policy development. Results from our adjusted difference-in-differences models showed variation in association between insurer participation in population health networks and Medicaid expansion (Table 2). Population health networks in expansion states experienced significant increases insurer participation in assessment (4.48 percentage points, P < .05) and policy and planning (7.66 percentage points, P < .05) activities. Encouraging insurance coverage gains through policy mechanisms like Medicaid expansion may not only improve access to healthcare services but can also act as a driver of insurer integration into population health networks.

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.

Back to basics: A mediation analysis approach to addressing the fundamental questions of integrated care evaluations

Health systems around the world are aiming to improve the integration of health and social care services to deliver better care for patients. Existing evaluations have focused exclusively on the impact of care integration on health outcomes and found little effect. That suggests the need to take a step back and ask whether integrated care programmes actually lead to greater clinical integration of care and indeed whether greater integration is associated with improved health outcomes. We propose a mediation analysis approach to address these two fundamental questions when evaluating integrated care programmes. We illustrate our approach by re-examining the impact of an English integrated care program on clinical integration and assessing whether greater integration is causally associated with fewer admissions for ambulatory care sensitive conditions. We measure clinical integration using a concentration index of outpatient referrals at the general practice level. While we find that the scheme increased integration of primary and secondary care, clinical integration did not mediate a decrease in unplanned hospital admissions. Our analysis emphasizes the need to better understand the hypothesized causal impact of integration on health outcomes and demonstrates how mediation analysis can inform future evaluations and program design.

Spillover effects of financial incentives for providers onto non-targeted patients: daycase surgery in English hospitals

BACKGROUND

Incentives for healthcare providers may also affect non-targeted patients. These spillover effects have important implications for the full impact and evaluation of incentive schemes. However, there are few studies on the extent of such spillovers in health care. We investigated whether incentives to perform surgical procedures as daycases affected whether other elective procedures in the same specialties were also treated as daycases.

DATA

8,505,754 patients treated for 92 non-targeted procedures in 127 hospital trusts in England between April and March 2016.

METHODS

Interrupted time series analysis of the probability of being treated as a daycase for non-targeted patients treated in six specialties where targeted patients were also treated and three specialties where they were not.

RESULTS

The daycase rate initially increased (1.04 percentage points, SE: 0.30) for patients undergoing a non-targeted procedure in incentivised specialties but then reduced over time. Conversely, the daycase rate gradually decreased over time for patients treated in a non-incentivised specialty.

DISCUSSION

Spillovers from financial incentives have variable effects over different activities and over time. Policymakers and researchers should consider the possibility of spillovers in the design and evaluation of incentive schemes.

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.

The influence of strong and weak ties in physician peer networks on new drug adoption

Physicians interact and exchange information through various social networks. Understanding peer effects through different networks can help accelerate new medical technology and innovative treatment adoption. In this research, we measure the influence of strong-tie and weak-tie connections on new drug adoption and study the overlap between advice-discussion and patient-sharing network. We construct two physician networks with strong and weak ties from peer nomination surveys and commercial medical claims data. We design a dynamic system to define peer adoption status and build patient-level hierarchical logistic models to measure the peer influence on new product adoption for treating new-to-therapy patients. Our results show that A strong-tie early adoption peer has six times more influence on new drug adoption than a weak-tie peer. Weak tie peers collectively exert as much or higher influence than strong-tie peers because of the larger network size. In the case of inaccessibility to strong-tie data, researchers can still reliably use the influence of the weak tie data only even though they will lose the effect of the omitted strong ties.

Social network analysis of nationwide interhospital emergency department transfers in Taiwan

Transferring patients between emergency departments (EDs) is a complex but important issue in emergency care regionalization. Social network analysis (SNA) is well-suited to characterize the ED transfer pattern. We aimed to unravel the underlying transfer network structure and to identify key network metrics for monitoring network functions. This was a retrospective cohort study using the National Electronic Referral System (NERS) database in Taiwan. All interhospital ED transfers from 2014 to 2016 were included and transfer characteristics were retrieved. Descriptive statistics and social network analysis were used to analyze the data. There were a total of 218,760 ED transfers during the 3-year study period. In the network analysis, there were a total of 199 EDs with 9516 transfer ties between EDs. The network demonstrated a multiple hub-and-spoke, regionalized pattern, with low global density (0.24), moderate centralization (0.57), and moderately high clustering of EDs (0.63). At the ED level, most transfers were one-way, with low reciprocity (0.21). Sending hospitals had a median of 5 transfer-out partners [interquartile range (IQR) 3-7), while receiving hospitals a median of 2 (IQR 1-6) transfer-in partners. A total of 16 receiving hospitals, all of which were designated base or co-base hospitals, had 15 or more transfer-in partners. Social network analysis of transfer patterns between hospitals confirmed that the network structure largely aligned with the planned regionalized transfer network in Taiwan. Understanding the network metrics helps track the structure and process aspects of regionalized care.

Commentary on "Integrating network theory into the study of integrated healthcare"

As medicine continues to advance, fragmentation problems in care delivery - and the promise of care integration to solve them - will remain central. But focused research over the past thirty years has yet to uncover the key factors that enable integrated care. In their paper, Burns and colleagues offer a path to new discovery in this well-trodden area: drawing on network theory to better understand the social processes through which integrated care is produced. Social processes are a vital and understudied aspect of integration, and applying network theory may help to refocus integration in a more comprehensive way. However, to transform our understanding of integrated care - and to enable impact in practice - will require expansion beyond the usual network approaches to also capture the communication and work processes that occur among entities. This is no small endeavor. It will take considerable humility, open-mindedness, and focus.

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.

Impact of Clinician Linkage on Unequal Access to High-Volume Hospitals for Colorectal Cancer Surgery

BACKGROUND

Understanding drivers of persistent surgical disparities remains an important area of cancer care delivery and policy. The degree to which clinician linkages contribute to disparities in access to quality colorectal cancer surgery is unknown. Using hospital surgical volume as a proxy for quality, the study team evaluated how clinician linkages impact access to colorectal cancer surgery at high-volume hospitals (HVHs).

STUDY DESIGN

Maryland's Health Services Cost Review Commission was used to evaluate 6,909 patients who underwent colon or rectal cancer operations from 2013 to 2018. Two linkages based on patient sharing were examined separately for colon and rectal cancer surgery: (1) from primary care clinicians to specialists (gastroenterologist or medical oncologist) and (2) from specialists to surgeons (general or colorectal). A referral link was defined as 9 or more shared patients between 2 clinicians. Adjusted regression models examined associations between referral links and odds of receiving colon or rectal cancer operations at HVHs.

RESULTS

The cohort included 5,645 colon and 1,264 rectal cancer patients across 52 hospitals. Every additional referral link between a primary care clinician and a specialist connected to a HVH was associated with a 12% and 14% increased likelihood of receiving colon (odds ratio [OR] 1.12, CI 1.07 to 1.17) and rectal (OR 1.14, CI 1.08 to 1.20]) cancer operations at a HVH, respectively. Every additional referral link between a specialist and a surgeon at a HVH was associated with at least a 25% increased likelihood of receiving colon (OR 1.28, CI 1.20 to 1.36) and rectal (OR 1.25, CI 1.15 to 1.36) cancer operation at a HVH.

CONCLUSIONS

Patients of clinicians with linkages to HVHs are more likely to have their colorectal cancer operations at these hospitals. These findings suggest that policy interventions targeting clinician relationships are an important step in providing equitable surgical care.

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.

Using social network analysis methods to identify networks of physicians responsible for the care of specific patient populations

BACKGROUND

Coordinating health care within and among sectors is crucial to improving quality of care and avoiding undesirable negative health outcomes, such as avoidable hospitalizations. Quality circles are one approach to strengthening collaboration among health care providers and improving the continuity of care. However, identifying and including the right health professionals in such meetings is challenging, especially in settings with no predefined patient pathways. Based on the Accountable Care in Germany (ACD) project, our study presents a framework for and investigates the feasibility of applying social network analysis (SNA) to routine data in order to identify networks of ambulatory physicians who can be considered responsible for the care of specific patients.

METHODS

The ACD study objectives predefined the characteristics of the networks. SNA provides a methodology to identify physicians who have patients in common and ensure that they are involved in health care provision. An expert panel consisting of physicians, health services researchers, and data specialists examined the concept of network construction through informed decisions. The procedure was structured by five steps and was applied to routine data from three German states.

RESULTS

In total, 510 networks of ambulatory physicians met our predefined inclusion criteria. The networks had between 20 and 120 physicians, and 72% included at least ten different medical specialties. Overall, general practitioners accounted for the largest proportion of physicians in the networks (45%), followed by gynecologists (10%), orthopedists, and ophthalmologists (5%). The specialties were distributed similarly across the majority of networks. The number of patients this study allocated to the networks varied between 95 and 45,268 depending on the number and specialization of physicians per network.

CONCLUSIONS

The networks were constructed according to the predefined characteristics following the ACD study objectives, e.g., size of and specialization composition in the networks. This study shows that it is feasible to apply SNA to routine data in order to identify groups of ambulatory physicians who are involved in the treatment of a specific patient population. Whether these doctors are also mainly responsible for care and if their active collaboration can improve the quality of care still needs to be examined.

Perioperative magnetic resonance imaging in breast cancer care: Distinct adoption trajectories among physician patient-sharing networks

BACKGROUND

Despite no proven benefit in clinical outcomes, perioperative magnetic resonance imaging (MRI) was rapidly adopted into breast cancer care in the 2000's, offering a prime opportunity for assessing factors influencing overutilization of unproven technology.

OBJECTIVES

To examine variation among physician patient-sharing networks in their trajectory of adopting perioperative MRI for breast cancer surgery and compare the characteristics of patients, providers, and mastectomy use in physician networks that had different adoption trajectories.

METHODS AND FINDINGS

Using the Surveillance, Epidemiology, and End Results-Medicare database in 2004-2009, we identified 147 physician patient-sharing networks (caring for 26,886 patients with stage I-III breast cancer). After adjusting for patient clinical risk factors, we calculated risk-adjusted rate of perioperative MRI use for each physician network in 2004-2005, 2006-2007, and 2008-2009, respectively. Based on the risk-adjusted rate, we identified three distinct trajectories of adopting perioperative MRI among physician networks: 1) low adoption (risk-adjusted rate of perioperative MRI increased from 2.8% in 2004-2005 to 14.8% in 2008-2009), 2) medium adoption (8.8% to 45.1%), and 3) high adoption (33.0% to 71.7%). Physician networks in the higher adoption trajectory tended to have a larger proportion of cancer specialists, more patients with high income, and fewer patients who were Black. After adjusting for patients' clinical risk factors, the proportion of patients undergoing mastectomy decreased from 41.1% in 2004-2005 to 38.5% in 2008-2009 among those in physician networks with low MRI adoption, but increased from 27.0% to 31.4% among those in physician networks with high MRI adoption (p = 0.03 for the interaction term between trajectory group and time).

CONCLUSIONS

Physician patient-sharing networks varied in their trajectory of adopting perioperative MRI. These distinct trajectories were associated with the composition of patients and providers in the networks, and had important implications for patterns of mastectomy use.

Examining Diurnal Differences in Multidisciplinary Care Teams at a Pediatric Trauma Center Using Electronic Health Record Data: Social Network Analysis

BACKGROUND

The care of pediatric trauma patients is delivered by multidisciplinary care teams with high fluidity that may vary in composition and organization depending on the time of day.

OBJECTIVE

This study aims to identify and describe diurnal variations in multidisciplinary care teams taking care of pediatric trauma patients using social network analysis on electronic health record (EHR) data.

METHODS

Metadata of clinical activities were extracted from the EHR and processed into an event log, which was divided into 6 different event logs based on shift (day or night) and location (emergency department, pediatric intensive care unit, and floor). Social networks were constructed from each event log by creating an edge among the functional roles captured within a similar time interval during a shift. Overlapping communities were identified from the social networks. Day and night network structures for each care location were compared and validated via comparison with secondary analysis of qualitatively derived care team data, obtained through semistructured interviews; and member-checking interviews with clinicians.

RESULTS

There were 413 encounters in the 1-year study period, with 65.9% (272/413) and 34.1% (141/413) beginning during day and night shifts, respectively. A single community was identified at all locations during the day and in the pediatric intensive care unit at night, whereas multiple communities corresponding to individual specialty services were identified in the emergency department and on the floor at night. Members of the trauma service belonged to all communities, suggesting that they were responsible for care coordination. Health care professionals found the networks to be largely accurate representations of the composition of the care teams and the interactions among them.

CONCLUSIONS

Social network analysis was successfully used on EHR data to identify and describe diurnal differences in the composition and organization of multidisciplinary care teams at a pediatric trauma center.

Association of Physician Referrals with Timely Cancer Care Using Tumor Registry and Claims Data

More Americans are being screened for and more are surviving colorectal cancer due to advanced treatments and better quality of care; however, these benefits are not equitably distributed among diverse or older populations. Differential care delivery outcomes are driven by multiple factors, including access to timely treatment that comes from high-quality care coordination. Providers help ensure such coordinated care, which includes timely referrals to specialists. Variation in referrals between providers can also result in differences in treatment plans and outcomes. Patients who are more often referred between the same diagnosing and treating providers may benefit from more timely care compared to those who are not. Our objective is to examine patterns of referral, or patient-sharing networks (PSNs), and our outcome, treatment delay of 30-days (yes/no). We hypothesize that if a patient is in a PSN they will have lower odds of a 30-day treatment initiation delay. Our observational population-based analysis using the National Cancer Institute (NCI)-linked tumor registry and Medicare claims database includes records for 27,689 patients diagnosed with colorectal cancer from 2001 to 2013, and treated with either chemotherapy, radiotherapy, or surgery. We modeled the adjusted odds of a delay and found 17.04% of patients experienced a 30-day delay in initial treatment. Factors that increased odds of a delay were lack of membership in a PSN (adjusted odds ratio [AOR]: 2.20; 95% confidence interval [CI]: 1.71-2.84), racial/ethnic minority status, and having multiple comorbidities. Provider characteristics significantly associated with greater odds of a delay were if dyads were not in the same facility (AOR: 1.95; 95% CI: 1.81-2.10), if providers were different genders, most notably male (diagnosing) and female (treating) [AOR: 1.23; 95% CI: 1.08-1.40, p = 0.0015]. PSNs appear to be associated with reduced of a care delay. The associations observed in our study address the demand for developing multilevel interventions to improve the delivery and coordination of high-quality of care for older cancer patients.

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.

Review of social networks of professionals in healthcare settings-where are we and what else is needed?

Background

Social Network Analysis (SNA) demonstrates great potential in exploring health professional relationships and improving care delivery, but there is no comprehensive overview of its utilization in healthcare settings. This review aims to provide an overview of the current state of knowledge regarding the use of SNA in understanding health professional relationships in different countries.

Methods

We conducted an umbrella review by searching eight academic databases and grey literature up to April 30, 2021, enhanced by citation searches. We completed study selection, data extraction and quality assessment using predetermined criteria. The information abstracted from the reviews was synthesized quantitatively, qualitatively and narratively.

Results

Thirteen reviews were included in this review, yielding 330 empirical studies. The degree of overlaps of empirical studies across included reviews was low (4.3 %), indicating a high diversity of included reviews and the necessity of this umbrella review. Evidence from low- and middle-income countries (LMIC), particularly Asian countries, was limited. The earliest review was published in 2010 and the latest in 2019. Six reviews focused on the construction or description of professional networks and seven reviews reported factors or influences of professional networks. We synthesized existing literature on social networks of health care professionals in the light of (i) theoretical frameworks, (ii) study design and data collection, (iii) network nodes, measures and analysis, and (iv) factors of professional networks and related outcomes. From the perspective of methodology, evidence lies mainly in cross-sectional study design and electronic data, especially administrative data showing “patient-sharing” relationships, which has become the dominant data collection method. The results about the impact of health professional networks on health-related consequences were often contradicting and not truly comparable.

Conclusions

Methodological limitations, inconsistent findings, and lack of evidence from LMIC imply an urgent need for further investigations. The potential for broader utilization of SNA among providers remains largely untapped and the findings of this review may contain important value for building optimal healthcare delivery networks.

PROSPERO registration number

The protocol was published and registered with PROSPERO, the International Prospective Register of Systematic Reviews (CRD42020205996).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12992-021-00772-7.

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.

Longitudinal trends in physician preferences for referrals to same-sex surgeons: a population-based study

This population-based study of over 27 million referrals to surgeons demonstrated that patient sharing among physicians and surgeons is influenced by the sex of the patient, referring physician, and surgeon. Importantly, the study demonstrated that male patients are unlikely to be referred to female surgeons, particularly if seen by a male physician. These disparities are not improving over time .

Sex Differences in the Pattern of Patient Referrals to Male and Female Surgeons

Importance

Studies have found that female surgeons have fewer opportunities to perform highly remunerated operations, a circumstance that contributes to the sex-based pay gap in surgery. Procedures performed by surgeons are, in part, determined by the referrals they receive. In the US and Canada, most practicing physicians who provide referrals are men. Whether there are sex-based differences in surgical referrals is unknown.

Objective

To examine whether physicians' referrals to surgeons are influenced by the sex of the referring physician and/or surgeon.

Design, Setting, and Participants

This cross-sectional, population-based study used administrative databases to identify outpatient referrals to surgeons in Ontario, Canada, from January 1, 1997, to December 31, 2016, with follow-up to December 31, 2018. Data analysis was performed from April 7, 2019, to May 14, 2021.

Exposures

Referring physician sex.

Main Outcomes and Measures

This study compared the proportion of referrals (overall and those referrals that led to surgery) made by male and female physicians to male and female surgeons to assess associations between surgeon, referring physician, or patient characteristics and referral decisions. Discrete choice modeling was used to examine the extent to which sex differences in referrals were associated with physicians' preferences for same-sex surgeons.

Results

A total of 39 710 784 referrals were made by 44 893 physicians (27 792 [61.9%] male) to 5660 surgeons (4389 [77.5%] male). Female patients made up a greater proportion of referrals to female surgeons than to male surgeons (76.8% vs 55.3%, P < .001). Male surgeons accounted for 77.5% of all surgeons but received 87.1% of referrals from male physicians and 79.3% of referrals from female physicians. Female surgeons less commonly received procedural referrals than male surgeons (25.4% vs 33.0%, P < .001). After adjusting for patient and referring physician characteristics, male physicians referred a greater proportion of patients to male surgeons than did female physicians; differences were greatest among referrals from other surgeons (rate ratio, 1.14; 95% CI, 1.13-1.16). Female physicians had a 1.6% (95% CI, 1.4%-1.9%) greater odds of same-sex referrals, whereas male physicians had a 32.0% (95% CI, 31.8%-32.2%) greater odds of same-sex referrals; differences did not attenuate over time.

Conclusions and Relevance

In this cross-sectional, population-based study, male physicians appeared to have referral preferences for male surgeons; this disparity is not narrowing over time or as more women enter surgery. Such preferences lead to lower volumes of and fewer operative referrals to female surgeons and are associated with sex-based inequities in medicine.

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

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

Empirically identified networks of healthcare providers for adults with mental illness

Background

Policies target networks of providers who treat people with mental illnesses, but little is known about the empirical structures of these networks and related variation in patient care. The goal of this paper is to describe networks of providers who treat adults with mental illness in a multi-payer database based medical claims data in a U.S. state.

Methods

Provider networks were identified and characterized using paid inpatient, outpatient and pharmacy claims related to care for people with a mental health diagnosis from an all-payer claims dataset that covers both public and private payers.

Results

Three nested levels of network structures were identified: an overall network, which included 21% of providers (N = 8256) and 97% of patients (N = 476,802), five communities and 24 sub-communities. Sub-communities were characterized by size, provider composition, continuity-of-care (CoC), and network structure measures including mean number of connections per provider (degree) and average number of connections who were connected to each other (transitivity). Sub-community size was positively associated with number of connections (r = .37) and the proportion of psychiatrists (r = .41) and uncorrelated with network transitivity (r = −.02) and continuity of care (r = .00). Network transitivity was not associated with CoC after adjustment for provider type, number of patients, and average connection CoC (p = .85).

Conclusions

These exploratory analyses suggest that network analysis can provide information about the networks of providers that treat people with mental illness that is not captured in traditional measures and may be useful in designing, implementing, and studying interventions to improve systems of care. Though initial results are promising, additional empirical work is needed to develop network-based measures and tools for policymakers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06798-2.

Association of Low-Value Care Exposure With Health Care Experience Ratings Among Patient Panels

IMPORTANCE

Patient reviews of health care experiences are increasingly used for public reporting and alternative payment models. Critics have argued that this incentivizes physicians to provide more care, including low-value care, undermining efforts to reduce wasteful practices.

OBJECTIVE

To assess associations between rates of low-value service provision to a primary care professional (PCP) patient panel and patients' ratings of their health care experiences.

DESIGN, SETTING, AND PARTICIPANTS

This quality improvement study used Medicare fee-for-service claims from January 1, 2007, to December 31, 2014, for a random 20% sample of beneficiaries to identify beneficiaries for whom each of 8 low-value services could be ordered but would be considered unnecessary. The study also used health care experience reports from independently sampled beneficiaries who responded to the 2010-2015 Consumer Assessment of Healthcare Providers and Systems (CAHPS) Medicare fee-for-service survey. Statistical analysis was performed from January 1, 2019, to December 9, 2020.

MAIN OUTCOMES AND MEASURES

The main outcomes were health care experience ratings from Medicare beneficiaries who responded to the CAHPS survey from 2 domains, namely "Your Health Care in the Last 6 Months" (overall health care, office wait time, timely access to nonurgent care, and timely access to urgent care) and "Your Personal Doctor" (overall personal physician and a composite score for interactions with personal physician). Beneficiaries in both samples were attributed to the PCP with whom they had the most spending. For each PCP, a composite score of low-value service exposure was constructed using the 20% sample; this score represented the adjusted relative propensity of the PCP patient panel to receive low-value care. The association between low-value service exposure and health care experience ratings reported by the CAHPS respondents in the PCP patient panel was estimated using regression analysis.

RESULTS

The final sample had 100 743 PCPs, with a mean of approximately 258 patients per PCP. Only 1 notable association was found; more low-value care exposure was associated with more frequent reports of having to wait more than 15 minutes after the scheduled time of an appointment (a mean of 0.448 points lower CAHPS score on a 10-point scale for PCP patient panels who received the most low-value care vs the least low-value care). Although some other associations were statistically significant, their magnitudes were substantially smaller than those typically considered meaningful in other CAHPS literature and were inconsistent in direction across levels of low-value service exposure.

CONCLUSIONS AND RELEVANCE

This quality improvement study found that more low-value care exposure for a PCP patient panel was not associated with more favorable patient ratings of their health care experiences.

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.

Edge Overlap in Weighted and Directed Social Networks

With the increasing availability of behavioral data from diverse digital sources, such as social media sites and cell phones, it is now possible to obtain detailed information about the structure, strength, and directionality of social interactions in varied settings. While most metrics of network structure have traditionally been defined for unweighted and undirected networks only, the richness of current network data calls for extending these metrics to weighted and directed networks. One fundamental metric in social networks is edge overlap, the proportion of friends shared by two connected individuals. Here we extend definitions of edge overlap to weighted and directed networks, and present closed-form expressions for the mean and variance of each version for the Erdős-Rényi random graph and its weighted and directed counterparts. We apply these results to social network data collected in rural villages in southern Karnataka, India. We use our analytical results to quantify the extent to which the average overlap of the empirical social network deviates from that of corresponding random graphs and compare the values of overlap across networks. Our novel definitions allow the calculation of edge overlap for more complex networks and our derivations provide a statistically rigorous way for comparing edge overlap across networks.

A Network Approach for the Study of Drug Prescriptions: Analysis of Administrative Records from a Local Health Unit (ASL TO4, Regione Piemonte, Italy)

In a Drug Prescription Network (DPN), each drug is represented as a node and two drugs co-prescribed to the same patient are represented as an edge linking the nodes. The use of DPNs is a novel approach that has been proposed as a means to study the complexity of drug prescription. The aim of this study is to demonstrate the analytical power of the DPN-based approach when it is applied to the analysis of administrative data. Drug prescription data that were collected at a local health unit (ASL TO4, Regione Piemonte, Italy), over a 12-month period (July 2018–June 2019), were used to create several DPNs that correspond to the five levels of the Anatomical Therapeutic Chemical classification system. A total of 5,431,335 drugs prescribed to 361,574 patients (age 0–100 years; 54.7% females) were analysed. As indicated by our results, the DPNs were dense networks, with giant components that contain all nodes. The disassortative mixing of node degrees was observed, which implies that non-random connectivity exists in the networks. Network-based methods have proven to be a flexible and efficient approach to the analysis of administrative data on drug prescription.

Differences in nursing home admission between functionally defined populations in Germany and the association with quality of health care

Background

People prefer to age in place and not move into a nursing home as long as possible. The prevention of cognitive and functional impairments is feasible to support this goal. Health services play a key role in providing support for underlying medical conditions. We examined differentials in nursing home admissions between patient sharing networks in Germany and whether potential variations can be attributed to indicators of health care provision.

Methods

We conducted an ecological study using data of patients of 65 years and above from all 11 AOK statutory health insurance companies in Germany. Nursing home admissions were observed in a cohort of persons becoming initially care-dependent in 2006 (n = 118,213) with a follow-up of up to 10 years. A patient sharing network was constructed and indicators for quality of health care were calculated based on data of up to 6.6 million patients per year. Community detection was applied to gain distinct patient populations. Analyses were conducted descriptively and through regression analyses to identify the variation explained by included quality indicators.

Results

The difference in the proportion of nursing home admissions between identified clusters shows an interquartile range (IQR) of 12.6% and the average time between onset of care-dependency and admission to a nursing home an IQR of 10,4 quarters. Included quality indicators attributed for 40% of these variations for the proportion of nursing home admissions and 49% for the time until nursing home admission, respectively. Indicators of process quality showed the single highest contribution. Effects of single indicators were inconclusive.

Conclusions

Health services can support persons in their preference to age in place. Research and discussion on adequate health care for care-dependent persons and on conditions, where nursing home admission may be beneficial, is necessary.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-06196-8.

Use of Electronic Health Data for Disease Prediction: A Comprehensive Literature Review

Disease prediction has the potential to benefit stakeholders such as the government and health insurance companies. It can identify patients at risk of disease or health conditions. Clinicians can then take appropriate measures to avoid or minimize the risk and in turn, improve quality of care and avoid potential hospital admissions. Due to the recent advancement of tools and techniques for data analytics, disease risk prediction can leverage large amounts of semantic information, such as demographics, clinical diagnosis and measurements, health behaviours, laboratory results, prescriptions and care utilisation. In this regard, electronic health data can be a potential choice for developing disease prediction models. A significant number of such disease prediction models have been proposed in the literature over time utilizing large-scale electronic health databases, different methods, and healthcare variables. The goal of this comprehensive literature review was to discuss different risk prediction models that have been proposed based on electronic health data. Search terms were designed to find relevant research articles that utilized electronic health data to predict disease risks. Online scholarly databases were searched to retrieve results, which were then reviewed and compared in terms of the method used, disease type, and prediction accuracy. This paper provides a comprehensive review of the use of electronic health data for risk prediction models. A comparison of the results from different techniques for three frequently modelled diseases using electronic health data was also discussed in this study. In addition, the advantages and disadvantages of different risk prediction models, as well as their performance, were presented. Electronic health data have been widely used for disease prediction. A few modelling approaches show very high accuracy in predicting different diseases using such data. These modelling approaches have been used to inform the clinical decision process to achieve better outcomes.

Underemployment of Female Surgeons?

OBJECTIVE

To compare the complexity of operations performed by female versus male surgeons.

BACKGROUND

Prior literature has suggested that female surgeons are relatively underemployed when compared to male surgeons, with regards to operative case volume and specialization.

METHODS

Operative case records from a large academic medical center from 1997 to 2018 were evaluated. The primary end point was work relative value unit (wRVU) for each case with a secondary end point of total wRVU per month for each surgeon. Multivariate linear analysis was performed, adjusting for surgeon race, calendar year, seniority, and clinical subspecialty.

RESULTS

A total of 551,047 records were analyzed, from 131 surgeons and 13,666 surgeon-months. Among them, 104,424 (19.0%) of cases were performed by female surgeons, who make up 20.6% (n = 27) of the surgeon population, and 2879 (21.1%) of the surgeon months. On adjusted analysis, male surgeons earned an additional 1.65 wRVU per case, compared to female surgeons (95% confidence interval 1.57-1.74). Subset analyses found that sex disparity increased with surgeon seniority, and did not improve over the 20-year study period.

CONCLUSIONS

Female surgeons perform less complex cases than their male peers, even after accounting for subspecialty and seniority. These sex differences are not due to availability from competing professional or familial obligations. Future work should focus on determining the cause and mitigating this underemployment of female surgeons.

Patient-sharing relationship between Chinese medicine doctors and other physicians: costs and outcomes of breast cancer survivorship care

PURPOSE

Breast cancer survivors represent a unique group of patients who need complex and continuous care after their cancer treatment. These patients often see several providers in various specialties. This study aimed to analyze how traditional Chinese medicine (TCM) integration within care networks of patients with breast cancer might be related to health care costs and patient outcomes under the National Health Insurance program in Taiwan.

METHODS

We enrolled all patients who underwent definitive mastectomy for newly diagnosed breast cancer between 2007 and 2015. We analyzed the presence of TCM physicians and the patient-sharing relationship between TCM physicians and other physicians during the first year after mastectomy. The outcomes included all-cause mortality, avoidable hospitalization, and medical expenditures.

RESULTS

There were 68,987 patients with breast cancer, with a median age of 53 years. After propensity score matching, patients whose TCM doctors had the highest connectedness with other physicians had the lowest odds of avoidable hospitalization (adjusted odds ratio 0.86; 95% confidence interval [CI], 0.78-0.96) and lowest hazard of mortality (adjusted hazard ratio, 0.82; 95% CI, 0.72-0.93), followed by those with TCM doctors with medium connectedness, then low connectedness, and lastly those patients with no TCM doctor in their care network.

CONCLUSIONS

A dose-response pattern was observed regarding the relationship between TCM doctor's connectedness with other physicians within a patient's care network and patient outcomes.

IMPLICATIONS FOR CANCER SURVIVORS

The findings demonstrated that stronger connectedness between TCM and other physicians could help improve the health outcomes of breast cancer survivors.

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.

Determinants of physician networks: an ethnographic study examining the processes that inform patterns of collaboration and referral decision-making among physicians

OBJECTIVE

Most scholarly attention to studying collaborative ties in physician networks has been devoted to quantitatively analysing large, complex datasets. While valuable, such studies can reduce the dynamic and contextual complexities of physician collaborations to numerical values. Qualitative research strategies can contribute to our understanding by addressing the gaps left by more quantitative approaches. This study seeks to contribute to the literature that applies network science approaches to the context of healthcare delivery. We use qualitative, observational and interview, methods to pursue an in-depth, micro-level approach to the deeply social and discursive processes that influence patterns of collaboration and referral decision-making in physician networks.

DESIGN

Qualitative methodologies that paired ethnographic field observations, semistructured interviews and document analysis were used. An inductive thematic analysis approach was used to analyse, identify and describe patterns in those data.

SETTING

This study took place in a high-volume cardiovascular department at a major academic medical centre (AMC) located in the Midwest region of the USA.

PARTICIPANTS

Purposive and snowballing sampling were used to recruit study participants for both the observational and face-to-face in-depth interview portions of the study. In total, 25 clinicians and 43 patients participated in this study.

RESULTS

Two primary thematic categories were identified: (1) circumstances for external engagement; and (2) clinical conditions for engagement. Thematic subcategories included community engagement, scientific engagement, reputational value, experiential information, professional identity, self-awareness of competence, multidisciplinary programmes and situational factors.

CONCLUSION

This study adds new contextual knowledge about the mechanisms that characterise referral decision-making processes and how these impact the meaning of physician relationships, organisation of healthcare delivery and the knowledge and beliefs that physicians have about their colleagues. This study highlights the nuances that influence how new collaborative networks are formed and maintained by detailing how relationships among physicians develop and evolve over time.

Assessment of Racial Disparities in Primary Care Physician Specialty Referrals

IMPORTANCE

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

OBJECTIVE

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

DESIGN, SETTING, AND PARTICIPANTS

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

EXPOSURES

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

MAIN OUTCOMES AND MEASURES

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

RESULTS

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

CONCLUSIONS AND RELEVANCE

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

Assessing Association Between Team Structure and Health Outcome and Cost by Social Network Analysis

BACKGROUND/OBJECTIVE

To assess the impact of team structure composition and degree of collaboration among various providers on process and outcomes of primary care.

DESIGN

Cross-sectional study.

SETTING

Data from 20% randomly selected primary care service areas in the 2015 Medicare claims were used to identify primary care practices.

PARTICIPANTS

449,460 patients with diabetes, heart failure, or chronic obstructive pulmonary disease cared for by the identified primary care practices.

MEASUREMENTS

Social network analysis measures, including edge density, degree centralization, and betweenness centralization for each practice.

RESULTS

When compared with practices with MDs and nurse practitioners (NPs) or/and physicians assistants (PAs), the practices with MDs had only lower degree of centralization and higher MD-to-MD connectedness. Within the primary care practices comprising MDs, NPs, or/and PAs, the nonphysician providers were more connected (measured as edge density) to all providers in the practice but with higher degree of centralization compared with the MDs in the practice. After adjusting for patient characteristics and type of practice, higher edge density was associated with lower odds of hospitalization (odds ratio (OR) = 0.89, 95% confidence interval (CI) = 0.79-0.99), emergency department (ER) admission (OR = 0.80, 95% CI = 0.70-0.92), and total spending (cost ratio (CR) = 0.86, standard error of the mean (SE) = 0.038). Conversely, higher degree centralization was associated with higher rates of hospitalization (OR = 1.15, 95% CI = 1.03-1.28), ER admission (OR = 1.23, 95% CI = 1.08-1.40), and total spending (CR = 1.14, SE = 0.037). However, higher degree centralization was associated with lower rates of potentially inappropriate medications (OR = 0.90, 95% CI = 0.81-0.99). Team leadership by an NP versus an MD was similar in the rate of ER admissions, hospitalizations, or total spending.

CONCLUSION

Our findings showed that highly connected primary care practices with high collaborative care and less top-down MD-centered authority have lower odds of hospitalization, fewer ER admissions, and less total spending; findings likely reflecting better communication and more coordinated care of older patients.

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.

Web-Based Interactive Tool to Identify Facilities at Risk of Receiving Patients with Multidrug-Resistant Organisms

To identify facilities at risk of receiving patients colonized or infected with multidrug-resistant organisms (MDROs), we developed an interactive web-based interface for visualization of patient-sharing networks among healthcare facilities in Tennessee, USA. Using hospital discharge data and the Centers for Medicare and Medicaid Services' claims and Minimum Data Set, we constructed networks among hospitals and skilled nursing facilities. Networks included direct and indirect transfers, which accounted for <365 days in the community outside of facility admissions. Authorized users can visualize a facility of interest and tailor visualizations by year, network dataset, length of time in the community, and minimum number of transfers. The interface visualizes the facility of interest with its connected facilities that receive or send patients, the number of interfacility transfers, and facilities at risk of receiving transfers from the facility of interest. This tool will help other health departments enhance their MDRO outbreak responses.

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.