Analysis of the U.S. patient referral network

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

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

Methodology for supervised optimization of the construction of physician shared-patient networks

There is growing use of shared-patient physician networks in health services research and practice, but minimal study of the consequences of decisions made in constructing them. To address this gap, we surveyed physician employees of a National Physician Organization (NPO) on their peer physician relationships. Using the physicians' survey nominations as ground truths, we evaluated the diagnostic accuracy of shared-patient edge-weights and the optimal construction of physician networks from sequences of patient-physician encounters. To further improve diagnostic accuracy, we optimized network construction with respect to the within-dyad difference and summation of edge-strength (two orthogonal measures), optimally combining them to form a final edge-weight. To achieve these goals, we develop statistical procedures to quantify the extent that directionality and other features of referral paths yield edge-weights with improved diagnostic properties. We also develop network models of the survey nominations incorporating directed (edge) and undirected (dyadic) shared-patient network measures as edge and dyad attributes to demonstrate that the measurement of the network as a whole is improved. Finally, we estimate the association of the physicians' centrality in the NPO shared-patient network (a sociocentric feature that cannot be evaluated for the partially-measured survey-based network) with their beliefs regarding physician peer-influence.

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

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

Exploiting relationship directionality to enhance statistical modeling of peer-influence across social networks

Risky-prescribing is the excessive or inappropriate prescription of drugs that singly or in combination pose significant risks of adverse health outcomes. In the United States, prescribing of opioids and other "risky" drugs is a national public health concern. We use a novel data framework-a directed network connecting physicians who encounter the same patients in a sequence of visits-to investigate if risky-prescribing diffuses across physicians through a process of peer-influence. Using a shared-patient network of 10 661 Ohio-based physicians constructed from Medicare claims data over 2014-2015, we extract information on the order in which patients encountered physicians to derive a directed patient-sharing network. This enables the novel decomposition of peer-effects of a medical practice such as risky-prescribing into directional (outbound and inbound) and bidirectional (mutual) relationship components. Using this framework, we develop models of peer-effects for contagion in risky-prescribing behavior as well as spillover effects. The latter is measured in terms of adverse health events suspected to be related to risky-prescribing in patients of peer-physicians. Estimated peer-effects were strongest when the patient-sharing relationship was mutual as opposed to directional. Using simulations we confirmed that our modeling and estimation strategies allows simultaneous estimation of each type of peer-effect (mutual and directional) with accuracy and precision. We also show that failing to account for these distinct mechanisms (a form of model mis-specification) produces misleading results, demonstrating the importance of retaining directional information in the construction of physician shared-patient networks. These findings suggest network-based interventions for reducing risky-prescribing.

Implications of interhospital patient transfers for emergency medical services transportation systems in the Netherlands: a retrospective study

OBJECTIVES

Interhospital patient transfers have become routine. Known drivers are access to specialty care and non-clinical reasons, such as limited capacity. While emergency medical services (EMS) providers act as main patient transfer operators, the impact of interhospital transfers on EMS service demand and fleet management remains understudied. This study aims to identify patterns in regional interhospital patient transfer volumes and their spatial distribution, and to discuss their potential implications for EMS service demand and fleet management.

DESIGN

A retrospective study was performed analysing EMS transport data from the province of Drenthe in the Netherlands between 2013 and 2019 and public hospital listings. Yearly volume changes in urgent and planned interhospital transfers were quantified. Further network analysis, including geomapping, was used to study how transfer volumes and their spatial distribution relate to hospital specialisation, and servicing multihospital systems. Organisational data were considered for relating transfer patterns to fleet changes.

SETTING

EMS in the province of Drenthe, the Netherlands, 492 167 inhabitants.

PARTICIPANTS

Analyses are based on routinely collected patient data from EMS records, entailing all 248 114 transports (137 168 patients) of the Drenthe EMS provider (2013-2019). From these interhospital transports were selected (24 311 transports).

RESULTS

Interhospital transfers represented a considerable (9.8%) and increasing share of transports (from 8.6% in 2013 to 11.3% in 2019). Most transfers were related to multihospital systems (47.3%, 11 509 transports), resulting in a considerable growth of planned EMS transports (from 2093 in 2013 to 3511 in 2019). Geomapping suggests increasing transfer distances and diminishing resource efficiencies due to lacking follow-up rides. Organisational data clarify how EMS fleets were adjusted by expanding resources and reorganising fleet operation.

CONCLUSIONS

Emerging interhospital network transfers play an important role in EMS service demand. Increased interhospital transport volumes and geographical spread require a redesign of current EMS fleets and management along regional lines.

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.

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

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

Maintenance of medium- and high-complexity health services in the context of high patient transition: a time series ecological study of the state of Rio de Janeiro, Brazil

The study addresses the historical disparities in the distribution of the medium- and high-complexity health network and the limits to budget adjustments between the municipality of Rio de Janeiro and its neighboring municipalities of the Metropolitan region 1. An ecological study was conducted with data related to the municipality of Rio de Janeiro, chosen because it has a large assistance network, while located on the borders of vulnerable and underprivileged areas, characterizing a locus that is representative of the situations faced throughout the country. A decrease in the gross values of the programmed quotas in all municipalities of Rio de Janeiro was observed from 2016 onwards. The temporal trend of the programmed quotas remained stable for all municipalities in the Metropolitan Region 1, even with significant increases in the accomplished quotas for neighboring municipalities. The resulting overload in local expenditure prevents the increase of capacity to anticipate fluctuations in demand, both known and unexpected ones, compromising the responsiveness of the health system regarding its regular operation, as well as the ability to adjust to cope with extraordinary events, essential characteristics of resilience.

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

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

Network Analysis Examining Intrahospital Traffic of Patients With Traumatic Hip Fracture

INTRODUCTION

Increased intrahospital traffic (IHT) is associated with adverse events and infections in hospitalized patients. Network science has been used to study patient flow in hospitals but not specifically for patients with traumatic injuries.

METHODS

This retrospective analysis included 103 patients with traumatic hip fractures admitted to a level I trauma center between April 2021 and September 2021. Associations with IHTs (moves within the hospital) were analyzed using R (4.1.2) as a weighted directed graph.

RESULTS

The median (interquartile range) number of moves was 8 (7-9). The network consisted of 16 distinct units and showed mild disassortativity (-0.35), similar to other IHT networks. The floor and intensive care unit (ICU) were central units in the flow of patients, with the highest degree and betweenness. Patients spent a median of 20-28 hours in the ICU, intermediate care unit, or floor. The number of moves per patient was mildly correlated with hospital length of stay (ρ = 0.26, p = .008). Intrahospital traffic volume was higher on weekdays and during daytime hours. Intrahospital traffic volume was highest in patients aged <65 years ( p = .04), but there was no difference in IHT volume by dependent status, complications, or readmissions.

CONCLUSIONS

Network science is a useful tool for trauma patients to plan IHT, flow, and staffing.

A Bibliometric Analysis and Visualization of Decision Support Systems for Healthcare Referral Strategies

BACKGROUND

The referral process is an important research focus because of the potential consequences of delays, especially for patients with serious medical conditions that need immediate care, such as those with metastatic cancer. Thus, a systematic literature review of recent and influential manuscripts is critical to understanding the current methods and future directions in order to improve the referral process.

METHODS

A hybrid bibliometric-structured review was conducted using both quantitative and qualitative methodologies. Searches were conducted of three databases, Web of Science, Scopus, and PubMed, in addition to the references from the eligible papers. The papers were considered to be eligible if they were relevant English articles or reviews that were published from January 2010 to June 2021. The searches were conducted using three groups of keywords, and bibliometric analysis was performed, followed by content analysis.

RESULTS

A total of 163 papers that were published in impactful journals between January 2010 and June 2021 were selected. These papers were then reviewed, analyzed, and categorized as follows: descriptive analysis (n = 77), cause and effect (n = 12), interventions (n = 50), and quality management (n = 24). Six future research directions were identified.

CONCLUSIONS

Minimal attention was given to the study of the primary referral of blood cancer cases versus those with solid cancer types, which is a gap that future studies should address. More research is needed in order to optimize the referral process, specifically for suspected hematological cancer patients.

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

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

Analyzing annual changes in network structures of a social media application-based information-sharing system in a Japanese community

Background

Understanding the evolution of social network services (SNSs) can provide insights into the functions of interprofessional information-sharing systems. Using social network analysis, we aimed to analyze annual changes in the network structure of SNS-based information sharing among healthcare professionals over a 3-year period in Japan.

Methods

We analyzed data on SNS-based information sharing networks with online message boards for healthcare professionals for 2018, 2019, and 2020 in a Japanese community.

These networks were created for each patient so that healthcare professionals could post and view messages on the web platform. In the social network analysis (SNA), healthcare professionals registered with a patient group were represented as nodes, and message posting and viewing relationships were represented as links. We investigated the structural characteristics of the networks using several measures for SNA, including reciprocity, assortativity and betweenness centrality, which reflect interrelational links, the prevalence of similar nodes with neighbors, and the mediating roles of other nodes, respectively. Next, to compare year-to-year trends in networks of patients overall, and between receiving nursing care levels 1–3 (lighter care requirement) and levels 4–5 (heavier care requirement), we described the annual structural differences and analyzed each measure for SNA using the Steel–Dwass test.

Results

Among 844, 940, and 1063 groups in each year, groups for analysis in care levels 1–3/4–5 were identified as 106/135, 79/89, and 57/57, respectively. The overall annual assessment showed a trend toward increased diameter and decreased density, but the differences were not significant. For those requiring care levels 1–3, assortativity decreased significantly, while for those requiring care levels 4–5, reciprocity decreased and betweenness centrality increased significantly. No significant differences were found in the other items.

Discussion

This study revealed that the network of patients with a lighter care requirement had more connections consisting of nodes with different links, whereas the network of patients with a heavier care requirement had more fixed intermediary roles and weaker interrelationships among healthcare professionals. Clarifying interprofessional collaborative mechanisms underlying development patterns among healthcare professionals can contribute to future clinical quality improvement.

Using network analysis to model the effects of the SARS Cov2 pandemic on acute patient care within a healthcare system

Consolidation of healthcare in the US has resulted in integrated organizations, encompassing large geographic areas, with varying services and complex patient flows. Profound changes in patient volumes and behavior have occurred during the SARS Cov2 pandemic, but understanding these across organizations is challenging. Network analysis provides a novel approach to address this. We retrospectively evaluated hospital-based encounters with an index emergency department visit in a healthcare system comprising 18 hospitals, using patient transfer as a marker of unmet clinical need. We developed quantitative models of transfers using network analysis incorporating the level of care provided (ward, progressive care, intensive care) during pre-pandemic (May 25, 2018 to March 16, 2020) and mid-pandemic (March 17, 2020 to March 8, 2021) time periods. 829,455 encounters were evaluated. The system functioned as a non-small-world, non-scale-free, dissociative network. Our models reflected transfer destination diversification and variations in volume between the two time points - results of intentional efforts during the pandemic. Known hub-spoke architecture correlated with quantitative analysis. Applying network analysis in an integrated US healthcare organization demonstrates changing patterns of care and the emergence of bottlenecks in response to the SARS Cov2 pandemic, consistent with clinical experience, providing a degree of face validity. The modelling of multiple influences can identify susceptibility to stress and opportunities to strengthen the system where patient movement is common and voluminous. The technique provides a mechanism to analyze the effects of intentional and contextual changes on system behavior.

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.

Medicare Patient Referral Networks to Pelvic Floor Physical Therapy Across the United States

OBJECTIVE

The purpose of this study is to evaluate the distribution of referrals to pelvic floor physical therapy throughout the United States and to identify specialties with the highest and lowest referral rates. Referral networks to pelvic floor physical therapy were identified, and factors associated with referral connections were determined.

METHODS

This retrospective network analysis of referrals examined U.S. Centers for Medicare and Medicaid Services data from 2009 to 2017. Pelvic floor physical therapists were identified, and their patient-sharing networks were modeled using social network analytics.

RESULTS

There were 18,740 Medicare beneficiaries referred to pelvic floor physical therapists between 2009 and 2017. The mean number of referrals to each physical therapy provider or practice was 82 (SD ±46.3). Half of the referrals were made by a general acute care hospital. The remainder were referred by female pelvic medicine and reconstructive surgeons, nurse practitioners, colorectal surgeons, internal medicine, and obstetrician-gynecologists.The number of individual pelvic floor physical therapists, as well as the referrals, increased each year. The geographic representation of the patient referral networks is illustrated. The map reveals that pelvic floor physical therapists often work in groups and treat patients in their geographic vicinity. In this study, we demonstrate intensely fractured referral networks.

CONCLUSION

Our network analysis of pelvic floor physical therapy referrals in Medicare patients across the United States shows fractured networks with dense geographic connections in some areas, whereas sparse in others. Multidisciplinary approaches and early referrals to pelvic floor physical therapy are recommended as some ways to amend these fractured networks.

The Effect of Interdependences of Referral Behaviors on the Quality of Ambulatory Care: Evidence from Taiwan

Purpose

The purpose of this study is to investigate the effect of interdependences of healthcare providers’ referral behaviors on the quality of ambulatory care. The significance of this study is to address the concern regarding the low quality of ambulatory care due to the lack of a compulsory referral system under Taiwan’s National Health Insurance system.

Methods

We applied the dynamic connectedness network analysis to estimate the total connectedness index of the referral behavior network, which was separated into the horizontal and vertical referral behavior components in order to measure the interdependences of horizontal and vertical referral behaviors across hospitals and local clinics, respectively.

Results

Our results suggest that the interdependences of referral behaviors increase the quality of ambulatory care. The harmful effect on the quality of ambulatory care from the interdependences of horizontal referral behaviors within the local clinics sector is more significant than that from the interdependences of horizontal referral behaviors within the hospital sector, and the negative effect on the overall and chronic composite measures of avoidable hospital admissions from the interdependences of vertical behaviors associated with local clinics is more substantial than that from the interdependences of vertical behaviors within the hospital sector.

Conclusion

These results not only highlight the significance of care collaboration between local clinics and hospitals to restrain avoidable hospital admissions of chronic diseases for a better overall quality of ambulatory care, but they also suggest that the surveillance system established for the quality of ambulatory care under the global budget payment scheme for the local clinics sector should target ambulatory care for patients with acute conditions.

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.

Frequent attendance at the emergency department shows typical features of complex systems: analysis of multicentre linked data

OBJECTIVE

Frequent attendance at the ED is a worldwide problem. We hypothesised that frequent attendance could be understood as a feature of a complex system comprising patients, healthcare and society. Complex systems have characteristic statistical properties, with stable patterns at the level of the system emerging from unstable patterns at the level of individuals who make up the system.

METHODS

Analysis of a linked dataset of routinely collected health records from all 13 hospital trusts providing ED care in the Yorkshire and Humber region of the UK (population 5.5 million). We analysed the distribution of attendances per person in each of 3 years and measured the transition of individual patients between frequent, infrequent and non-attendance. We fitted data to power law distributions typically seen in complex systems using maximum likelihood estimation.

RESULTS

The data included 3.6 million attendances at EDs in 13 hospital trusts. 29/39 (74.3%) analyses showed a statistical fit to a power law; 2 (5.1%) fitted an alternative distribution. All trusts' data fitted a power law in at least 1 year. Differences over time and between hospital trusts were small and partly explained by demographics. In contrast, individual patients' frequent attendance was unstable between years.

CONCLUSIONS

ED attendance patterns are stable at the level of the system, but unstable at the level of individual frequent attenders. Attendances follow a power law distribution typical of complex systems. Interventions to address ED frequent attendance need to consider the whole system and not just the individual frequent attenders.

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.

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.

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

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

The association between patient sharing network structure and healthcare costs

STUDY QUESTION

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

DATA SOURCE

Data used are the 2011 Massachusetts All Payer Claims Database.

STUDY DESIGN

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

DATA COLLECTION

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

PRINCIPAL FINDINGS

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

CONCLUSIONS

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

WHAT THIS STUDY ADDS

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

Understanding unwarranted variation in clinical practice: a focus on network effects, reflective medicine and learning health systems

In the past decades, extensive research has been performed on the phenomenon of unwarranted clinical variation in clinical practice. Many studies have been performed on signaling, describing and visualizing clinical variation. We argue that it is time for next steps in practice variation research. In addition to describing and signaling variation patterns, we argue that a better understanding of causes of variation should be gained. Moreover, target points for improving and decreasing clinical variation should be created. Key elements in this new focus should be research on the complex interaction of networks, reflective medicine, patient beliefs and objective criteria for treatment choices. By combining these different concepts, alternative research objectives and new targets for improving and reducing unwarranted variation may be defined. In this perspective, we reflect on these concepts and propose target points for future research.

A Systematic Review of Network Studies Based on Administrative Health Data

Effective and efficient delivery of healthcare services requires comprehensive collaboration and coordination between healthcare entities and their complex inter-reliant activities. This inter-relation and coordination lead to different networks among diverse healthcare stakeholders. It is important to understand the varied dynamics of these networks to measure the efficiency of healthcare delivery services. To date, however, a work that systematically reviews these networks outlined in different studies is missing. This article provides a comprehensive summary of studies that have focused on networks and administrative health data. By summarizing different aspects including research objectives, key research questions, adopted methods, strengths and weaknesses, this research provides insights into the inherently complex and interlinked networks present in healthcare services. The outcome of this research is important to healthcare management and may guide further research in this area.

Can network science reveal structure in a complex healthcare system? A network analysis using data from emergency surgical services

INTRODUCTION

Hospitals are complex systems and optimising their function is critical to the provision of high quality, cost effective healthcare. Metrics of performance have to date focused on the performance of individual elements rather than the whole system. Manipulation of individual elements of a complex system without an integrative understanding of its function is undesirable and may lead to counterintuitive outcomes and a holistic metric of hospital function might help design more efficient services.

OBJECTIVES

We aimed to use network analysis to characterise the structure of the system of perioperative care for emergency surgical admissions in our tertiary care hospital.

DESIGN

We constructed a weighted directional network representation of the emergency surgical services using patient location data from electronic health records.

SETTING

A single-centre tertiary care hospital in the UK.

PARTICIPANTS

We selected data from the retrospective electronic health record data of all unplanned admissions with a surgical intervention during their stay during a 3.5-year period, which resulted in a set of 16 500 individual admissions.

METHODS

We then constructed and analysed the structure of this network using established methods from network science such as degree distribution, betweenness centrality and small-world characteristics.

RESULTS

The analysis showed the service to be a complex system with scale-free, small-world network properties. We also identified such potential hubs and bottlenecks in the system.

CONCLUSIONS

Our holistic, system-wide description of a hospital service may provide tools to inform service improvement initiatives and gives us insights into the architecture of a complex system of care. The implications for the structure and resilience of the service is that while being robust in general, the system may be vulnerable to outages at specific key nodes.

Exponential random graph model parameter estimation for very large directed networks

Exponential random graph models (ERGMs) are widely used for modeling social networks observed at one point in time. However the computational difficulty of ERGM parameter estimation has limited the practical application of this class of models to relatively small networks, up to a few thousand nodes at most, with usually only a few hundred nodes or fewer. In the case of undirected networks, snowball sampling can be used to find ERGM parameter estimates of larger networks via network samples, and recently published improvements in ERGM network distribution sampling and ERGM estimation algorithms have allowed ERGM parameter estimates of undirected networks with over one hundred thousand nodes to be made. However the implementations of these algorithms to date have been limited in their scalability, and also restricted to undirected networks. Here we describe an implementation of the recently published Equilibrium Expectation (EE) algorithm for ERGM parameter estimation of large directed networks. We test it on some simulated networks, and demonstrate its application to an online social network with over 1.6 million nodes.

The formation of physician patient sharing networks in medicare: Exploring the effect of hospital affiliation

This study explores the forces that drive the formation of physician patient sharing networks. In particular, I examine the degree to which hospital affiliation drives physicians' sharing of Medicare patients. Using a revealed preference framework where observed network links are taken to be pairwise stable, I estimate the physicians' pair-specific values using a tetrad maximum score estimator that is robust to the presence of unobserved physician specific characteristics. I also control for a number of potentially confounding patient sharing channels, such as (a) common physician group or hospital system affiliation, (b) physician homophily, (c) knowledge complementarity, (d) patient side considerations related to both geographic proximity and insurance network participation, and (e) spillover from other collaborations. Focusing on the Chicago hospital referral region, I find that shared hospital affiliation accounts for 36.5% of the average pair-specific utility from a link. Implications for reducing care fragmentation are discussed.

Taking upstairs care outside

Background: Critical care is a clinically complex and resource intensive discipline, the world over. Consequently, the delivery of these services has been compounded by the need to sustain a specialized workforce, while maintaining consistent and high standards.1,2 The regionalization of critical care resources and the creation of referral networks has been one approach that has led to success in this area.2-7 However, as steps have been made towards regionalization, so too has the need to transfer patients between facilities in order to access these services. The effects of this are already apparent, where estimates in the United States have found that 1 in 20 patients requiring intensive and critical care resulted in transfer to another facility.2 The need for such transfers are equally varied as they are common and include: no critical care facilities at the referring facility; no staffed critical care bed availability at referring facility; requirements for expertise and/or specialists facilitates not available at referring site; and the repatriation of patients back to their original facility.6,8 An increase in the number of patients requiring the continuation of critical care in-transit has led to a need to expand the borders of traditional intensive care beyond the confines of the hospital. Such a concept fits with the assertions of Peter Safar, a pioneer of modern critical care, who proposed that critical care should not be defined by geographic location, but rather a set of principles designed to deliver appropriate and timely care to patients who need it.9 Specialised transfer services: The advent and implementation of critical care transfer and retrieval services has been the bridge to this divide, lying at the confluence of prehospital emergency care, in-hospital emergency medicine, and intensive care. Undertaking the transfer of a patient requiring the initiation or continuation of critical care is no simple task. Variations in patient type and severity of their medical condition, as well as the expectations of the transfer team are significant. Reports regarding the transfer of patients ranging from critical neonates, to the multi-comorbid geriatric; with complex underlying surgical and medical diagnoses; involving the concomitant administration of multiple vasoactive and sedative medications; with a variety of oxygenation and ventilation requirements, are commonplace in the literature.6,8,10-16 Consequently, moving these patients from the safety and security of one facility to another is an immense logistical challenge and fraught with risks. In addition to the severity of the patients underlying condition, limitations in space, personnel and equipment, as well an unpredictable operating environment are several of the potential hazards faced during the transfer of these patients. These hazards are evident in the incidence of adverse events found in the literature. Incorrect referral triage; inadequate transfer team; patients requiring stabilization prior to transfer; equipment and/or technical failures; adverse drug events and medication errors are amongst the most common reported events.6,8,10-17 Further to this, the movement of patients alone has in itself been shown to have an impact on a patient's baseline status, without the occurrence of negative or untoward events.10,13,15,16 As a result, patient safety and quality of care have become essential components of modern critical care transfer and retrieval services, with the role of clinical audit central to their ability to learn and improve from previous cases and events. The local solution: Despite the relatively small size of the State of Qatar, critical care transfer and retrieval has nonetheless become a necessity within the country's healthcare system. Figure 1 highlights the locations of the main hospitals. Starting in 2014, a dedicated program was initiated to facilitate the transfer and retrieval of critical care patients across the country.18 The Specialized High Acuity Adult Retrieval Program (SHAARP) is a joint initiative between the Hamad Medical Corporation Ambulance Service (HMCAS) and the Hamad Medical Corporation (HMC) Critical Care Network (CCN). It consists of a single dedicated purpose-built ambulance, manned and run 24 hours a day, seven days a week by a variety of staff from both HMCAS and the CCN and deployed primarily for the transfer and retrieval of critical care patients across Qatar.19 The program was further developed in 2016 and formalized under the Transfer and Retrieval division of the HMCAS, with dedicated HMCAS and CCN staff receiving bespoke training and continued education;18 the addition of specialized and dedicated communications staff for call taking, dispatch and monitoring; and focused governance and audit to maintain the highest quality of patient safety and quality of care. Since then, the program has seen considerable success and uptake within the country's health system. The activity of the unit echoes much of what can be found in the literature and further reinforces the need for such a specialized service, regardless of setting (Table 1). It further highlights the importance of the relationship and cooperation between the HMCAS and CCN regarding the expertise and resources that each component adds to the overall service. This is particularly evident in the expectations of the team regarding their duties of care whilst in transit. A significant proportion of the patients transferred by the program have required the maintenance of a high-level of care between facilities, under conditions that are far more challenging than that seen in any regular hospital ward or intensive care unit (Table 2). Conclusion: In modern healthcare, to deliver a consistent and high-level critical care service in any setting, the movement of patients is inevitable. However, in order to ensure the continuum of this level of care and maintain the highest standards of patient safety and quality of care in-transit, specialized transfer services are a necessity. The multidisciplinary nature of critical care transfer and retrieval dictates the cooperation between multiple in-hospital and out of hospital specialties and is a fundamental underlying concept in the success of such services.

Healthcare network analysis of patients with diabetes and their physicians

OBJECTIVES

To illustrate methods using administrative data on patients with diabetes that can offer a foundation for using network analyses in managed care.

STUDY DESIGN

The study used an administrative claims database to analyze patients with diabetes in a large health plan in Hawaii in 2010.

METHODS

The networks were explored graphically and analyzed at several levels of complexity. Levels ranged from major components comprising the majority in the networks to smaller, highly connected cliques to communities of patients and physicians grouped by a network algorithm. The attributes of patients linked by seeing the same primary physicians were evaluated using an exponential random graph model that predicted links in the network.

RESULTS

The study included 41,941 patients with diabetes of Native Hawaiian (16.3%), Filipino (14.2%), Japanese (46.7%), white (11.2%), and other (11.6%) ethnicity. About half were 65 years or older. When examined by Hawaiian island of residence, at least 95% of patients and at least 78% of physicians belonged to loosely connected major components within a network. Smaller communities of patients, identified by being closely linked together, averaged 150 to 177 patients; communities of physicians averaged 3 to 8 physicians. The average numbers of patients sharing physicians and physicians sharing patients were greater on the island of Oahu than on the rural neighboring islands. Patients of the same ethnicity were significantly more likely to share the same primary physician.

CONCLUSIONS

Network analyses reveal structures and links that health plans could leverage to strengthen quality improvement and disease management programs.

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

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

Physician network position and patient outcomes following implantable cardioverter defibrillator therapy

OBJECTIVE

To evaluate two novel measures of physician network centrality and their associations with implantable cardioverter defibrillator (ICD) procedure volume and health outcomes.

DATA SOURCES

Medicare claims and the National Cardiovascular Data Registry data from 2007 to 2011.

STUDY DESIGN

We constructed a national cardiovascular disease patient-sharing physician network and used network analysis to characterize physician network centrality with two measures: within-hospital degree centrality (number of connections within a hospital) and across-hospital degree centrality (number of connections across hospitals). The primary outcome was risk-adjusted 2-year case fatality. Hierarchical logistic regression estimated the effects of physician's within-hospital and across-hospital degree centrality on case fatality. We included 105 109 ICD therapy patients and 3474 ICD implanting physicians in our analyses.

PRINCIPAL FINDINGS

After controlling for other physician and hospital characteristics, we observed greater risk-adjusted case fatality among patients treated by physicians in the highest across-hospital degree tertile compared to lowest tertile (OR [95% CI] = 1.10 [1.04-1.16], P = 0.001) and lowest tertile volume physicians compared with highest volume (OR [95% CI] = 0.90 [0.84-0.95], P < 0.001). Physician's within-hospital degree tertile was inversely associated with case fatality but not statistically significant.

CONCLUSIONS

Degree centrality measures capture information independent of procedure volume and raise questions about the quality of physicians with networks that predict worse health outcomes.

Referral paths in the U.S. physician network

In this paper, we analyze the millions of referral paths of patients' interactions with the healthcare system for each year in the 2006-2011 time period and relate them to U.S. cardiovascular treatment records. For a patient, a "referral path" records the chronological sequence of physicians encountered by a patient (subject to certain constraints on the times between encounters). It provides a basic unit of analysis in a broader referral network that encodes the flow of patients and information between physicians in a healthcare system. We consider referral networks defined over a range of interactions as well as the characteristics of referral paths, producing a characterization of the various networks as well as the physicians they comprise. We further relate these metrics and findings to outcomes in the specific area of cardiovascular care. In particular, we match a referral path to occurrences of Acute Myocardial Infarction (AMI) and use the summary measures of the referral path to predict the treatment a patient receives and medical outcomes following treatment. Some referral path features are more significant with respect to their ability to boost a tree-based predictive model, and have stronger correlations with numerical treatment outcome variables. The patterns of referral paths and the derived informative features illustrate the potential for using network science to optimize patient referrals in healthcare systems for improved treatment outcomes and more efficient utilization of medical resources.

The Role of Organizational Affiliations in Physician Patient-Sharing Relationships

Provider consolidation may enable improved care coordination, but raises concerns about lack of competition. Physician patient-sharing relationships play a key role in constructing patient care teams, but it is unknown how organization affiliations affect these. We use the Massachusetts All Payer Claims Database to examine whether patient-sharing relationships are associated with sharing a practice site, medical group, and/or physician contracting network. Physicians were 17 percentage points more likely to have a patient-sharing relationship if they shared a practice site and 4 percentage points more likely if they shared a medical group, as compared with sharing no affiliation. However, there was no detectable increased probability of a patient-sharing relationship within the same physician contracting network. Our finding that physician patient-sharing relationships are concentrated within organizational boundaries at practice site and medical group levels helps illuminate referral incentives and provide insight into the role of organizational affiliations in patient care team construction.