Mapping and modeling of physician collaboration network

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

OBJECTIVES

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

DESIGN

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

SETTING

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

PARTICIPANTS

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

PRIMARY OUTCOME MEASURES

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

RESULTS

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

CONCLUSIONS

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

Impact of artificial intelligence-based and traditional image preprocessing and resampling on MRI-based radiomics for classification of papillary thyroid carcinoma

Objectives

This study aims to evaluate the impact of image preprocessing methods, including traditional and artificial intelligence (AI)-based techniques, on the performance of MRI-based radiomics for predicting tumour aggressiveness in papillary thyroid carcinoma (PTC).

Methods

We retrospectively analysed MRI data from 69 patients with PTC, acquired between January 2011 and April 2023, alongside corresponding histopathology. MRI scans underwent N4 bias field correction and resampling using 10 traditional methods and an AI-based technique, synthetic multi-orientation resolution enhancement (SMORE). Radiomic features were extracted from the original and preprocessed images. Recursive feature elimination with random forests was used for feature selection, and predictive models were developed using XGBoost. The performance of the model was assessed by calculating the area under the receiver operating characteristic curve (AUC) across 1000 iterations.

Results

The combination of the correction of the bias field of N4 with SMORE resampling produced the highest mean AUC (0.75), significantly outperforming all traditional resampling methods ( P < .001 ). The lowest mean AUC (0.66) was observed with nearest neighbour resampling. Texture-based radiomic features, particularly the standard deviation of the grey-level co-occurrence matrix autocorrelation, were frequently selected in models using SMORE-resampled images.

Conclusions

Preprocessing techniques critically influence the predictive performance of MRI-based radiomics in PTC. The combination of N4 bias field correction and SMORE resampling enhances accuracy, highlighting the necessity of optimizing preprocessing pipelines.

Advances in knowledge

This study demonstrates the superiority of AI-driven preprocessing techniques, such as SMORE, in improving MRI radiomic models, paving the way for enhanced clinical decision-making in PTC management.

Collaboration Between Physicians from Different Medical Specialties in Hospital Settings: A Systematic Review

Health care today is characterized by an increasing number of patients with comorbidities for whom interphysician collaboration seems very important. We reviewed the literature to understand what factors affect interphysician collaboration, determine how interphysician collaboration is measured, and determine its effects. We systematically searched six major databases. Based on 63 articles, we identified five categories that influence interphysician collaboration: personal factors, professional factors, preconditions and tools, organizational elements, and contextual characteristics. We identified a diverse set of mostly unvalidated tools for measuring interphysician collaboration that focus on information being transferred and understood, frequency of interaction and tone of the relationship, and value judgements about quality or satisfaction. We found that interphysician collaboration increased clinical outcomes as well as patient and staff satisfaction, while error rates and length of stay were reduced. The results should, however, be interpreted with caution, as most of the studies provide a low level of evidence.

Regional medical inter-institutional cooperation in medical provider network constructed using patient claims data from Japan

The aging world population requires a sustainable and high-quality healthcare system. To examine the efficiency of medical cooperation, medical provider and physician networks were constructed using patient claims data. Previous studies have shown that these networks contain information on medical cooperation. However, the usage patterns of multiple medical providers in a series of medical services have not been considered. In addition, these studies used only general network features to represent medical cooperation, but their expressive ability was low. To overcome these limitations, we analyzed the medical provider network to examine its overall contribution to the quality of healthcare provided by cooperation between medical providers in a series of medical services. This study focused on: i) the method of feature extraction from the network, ii) incorporation of the usage pattern of medical providers, and iii) expressive ability of the statistical model. Femoral neck fractures were selected as the target disease. To build the medical provider networks, we analyzed the patient claims data from a single prefecture in Japan between January 1, 2014 and December 31, 2019. We considered four types of models. Models 1 and 2 use node strength and linear regression, with Model 2 also incorporating patient age as an input. Models 3 and 4 use feature representation by node2vec with linear regression and regression tree ensemble, a machine learning method. The results showed that medical providers with higher levels of cooperation reduce the duration of hospital stay. The overall contribution of the medical cooperation to the duration of hospital stay extracted from the medical provider network using node2vec is approximately 20%, which is approximately 20 times higher than the model using strength.

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.

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.

Evaluation of Physician Network-Based Measures of Care Coordination Using Medicare Patient-Reported Experience Measures

BACKGROUND

There is significant promise in analyzing physician patient-sharing networks to indirectly measure care coordination, yet it is unknown whether these measures reflect patients' perceptions of care coordination.

OBJECTIVE

To evaluate the associations between network-based measures of care coordination and patient-reported experience measures.

DESIGN

We analyzed patient-sharing physician networks within group practices using data made available by the Centers for Medicare and Medicaid Services.

SUBJECTS

Medicare beneficiaries who provided responses to the Consumer Assessment of Healthcare Providers and Systems (CAHPS) Survey in 2016 (data aggregated by physician group practice made available through the Physician Compare 2016 Group Public Reporting).

MAIN MEASURES

The outcomes of interest were patient-reported experience measures reflecting aspects of care coordination (CAHPS). The predictor variables of interests were physician group practice density (the number of physician pairs who share patients adjusting for the total number of physician pairs) and clustering (the extent to which sets of three physicians share patients).

KEY RESULTS

Four hundred seventy-six groups had patient-reported measures available. Patients' perception of "Clinicians working together for your care" was significantly positively associated with both physician group practice density (Est (95 % CI) = 5.07(0.83, 9.33), p = 0.02) and clustering (Est (95 % CI) = 3.73(1.01, 6.44), p = 0.007). Physician group practice clustering was also significantly positively associated with "Getting timely care, appointments, and information" (Est (95 % CI) = 4.63(0.21, 9.06), p = 0.04).

CONCLUSIONS

This work suggests that network-based measures of care coordination are associated with some patient-reported experience measures. Evaluating and intervening on patient-sharing networks may provide novel strategies for initiatives aimed at improving quality of care and the patient experience.

Collaboration in Complex Systems: Multilevel Network Analysis for Community-Based Obesity Prevention Interventions

Community-based systems interventions represent a promising, but complex approach to the prevention of childhood obesity. Existing studies suggest that the implementation of multiple actions by engaged community leaders (steering committees) is of critical importance to influence a complex system. This study explores two key components of systems interventions: (1) steering committees; and (2) causal loop diagrams (CLDs), used to map the complex community-level drivers of obesity. The interactions between two components create an entangled, complex process difficult to measure, and methods to analyse the dependencies between these two components in community-based systems interventions are limited. This study employs multilevel statistical models from social network analysis to explore the complex interdependencies between steering committee collaboration and their actions in the CLD. Steering committee members from two communities engaged in obesity prevention interventions reported on their collaborative relationships with each other, and where their actions are situated in a locally developed CLD. A multilevel exponential random graph model (MERGM) was developed for each community to explore the structural configurations of the collaboration network, actions in the CLD, and cross-level interactions. The models showed the tendency for reciprocated and transitive collaboration among committee members, as well as some evidence of more complex multilevel configurations that may indicate integrated solutions and collective action. The use of multilevel network analysis represents a step toward unpacking the complexities inherent in community-based systems interventions for obesity prevention.

Team Collaboration Networks and Multidisciplinarity in Diabetes Care: Implications for Patient Outcomes

Prevalence of type 2 diabetes mellitus (T2DM) has almost doubled in recent decades and commonly presents comorbidities and complications. T2DM is a multisystemic disease, requiring multidisciplinary treatment provided by teams working in a coordinated and collaborative manner. The application of social network analysis techniques in the healthcare domain has allowed researchers to analyze interaction between professionals and their roles inside care teams. We studied whether the structure of care teams, modeled as complex social networks, is associated with patient progression. For this, we illustrate a data-driven methodology and use existing social network analysis metrics and metrics proposed for this research. We analyzed appointment and HbA1c blood test result data from patients treated at three primary health care centers, representing six different practices. Patients with good metabolic control during the analyzed period were treated by teams that were more interactive, collaborative and multidisciplinary, whereas patients with worsening or unstable metabolic control were treated by teams with less collaboration and more continuity breakdowns. Results from the proposed metrics were consistent with the previous literature and reveal relevant aspects of collaboration and multidisciplinarity.

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.

A scoping review of patient-sharing network studies using administrative data

There is a robust literature examining social networks and health, which draws on the network traditions in sociology and statistics. However, the application of social network approaches to understand the organization of health care is less well understood. The objective of this work was to examine approaches to conceptualizing, measuring, and analyzing provider patient-sharing networks. These networks are constructed using administrative data in which pairs of physicians are considered connected if they both deliver care to the same patient. A scoping review of English language peer-reviewed articles in PubMed and Embase was conducted from inception to June 2017. Two reviewers evaluated article eligibility based upon inclusion criteria and abstracted relevant data into a database. The literature search identified 10,855 titles, of which 63 full-text articles were examined. Nine additional papers identified by reviewing article references and authors were examined. Of the 49 papers that met criteria for study inclusion, 39 used a cross-sectional study design, 6 used a cohort design, and 4 were longitudinal. We found that studies most commonly theorized that networks reflected aspects of collaboration or coordination. Less commonly, studies drew on the strength of weak ties or diffusion of innovation frameworks. A total of 180 social network measures were used to describe the networks of individual providers, provider pairs and triads, the network as a whole, and patients. The literature on patient-sharing relationships between providers is marked by a diversity of measures and approaches. We highlight key considerations in network identification including the definition of network ties, setting geographic boundaries, and identifying clusters of providers, and discuss gaps for future study.

Applications of network analysis to routinely collected health care data: a systematic review

Objective

To survey network analyses of datasets collected in the course of routine operations in health care settings and identify driving questions, methods, needs, and potential for future research.

Materials and Methods

A search strategy was designed to find studies that applied network analysis to routinely collected health care datasets and was adapted to 3 bibliographic databases. The results were grouped according to a thematic analysis of their settings, objectives, data, and methods. Each group received a methodological synthesis.

Results

The search found 189 distinct studies reported before August 2016. We manually partitioned the sample into 4 groups, which investigated institutional exchange, physician collaboration, clinical co-occurrence, and workplace interaction networks. Several robust and ongoing research programs were discerned within (and sometimes across) the groups. Little interaction was observed between these programs, despite conceptual and methodological similarities.

Discussion

We use the literature sample to inform a discussion of good practice at this methodological interface, including the concordance of motivations, study design, data, and tools and the validation and standardization of techniques. We then highlight instances of positive feedback between methodological development and knowledge domains and assess the overall cohesion of the sample.

The Impact of Provider Networks on the Co-Prescriptions of Interacting Drugs: A Claims-Based Analysis

INTRODUCTION

Multiple provider prescribing of interacting drugs is a preventable cause of morbidity and mortality, and fragmented care is a major contributing factor. We applied social network analysis to examine the impact of provider patient-sharing networks on the risk of multiple provider prescribing of interacting drugs.

METHODS

We performed a retrospective analysis of commercial healthcare claims (years 2008-2011), including all non-elderly adult beneficiaries (n = 88,494) and their constellation of care providers. Patient-sharing networks were derived based on shared patients, and care constellation cohesion was quantified using care density, defined as the ratio between the total number of patients shared by provider pairs and the total number of provider pairs within the care constellation around each patient.

RESULTS

In our study, 2% (n = 1796) of patients were co-prescribed interacting drugs by multiple providers. Multiple provider prescribing of interacting drugs was associated with care density (odds ratio per unit increase in the natural logarithm of the value for care density 0.78; 95% confidence interval 0.74-0.83; p < 0.0001). The effect of care density was more pronounced with increasing constellation size: when constellation size exceeded ten providers, the risk of multiple provider prescribing of interacting drugs decreased by nearly 37% with each unit increase in the natural logarithm of care density (p < 0.0001). Other predictors included increasing age of patients, increasing number of providers, and greater morbidity.

CONCLUSION

Improved care cohesion may mitigate unsafe prescribing practices, especially in larger care constellations. There is further potential to leverage network analytics to implement large-scale surveillance applications for monitoring prescribing safety.

Analysis of the U.S. patient referral network

In this paper, we analyze the US Patient Referral Network (also called the Shared Patient Network) and various subnetworks for the years 2009 to 2015. In these networks, two physicians are linked if a patient encounters both of them within a specified time interval, according to the data made available by the Centers for Medicare and Medicaid Services. We find power law distributions on most state-level data as well as a core-periphery structure. On a national and state level, we discover a so-called small-world structure as well as a "gravity law" of the type found in some large-scale economic networks. Some physicians play the role of hubs for interstate referral. Strong correlations between certain network statistics with health care system statistics at both the state and national levels are discovered. The patterns in the referral network evinced using several statistical analyses involving key metrics derived from the network illustrate the potential for using network analysis to provide new insights into the health care system and opportunities or mechanisms for catalyzing improvements.

Bayesian exponential random graph modelling of interhospital patient referral networks

Using original data that we have collected on referral relations between 110 hospitals serving a large regional community, we show how recently derived Bayesian exponential random graph models may be adopted to illuminate core empirical issues in research on relational coordination among healthcare organisations. We show how a rigorous Bayesian computation approach supports a fully probabilistic analytical framework that alleviates well-known problems in the estimation of model parameters of exponential random graph models. We also show how the main structural features of interhospital patient referral networks that prior studies have described can be reproduced with accuracy by specifying the system of local dependencies that produce - but at the same time are induced by - decentralised collaborative arrangements between hospitals. Copyright © 2017 John Wiley & Sons, Ltd.

Properties of healthcare teaming networks as a function of network construction algorithms

Network models of healthcare systems can be used to examine how providers collaborate, communicate, refer patients to each other, and to map how patients traverse the network of providers. Most healthcare service network models have been constructed from patient claims data, using billing claims to link a patient with a specific provider in time. The data sets can be quite large (106-108 individual claims per year), making standard methods for network construction computationally challenging and thus requiring the use of alternate construction algorithms. While these alternate methods have seen increasing use in generating healthcare networks, there is little to no literature comparing the differences in the structural properties of the generated networks, which as we demonstrate, can be dramatically different. To address this issue, we compared the properties of healthcare networks constructed using different algorithms from 2013 Medicare Part B outpatient claims data. Three different algorithms were compared: binning, sliding frame, and trace-route. Unipartite networks linking either providers or healthcare organizations by shared patients were built using each method. We find that each algorithm produced networks with substantially different topological properties, as reflected by numbers of edges, network density, assortativity, clustering coefficients and other structural measures. Provider networks adhered to a power law, while organization networks were best fit by a power law with exponential cutoff. Censoring networks to exclude edges with less than 11 shared patients, a common de-identification practice for healthcare network data, markedly reduced edge numbers and network density, and greatly altered measures of vertex prominence such as the betweenness centrality. Data analysis identified patterns in the distance patients travel between network providers, and a striking set of teaming relationships between providers in the Northeast United States and Florida, likely due to seasonal residence patterns of Medicare beneficiaries. We conclude that the choice of network construction algorithm is critical for healthcare network analysis, and discuss the implications of our findings for selecting the algorithm best suited to the type of analysis to be performed.

Exploring the impact of different multi-level measures of physician communities in patient-centric care networks on healthcare outcomes: A multi-level regression approach

A patient-centric care network can be defined as a network among a group of healthcare professionals who provide treatments to common patients. Various multi-level attributes of the members of this network have substantial influence to its perceived level of performance. In order to assess the impact different multi-level attributes of patient-centric care networks on healthcare outcomes, this study first captured patient-centric care networks for 85 hospitals using health insurance claim dataset. From these networks, this study then constructed physician collaboration networks based on the concept of patient-sharing network among physicians. A multi-level regression model was then developed to explore the impact of different attributes that are organised at two levels on hospitalisation cost and hospital length of stay. For Level-1 model, the average visit per physician significantly predicted both hospitalisation cost and hospital length of stay. The number of different physicians significantly predicted only the hospitalisation cost, which has significantly been moderated by age, gender and Comorbidity score of patients. All Level-1 findings showed significance variance across physician collaboration networks having different community structure and density. These findings could be utilised as a reflective measure by healthcare decision makers. Moreover, healthcare managers could consider them in developing effective healthcare environments.

A framework for administrative claim data to explore healthcare coordination and collaboration

Previous studies have documented the application of electronic health insurance claim data for health services research purposes. In addition to administrative and billing details of healthcare services, insurance data reveal important information regarding professional interactions and/or links that emerge among healthcare service providers through, for example, informal knowledge sharing. By using details of such professional interactions and social network analysis methods, the aim of the present study was to develop a research framework to explore health care coordination and collaboration. The proposed framework was used to analyse a patient-centric care coordination network and a physician collaboration network. The usefulness of this framework and its applications in exploring collaborative efforts of different healthcare professionals and service providers is discussed. What is known about the topic? Application of methods and measures of social network analytics in exploring different health care collaboration and coordination networks is a comparatively new research direction. It is apparent that no other study in the present healthcare literature proposes a generic framework for examining health care collaboration and coordination using an administrative claim dataset. What does this paper add? Using methods and measures of social network analytics, this paper proposes a generic framework for analysing various health care collaboration and coordination networks extracted from an administrative claim dataset. What are the implications for the practitioners? Healthcare managers or administrators can use the framework proposed in the present study to evaluate organisational functioning in terms of effective collaboration and coordination of care in their respective healthcare organisations.