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 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.

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.

Determining the influence of the primary and specialist network of care on patient and system outcomes among patients with a new diagnosis of chronic obstructive pulmonary disease (COPD)

Introduction

Care for patients with chronic obstructive pulmonary disease (COPD) is provided by both family physicians (FP) and specialists. Ideally, patients receive comprehensive and coordinated care from this provider team. The objectives for this study were: 1) to describe the family and specialist physician network of care for Ontario patients newly diagnosed with COPD and 2) to determine the associations between selected characteristics of the physician network and unplanned healthcare utilization.

Methods

We conducted a retrospective cohort study using Ontario health administrative data housed at ICES (formerly the Institute for Clinical Evaluative Sciences). Ontario patients, ≥ 35 years, newly diagnosed with COPD were identified between 2005 and 2013. The FP and specialist network of care characteristics were described, and the relationship between selected characteristics (i.e., continuity of care) with unplanned healthcare utilization during the first 5 years after COPD diagnosis were determined in multivariate models.

Results

Our cohort consisted of 450,837 patients, mean age 61.5 (SD 14.6) years. The FP was the predominant provider of care for 86.4% of the patients. Using the Bice-Boxerman’s Continuity of Care Index (COCI), a measure reflecting care across different providers, 227,082 (50.4%) were categorized in a low COCI group based on a median cut-off. In adjusted analyses, patients in the low COCI group were more likely to have a hospital admission (OR = 2.27, 95% CI 2.20,2.22), 30-day readmission (OR = 2.44, 95% CI 2.39, 2.49) and ER visit (OR = 2.27, 95% CI 2.25, 2.29).

Conclusion

Higher indices of continuity of care are associated with reduced unplanned hospital use for patients with COPD. Primary care-based practice models to enhance continuity through coordination and integration of both primary and specialist care have the potential to enhance the health experience for patients with COPD and should be a health service planning priority.

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.

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.

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

OBJECTIVE

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

STUDY DESIGN

This is a retrospective cohort study.

METHODS

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

RESULTS

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

CONCLUSIONS

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

A 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.

Modeling Care Team Structures in the Neonatal Intensive Care Unit through Network Analysis of EHR Audit Logs

BACKGROUND

 In the neonatal intensive care unit (NICU), predefined acuity-based team care models are restricted to core roles and neglect interactions with providers outside of the team, such as interactions that transpire via electronic health record (EHR) systems. These unaccounted interactions may be related to the efficiency of resource allocation, information flow, communication, and thus impact patient outcomes. This study applied network analysis methods to EHR audit logs to model the interactions of providers beyond their core roles to better understand the interaction network patterns of acuity-based teams and relationships of the network structures with postsurgical length of stay (PSLOS).

METHODS

 The study used the EHR log data of surgical neonates from a large academic medical center. The study included 104 surgical neonates, for whom 9,206 unique actions were performed by 457 providers in their EHRs. We applied network analysis methods to model EHR provider interaction networks of acuity-based teams in NICU postoperative care. We partitioned each EHR network into three subnetworks based on interaction types: (1) interactions between known core providers who were documented in scheduling records (core subnetwork); (2) interactions between core and noncore providers (extended subnetwork); and (3) interactions between noncore providers (extended subnetwork). For each core subnetwork, we assessed its capability to replicate predefined core-provider relations as documented in scheduling records. We further compared each EHR network, as well as its subnetworks, using standard network measures to determine its differences in network topologies. We conducted a case study to learn provider interaction networks taking care of 15 neonates who underwent gastrostomy tube placement surgery from EHR log data and measure the effectiveness of the interaction networks on PSLOS by the proportional-odds model.

RESULTS

 The provider networks of four acuity-based teams (two high and two low acuity), along with their subnetworks, were discovered. We found that beyond capturing the predefined core-provider relations, EHR audit logs can also learn a large number of relations between core and noncore providers or among noncore providers. Providers in the core subnetwork exhibited a greater number of connections with each other than with providers in the extended subnetworks. Many more providers in the core subnetwork serve as a hub than those in the other types of subnetworks. We also found that high-acuity teams exhibited more complex network structures than low-acuity teams, with high-acuity team generating 6,416 interactions between 407 providers compared with 931 interactions between 124 providers, respectively. In addition, we discovered that high-acuity and low-acuity teams shared more than 33 and 25% of providers with each other, respectively, but exhibited different collaborative structures demonstrating that NICU providers shift across different acuity teams and exhibit different network characteristics. Results of case study show that providers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those who treated higher PSLOS patients (p = 0.03).

CONCLUSION

 Network analysis can be applied to EHR log data to model acuity-based NICU teams capturing interactions between providers within the predesigned core team as well as those outside of the core team. In the NICU, dissemination of information may be linked to reduced PSLOS. EHR log data provide an efficient, accessible, and research-friendly way to study provider interaction networks. Findings should guide improvements in the EHR system design to facilitate effective interactions between providers.

Quantification of the resilience of primary care networks by stress testing the health care system

We shock a full-scale simulation model of a national health care system by locally removing health care providers. We measure resilience of the system in terms of how fast and to what extent it can recover its ability to deliver adequate health services to the population. The model is based on actual regional primary care networks in Austria, where all patients and physicians are represented as anonymized avatars that are calibrated with nationwide data. After removal of a critical fraction of physicians, networks generically undergo a transition from resilient to nonresilient behavior, where it is impossible to maintain coverage for all patients. These “stress tests” allow us to quantify regional health care resilience and identify systemically risky health care providers.

There are practically no quantitative tools for understanding how much stress a health care system can absorb before it loses its ability to provide care. We propose to measure the resilience of health care systems with respect to changes in the density of primary care providers. We develop a computational model on a 1-to-1 scale for a countrywide primary care sector based on patient-sharing networks. Nodes represent all primary care providers in a country; links indicate patient flows between them. The removal of providers could cause a cascade of patient displacements, as patients have to find alternative providers. The model is calibrated with nationwide data from Austria that includes almost all primary care contacts over 2 y. We assign 2 properties to every provider: the “CareRank” measures the average number of displacements caused by a provider’s removal (systemic risk) as well as the fraction of patients a provider can absorb when others default (systemic benefit). Below a critical number of providers, large-scale cascades of patient displacements occur, and no more providers can be found in a given region. We quantify regional resilience as the maximum fraction of providers that can be removed before cascading events prevent coverage for all patients within a district. We find considerable regional heterogeneity in the critical transition point from resilient to nonresilient behavior. We demonstrate that health care resilience cannot be quantified by physician density alone but must take into account how networked systems respond and restructure in response to shocks. The approach can identify systemically relevant providers.

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.

Hierarchical patient-centric caregiver network method for clinical outcomes study

In clinical outcome studies, analysis has traditionally been performed using patient-level factors, with minor attention given to provider-level features. However, the nature of care coordination and collaboration between caregivers (providers) may also be important in determining patient outcomes. Using data from patients admitted to intensive care units at a large tertiary care hospital, we modeled the caregivers that provided medical service to a specific patient as patient-centric subnetwork embedded within larger caregiver networks of the institute. The caregiver networks were composed of caregivers who treated either a cohort of patients with particular disease or any patient regardless of disease. Our model can generate patient-specific caregiver network features at multiple levels, and we demonstrate that these multilevel network features, in addition to patient-level features, are significant predictors of length of hospital stay and in-hospital mortality.

An exploratory study of networks constructed using access data from an electronic health record

Network analysis may be a powerful tool for studying interprofessional practice. Using electronic health record data and social network analysis, the network of healthcare professionals involved in colorectal cancer care at a large, urban academic medical center were mapped and studied. A total of 100 surgical colorectal cancer patients receiving treatment in 2013 and 2014 were selected at random. We used detailed access logs for the EHR to map the network of all healthcare professionals for each patient, including inpatient and outpatient settings. Approximately 2.45 million records of access logs from more than 6,800 unique users, representing over 150 roles or occupations were analyzed. Across all networks, professionals were connected to an average of 5.8 other professionals, but some were rarely connected with others while over 20 were very highly connected (> 100 other professionals). Housestaff, attending physicians, and nurses played central roles in the global network with a high number of inter- and intra-professional connections. Clusters of professionals with frequent interaction were demonstrated but, based on the size and complexity of the network, serendipitous interactions were unlikely. Settings for care seemed to influence these clusters. Patient-centric care networks were similar to the global network with some potentially important differences. Access-log information from electronic health records can be an important source of information about relationships between healthcare professionals. Findings from analyses such as this one may help define the state of current networks and potential targets for interventions to improve the quality of care.

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.

Lower fragmentation of coordination in primary care is associated with lower prescribing drug costs-lessons from chronic illness care in Hungary

Improving patient care coordination is critical for achieving better health outcome measures at reduced cost. However, assessing the results of patient care coordination at system level is lacking. In this report, based on administrative healthcare data, a provider-level care coordination measure is developed to assess the function of primary care at system level. In a sample of 31 070 patients with diabetes we find that the type of collaborative relationship general practitioners build up with specialists is associated with prescription drug costs. Regulating access to secondary care might result in cost savings through improved care coordination.

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.

Analysis of stakeholders networks of infant and young child nutrition programmes in Sri Lanka, India, Nepal, Bangladesh and Pakistan

Background

Effective public policies are needed to support appropriate infant and young child feeding (IYCF) to ensure adequate child growth and development, especially in low and middle income countries. The aim of this study was to: (i) capture stakeholder networks in relation to funding and technical support for IYCF policy across five countries in South Asia (i.e. Sri Lanka, India, Nepal, Bangladesh and Pakistan); and (ii) understand how stakeholder networks differed between countries, and identify common actors and their patterns in network engagement across the region.

Methods

The Net-Map method, which is an interview-based mapping technique to visualise and capture connections among different stakeholders that collaborate towards achieving a focused goal, has been used to map funding and technical support networks in all study sites. Our study was conducted at the national level in Bangladesh, India, Nepal, and Sri Lanka, as well as in selected states or provinces in India and Pakistan during 2013–2014. We analysed the network data using a social network analysis software (NodeXL).

Results

The number of stakeholders identified as providing technical support was higher than the number of stakeholders providing funding support, across all study sites. India (New Delhi site – national level) site had the highest number of influential stakeholders for both funding (43) and technical support (86) activities. Among all nine study sites, India (New Delhi – national level) and Sri Lanka had the highest number of participating government stakeholders (22) in their respective funding networks. Sri Lanka also had the highest number of participating government stakeholders for technical support (34) among all the study sites. Government stakeholders are more engaged in technical support activities compared with their involvement in funding activities. The United Nations Children’s Emergency Fund (UNICEF) and the World Health Organization (WHO) were highly engaged stakeholders for both funding and technical support activities across all study sites.

Conclusion

International stakeholders were highly involved in both the funding and technical support activities related to IYCF practices across these nine study sites. Government stakeholders received more support for funding and technical support activities from other stakeholders compared with the support that they offered. Stakeholders were, in general, more engaged for technical support activities compared with the funding activities.