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

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

BACKGROUND

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

METHODS

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

FINDINGS

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

CONCLUSIONS

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

Can peer effects explain prescribing appropriateness? a social network analysis

BACKGROUND

Optimizing prescribing practices is important due to the substantial clinical and financial costs of polypharmacy and an increasingly aging population. Prior research shows the importance of social relationships in driving prescribing behaviour. Using social network analysis, we examine the relationship between a physician practices' connectedness to peers and their prescribing performance in two German regions.

METHODS

We first mapped physician practice networks using links established between two practices that share 8 or more patients; we calculated network-level (density, average path length) and node-level measures (degree, betweenness, eigenvector). We defined prescribing performance as the total number of inappropriate medications prescribed or appropriate medications not prescribed (PIMs) to senior patients (over the age of 65) during the calendar year 2016. We used FORTA (Fit fOR The Aged) algorithm to classify medication appropriateness. Negative binomial regression models estimate the association between node-level measures and prescribing performance of physician practices controlling for patient comorbidity, provider specialization, percentage of seniors in practice, and region. We conducted two sensitivity analyses to test the robustness of our findings - i) limiting the network mapping to patients younger than 65; ii) limiting the network ties to practices that share more than 25 patients.

RESULTS

We mapped two patient-sharing networks including 436 and 270 physician practices involving 28,508 and 20,935 patients and consisting of 217,126 and 154,274 claims in the two regions respectively. Regression analyses showed a practice's network connectedness as represented by degree, betweenness, and eigenvector centrality, is significantly negatively associated with prescribing performance (degree-bottom vs. top quartile aRR = 0.04, 95%CI: 0.035,0.045; betweenness-bottom vs. top quartile aRR = 0.063 95%CI: 0.052,0.077; eigenvector-bottom vs. top quartile aRR = 0.039, 95%CI: 0.034,0.044).

CONCLUSIONS

Our study provides evidence that physician practice prescribing performance is associated with their peer connections and position within their network. We conclude that practices occupying strategic positions at the edge of networks with advantageous access to novel information are associated with better prescribing outcomes, whereas highly connected practices embedded in insulated information environments are associated with poor prescribing performance.

Care coordination across healthcare systems: development of a research agenda, implications for practice, and recommendations for policy based on a modified Delphi panel

OBJECTIVE

For large, integrated healthcare delivery systems, coordinating patient care across delivery systems with providers external to the system presents challenges. We explored the domains and requirements for care coordination by professionals across healthcare systems and developed an agenda for research, practice and policy.

DESIGN

The modified Delphi approach convened a 2-day stakeholder panel with moderated virtual discussions, preceded and followed by online surveys.

SETTING

The work addresses care coordination across healthcare systems. We introduced common care scenarios and differentiated recommendations for a large (main) healthcare organisation and external healthcare professionals that contribute additional care.

PARTICIPANTS

The panel composition included health service providers, decision makers, patients and care community, and researchers. Discussions were informed by a rapid review of tested approaches to fostering collaboration, facilitating care coordination and improving communication across healthcare systems.

OUTCOME MEASURES

The study planned to formulate a research agenda, implications for practice and recommendations for policy.

RESULTS

For research recommendations, we found consensus for developing measures of shared care, exploring healthcare professionals' needs in different care scenarios and evaluating patient experiences. Agreed practice recommendations included educating external professionals about issues specific to the patients in the main healthcare system, educating professionals within the main healthcare system about the roles and responsibilities of all involved parties, and helping patients better understand the pros and cons of within-system and out-of-system care. Policy recommendations included supporting time for professionals with high overlap in patients to engage regularly and sustaining support for care coordination for high-need patients.

CONCLUSIONS

Recommendations from the stakeholder panel created an agenda to foster further research, practice and policy innovations in cross-system care coordination.

Greater patient sharing between hospitals is associated with better outcomes for transferred emergency general surgery patients

BACKGROUND

Access to emergency surgical care has declined as the rural workforce has decreased. Interhospital transfers of patients are increasingly necessary, and care coordination across settings is critical to quality care. We characterize the role of repeated hospital patient sharing in outcomes of transfers for emergency general surgery (EGS) patients.

METHODS

A multicenter study of Wisconsin inpatient acute care hospital stays that involved transfer of EGS patients using data from the Wisconsin Hospital Association, a statewide hospital discharge census for 2016 to 2018. We hypothesized that higher proportion of patients transferred between hospitals would result in better outcomes. We examined the association between the proportion of EGS patients transferred between hospitals and patient outcomes, including in-hospital morbidity, mortality, and length of stay. Additional variables included hospital organizational characteristics and patient sociodemographic and clinical characteristics.

RESULTS

One hundred eighteen hospitals transferred 3,197 emergency general surgery patients over the 2-year study period; 1,131 experienced in-hospital morbidity, mortality, or extended length of stay (>75th percentile). Patients were 62 years old on average, 50% were female, and 5% were non-White. In the mixed-effects model, hospitals' proportion of patients shared was associated with lower odds of an in-hospital complication; specifically, when the proportion of patients shared between two hospitals doubled, the relative odds of any outcome changed by 0.85.

CONCLUSION

Our results suggest the importance of emergent relationships between hospital dyads that share patients in quality outcomes. Transfer protocols should account for established efficiencies, familiarity, and coordination between hospitals.

LEVEL OF EVIDENCE

Prognostic and Epidemiological; Level III.

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

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

Patterns of pregabalin prescribing in four German federal states: analysis of routine data to investigate potential misuse of pregabalin

OBJECTIVES

The objectives of this study were to investigate the utilisation patterns of pregabalin, to identify users potentially misusing pregabalin and to compare this group of patients to patients prescribed recommended doses of pregabalin concerning their personal characteristics and the coordination among their prescribers. Unintended coprescription of drugs with addictive potential might occur when care is insufficiently coordinated.

DESIGN

Secondary data analysis of linked data from three regional sickness funds in Germany (AOK) for the years 2014-2016.

SETTING

Ambulatory and hospital care sector in four German federal states.

METHODS

On the basis of routine data, patients who received at least three prescriptions of pregabalin were identified and classified into patients prescribed pregabalin as recommended and those dispensed with a higher than recommended dose (>600 mg/day). Social network analysis was applied to identify prescription networks and to analyse cooperation among the prescribers. With descriptive statistics and univariate statistical tests, typical characteristics of the group of patients potentially misusing pregabalin were compared with the others.

RESULTS

Among the 53 049 patients prescribed pregabalin, about 2% (877) were classified as potentially misusing pregabalin. The majority of this group was male and aged between 30 and 60 years. Of the patients misusing pregabalin, 365 (42%) had a diagnosed history of substance use disorders and 359 (41%) had been prescribed another drug with addictive potential (opioids) before. The prescribers of those patients potentially misusing pregabalin were more loosely connected within networks compared with prescribers of patients prescribed pregabalin as recommended.

CONCLUSION

This study found that patients could exceed recommended doses of pregabalin by getting prescriptions from multiple physicians. Specific patients were at increased risk of potentially misusing pregabalin, and these patients sought to obtain their prescriptions from physicians who were as loosely connected as possible. Coordination and sharing a relevant number of patients seem to be levers to avoid these problems of unintended coprescribing.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND

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

OBJECTIVES

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

METHODS AND FINDINGS

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

CONCLUSIONS

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

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

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

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

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

Integrating network theory into the study of integrated healthcare

Healthcare policy in the United States (U.S.) has focused on promoting integrated healthcare to combat fragmentation (e.g., 1993 Health Security Act, 2010 Affordable Care Act). Researchers have responded by studying coordination and developing typologies of integration. Yet, after three decades, research evidence for the benefits of coordination and integration are lacking. We argue that research efforts need to refocus in three ways: (1) use social networks to study relational coordination and integrated healthcare, (2) analyze integrated healthcare at three levels of analysis (micro, meso, macro), and (3) focus on clinical integration as the most proximate impact on patient outcomes. We use examples to illustrate the utility of such refocusing and present avenues for future research.

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

Background

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

Methods

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

Results

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

Conclusions

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

PROSPERO registration number

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

Supplementary Information

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

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.

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

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

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

Background

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

Methods

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

Discussion

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

Trial registration

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

Supplementary Information

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

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

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

Influence of physician networks on prescribing a new ingredient combination in heart failure: a longitudinal claim data-based study

Background

Since 2016, the combination of sacubitril/valsartan, which combines an angiotensin receptor and neprilysin inhibitor (ARNI), has been recommended in the guidelines for the treatment of heart failure. The adoption of new drugs may be influenced by collaboration and exchange between physicians. We aimed to determine whether characteristics of the professional networks of prescribing physicians were associated with the prescribing of ARNI in Germany.

Methods

We conducted a longitudinal analysis based on claims data in 2016–2018 in Germany. The characteristics of ambulatory care physicians’ networks were determined in the analysis of the patient-sharing networks of physicians in 2017. Binary logistic regression analysis with the outcome ‘prescribes ARNI in 2018’ (present or absent) was carried out, using network characteristics as predictors, adjusted for specialty and sociodemographic characteristics of physicians.

Results

The network analysis included 8370 physicians, who had 144,636 connections. Prescribers had more connections to other physicians compared to non-prescribers (median 31 vs. 23). Regression analysis showed that the numbers of linkages to prescribers of ARNI were positively associated with prescribing ARNI. For 6–10 connections, the average marginal effect (AME) was 0.04 (confidence interval [CI] 95% 0.01–0.06) and for > 10 links the AME 0.07 (CI 95% 0.05–0.10) compared to 0–5 connections to prescriber.

Conclusion

Physicians who shared patients with many other physicians were more likely to prescribe ARNI, independent of physicians’ specialty. This suggested that collaboration and exchange on the basis of patient-sharing with other physicians influenced their medication prescribing decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13012-021-01150-y.

Tracing the potential of networks to improve community cancer care: an in-depth single case study

Background

Despite overall declines in cancer mortality in the USA over the past three decades, many patients in community settings fail to receive evidence-based cancer care. Networks that link academic medical centers (AMCs) and community providers may reduce disparities by creating access to specialized expertise and care, but research on network effectiveness is mixed. The objective of this study was to identify factors related to whether and how an exemplar AMC network served to provide advice and referral access in community settings.

Methods

An embedded in–depth single case study design was employed to study a network in the Midwest USA that connects a leading cancer specialty AMC with community practices. The embedded case units were a subset of 20 patients with young-onset colorectal cancer or risk-related conditions and the providers involved in their care. The electronic health record (EHR) was reviewed from January 1, 1990, to February 28, 2018. Social network analysis identified care, advice, and referral relationships. Within-case process tracing provided detailed accounts of whether and how the network provided access to expert, evidence-based care or advice in order to identify factors related to network effectiveness.

Results

The network created access to evidence-based advice or care in some but not all case units, and there was variability in whether and how community providers engaged the network, including the path for referrals to the AMC and the way in which advice about an evidence-based approach to care was communicated from AMC specialists to community providers. Factors related to instances when the network functioned as intended included opportunities for both rich and lean communication between community providers and specialists, coordinated referrals, and efficient and adequately utilized documentation systems.

Conclusions

Network existence alone is insufficient to open up access to evidence-based expertise or care for patients in community settings. In-depth understanding of how this network operated provides insight into factors that support or inhibit the potential of networks to minimize disparities in access to evidence-based community cancer care, including both personal and organizational factors.

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.

Physician trajectories of abandoning long-course breast radiotherapy and their cost impact

OBJECTIVE

To examine variation in trajectories of abandoning conventionally fractionated whole-breast irradiation (CF-WBI) for adjuvant breast radiotherapy among physician peer groups and the associated cost implications.

DATA SOURCES

Medicare claims data were obtained from the Chronic Conditions Data Warehouse for fee-for-service beneficiaries with breast cancer in 2011-2014.

STUDY DESIGN

We used social network methods to identify peer groups of physicians that shared patients. For each physician peer group in each time period (T1 = 2011-2012 and T2 = 2013-2014), we calculated a risk-adjusted rate of CF-WBI use among eligible women, after adjusting for patient clinical characteristics. We applied a latent class growth analysis to these risk-adjusted rates to identify distinct trajectories of CF-WBI use among physician peer groups. We further estimated potential savings to the Medicare program by accelerating abandonment of CF-WBI in T2 using a simulation model.

DATA COLLECTION/EXTRACTION METHODS

Use of conventionally fractionated whole-breast irradiation was determined from Medicare claims among women ≥ 66 years of age who underwent adjuvant radiotherapy after breast conserving surgery.

PRINCIPAL FINDINGS

Among 215 physician peer groups caring for 16 988 patients, there were four distinct trajectories of abandoning CF-WBI: (a) persistent high use (mean risk-adjusted utilization rate: T1 = 94.3%, T2 = 90.6%); (b) decreased high use (T1 = 81.3%, T2 = 65.3%); (c) decreased medium use (T1 = 60.1%, T2 = 44.0%); and (d) decreased low use (T1 = 31.6%, T2 = 23.6%). Peer groups with a smaller proportion of patients treated at free-standing radiation facilities and a larger proportion of physicians that were surgeons tended to follow trajectories with lower use of CF-WBI. If all physician peer groups had practice patterns in T2 similar to those in the "decreased low use" trajectory, the Medicare program could save $83.3 million (95% confidence interval: $58.5 million-$112.2 million).

CONCLUSIONS

Physician peer groups had distinct trajectories of abandoning CF-WBI. Physician composition and setting of radiotherapy were associated with the different trajectories. Distinct practice patterns across the trajectories had important cost implications.

On the Network Transmission Mechanisms of Disease-Specific Healthcare Expenditure Spillovers: Evidence from the Connectedness Network Analyses

Previous studies investigating factors influencing healthcare expenditure growth ignored the network transmission mechanisms of disease-specific healthcare expenditure spillovers and regarded the processes culminating in healthcare expenditure growth as a black box. In this study, we investigated factors influencing the network transmission mechanisms underlying the determinants of healthcare expenditure growth through the dynamic connectedness network and the robust least square regression analyses. Our results indicate that demographic transition and business cycles are key factors increasing interconnectedness of different disease-specific healthcare expenditures, and that promotion of primary care utilization would reduce total healthcare expenditure spillovers. In order to reduce diffusion of disease-specific healthcare expenditures, health promotion activities should focus on those clinical diagnosis-related groups of diseases classified as pure net transmitters of spillover, and preventive interventions targeting different diseases should be activated in different phrases of the business cycle.

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 network-based method with privacy-preserving for identifying influential providers in large healthcare service systems

In data science, networks provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and physical systems. Healthcare service systems are one of the main social systems that can also be understood using network-based approaches, for example, to identify and evaluate influential providers. In this paper, we propose a network-based method with privacy-preserving for identifying influential providers in large healthcare service systems. First, the provider-interacting network is constructed by employing publicly available information on locations and types of healthcare services of providers. Second, the ranking of nodes in the generated provider-interacting network is conducted in parallel on the basis of four nodal influence metrics. Third, the impact of the top-ranked influential nodes in the provider-interacting network is evaluated using three indicators. Compared with other research work based on patient-sharing networks, in this paper, the provider-interacting network of healthcare service providers can be roughly created according to the locations and the publicly available types of healthcare services, without the need for personally private electronic medical claims, thus protecting the privacy of patients. The proposed method is demonstrated by employing Physician and Other Supplier Data CY 2017, and can be applied to other similar datasets to help make decisions for the optimization of healthcare resources in the response to public health emergencies.

The association between patient sharing network structure and healthcare costs

STUDY QUESTION

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

DATA SOURCE

Data used are the 2011 Massachusetts All Payer Claims Database.

STUDY DESIGN

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

DATA COLLECTION

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

PRINCIPAL FINDINGS

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

CONCLUSIONS

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

WHAT THIS STUDY ADDS

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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.

A Framework to Understand the Progression of Cardiovascular Disease for Type 2 Diabetes Mellitus Patients Using a Network Approach

The prevalence of chronic disease comorbidity has increased worldwide. Comorbidity-i.e., the presence of multiple chronic diseases-is associated with adverse health outcomes in terms of mobility and quality of life as well as financial burden. Understanding the progression of comorbidities can provide valuable insights towards the prevention and better management of chronic diseases. Administrative data can be used in this regard as they contain semantic information on patients' health conditions. Most studies in this field are focused on understanding the progression of one chronic disease rather than multiple diseases. This study aims to understand the progression of two chronic diseases in the Australian health context. It specifically focuses on the comorbidity progression of cardiovascular disease (CVD) in patients with type 2 diabetes mellitus (T2DM), as the prevalence of these chronic diseases in Australians is high. A research framework is proposed to understand and represent the progression of CVD in patients with T2DM using graph theory and social network analysis techniques. Two study cohorts (i.e., patients with both T2DM and CVD and patients with only T2DM) were selected from an administrative dataset obtained from an Australian health insurance company. Two baseline disease networks were constructed from these two selected cohorts. A final disease network from two baseline disease networks was then generated by weight adjustments in a normalized way. The prevalence of renal failure, fluid and electrolyte disorders, hypertension and obesity was significantly higher in patients with both CVD and T2DM than patients with only T2DM. This showed that these chronic diseases occurred frequently during the progression of CVD in patients with T2DM. The proposed network-based model may potentially help the healthcare provider to understand high-risk diseases and the progression patterns between the recurrence of T2DM and CVD. Also, the framework could be useful for stakeholders including governments and private health insurers to adopt appropriate preventive health management programs for patients at a high risk of developing multiple chronic diseases.

Investigating Coordination of Hospital Departments in Delivering Healthcare for Acute Coronary Syndrome Patients Using Data-Driven Network Analysis

Healthcare systems are challenged to deliver high-quality and efficient care. Studying patient flow in a hospital is particularly fundamental as it demonstrates effectiveness and efficiency of a hospital. Since hospital is a collection of physically nearby services under one administration, its performance and outcome are shaped by the interaction of its discrete components. Coordination of processes at different levels of organizational structure of a hospital can be studied using network analysis. Hence, this article presents a data-driven static and temporal network of departments. Both networks are directed and weighted and constructed using seven years’ (2010–2016) empirical data of 24902 Acute Coronary Syndrome (ACS) patients. The ties reflect an episode-based transfer of ACS patients from department to department in a hospital. The weight represents the number of patients transferred among departments. As a result, the underlying structure of network of departments that deliver healthcare for ACS patients is described, the main departments and their role in the diagnosis and treatment process of ACS patients are identified, the role of departments over seven years is analyzed and communities of departments are discovered. The results of this study may help hospital administration to effectively organize and manage the coordination of departments based on their significance, strategic positioning and role in the diagnosis and treatment process which, in-turn, nurtures value-based and precision healthcare.

Data-driven modeling of diabetes care teams using social network analysis

OBJECTIVE

We assess working relationships and collaborations within and between diabetes health care provider teams using social network analysis and a multi-scale community detection.

MATERIALS AND METHODS

Retrospective analysis of claims data from a large employer over 2 years was performed. The study cohort contained 827 patients diagnosed with diabetes. The cohort received care from 2567 and 2541 health care providers in the first and second year, respectively. Social network analysis was used to identify networks of health care providers involved in the care of patients with diabetes. A multi-scale community detection was applied to the network to identify groups of health care providers more densely connected. Social network analysis metrics identified influential providers for the overall network and for each community of providers.

RESULTS

Centrality measures identified medical laboratories and mail-order pharmacies as the central providers for the 2 years. Seventy-six percent of the detected communities included primary care physicians, and 97% of the communities included specialists. Pharmacists were detected as central providers in 24% of the communities.

DISCUSSION

Social network analysis measures identified the central providers in the network of diabetes health care providers. These providers could be considered as influencers in the network that could enhance the implication of promotion programs through their access to a large number of patients and providers.

CONCLUSION

The proposed framework provides multi-scale metrics for assessing care team relationships. These metrics can be used by implementation experts to identify influential providers for care interventions and by health service researchers to determine impact of team relationships on patient outcomes.

Impact of care provider network characteristics on patient outcomes: Usage of social network analysis and a multi-scale community detection

Objective

We assess healthcare provider collaboration and the impact on patient outcomes using social network analysis, a multi-scale community detection algorithm, and generalized estimating equations.

Material and methods

A longitudinal analysis of health claims data of a large employer over a 3 year period was performed to measure how provider relationships impact patient outcomes. The study cohort included 4,230 patients with 167 providers. Social network analysis with a multi-scale community detection algorithm was used to identify groups of healthcare providers more closely working together. Resulting measures of provider collaboration were: 1) degree, 2) betweenness, and 3) closeness centrality. The three patient outcome measures were 1) emergency department visit, 2) inpatient hospitalization, and 3) unplanned hospitalization. Relationships between provider collaboration and patient outcomes were assessed using generalized estimating equations. General practitioner, family practice, and internal medicine were labeled as primary care. Cardiovascular, endocrinologists, etc. were labeled as specialists, and providers such as radiology and social workers were labeled as others.

Results

Higher connectedness (degree) and higher access (closeness) to other providers in the community were significant for reducing inpatient hospitalization and emergency department visits. Patients of specialists (e.g. cardiovascular) and providers specified as others (e.g. social worker) had higher rate of hospitalization and emergency department visits compared to patients of primary care providers.

Conclusion

Application of social network analysis for developing healthcare provider networks can be leveraged by community detection algorithms and predictive modeling to identify providers’ network characteristics and their impacts on patient outcomes. The proposed framework presents multi-scale measures to assess characteristics of healthcare providers and their impact on patient outcomes. This approach can be used by implementation experts for informed decision-making regarding the design of insurance coverage plans, and wellness promotion programs. Health services researchers can use the study approach for assessment of provider collaboration and impacts on patient outcomes.

Shared-patient physician networks and their impact on the uptake of genomic testing in breast cancer

BACKGROUND

Factors influencing the adoption of genomic testing are poorly understood, which may lead to inequitable and suboptimal treatment in cancer patients. Oncotype DX (ODX) is one of the first and most widely used genomic assays to stratify risk in women with early-stage breast cancer (BC). Physician networks have emerged as a significant and modifiable driver of emerging medical technology adoption.

OBJECTIVE

To investigate the association between physician network connections and the use of ODX testing.

METHODS

A retrospective study of women diagnosed with BC using SEER-Medicare from 2008 to 2012 was used. Medical oncologists were "connected" if they shared two or more patients during the early-adoption period (2008-2009). Parallel physician- and patient-level analyses employed logistic mixed models to determine the impact of being "connected" to an early-adopting oncologist on ODX use in 2011-2012.

RESULTS

24,463 women met study criteria; 12,874 were diagnosed with BC in the early-adoption time period. 2129 medical oncologists treated these patients from 2008 to 2009. Medical oncologists had a median number of peer connections of 4 (IQR: 2-7). Peer connection to an early-adopting provider in 2008-2009 was associated with a 1.7-fold increase in providers' adopting ODX (95% CI: 1.1-2.6) and a 1.5-fold increase in their patients receiving ODX (95% CI: 1.1-2.0) in 2010-2012.

CONCLUSIONS

Peer connectedness to an early-adopting physician predicts ODX adoption in both physician-level and patient-level analyses. Provider networks may provide a potent and modifiable means to modulate the diffusion of emerging medical technologies. Efforts to increase testing, where appropriate, may benefit from peer-to-peer-based connection strategies.