When Clinicians Group Together: A Systematic Scoping Review of Clustering in Patient-Sharing Networks

doi:10.21203/rs.3.rs-4437662/v1

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Research Article

When Clinicians Group Together: A Systematic Scoping Review of Clustering in Patient-Sharing Networks

https://doi.org/10.21203/rs.3.rs-4437662/v1

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Background

. Improvements in patient safety and quality of care can be achieved by improvements in clinicians’ teamwork, coordination and communication. Growing research examines the structure and dynamics of clinician networks using social network analysis. Such networks can have clusters of healthcare professionals within them, but systematized knowledge on these clusters is lacking. Our goal was to review the evidence on determinants and characteristics of healthcare professional clustering in patient-sharing networks and their associations with patient outcomes.

Methods.

We searched for English-language peer-reviewed studies published up until January 4, 2021 using PubMed and EMBASE and an existing scoping review on patient-sharing by DuGoff et al (2018). We performed a systematic scoping review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We performed title and abstract screening and full-text screening to identify studies that used social network analysis to examine relationships between patient-sharing network clusters and health outcomes. From the twelve eligible studies, we extracted study information such as study design and setting, population, patient-sharing definition, network measures, clustering definition, health outcomes, and reported associations.

Results.

The studies varied considerably in definitions and measures of patient-sharing relations, definitions and structural measures of network clusters, settings, study population, and health outcomes. The general patterns indicate that busier physician networks (i.e., networks with more connections among physicians) are associated with worse health outcomes and better-connected physician networks are associated with better health outcomes.

Conclusion.

The majority of existing studies are exploratory. Rigorous theoretical grounding, interventional studies, and mixed-methods studies would help to strengthen patient-sharing research and advance our understanding of how patient-sharing clustering affects patient outcomes.

patient-sharing

social network analysis

network clusters

physician networks

health outcomes

Patient-sharing network analysis is a novel approach to reveal the patterns and dynamics of relationships among healthcare professionals (HCPs) using existing administrative data (e.g., insurance claims) or electronic medical records.[1, 2] Patient-sharing relations are usually assumed when multiple HCPs provide care to a patient, hence are an indirect indicator of provider communication, referral, collaboration, and care coordination. Increasing healthcare integration and the use of technology facilitate the process of referral and coordination.[3] Naturally, integrated care models involve a high level of patient-sharing; and patients, particularly those with complex needs, often receive care from multiple HCPs. Improving the processes of patient-sharing between HCPs is a key component of safe and high-quality care.[4]

In the last decade, there has been growing interest in analyzing the structure and dynamics of patient-sharing networks using social network analysis.[2, 3, 5] Social network analysis allows studying the patterns of relationship among HCPs (actors of the network), using visual mapping of relationship networks as well as various structural measures of network composition, existence and characteristics of clusters, relationship strength, actors’ roles and positions, etc.[6] Social network analysis of patient-sharing networks can be used to describe behavioral dynamics in a healthcare system, to analyze care coordination, and team-based care. It can also be used to examine the process and impact of social influence among network actors (e.g., adopting new clinical guideline by peer influence).[7] While some of these questions require collecting rich data from the network actors themselves, most network analysis studies of HCP networks rely on administrative data such as insurance claims and hospital discharge records, given their large scale, ease of analysis, and natural connection to patient outcomes.

Better communication and coordination among HCPs should positively affect patient care and ultimately patient outcomes.[4, 8] Therefore, many patient-sharing network studies examine how network characteristics affect patient outcomes. However, a previous scoping review of patient-sharing network studies did not find consistent patterns in the relationships between network measures and patient outcomes, noting a substantial range of data sources, network measures and statistical approaches to estimation.[2]

One important, yet less studied, aspect of patient-sharing network analysis is studying how HCP groups, clusters, or communities are formed within networks, what these clusters look like, and whether clustering among HCPs affects patient outcomes. Network clusters are groups of actors who are better connected to each other, with looser connections to others outside the cluster.[9] Studying network clusters provides valuable insights to the meso-level patterns and dynamics of connectivity, beyond the dyadic relations between individuals. The notion of clustering in human networks has been studied for decades. Humans join clusters to maximize the benefit and safety of a group,[10] and then adhere to cluster norms and cultures,[11] which themselves reshape over time. One consistent theme for the formation of clusters among professionals is homophily (or the tendency to connect to others with similar characteristics). Some studies have shown that HCPs are more likely to refer patients to colleagues that they personally know (through common demographic backgrounds or education,[12] or common activities such as journal clubs, etc.[13]), are geographically closer (e.g., in the same building or area),[12] have facilitated communication means (e.g., through face to face communication or electronic health records),[13, 14] share insurance plans, or are affiliated with the same health systems.[12, 15]

Studying clusters of HCP networks is important to understand how healthcare is delivered and how healthcare systems can improve patient outcomes via changes in structures and processes. Such understanding can be developed by examining how these clusters are formed (Why do some HCPs prefer to group together?) and how clusters affect HCP behavior (Do HCPs adopt the normative practice of the cluster?) and patient outcomes (Do patients benefit differently if their HCPs belong to the same cluster or particular clusters?). Our systematic scoping review aims to analyze the existing evidence on HCP clustering and its relationship with patient outcomes in patient-sharing network analysis studies.

Identifying relevant literature

We followed the five-step framework proposed by Arksey and O’Malley for scoping reviews.[16] Previously, DuGoff et al (2018)[2] conducted a systematic review of patient-sharing networks using a structured search strategy. We expanded and updated their search strategy and systematically searched PubMed and EMBASE databases in January 2021. Our detailed search strategy is provided in the Appendix.

Selecting studies

We included peer-reviewed articles in English published up until January 2021 that examined the clustering of HCPs in patient-sharing networks and reported at least one patient outcome, either at individual or systemic levels. HCP network clusters could be defined by regional (e.g., a catchment area or a city) or institutional (e.g., a hospital) boundaries, or determined by cohesive patterns (e.g., community detection algorithms) in patient-sharing networks. The latter means that HCPs tended to share patients within a cluster more than sharing outside the cluster. To be eligible for inclusion, a published study had to employ network analysis, examine clustering of providers, examine at least one health outcome, and be published in English. We excluded ongoing studies, protocols, and conference abstracts.

Abstracts were screened for eligibility by pairs of reviewers. Sixty-six selected articles underwent a full-text screening: each article was reviewed by two blind independent reviewers (articles were divided between two pairs of reviewers) and agreement on each article was achieved. Discrepancies were discussed and resolved in meetings. During full-text screening, the study of HCP clusters and a focus on patient outcomes were ascertained and articles that did not meet these criteria were excluded (Fig. 1).

Extracting and charting

To assess the methodological quality of included studies, we applied the New Castle-Ottawa quality assessment scale for cross-sectional[17] cohort and case-control studies. All included studies used patient records to determine HCP clusters as well as outcome measures, making a few items of the scale inapplicable to our study context. We simplified the scale and focused on the study design and the existence and nature of comparison groups and follow-ups.

The included studies were divided between pairs of reviewers. Within pairs, reviewers independently extracted the following data from each article: population, study design, source of data, patient-sharing definition, patient-sharing measure(s), patient-sharing timeframe, clustering definitions, patient outcome(s), and reported associations between patient-sharing clustering measures and patient outcome(s). Each pair then reached agreement; discrepancies were discussed and resolved in meetings.

Data synthesis and analysis

We assessed for clinical heterogeneity, presented as diversity in terms of patient populations, HCPs, definition of patient sharing, measures of clustering, and outcome assessment. Given these methodological and clinical heterogeneities, we summarized the study findings narratively. We descriptively summarized the characteristics of studies, methods of clustering, and outcome measures. We qualitatively synthesized the association between clustering and health outcomes. We summarized the main findings of each study, as presented by the authors, provided a narrative summary of findings, and thematically categorized the findings across studies, following a thematic analysis approach.[18] The team met regularly to discuss each study and the process of the formation of themes.

Review process and retrieval

We identified 3,034 citations from the Bibliographic search in PubMed and EMBASE (Fig. 1) and 32 citations from DuGoff et al (2018).[2] We removed 21 duplicate records and excluded 2,979 ineligible abstracts. We reviewed the full texts of 66 papers and excluded 54 for reasons listed in the PRISMA diagram (Fig. 1). We included 12 empirical studies that assessed HCP clustering and patient outcomes. The characteristics of included studies are summarized in Table 1.

Table 1

Characterization of the Studies Included in the Review
Study	Quality Assessment	Data Source	Sample Size	Setting	Definition of Clusters	Ties and Actors
Barnett et al, 2012	Cross-sectional study Comparison group: no Follow up: no The study adjusts for hospital characteristics (number of hospital beds, number of physicians assigned to a hospital, proportion of PCPs among assigned physicians, mean shared patient volume per physician at a hospital, urban location, teaching status, ownership, nurse full time equivalents per 1000 inpatient days, percentage of admissions from Medicare and Medicaid patients).	Medicare claims, Part A and Part B, 2006	2.6 million Medicare patients, 61,146 physicians, 528 hospitals, 51 HRRs	Hospitals in 50 randomly sampled HRRs and Boston, the United States	Hospitals.	Physicians who each had a significant encounter with one or more common patient. A significant encounter was defined as face-to-face visits or meaningful procedures with a value of at least 2 RVUs.
Casalino et al, 2015	Cross-sectional data over one year Comparison group: no Follow-up: no The study controls for patient characteristics (age, sex, dual eligibility status, race/ethnicity, number of chronic conditions) and physician characteristics (U.S. trained, sex, age, board-certified).	Medicare claims Part B outpatient files, 2008	782,595 patients, 54,202 physicians, 386 physician practice communities .	Hospitals; 5 states in the United States (OH, PA, TN, WA, WI)	Clusters (termed physician practice communities, or PPCs) were defined as "smaller networks of physicians who share patients with each other much more often than they share patients with anyone else". PPCs were identified using community detection algorithms.	Physicians who provided outpatient care.
Donker et al, 2012	Cross-sectional study over 8 years Comparison group: England vs the Netherlands Follow-up: no The study controls for cluster specific mean MRSA rates.	The NHS Hospital Episode Statistics (HES) data (England) and the Dutch National Medical Registry data (Netherlands), April 2006 - March 2007.	England: 7,420,219 patients admitted to 146 hospitals, for a total of 12,929,171 admissions Netherlands: 1,676,704 patients admitted to 98 hospitals, for a total of 2,611,452 admissions	Acute care hospitals in England, hospital regional clusters in England, England vs the Netherlands	Hospitals were grouped into hospital regional clusters, based on the weights of the connections between all hospitals, using a community detection algorithm.	Hospitals are the actors and patient sharing happened as a result of patient referrals from one hospital to another.
DuGoff et al, 2018	Longitudinal study Comparison group: no Follow-up: 1 and 2 years The study controls for the hospital characteristics (median number of physician pairs per primary care physician (PCP), median number of co-occurrences, PCPs and specialist supply, hospital beds, total number and characteristics of Medicare fee-for-service beneficiaries, and region of the country).	Medicare claims, 2012–2014.	142,016 PCPs connected to at least one other physician in 2012, representing 12,190,803 physician pairs	HRRs; the United States	Clusters are HRRs.	Referrals between PCPs and specialists.
Hollingsworth et al, 2016	Cross-sectional study over 4 years Comparison group: no Follow-up: 60 days post-surgery The study adjusts for hospital and hospital service area characteristics as well as the number of physicians.	Medicare Provider Analysis and Review (MedPAR), 2008–2011	251,630 Medicare beneficiaries who underwent CABG. 466,243 physicians, 1,186 health systems	Hospitals; the United States	Clusters are health systems.	Physicians directly involved in the care of patients with coronary artery bypass graft 30 days prior and 60 days after admission.
Moen et al, 2016	Cross-sectional study Comparison group: Two HRRs with difference in rates of receiving implantable cardioverter defibrillator (ICD) therapy Follow-up: no The study controls for patient characteristics (age, sex, race).	Medicare Part B claim data, 2008	Gary, IN: 10,350 cardiovascular patients,481 physicians South Bend, IN: 9,653 cardiovascular patients, 639 physicians.	Two HRRs; Indiana, United States	Physicians and hospitals within two adjacent HRRs with disparate adherence to clinical guidelines regarding patient selection for ICD therapy.	Physicians who provided care to patients with cardiovascular disease.
Pollack et al, 2012	Observational and retrospective Comparison group: no Follow-up: no The study adjusts for patient clinical (Gleason score, tumor stage, PSA results, comorbidity) and sociodemographic characteristics (age, race, community-level income, marital status).	SEER-Medicare data from three cities, 2004–2005	4,520 men 2420 doctors	Three cities; the United States	Community detection algorithm (Girvan-Newman) to identify group of physicians with more frequent patient sharing in each city. Separate analysis was done in each city.	Physicians who provided care to prostate cancer patients, classified based on their role in the diagnosis, providing care, or administered the cancer treatments, within a 12-month window after the diagnosis.
Pollack et al, 2014	Retrospective, observational cohort study Comparison group: no Follow-up: one year The study controls for patient-level and community-level characteristics (comorbidities, race, ethnicity, census tract or zip level median income, urologist surgical volume).	SEER Medicare data, 2004–2005	For network construction: 13,465 men with prostate cancer without metastatic disease. For analyses: 2,677 men in 5 cities. The sample in each city ranged in size from 270 to 1,224 men.	Physician networks in five cities; the United States	Network subgroups were defined by Girvan-Newman algorithm (also called "community structure"); each doctor was assigned to a subgroup. Patients, however, may have doctors that were assigned to multiple subgroups. For the purposes of the analyses, patients were assigned to the network subgroup of the urologist who performed their prostatectomy.	Urologists who provided care to the same patients with localized prostate cancer who underwent radical prostatectomy.
Stein et al, 2017	Cross-sectional study Comparison group: no Follow-up: no The study controls for county characteristics including the total number of providers in the provider communities seen by the patient, the total number of patients in the provider communities seen by the provider, and state-fixed effects.	Medicaid claims data, 2008–2009	29,611 Medicaid enrollees	12 states; the United States	Provider communities based on the frequency with which different providers were treating the same patients, all of whom had been diagnosed with opioid use disorders. Communities were defined using modularity maximization community detection.	Providers who cared for the same patients with opioid use disorder during 2009.
Uddin et al, 2012	Cross-sectional study over four years and two months. Comparison group: no Follow-up: no	One health insurance company’s claims, Jan 2005 – Feb 2009	2229 patients,* 85 hospitals	Hospitals; Australia	Physician collaboration networks were hospitals.	Physicians who provided care to hip replacement patients during their hospitalization period.
Uddin et al, 2015	Cross-sectional data over 4 years and 2 months Comparison group: no Follow-up: no	One health insurance company’s claims, January 2005 - February 2009	2352 patients, 2229 physicians, 85 hospitals.	Hospitals; Australia	Community detection algorithm to determine communities of doctors within each physician collaboration network (i.e. hospital)	Physicians who provided care to hip replacement patients during their hospitalization.
Uddin 2016	Cross-sectional data “over 5 years”** Comparison group: no Follow-up: no The study controls for patient-level characteristics (age, gender, comorbidity index (Charlson-Deyo index)).	One health insurance company’s claims “over 5 years”**	2352 patients, 2229 physicians, 85 hospitals	Hospitals; Australia	Community detection firefly algorithm[33] optimization approach: maximization of internal links and minimization of external links	Physicians who provided care to hip replacement patients during their hospitalization
Notes: SEER = The Surveillance, Epidemiology, and End Results, a program of the National Cancer Institute. MRSA = Methicillin-resistant Staphylococcus aureus, a hospital-acquired infection. HRR = Hospital referral region. RVU = relative value unit, a measure of work or effort performed on a patient that is correlated to the revenue potential.[46] NHS = The National Health Services. CABG = coronary artery bypass graft. PSA = prostate specific antigen.
* This article states that data from 2229 patients were used, which may be a typo, because two other studies that use the same data (Uddin et al, 2015; Uddin 2016) list 2229 physicians and 2352 patients.
** This article states “over a period of five years” but does not specify which years; it appears that the data source is exactly the same as in Uddin et al, 2012 and Uddin et al, 2015, which is Jan 2005 – Feb 2009, which is four years and two months.

Study setting and data sources

Eight studies were conducted in the United States,[19–26] three in Australia,[27–29] and one in the UK.[30] The settings of seven studies were hospitals and emergency departments,[20, 24, 25, 27–30] 3 studies were hospital referral region,[21, 22, 26] and two studies were geographical regions (e.g., cities).[19, 23]

Five studies used Medicare data[20–22, 24, 26] and two used Medicare-SEER linked data.[19, 23] One used Medicaid data,[25] three used Australian insurance organization data,[27–29] and one used the National Health Service (NHS) Hospital Statistics data.[30]

Study design

Nine studies were cross-sectional.[20–22, 24, 25, 27–30] Of those, three were repeated cross-sectional studies.[24, 29, 30] Three studies were longitudinal.[19, 23, 26]

Patient-sharing networks, actors, and ties

Network actors were HCPs[19–29] and hospitals.[30] Patient-sharing definition varied across studies. Patient-sharing relations between pairs of HCPs or hospitals were defined as caring for the same patient within a specific time frame, based on care context, a care episode, or professional relationships. Four studies based their definition on care episodes, e.g., during a hospitalization or an ambulatory care episode;[20, 27–29] five studies were based on clinical conditions like cardiovascular disease, prostate cancer, opioid use disorder, and life-threatening chronic disease;[19, 21, 23–25] one study was based on direct referrals from primary care physicians to specialists,[26] and one study was based on transfers between hospitals.[30] One study did not specify care setting or clinical conditions.[22] Three studies used a specific time window of providing care for the same patient as the definition of patient-sharing relations.[23, 24, 26]

Definitions of clusters

Seven studies used community detection algorithms to identify HCP clusters.[19, 20, 23, 25, 27, 29, 30] Community detection algorithms divide network nodes based on their relationship patterns into sets to maximize within-group and minimize between-group ties.[20] It is used to identify denser connected clusters within a larger network.

Three studies[19, 23, 29] used the Girvan-Newman community detection algorithm, which relies on modularity maximization. It involves an iterative removal of high betweenness ties with the highest number of shortest paths between nodes passing through them (in other words, identifying the network weak points).[19] These ties probably bridge loosely connected clusters in the network. Girvan-Newman is one of the traditional community detection methods and is best for smaller networks.[31]

Two studies[25, 30] use the Fast Greedy algorithm. This method is based on the notion of local modularity.[32] It starts with considering individual nodes as their own clusters, then adding nodes to the clusters one at a time in a greedy approach (examining local optimization) to create new clusters, aiming to maximize network modularity. Merging nearby clusters continues until there is no improvement in modularity.[31, 32] One study[20] used an algorithm called the Blondel model or the Louvain algorithm.[31] Similar to the Fast Greedy algorithm, this hierarchical clustering method starts with separate nodes and adds nodes to clusters in a greedy way to optimize modularity. To accommodate for larger network size, this model iteratively aggregates new clusters into “super-nodes” in a new network, and then repeats this procedure with the new aggregated network until there is no improvement in modularity. One paper[27] used the algorithm introduced by Amiri et al.[33] The Amiri algorithm is an evolutionary technique[34] that learns from previous steps, aiming for maximizing within-cluster relations and minimizing between-cluster relations at the same time.

Four of these studies applied a multi-level approach, identifying collaboration networks based on hospitals[27, 29] or cities,[19, 23] and then applying a community detection algorithm to each sub-network separately.

Five studies did not apply any community detection algorithm and a priori defined clusters as hospitals, medical practices, clinics, health systems, or hospital referral regions.[21, 22, 24, 26, 28]

Measures of cluster characteristics

Cluster structure

The size of clusters (number of providers in each cluster) was the most commonly reported measure.[19, 22, 23, 27, 29] Uddin et al. (2015)[29] assessed the physician-to-patient ratio in physician communities. Studies used different measures to assess collaboration between HCPs within a cluster. Network density was used most to describe the connectivity of actors (e.g., physicians or hospitals). Hollingsworth et al. (2016)[24] used the bipartite clustering coefficient to measure in the tendency to form dense groups in clusters (health systems). Casalino et al. (2015) calculated the mean adjusted valued degree of physicians within the community.[20]

Cluster composition

Pollack et al. (2012) measured the percentage of patients and providers in large clusters (defined as clusters of more than 50 physicians).[23] Casalino et al. (2015)[20] and Pollack et al. (2012)[23] assessed the proportion of primary care physicians (PCPs) and urologists in each cluster, respectively. DuGoff et al. (2018)[26] used the median number of physician pairs per PCP in a cluster to evaluate the breadth of PCP network.

Actors’ position

The position of actors in clusters was assessed in three studies.[20, 22, 28] Casalino et al. (2015)[20] measured the betweenness centrality of PCPs and cluster-level relative betweenness centrality by dividing the PCPs betweenness centrality (an indicator of their intermediary role) by the average betweenness centrality of other HCPs to evaluate the bridging ability of PCPs. Barnett et al. (2012)[22] measured degree centrality for PCPs and relative degree centrality using similar approach to evaluate the popularity of PCPs. Uddin et al. (2012)[28] assessed both betweenness centrality and degree centrality of actors in network clusters, to identify central actors.

Cluster dynamics

The persistence of connections over time was assessed in one study.[26] DuGoff et al. (2018)[26] evaluated persistence of ties between providers by assessing whether the ties were present across years, and calculated the proportion of ties that persisted in each cluster.

Behavior in clusters

Studies also used cluster-level measures in relation to physician behaviors. Stein et al. (2017)[25] assessed the percentage of individuals prescribed opioid analgesics in each cluster to understand how provider communities differed in prescription patterns.

Measures of cluster characteristics are defined in Table 2.

Table 2

Measures of Cluster Characteristics.
Structural measures	Explanation	Studies
CLUSTER LEVEL
Cluster size	The total number of individuals (actors) in the cluster	Uddin et al, 2016; Pollack et al, 2014; Barnett et al, 2012; Uddin et al, 2015; Pollack et al, 2012.
Density	The proportion of all possible connections that actors may have (actual connections divided by all possible connections)	Uddin et al, 2016; Uddin et al, 2012; Moen et al, 2016; Donker et al, 2012.
Bipartite clustering coefficient	The tendency of individuals (actors) to form clusters.	Hollingsworth et al, 2016.
Distance	The average distances between all pairs of actors in a cluster	Uddin et al, 2012.
Strength of ties	The number of patients shared by pairs of HCPs (actors)	Pollack et al, 2014; Casalino et al, 2015; Stein et al, 2017; DuGoff et al, 2018.
Persistence of ties	The proportion of ties that persisted overtime.	DuGoff et al, 2018.
Physician-to-patient ratio	The average number of HCPs per patient	Uddin et al, 2015.
Physician specialty characteristics	The proportion of physicians with certain specialties (e.g. PCP) in the cluster	Casalino et al, 2015; Pollack et al, 2012; Stein et al, 2017; DuGoff et al, 2018.
ACTOR LEVEL
Degree centrality	The number of connections that an individual has. At cluster level the average centrality of actors was calculated. Sometimes the relative average centrality of PCPs compared to other doctors was calculated for each cluster.	Uddin et al, 2012; Barnett et al, 2012.
Betweenness centrality	The extent to which an individual mediates indirect connections between others who are not connected to each other. At cluster level, the average betweenness of all actors was calculated. In one study, the ratio of the average betweenness of PCPs over specialists was calculated. One study calculated the variations in betweenness centrality among actors in a cluster.	Casalino et al, 2015; Uddin et al, 2012.

Association of clustering with patient outcomes

As the above sections elaborate, the included studies are heterogeneous in terms of study design, study setting, population, network and clustering measures, and statistical approaches used to estimate associations of these measures with patient outcomes. The findings from these studies on associations of clustering with patient outcomes are summarized in Table 3. Four papers assessed service use and intensity (such as emergency department (ED) visits, hospitalization days).[22, 24, 26, 27] Four papers assessed readmissions.[24, 26, 28, 29] Four papers examined costs and spending in health care.[22, 27–29] Two studies examined provider choice of treatment,[21, 23] one study focused on opioid and benzodiazepine prescriptions,[25] and one study focused on a hospital-acquired infection.[30] Below we synthesize these findings narratively, by grouping study findings based on the identified patterns.

Table 3

Associations between Network Cluster Measures and Patient Outcomes.
Study	Summary of findings	Interpretation of findings
Health care use and intensity
Barnett et al, 2012	For the average-sized urban hospital, an increase of one standard deviation (SD) in the median adjusted degree of doctors in the hospital network (implying connection to more other doctors for each patient) was associated with 17.4% more hospital days and 23.8% more physician visits in the last two years of life. One SD increase in the relative centrality of PCPs to other doctors (implying their prominence in the network) within hospital networks was associated with 8.6% fewer physician visits and 14.7% fewer medical specialist visits.	Hospitals with doctors with more connections have more intensive care. Hospitals with primary care-centered networks have less intensive care.
Uddin, 2016	Inclusion of cluster size and density as random effect variables in models predicting the length of stay for patients undergoing total hip replacement improved the performance of the model.	There was a significant variation among clusters in terms of the association between physician networks and length of stay. This supports the importance of cluster-level characteristics on patient-level relationships.
DuGoff et al, 2018	An additional physician connected to each PC physician in an HRR at baseline is associated with 1.2 more emergency department (ED) visits per 1,000 Medicare enrollees the following year. There was a non-significant positive association after 2 years. A 10% increase in tie persistence for 2 and 3 years (i.e., number of ties that persisted after 2 and 3 years in an HRR) is associated with 57 and 33 fewer ED visits per 1,000 Medicare beneficiaries, respectively.	Hospital referral regions (HRRs) where PC physicians had more physician connections were more likely to have higher ED visit rates after one year. HRRs with more persistent physician relationships is associated with fewer ED visits.
Hollingsworth et al, 2017	High level of clustering in physician teamwork (measured by the bipartite clustering coefficient) around CABG episodes was associated with a 24.6% lower 60-day ED visit rate, relative to a low level of physician teamwork.	Lower postoperative ED visit in health systems in which physicians worked together in tightly knit groups during CABG episodes.
Quality of care: readmissions
Uddin et al, 2012	higher density, higher average distance, and lower betweenness centralization (variation in betweenness centrality among physicians) in physician collaboration networks (PCNs; i.e., hospitals) were associated with lower readmissions rates (time frame for readmission not mentioned) among total hip replacement patients	Lower readmissions rates in PCNs with higher level of connectedness between physicians, and a more even distribution of mediatory connections (smaller betweenness inequality)
Uddin et al, 2015	PCNs (hospitals) with more physician communities (identified through community detection) and fewer physicians per community had lower 28-day readmission rates	Hospitals that are comprised of more small communities (corresponding to better communication and information sharing in smaller clusters) had lower readmission rates..
Hollingsworth et al, 2017	High level of clustering in physician networks (measured by the bipartite clustering coefficient) around CABG episodes was associated with 24.4% lower readmission rate, relative to a low level of physician teamwork.	Lower readmission rates in health systems in which physicians worked together in tightly knit groups during CABG episodes.
DuGoff et al, 2018	30-day readmission rates were not associated with more average physician ties (i.e., additional physician pairs per PC physician in an HRR) or with two-year persistence in these physician ties (i.e., number of ties that persisted after 2 years in an HRR).	-
Quality of care: other outcomes
Pollack et al, 2014	A significant variation among physician clusters and the rates of complications following radical prostatectomy: 30-day surgical complications, late urinary complications, and long-term incontinence. As for subgroup characteristics, average urologist centrality was significantly associated with the outcomes in some cities, but not always in the same direction.	Network subgroups (clusters) may explain a portion of the observed variation in surgical complications. -
Casalino et al., 2015	Physician practice community (PPC) fixed effects were jointly statistically significant when modeling ambulatory care-sensitive hospital admission (ACSA) rates. PPCs with a 1 SD higher percentage of primary care physicians had 4.7% higher ACSA rates. PPCs with a 1 SD increase in physicians’ mean adjusted value degree had 5.0% higher ACSA rates. No evidence of an association between ACSA rates and other PPC characteristics: percentage of US-trained physicians, percentage of board-certified physicians, primary care centrality ratio (i.e., the mean betweenness centrality of primary care physicians divided by the mean centrality of specialists), or PPC size.	PPCs were associated with ACSA rates, i.e., PPCs within networks matter. PPCs with larger proportion of primary care physicians had higher ACSA rates. PPCs in which physicians share patients with more other physicians had higher ACSA rates. -
Hollingsworth et al, 2017	High level of clustering in physician teamwork (measured by the bipartite clustering coefficient) around CABG episodes was associated with a 28.4% lower mortality rate, relative to a low level of physician teamwork.	Lower mortality rates in health systems in which physicians worked together in tightly knit groups during CABG episodes.
Costs and spending
Uddin et al, 2012	Higher hospitalization costs among total hip replacement patients in PCNs with lower density, higher distance, and higher betweenness centralization.	Higher hospitalization costs among total hip replacement patients in PCNs with lower level of connectedness between physicians and higher inequity in distribution of mediatory connections
Barnett et al, 2012	One SD increase in the adjusted degree of doctors in the hospital network (implying connection to more doctors for each patient) was associated with a 17.8% increase in total Medicare spending, along with over 20% increases in spending on imaging and tests. One SD increase in the relative centrality of PCPs to other doctors was associated with 6.0% lower total Medicare spending, 9.2% lower spending on imaging, and 12.9% lower spending on tests.	Hospitals with doctors with more connections have higher costs. Hospitals with primary care-centered networks have lower costs.
Uddin et al, 2015	Higher hospitalization costs in PCNs (hospitals) with a higher physician-to-patient ratio.	The association may indicate redundant physician visits to patients in hospitals with more physicians per patient.
Uddin, 2016	Inclusion of cluster size and density as random effect variables in models predicting the hospitalization cost for patients undergoing total hip replacement improved the performance of the model.	There was a significant variation among clusters in terms of the association between physician networks and hospitalization cost. This supports the importance of cluster-level characteristics on patient-level relationships.
Provider choice of treatment
Pollack et al, 2012	The differences in prostatectomy between different urologist clusters were statistically significant.	Clustering of urologists caring for patients with prostate cancer were associated with the likelihood of prostatectomy.
Moen et al, 2016	Associations between the use of implantable cardioverter defibrillators (ICDs), which is supported by clinical guidelines, and hospital centrality measures (betweenness centrality, closeness centrality, eigenvector centrality of hospitals in an aggregated network) were not statistically significant at 0.05 level. Including ICD-capable physician node strength and closeness centrality as covariates without hospital-level centrality measures had the largest impact on reducing the HRR effect.	- The variation in adherence to clinical guidelines is primarily driven by relationships between physicians.
Opioid and benzodiazepine prescriptions
Stein et al, 2017	Patients were more likely to receive opioid analgesic, benzodiazepine, and combined if they visited more provider communities (ORs 1.11, 1.16, 1.15, respectively). There was variation among prescription patterns across provider communities. In the provider communities in the highest quartile of the opioid analgesic, benzodiazepine, or combined prescribing, 2.5-5 times more patients received this prescription than in the lowest quartile.	Receiving treatment from prescribers in multiple provider communities was positively associated with receiving both drugs. It may be that jointly managing care becomes more difficult as prescribers from more communities are involved in a patient’s care and do not have history and collaboration channels. Also it might be the sign of doctor shopping across communities. Provider community norms influence providers’ prescribing behavior
Hospital-acquired infections
Donker et al, 2012	MRSA (Methicillin-resistant Staphylococcus aureus) incidence rate of an individual hospital was positively associated with the number of patients it shared (through referrals) with other hospitals within its cluster. Hospitals from larger clusters had a higher MRSA incidence rate than hospitals from smaller clusters.	More connected hospitals have higher MRSA rates. Larger clusters have a higher chance of experiencing a founding event (MRSA introduction and dispersal in one hospital).

Clusters matter

Pollack et al. (2014), Moen et al. (2016), and Stein et al. (2017) found a significant variation across cohesive natural clusters in terms of practice patterns (e.g., prostatectomy, adherence to guidelines, and analgesic prescription).[19, 21, 25] Pollack et al. (2014) and Casalino et al. (2015) found a significant variation across cohesive natural clusters in terms of surgical complications and preventable admission rates, respectively.[19, 20]

The disadvantages of connection to more peers

Barnet et al. (2012), DuGoff et al. (2018), and Casalino et al. (2015) found that a larger number of connections among physicians in clusters was associated with more adverse outcomes (including more intensive care, higher costs, higher ED visits, hospital admissions, and preventable hospital admissions).[20, 22, 26] Uddin et al. (2015) also found that in hospitals where physicians formed more small cohesive communities (rather than a large, interconnected network) there were lower readmission rates.[29] They interpreted this as higher complexity of collaboration and data sharing when physicians interact with more peers to care for the same patients, which adversely affected their quality of care.

The position of PCPs

Barnet et al. (2012) and Casalino et al. (2015) assessed the relative position of PCPs (compared to other specialties) in a cluster.[20, 22] Barnet et al. (2012) found that the average centrality of PCPs in a cluster was associated with positive outcomes (care intensity and cost),[22] while Casalino et al. (2015) did not find an association between relative centrality of PCPs and preventable admissions.[20] Casalino et al. (2015) and DuGoff et al. (2018) reported that the larger number of PCPs in a cluster and larger networks of PCPs were associated with adverse outcomes (such as more preventable hospital admissions and ED visits).[20, 26] The findings of two latter studies align with the previous theme of findings regarding the disadvantages of larger networks (i.e., being connected to more peers).

Advantages of tight-knit communities

DuGoff et al. (2018) found that more persistent connections among physicians in a cluster were associated with a better outcome (fewer ED visits).[26] Uddin et al. (2012) and Hollingsworth et al. (2017) showed that more cohesive connections among physicians (less average betweenness, higher clustering coefficient) in a cluster were associated with better outcomes (e.g., readmission rates, ED visits, hospitalization costs).[24, 28] Also, Uddin et al. (2015) found that the formation of cohesive communities among physicians in hospitals improved readmission rates among hip replacement patients.[29]

We reviewed studies that examined clustering of HCPs in patient-sharing networks. The studies varied considerably in terms of definitions and measures of patient-sharing relations, definitions and structural measures of network clusters, settings, study populations, and health outcomes.

Most studies[19–25, 27–29] defined patient-sharing networks based on all HCPs providing care to the same patients, while some studies specifically focused on patient-sharing across and within specialties or hospitals.[26, 30] The majority of the studies did not limit the geographic or systemic boundaries of the network as long as the care was provided to a select group of patients. In contrast, others limited the network boundaries to specific health systems or geographic regions. The diversity of networks and cluster definitions may reflect different behavioral and contextual dynamics. The motivation and constraints of HCPs to share certain patients with their peers and to form informal or formal clusters of care are moderated by various contextual and systemic factors as well as the nature of their practice. For example, physicians within a healthcare system or a hospital have more monetary and organizational incentives to share patients. Also, patient-sharing between a PCP and a specialist mainly presents as referrals, while patient-sharing among different PCPs is probably due to patients’ request to have a second opinion, changing their family doctor, or changes in insurance plans.

To identify provider clusters, a few studies used community-detection algorithms (mostly variations of Girvan-Newman or modularity), while a few others used organizational boundaries (e.g., hospital). Community detection algorithms identify clusters based on the behavioral patterns of HCPs in sharing patients with peers, while clustering based on organizational structure does not take the behavior into account. Even though a common assumption is that the providers within a healthcare system are more likely to share patients, we know little about the factors determining cluster formation and the extent of overlap between clusters based on practice patterns and formal organizational structures.

Variation in study concepts and methods aside, it is challenging to summarize the findings of the studies to inform policies, for two reasons. First, the studies were not clear in terms of the theoretical underpinnings for both the choice of network measures and the potential mechanisms of the effects of clustering on health outcomes. The rationale for the use of specific network measures was rarely provided. Given the wide range of network measures, it is key to ground analytical choices in the social network theories, to meaningfully interpret the findings. Also, few studies drew on behavioral, organizational, or health services theoretical models for how specific network characteristics are associated with cluster formation and its impact on health outcomes. In other words, most studies either had no clear hypotheses, or fell short to generate or support their hypotheses based on theory. Evidently, the majority of this literature is still exploratory.

Second, the use of administrative data, despite the strengths listed earlier, has its disadvantages. Policies and interventions informed by provider network analyses could focus on increasing provider collaboration, effective patient care coordination, or knowledge sharing, all of which require providers to communicate with each other and share resources. However, administrative data do not allow to measure actual collaboration and communication, which can only be rigorously measured through primary data collection.

Despite the heterogeneity of studies and their limitations, there are a few general themes in study findings. First, more connections between physicians (e.g. more HCPs in one cluster or being connected to more peers to care for a patient) in patient-sharing clusters are associated with worse health outcomes and higher spending. Second, better connectedness of physicians, tight-knit communities, and persistence of physician ties are associated with better outcomes and less costly care.

The quality of the relationships, rather than quantity, seems to lead to better patient outcomes. Theoretically, better connectivity between network actors would suggest better communication and teamwork[35] and would thus be associated with improved outcomes.[36] Prior research has shown a positive impact of network cohesion on outcomes for network actors, including better social capital[37] and support.[38, 39] In small cohesive networks, the connections are stronger and more intimate.[40] However, strong connectivity may limit the diversity, access to new knowledge resources, and innovation.[41] Tie persistence could be the result of satisfaction with the connection based on prior experience of patient referral and communication, implying that the strategically chosen network ties may last longer and lead to better care coordination.[26]

Larger and denser networks, on the other hand, are also probably more difficult to manage by physicians, who have busy schedules, especially when the infrastructure for referral and communication is not optimal. It is also more difficult to develop personal relationships with peers in a larger cluster.

The studies did not report differences in the themes of findings among pre-defined clusters (e.g., hospital, HRR) vs. naturally occurring cohesive clusters (i.e., identified through community detection). This implies that the dynamics of relationships (such as tie persistence and manageable size) associate more with better outcomes than factors determining the local formation of clusters, such as organizational boundaries or preference to connect.

We offer several recommendations for future research on patient-sharing and health outcomes. First, we recommend that researchers use social network theories to define networks and clusters and choose network measures in a rigorous way. Second, researchers should use theories from social and health sciences to argue for the expected effects of physician networks on outcomes. Next, while studies using administrative data have benefits, qualitative and mixed methods studies would help assess providers’ motivation to share patients with colleagues as well as examine relationships between network patterns and provider perspectives on patient-sharing. Regarding clustering within networks, qualitative studies could further illuminate the determinants and dynamics of cluster formation, from the viewpoint of network actors. Lastly, interventional studies are needed in order to produce high-quality systematic evidence on the causal mechanisms and development of network interventions. Understanding the mechanisms and testing interventions to improve cluster formation among HCPs have implications for system integration models in healthcare. Integrated systems, which generally have a high level of efficiency in patient-sharing, may produce better health outcomes through various mechanisms, including better care coordination, team-based care, streamlined communication and value-based payment. Interventions designed to promote clustering among healthcare providers (e.g., team-based care[42–45]) would help disentangle network-related effects on health outcomes.

Our findings are subject to several limitations. First, our findings are based on a qualitative analysis of the main themes in included studies and due to study inconsistencies, we were not able to pool the results for a meta-analysis. Our findings should not be interpreted as evidence of effects. Second, even though we assessed the quality of the studies, we did not use our assessment to exclude any studies. Third, errors in the data collection and extraction are possible. We tried to minimize errors by having two independent reviewers for each study and using structured tools for data extraction. Fourth, all studies under review report some statistically significant findings and potential publication bias may introduce bias into our summary.

Our systematic scoping review revealed that studies focused on patient-sharing network clusters varied substantially in key characteristics and approaches. More rigorous theoretical grounding, the use of interventional, qualitative and mixed-methods studies, and development of interventions to improve the composition and dynamics of HCP clusters will advance our understanding of the social dynamics of healthcare and development of better integrated health systems.

Acknowledgments

The authors thank Dr. Chinmayee Katragadda and Ms. Zhi Pan, who contributed to the data extraction.

Funding

No funding to declare.

Authors’ contributions

All authors contributed to the conceptualization. AD, PD, QQ, and SY performed the screening, data extraction, and analysis, under supervision of RYN. All authors contributed to the drafting the manuscript and approved the final text.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and material

The authors confirm that all data generated or analyzed during this study are included in this published article.

Competing interests

None to declare.

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When Clinicians Group Together: A Systematic Scoping Review of Clustering in Patient-Sharing Networks

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Abstract

Background

Methods.

Results.

Conclusion.

Figures

INTRODUCTION

METHODS

Identifying relevant literature

Selecting studies

Extracting and charting

Data synthesis and analysis

RESULTS

Review process and retrieval

Study setting and data sources

Study design

Patient-sharing networks, actors, and ties

Definitions of clusters

Measures of cluster characteristics

Cluster structure

Cluster composition

Actors’ position

Cluster dynamics

Behavior in clusters

Association of clustering with patient outcomes

Clusters matter

The disadvantages of connection to more peers

The position of PCPs

Advantages of tight-knit communities

DISCUSSION

CONCLUSIONS

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1