Social network analysis of patient sharing among hospitals in Orange County, California

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

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

Analysis of the patient-sharing network in hypertension management: a retrospective study in China

OBJECTIVE

To explore the robustness of the patient-sharing network and validate the association between strength and persistence of physicians' relationships in China.

DESIGN, SETTING AND PARTICIPANTS

We conducted a patient-sharing network analysis to describe the persistence of patient-sharing relationships and logistic regression to analyse factors associating with the persistence of patient-sharing relationships in the Yinzhou Health Information System from 1 January 2010 to 31 December 2018; all outpatient records that had a hypertension diagnosis were included in this study.

OUTCOME MEASURES

The persistence ratio was defined as the proportion of the patient-sharing relationships in a given year that continued to exist in the following year, the 1-, 2- and 3-year persistence to test the robustness of the findings.

RESULTS

This study included 3916 physicians from 42 public healthcare facilities in Yinzhou. The 1-year persistence ratio fluctuated around 80%, and the 3-year persistence ratio was around 60% over the study period. The strength of the relationship, tie characteristics and physician specialty were important factors associating with the persistence of the relationships. The persistence of the relationships increased significantly as the strength of the relationships increased (for relationships with strength ∈ [3, 5), OR=3.987, 95% CI 3.896 to 4.08; for relationships with strength ∈ [5, 7), OR=6.379, 95% CI 6.147 to 6.626; and for relationships with strength ∈ [7, 9), OR=8.373, 95% CI 7.941 to 8.829). Physicians from the same healthcare institution were more likely to form ties that persisted for at least 1 year compared with physicians from different institutions (OR=1.510, 95% CI 1.480 to 1.540).

CONCLUSIONS

Our study showed that physicians frequently formed relationships with other physicians through sharing patients in Yinzhou, China, and these relationships had similar rates of persistence to studies conducted in developed countries, which indicated that findings of social network analyses conducted in developed countries still hold value in developing countries.

A multi-center validation of the electronic health record admission source and discharge location fields against the clinical notes for identifying inpatients with long-term care facility exposure

Identifying long-term care facility (LTCF)-exposed inpatients is important for infection control research and practice, but ascertaining LTCF exposure is challenging. Across a large validation study, electronic health record data fields identified 76% of LTCF-exposed patients compared to manual chart review.

OBJECTIVE

Residence or recent stay in a long-term care facility (LTCF) is an important risk factor for antibiotic-resistant bacterial colonization. However, absent dedicated intake questionnaires or resource-intensive chart review, ascertaining LTCF exposure in inpatients is challenging. We aimed to validate the electronic health record (EHR) admission and discharge location fields against the clinical notes for identifying LTCF-exposed inpatients.

METHODS

We conducted a retrospective study of 1020 randomly sampled adult admissions between 2016 and 2021 across 12 University of Maryland Medical System hospitals. Using study-developed guidelines, we categorized the following data for LTCF exposure: each admission’s history & physical (H&P) note, each admission’s EHR-extracted “Admission Source,” and (3) the EHR-extracted admission and discharge locations for previous admissions (≤90 days). We estimated sensitivities, with 95% CIs, of H&P notes and of EHR admission/discharge location fields for detecting “current” and “any recent” (≤90 days, including current) LTCF exposure.

RESULTS

For detecting current LTCF exposure, the sensitivity of the index admission’s EHR-extracted “Admission Source” was 46% (95% CI: 35%–58%) and of the H&P note was 92% (83%–97%). For detecting any recent LTCF exposure, the sensitivity of “Admission Source” across the index and previous admissions was 32% (24%–41%), “Discharge Location” across previous admission(s) was 57% (47%–66%), and of the H&P note was 68% (59%–76%). The combined sensitivity of admission source and discharge location for detecting any recent LTCF exposure was 76% (67%–83%).

CONCLUSIONS

The EHR-obtained admission source and discharge location fields identified 76% of LTCF-exposed patients compared to chart review but disproportionately missed currently exposed patients.

Regional patient transfer patterns matter for the spread of hospital-acquired pathogens

Pathogens typically responsible for hospital-acquired infections (HAIs) constitute a major threat to healthcare systems worldwide. They spread via hospital (or hospital-community) networks by readmissions or patient transfers. Therefore, knowledge of these networks is essential to develop and test strategies to mitigate and control the HAI spread. Until now, no methods for comparing healthcare networks across different systems were proposed. Based on healthcare insurance data from four German federal states (Bavaria, Lower Saxony, Saxony and Thuringia), we constructed hospital networks and compared them in a systematic approach regarding population, hospital characteristics, and patient transfer patterns. Direct patient transfers between hospitals had only a limited impact on HAI spread. Whereas, with low colonization clearance rates, readmissions to the same hospitals posed the biggest transmission risk of all inter-hospital transfers. We then generated hospital-community networks, in which patients either stay in communities or in hospitals. We found that network characteristics affect the final prevalence and the time to reach it. However, depending on the characteristics of the pathogen (colonization clearance rate and transmission rate or even the relationship between transmission rate in hospitals and in the community), the studied networks performed differently. The differences were not large, but justify further studies.

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.

Effect of the Implementation of the French Hospital Regionalization Policy on Patient Mobility

A new law was voted in France in 2016 to increase cooperation between public sector hospitals. Hospitals were encouraged to work under the leadership of local referral centers and to share their support functions (e.g., information systems) with newly created hospital groups, called "Regional Hospital Groups." The law made it compulsory for each public sector hospital to become affiliated with one of 136 newly created hospital groups. The policy's aim was to ensure that all patients were sent to the hospital best qualified to treat their unique condition, among the hospitals available at the regional level. Therefore, we aimed to assess whether this regionalization policy was associated with changes in observed patterns of patient mobility between hospitals. This nationwide observational study followed an interrupted time series design. For each stay occurring from 2014 to 2019, we ascertained whether or not the stay was followed by mobility toward another hospital within 90 days, and whether or not the receiving hospital was part of the same Regional Hospital Group as the sender hospital. The proportion of mobility directed toward the same regional hospital group increased from 22.9% in 2014 (95% CI 22.7-23.1) to 24.6% in 2019 (95% CI 24.4-24.8). However, the absence of discontinuity during the policy change year was consistent with the hypothesis of a preexisting trend toward regionalization. Therefore, the policy did not achieve major changes in patterns of mobility between hospitals. Other objectives of the reform, including long-term consequences on the healthcare offer, remain to be assessed.

Introducing SoNHR-Reporting guidelines for Social Networks In Health Research

OBJECTIVE

The overall goal of this work is to produce a set of recommendations (SoNHR-Social Networks in Health Research) that will improve the reporting and dissemination of social network concepts, methods, data, and analytic results within health sciences research.

METHODS

This study used a modified-Delphi approach for recommendation development consistent with best practices suggested by the EQUATOR health sciences reporting guidelines network. An initial set of 28 reporting recommendations was developed by the author team. A group of 67 (of 147 surveyed) experienced network and health scientists participated in an online feedback survey. They rated the clarity and importance of the individual recommendations, and provided qualitative feedback on the coverage, usability, and dissemination opportunities of the full set of recommendations. After examining the feedback, a final set of 18 recommendations was produced.

RESULTS

The final SoNHR reporting guidelines are comprised of 18 recommendations organized within five domains: conceptualization (how study research questions are linked to network conceptions or theories), operationalization (how network science portions of the study are defined and operationalized), data collection & management (how network data are collected and managed), analyses & results (how network results are analyzed, visualized, and reported), and ethics & equity (how network-specific human subjects, equity, and social justice concerns are reported). We also present a set of exemplar published network studies which can be helpful for seeing how to apply the SoNHR recommendations in research papers. Finally, we discuss how different audiences can use these reporting guidelines.

CONCLUSIONS

These are the first set of formal reporting recommendations of network methods in the health sciences. Consistent with EQUATOR goals, these network reporting recommendations may in time improve the quality, consistency, and replicability of network science across a wide variety of important health research areas.

The spread of coronavirus disease 2019 (COVID-19) via staff work and household networks in residential aged-care services in Victoria, Australia, May-October 2020

OBJECTIVE

Morbidity and mortality from coronavirus disease 2019 (COVID-19) have been significant among elderly residents of residential aged-care services (RACS). To prevent incursions of COVID-19 in RACS in Australia, visitors were banned and aged-care workers were encouraged to work at a single site. We conducted a review of case notes and a social network analysis to understand how workplace and social networks enabled the spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) among RACS.

DESIGN

Retrospective outbreak review.

SETTING AND PARTICIPANTS

Staff involved in COVID-19 outbreaks in RACS in Victoria, Australia, May-October 2020.

METHODS

The Victorian Department of Health COVID-19 case and contact data were reviewed to construct 2 social networks: (1) a work network connecting RACS through workers and (2) a household network connecting to RACS through households. Probable index cases were reviewed to estimate the number and size (number of resident cases and deaths) of outbreaks likely initiated by multisite work versus transmission via households.

RESULTS

Among 2,033 cases linked to an outbreak as staff, 91 (4.5%) were multisite staff cases. Forty-three outbreaks were attributed to multisite work and 35 were deemed potentially preventable had staff worked at a single site. In addition, 99 staff cases were linked to another RACS outbreak through their household contacts, and 21 outbreaks were attributed to staff-household transmission.

CONCLUSIONS

Limiting worker mobility through single-site policies could reduce the chances of SARS-CoV-2 spreading from one RACS to another. However, initiatives that reduce the chance of transmission via household networks would also be needed.

Assessing Intrahospital Care Transition Structures Before and During the COVID-19 Pandemic: Network Analysis Study

Transitions of care (TOC) is essential for patients with complex medical needs to maintain the continuity of care. The COVID-19 pandemic may result in unexpected pressure on healthcare organizations' routine work and may burden the TOC system. The objective of this study is to assess TOC structures in pre- and intra-COVID-19 and quantify changes in the structures through the lens of network analysis. We investigated a trauma registry repository consisting of care transitions of 5,674 (2,699 and 2,975 in pre- and intra-COVID-19) inpatients admitted to Vanderbilt University Medical Center (VUMC) between January 2019 and May 2021. Network metrics, including assortativity, homophily, and small-world-ness were leveraged to measure TOC structures and their changes. Our results showed both pre- and intra-COVID-19 TOC structures were disassortative, homophily, and small-world- ness, and the COVID-19 pandemic had limited influences on the three characteristics of the TOC structures. The disassortative TOC structure indicates patients can be efficiently transferred between triage centers (highly connected units) and receivers (lowly connected units); the homophily structure demonstrates two connected care units serve similar patients, and the small-world-ness reveals a patient can be transferred to highly collaborative care units with a short length of transfer path.

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.

A Conceptual Framework for Optimizing the Equity of Hospital-Based Emergency Care: The Structure of Hospital Transfer Networks

Policy Points Current pay-for-performance and other payment policies ignore hospital transfers for emergency conditions, which may exacerbate disparities. No conceptual framework currently exists that offers a patient-centered, population-based perspective for the structure of hospital transfer networks. The hospital transfer network equity-quality framework highlights the external and internal factors that determine the structure of hospital transfer networks, including structural inequity and racism.

CONTEXT

Emergency care includes two key components: initial stabilization and transfer to a higher level of care. Significant work has focused on ensuring that local facilities can stabilize patients. However, less is understood about transfers for definitive care. To better understand how transfer network structure impacts population health and equity in emergency care, we proposea conceptual framework, the hospital transfer network equity-quality model (NET-EQUITY). NET-EQUITY can help optimize population outcomes, decrease disparities, and enhance planning by supporting a framework for understanding emergency department transfers.

METHODS

To develop the NET-EQUITY framework, we synthesized work on health systems and quality of health care (Donabedian, the Institute of Medicine, Ferlie, and Shortell) and the research framework of the National Institute on Minority Health and Health Disparities with legal and empirical research.

FINDINGS

The central thesis of our framework is that the structure of hospital transfer networks influences patient outcomes, as defined by the Institute of Medicine, which includes equity. The structure of hospital transfer networks is shaped by internal and external factors. The four main external factors are the regulatory, economic environment, provider, and sociocultural and physical/built environment. These environments all implicate issues of equity that are important to understand to foster an equitable population-based system of emergency care. The framework highlights external and internal factors that determine the structure of hospital transfer networks, including structural racism and inequity.

CONCLUSIONS

The NET-EQUITY framework provides a patient-centered, equity-focused framework for understanding the health of populations and how the structure of hospital transfer networks can influence the quality of care that patients receive.

Association of distance between hospitals and volume of shared admissions

Background

To assess whether decreasing distance between hospitals was associated with the number of shared patients (patients with an admission to one hospital and a readmission to another).

Methods

Data were from the Healthcare Cost and Utilization Project’s State Inpatient Databases (Florida, Georgia, Maryland, Utah [2017], New York, Vermont [2016]) and the American Hospital Association Annual Survey (2016 & 2017). This was a cross-sectional analysis of patients who had an index admission and subsequent readmission at different hospitals within the same year. We used unadjusted and adjusted linear regression to evaluate the association between the number of shared patients and the distance between admission-readmission hospital pairs.

Results

There were 691 hospitals in the sample (247 in Florida, 151 in Georgia, 50 in Maryland, 172 in New York, 58 in Utah, and 13 in Vermont), accounting for a total of 596,772 admission-readmission pairs. 32.6% of the admission-readmission pairs were shared between two hospitals. On average, a one-mile decrease in distance between two hospitals was associated with of 3.05 (95% CI, 3.02, 3.07) more shared admissions. However, variability between states was wide, with Utah having 0.37 (95% CI 0.35, 0.39) more shared admissions between hospitals per one-mile shorter distance, and Maryland having 4.98 (95% CI 4.87, 5.08) more.

Conclusions

We found that proximity between hospitals is associated with higher volumes of shared admissions.

Stress-testing the resilience of the Austrian healthcare system using agent-based simulation

Patients do not access physicians at random but rather via naturally emerging networks of patient flows between them. As mass quarantines, absences due to sickness, or other shocks thin out these networks, the system might be pushed to a tipping point where it loses its ability to deliver care. Here, we propose a data-driven framework to quantify regional resilience to such shocks via an agent-based model. For each region and medical specialty we construct patient-sharing networks and stress-test these by removing physicians. This allows us to measure regional resilience indicators describing how many physicians can be removed before patients will not be treated anymore. Our model could therefore enable health authorities to rapidly identify bottlenecks in access to care. Here, we show that regions and medical specialties differ substantially in their resilience and that these systemic differences can be related to indicators for individual physicians by quantifying their risk and benefit to the system.

Graph theoretical way of understanding protein-protein interaction in ovarian cancer

Cancer is a major research area in the medical field. Precise assessment of non-similar cancer types holds great significance in according to better treatment and reducing the risk of destructiveness in patients’ health. Cancer comprises a ambient that differs in response to therapy, signaling mechanisms, cytology and physiology. Netting theory and graph theory jointly gives a viable way to probe the proteomic specific data of cancer types such as ovarian, colon, breast, oral, cervical, prostate, and lung. We observe that the P2P(protein-protein) interaction Nettings of the cancerous tissues blended with the seven cancers and normal have same structural attributes. But some of these point to desultory changes from the disease Nettings to normal implying the variation in the dealings and bring out the redoing in the complicacy of various cancers. The Netting-based approach has a pertinent role in precision oncology. Cancer can be better dealt with through mutated pathways or Nettings in preference to individual mutations and that the utility value of repositioned drugs can be understood from disease modules in molecular Nettings. In this paper, we demonstrate how the graph theory and neural Nettings act as vital tools for understanding cancer and other types such as ovarian cancer at the zeroth level.

Beyond patient-sharing: Comparing physician- and patient-induced networks

The sharing of patients reflects collaborative relationships between various healthcare providers. Patient-sharing in the outpatient sector is influenced by both physicians' activities and patients' preferences. Consequently, a patient-sharing network arises from two distinct mechanisms: the initiative of the physicians on the one hand, and that of the patients on the other. We draw upon medical claims data to study the structure of one patient-sharing network by differentiating between these two mechanisms. Owing to the institutional requirements of certain healthcare systems rather following the Bismarck model, we explore different triadic patterns between general practitioners and medical specialists by applying exponential random graph models. Our findings imply deviation from institutional expectations and reveal structural realities visible in both networks.

Making sense of the French public hospital system: a network-based approach to hospital clustering using unsupervised learning methods

BACKGROUND

Hospitals in the public and private sectors tend to join larger organizations to form hospital groups. This increasingly frequent mode of functioning raises the question of how countries should organize their health system, according to the interactions already present between their hospitals. The objective of this study was to identify distinctive profiles of French hospitals according to their characteristics and their role in the French hospital network.

METHODS

Data were extracted from the national hospital database for year 2016. The database was restricted to public hospitals that practiced medicine, surgery or obstetrics. Hospitals profiles were determined using the k-means method. The variables entered in the clustering algorithm were: the number of stays, the effective diversity of hospital activity, and a network-based mobility indicator (proportion of stays followed by another stay in a different hospital of the same Regional Hospital Group within 90 days).

RESULTS

Three hospital groups were identified by the clustering algorithm. The first group was constituted of 34 large hospitals (median 82,100 annual stays, interquartile range 69,004 - 117,774) with a very diverse activity. The second group contained medium-sized hospitals (with a median of 258 beds, interquartile range 164 - 377). The third group featured less diversity regarding the type of stay (with a mean of 8 effective activity domains, standard deviation 2.73), a smaller size and a higher proportion of patients that subsequently visited other hospitals (11%). The most frequent type of patient mobility occurred from the hospitals in group 2 to the hospitals in group 1 (29%). The reverse direction was less frequent (19%).

CONCLUSIONS

The French hospital network is organized around three categories of public hospitals, with an unbalanced and disassortative patient flow. This type of organization has implications for hospital planning and infectious diseases control.

Modeling Interventions to Reduce the Spread of Multidrug-Resistant Organisms Between Health Care Facilities in a Region

IMPORTANCE

Multidrug-resistant organisms (MDROs) can spread across health care facilities in a region. Because of limited resources, certain interventions can be implemented in only some facilities; thus, decision-makers need to evaluate which interventions may be best to implement.

OBJECTIVE

To identify a group of target facilities and assess which MDRO intervention would be best to implement in the Shared Healthcare Intervention to Eliminate Life-threatening Dissemination of MDROs in Orange County, a large regional public health collaborative in Orange County, California.

DESIGN, SETTING, AND PARTICIPANTS

An agent-based model of health care facilities was developed in 2016 to simulate the spread of methicillin-resistant Staphylococcus aureus (MRSA) and carbapenem-resistant Enterobacteriaceae (CRE) for 10 years starting in 2010 and to simulate the use of various MDRO interventions for 3 years starting in 2017. All health care facilities (23 hospitals, 5 long-term acute care hospitals, and 74 nursing homes) serving adult inpatients in Orange County, California, were included, and 42 target facilities were identified via network analyses.

EXPOSURES

Increasing contact precaution effectiveness, increasing interfacility communication about patients' MDRO status, and performing decolonization using antiseptic bathing soap and a nasal product in a specific group of target facilities.

MAIN OUTCOMES AND MEASURES

MRSA and CRE prevalence and number of new carriers (ie, transmission events).

RESULTS

Compared with continuing infection control measures used in Orange County as of 2017, increasing contact precaution effectiveness from 40% to 64% in 42 target facilities yielded relative reductions of 0.8% (range, 0.5%-1.1%) in MRSA prevalence and 2.4% (range, 0.8%-4.6%) in CRE prevalence in health care facilities countywide after 3 years, averting 761 new MRSA transmission events (95% CI, 756-765 events) and 166 new CRE transmission events (95% CI, 158-174 events). Increasing interfacility communication of patients' MDRO status to 80% in these target facilities produced no changes in the prevalence or transmission of MRDOs. Implementing decolonization procedures (clearance probability: 39% in hospitals, 27% in long-term acute care facilities, and 3% in nursing homes) yielded a relative reduction of 23.7% (range, 23.5%-23.9%) in MRSA prevalence, averting 3515 new transmission events (95% CI, 3509-3521 events). Increasing the effectiveness of antiseptic bathing soap to 48% yielded a relative reduction of 39.9% (range, 38.5%-41.5%) in CRE prevalence, averting 1435 new transmission events (95% CI, 1427-1442 events).

CONCLUSIONS AND RELEVANCE

The findings of this study highlight the ways in which modeling can inform design of regional interventions and suggested that decolonization would be the best strategy for the Shared Healthcare Intervention to Eliminate Life-threatening Dissemination of MDROs in Orange County.

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.

Effects of incomplete inter-hospital network data on the assessment of transmission dynamics of hospital-acquired infections

In the year 2020, there were 105 different statutory insurance companies in Germany with heterogeneous regional coverage. Obtaining data from all insurance companies is challenging, so that it is likely that projects will have to rely on data not covering the whole population. Consequently, the study of epidemic spread in hospital referral networks using data-driven models may be biased. We studied this bias using data from three German regional insurance companies covering four federal states: AOK (historically “general local health insurance company”, but currently only the abbreviation is used) Lower Saxony (in Federal State of Lower Saxony), AOK Bavaria (in Bavaria), and AOK PLUS (in Thuringia and Saxony). To understand how incomplete data influence network characteristics and related epidemic simulations, we created sampled datasets by randomly dropping a proportion of patients from the full datasets and replacing them with random copies of the remaining patients to obtain scale-up datasets to the original size. For the sampled and scale-up datasets, we calculated several commonly used network measures, and compared them to those derived from the original data. We found that the network measures (degree, strength and closeness) were rather sensitive to incompleteness. Infection prevalence as an outcome from the applied susceptible-infectious-susceptible (SIS) model was fairly robust against incompleteness. At incompleteness levels as high as 90% of the original datasets the prevalence estimation bias was below 5% in scale-up datasets. Consequently, a coverage as low as 10% of the local population of the federal state population was sufficient to maintain the relative bias in prevalence below 10% for a wide range of transmission parameters as encountered in clinical settings. Our findings are reassuring that despite incomplete coverage of the population, German health insurance data can be used to study effects of patient traffic between institutions on the spread of pathogens within healthcare networks.

Patterns of patients’ transfer between different hospitals contribute crucially to the risk of hospital-acquired infections (HAIs) in the health care system. To quantify this risk, network models can be applied. The estimated risk can be inaccurate in the case of incomplete data on hospital admissions, which can be a consequence of the multiplicity of insurance companies as it is the case in Germany. To develop a better understanding of how incompleteness of data affects network measures and the simulated spread of HAI, we compared those measures derived from sampled, scale-up and original data, based on hospitalization data from three AOK insurance companies. We found that common network measures were affected by incompleteness, but the simulated prevalence as a measure of epidemic spread in the network was robust over a large range of incompleteness proportions. Epidemics and the transition of the infectious diseases may be modelled on hospital data with a coverage as low as 10% of the local population, whilst maintaining accuracy to within 10% of the true population prevalence.

How to Choose Target Facilities in a Region to Implement Carbapenem-resistant Enterobacteriaceae Control Measures

BACKGROUND

When trying to control regional spread of antibiotic-resistant pathogens such as carbapenem-resistant Enterobacteriaceae (CRE), decision makers must choose the highest-yield facilities to target for interventions. The question is, with limited resources, how best to choose these facilities.

METHODS

Using our Regional Healthcare Ecosystem Analyst-generated agent-based model of all Chicago metropolitan area inpatient facilities, we simulated the spread of CRE and different ways of choosing facilities to apply a prevention bundle (screening, chlorhexidine gluconate bathing, hand hygiene, geographic separation, and patient registry) to a resource-limited 1686 inpatient beds.

RESULTS

Randomly selecting facilities did not impact prevalence, but averted 620 new carriers and 175 infections, saving $6.3 million in total costs compared to no intervention. Selecting facilities by type (eg, long-term acute care hospitals) yielded a 16.1% relative prevalence decrease, preventing 1960 cases and 558 infections, saving $62.4 million more than random selection. Choosing the largest facilities was better than random selection, but not better than by type. Selecting by considering connections to other facilities (ie, highest volume of discharge patients) yielded a 9.5% relative prevalence decrease, preventing 1580 cases and 470 infections, and saving $51.6 million more than random selection. Selecting facilities using a combination of these metrics yielded the greatest reduction (19.0% relative prevalence decrease, preventing 1840 cases and 554 infections, saving $59.6 million compared with random selection).

CONCLUSIONS

While choosing target facilities based on single metrics (eg, most inpatient beds, most connections to other facilities) achieved better control than randomly choosing facilities, more effective targeting occurred when considering how these and other factors (eg, patient length of stay, care for higher-risk patients) interacted as a system.

How Long-Term Acute Care Hospitals Can Play an Important Role in Controlling Carbapenem-Resistant Enterobacteriaceae in a Region: A Simulation Modeling Study

Typically, long-term acute care hospitals (LTACHs) have less experience in and incentives to implementing aggressive infection control for drug-resistant organisms such as carbapenem-resistant Enterobacteriaceae (CRE) than acute care hospitals. Decision makers need to understand how implementing control measures in LTACHs can impact CRE spread regionwide. Using our Chicago metropolitan region agent-based model to simulate CRE spread and control, we estimated that a prevention bundle in only LTACHs decreased prevalence by a relative 4.6%-17.1%, averted 1,090-2,795 new carriers, 273-722 infections and 37-87 deaths over 3 years and saved $30.5-$69.1 million, compared with no CRE control measures. When LTACHs and intensive care units intervened, prevalence decreased by a relative 21.2%. Adding LTACHs averted an additional 1,995 carriers, 513 infections, and 62 deaths, and saved $47.6 million beyond implementation in intensive care units alone. Thus, LTACHs may be more important than other acute care settings for controlling CRE, and regional efforts to control drug-resistant organisms should start with LTACHs as a centerpiece.

Evaluating Movement of Patients With Carbapenem-resistant Enterobacteriaceae Infections in the Greater Atlanta Metropolitan Area Using Social Network Analysis

BACKGROUND

Carbapenem-resistant Enterobacteriaceae (CRE) are an urgent threat with potential for rapid spread. We evaluated the role of Medicare patient movement between facilities to model the spread of CRE within a region.

METHODS

Through population-based CRE surveillance in the 8-county Atlanta (GA) metropolitan area, all Escherichia coli, Enterobacter spp., or Klebsiella spp. resistant to ≥1 carbapenem were reported from residents. CRE was attributed to a facility based on timing of culture and facility exposures. Centrality metrics were calculated from 2016 Medicare data and compared to CRE-transfer derived centrality metrics by Spearman correlation.

RESULTS

During 2016, 283 incident CRE cases with concurrent or prior year facility stays were identified; cases were attributed mostly to acute care hospitals (ACHs; 141, 50%) and skilled nursing facilities (SNFs; 113, 40%), and less frequently to long-term acute care hospitals (LTACHs; 29, 10%). Attribution was widespread, originating at 17 of 20 ACHs (85%), 7 of 8 (88%) LTACHs, but only 35 of 65 (54%) SNFs. Betweenness of Medicare patient transfers strongly correlated with betweenness of CRE case-transfer data in ACHs (r = 0.75; P < .01) and LTACHs (r = 0.77; P = .03), but not in SNFs (r = 0.02; P = 0.85). We noted 6 SNFs with high CRE-derived betweenness but low Medicare-derived betweenness.

CONCLUSIONS

CRE infections originate from almost all ACHs and half of SNFs. We identified a subset of SNFs central to the CRE transfer network but not the Medicare transfer network; other factors may explain CRE patient movement in these facilities.

Dispersion in the hospital network of shared patients is associated with less efficient care

BACKGROUND

There is growing recognition that health care providers are embedded in networks formed by the movement of patients between providers. However, the structure of such networks and its impact on health care are poorly understood.

PURPOSE

We examined the level of dispersion of patient-sharing networks across U.S. hospitals and its association with three measures of care delivered by hospitals that were likely to relate to coordination.

METHODOLOGY/APPROACH

We used data derived from 2016 Medicare Fee-for-Service claims to measure the volume of patients that hospitals treated in common. We then calculated a measure of dispersion for each hospital based on how those patients were concentrated in outside hospitals. Using this measure, we created multivariate regression models to estimate the relationship between network dispersion, Medicare spending per beneficiary, readmission rates, and emergency department (ED) throughput rates.

RESULTS

In multivariate analysis, we found that hospitals with more dispersed networks (those with many low-volume patient-sharing relationships) had higher spending but not greater readmission rates or slower ED throughput. Among hospitals with fewer resources, greater dispersion related to greater readmission rates and slower ED throughput. Holding an individual hospital's dispersion constant, the level of dispersion of other hospitals in the hospital's network was also related to these outcomes.

CONCLUSION

Dispersed interhospital networks pose a challenge to coordination for patients who are treated at multiple hospitals. These findings indicate that the patient-sharing network structure may be an overlooked factor that shapes how health care organizations deliver care.

PRACTICE IMPLICATIONS

Hospital leaders and hospital-based clinicians should consider how the structure of relationships with other hospitals influences the coordination of patient care. Effective management of this broad network may lead to important strategic partnerships.

Modelling pathogen spread in a healthcare network: Indirect patient movements

Inter-hospital patient transfers (direct transfers) between healthcare facilities have been shown to contribute to the spread of pathogens in a healthcare network. However, the impact of indirect transfers (patients re-admitted from the community to the same or different hospital) is not well studied. This work aims to study the contribution of indirect transfers to the spread of pathogens in a healthcare network. To address this aim, a hybrid network–deterministic model to simulate the spread of multiresistant pathogens in a healthcare system was developed for the region of Lower Saxony (Germany). The model accounts for both, direct and indirect transfers of patients. Intra-hospital pathogen transmission is governed by a SIS model expressed by a system of ordinary differential equations. Our results show that the proposed model reproduces the basic properties of healthcare-associated pathogen spread. They also show the importance of indirect transfers: restricting the pathogen spread to direct transfers only leads to 4.2% system wide prevalence. However, adding indirect transfers leads to an increase in the overall prevalence by a factor of 4 (18%). In addition, we demonstrated that the final prevalence in the individual healthcare facilities depends on average length of stay in a way described by a non-linear concave function. Moreover, we demonstrate that the network parameters of the model may be derived from administrative admission/discharge records. In particular, they are sufficient to obtain inter-hospital transfer probabilities, and to express the patients’ transfers as a Markov process. Using the proposed model, we show that indirect transfers of patients are equally or even more important as direct transfers for the spread of pathogens in a healthcare network.

Direct patient transfers between hospitals have been shown to play an important role in the spread of pathogens in a healthcare network. However, readmission of patients from the community (indirect transfers) to the same or a different hospital is not well studied, and its role for the spread of pathogens in a healthcare network is not quantified. In this work, we developed a network model of a healthcare system to study the impact of indirect transfers on the prevalence in the individual hospitals as well as in the overall healthcare system. The model includes both, direct and indirect transfers of patients between the healthcare facilities due to transferring as well as readmission of infectious (colonized or infected) patients. Our results show that the readmission of patients (indirect transfers), either to the same or different facility, is an important potential channel of pathogen transmission. Such indirect transfers are of no less importance than direct patient transfers in controlling the spread of pathogens in a healthcare network.

How Specialist Aftercare Impacts Long-Term Readmission Risks in Elderly Patients With Metabolic, Cardiac, and Chronic Obstructive Pulmonary Diseases: Cohort Study Using Administrative Data

BACKGROUND

The health state of elderly patients is typically characterized by multiple co-occurring diseases requiring the involvement of several types of health care providers.

OBJECTIVE

We aimed to quantify the benefit for multimorbid patients from seeking specialist care in terms of long-term readmission risks.

METHODS

From an administrative database, we identified 225,238 elderly patients with 97 different diagnosis (ICD-10 codes) from hospital stays and contact with 13 medical specialties. For each diagnosis associated with the first hospital stay, we used multiple logistic regression analysis to quantify the sex-specific and age-adjusted long-term all-cause readmission risk (hospitalizations occurring between 3 months and 3 years after the first admission) and how specialist contact impacts these risks.

RESULTS

Men have a higher readmission risk than women (mean difference over all first diagnoses 1.9%, P<.001), but similar reduction in readmission risk after receiving specialist care. Specialist care can reduce readmission risk by almost 50%. We found the greatest reductions in risk when the first hospital stay was associated with diagnoses corresponding to complex chronic diseases such as acute myocardial infarction (57.6% reduction in readmission risk, SE 7.6% for men [m]; 55.9% reduction, SE 9.8% for women [w]), diabetic and other retinopathies (m: 62.3%, SE 8.0; w: 60.1%, SE 8.4%), chronic obstructive pulmonary disease (m: 63.9%, SE 7.8%; w: 58.1%, SE 7.5%), disorders of lipoprotein metabolism (m: 64.7%, SE 3.7%; w: 63.8%, SE 4.0%), and chronic ischemic heart diseases (m: 63.6%, SE 3.1%; w: 65.4%, SE 3.0%).

CONCLUSIONS

Specialist care can greatly reduce long-term readmission risk for patients with chronic and multimorbid diseases. Further research is needed to identify the specific reasons for these findings and to understand the detected sex-specific differences.

Web-Based Interactive Tool to Identify Facilities at Risk of Receiving Patients with Multidrug-Resistant Organisms

To identify facilities at risk of receiving patients colonized or infected with multidrug-resistant organisms (MDROs), we developed an interactive web-based interface for visualization of patient-sharing networks among healthcare facilities in Tennessee, USA. Using hospital discharge data and the Centers for Medicare and Medicaid Services' claims and Minimum Data Set, we constructed networks among hospitals and skilled nursing facilities. Networks included direct and indirect transfers, which accounted for <365 days in the community outside of facility admissions. Authorized users can visualize a facility of interest and tailor visualizations by year, network dataset, length of time in the community, and minimum number of transfers. The interface visualizes the facility of interest with its connected facilities that receive or send patients, the number of interfacility transfers, and facilities at risk of receiving transfers from the facility of interest. This tool will help other health departments enhance their MDRO outbreak responses.

Social Network Analysis of Patient Movement Across Health Care Entities in Orange County, Florida

OBJECTIVE

Multidrug-resistant organisms (MDROs) are continually emerging and threatening health care systems. Little attention has been paid to the effect of patient transfers on MDRO dissemination among health care entities in health care systems. In this study, the Florida Department of Health in Orange County (DOH-Orange) developed a baseline social network analysis of patient movement across health care entities in Orange County, Florida, and regionally, within 6 surrounding counties in Central Florida.

MATERIALS AND METHODS

DOH-Orange constructed 2 directed network sociograms-graphic visualizations that show the direction of relationships (ie, county and regional)-by using 2016 health insurance data from the Centers for Medicare & Medicaid Services, which include metrics that could be useful for local public health interventions, such as MDRO outbreaks.

RESULTS

We found that both our county and regional networks were sparse and centralized. The county-level network showed that acute-care hospitals had the highest influence on controlling the flow of patients between health care entities that would otherwise not be connected. The regional-level network showed that post-acute-care hospitals and other facilities (behavioral hospitals and mental health/substance abuse facilities) served as the primary controls for flow of patients between health care entities. The most prominent health care entities in both networks were the same 2 acute-care hospitals.

PRACTICE IMPLICATIONS

Social network analysis can help local public health officials respond to MDRO outbreak investigations by determining which health care facilities are the main contributors of dissemination of MDROs or are at high risk of receiving patients with MDROs. This information can help epidemiologists prioritize prevention efforts and develop county- or regional-specific interventions to control and halt MDRO transmission across a health care network.

Knowing More of the Iceberg: How Detecting a Greater Proportion of Carbapenem-Resistant Enterobacteriaceae Carriers Influences Transmission

BACKGROUND

Clinical testing detects a fraction of carbapenem-resistant Enterobacteriaceae (CRE) carriers. Detecting a greater proportion could lead to increased use of infection prevention and control measures but requires resources. Therefore, it is important to understand the impact of detecting increasing proportions of CRE carriers.

METHODS

We used our Regional Healthcare Ecosystem Analyst-generated agent-based model of adult inpatient healthcare facilities in Orange County, California, to explore the impact that detecting greater proportions of carriers has on the spread of CRE.

RESULTS

Detecting and placing 1 in 9 carriers on contact precautions increased the prevalence of CRE from 0% to 8.0% countywide over 10 years. Increasing the proportion of detected carriers from 1 in 9 up to 1 in 5 yielded linear reductions in transmission; at proportions >1 in 5, reductions were greater than linear. Transmission reductions did not occur for 1, 4, or 5 years, varying by facility type. With a contact precautions effectiveness of ≤70%, the detection level yielding nonlinear reductions remained unchanged; with an effectiveness of >80%, detecting only 1 in 5 carriers garnered large reductions in the number of new CRE carriers. Trends held when CRE was already present in the region.

CONCLUSION

Although detection of all carriers provided the most benefits for preventing new CRE carriers, if this is not feasible, it may be worthwhile to aim for detecting >1 in 5 carriers.

How Introducing a Registry With Automated Alerts for Carbapenem-resistant Enterobacteriaceae (CRE) May Help Control CRE Spread in a Region

BACKGROUND

Regions are considering the use of electronic registries to track patients who carry antibiotic-resistant bacteria, including carbapenem-resistant Enterobacteriaceae (CRE). Implementing such a registry can be challenging and requires time, effort, and resources; therefore, there is a need to better understand the potential impact.

METHODS

We developed an agent-based model of all inpatient healthcare facilities (90 acute care hospitals, 9 long-term acute care hospitals, 351 skilled nursing facilities, and 12 ventilator-capable skilled nursing facilities) in the Chicago metropolitan area, surrounding communities, and patient flow using our Regional Healthcare Ecosystem Analyst software platform. Scenarios explored the impact of a registry that tracked patients carrying CRE to help guide infection prevention and control.

RESULTS

When all Illinois facilities participated (n = 402), the registry reduced the number of new carriers by 11.7% and CRE prevalence by 7.6% over a 3-year period. When 75% of the largest Illinois facilities participated (n = 304), registry use resulted in a 11.6% relative reduction in new carriers (16.9% and 1.2% in participating and nonparticipating facilities, respectively) and 5.0% relative reduction in prevalence. When 50% participated (n = 201), there were 10.7% and 5.6% relative reductions in incident carriers and prevalence, respectively. When 25% participated (n = 101), there was a 9.1% relative reduction in incident carriers (20.4% and 1.6% in participating and nonparticipating facilities, respectively) and 2.8% relative reduction in prevalence.

CONCLUSIONS

Implementing an extensively drug-resistant organism registry reduced CRE spread, even when only 25% of the largest Illinois facilities participated due to patient sharing. Nonparticipating facilities garnered benefits, with reductions in new carriers.

Spread of Carbapenem-Resistant Klebsiella pneumoniae in Hub and Spoke Connected Health-Care Networks: A Case Study from Italy

The study describes the spread of carbapenem-resistant Klebsiella pneumoniae (CRKP) in a regional healthcare network in Italy. The project included several stages: (1) Establishment of a laboratory-based regional surveillance network, including all the acute care hospitals of the Marches Region (n = 20). (2) Adoption of a shared protocol for the surveillance of Multi-Drug Resistant Organisms (MDROs). Only the first CRKP isolate for each patient has been included in the surveillance in each hospital. The anonymous tracking of patients, and their subsequent microbial records within the hospital network, allowed detection of networks of inter-hospital exchange of CRKP and its comparison with transfer of patients within the hospital network. Pulsed-Field Gel Electrophoresis (PFGE) analysis has been used to study selected isolates belonging to different hospitals. 371,037 admitted patients have been included in the surveillance system. CRKP has shown an overall incidence rate of 41.0 per 100,000 days of stay (95% confidence interval, CI 38.5–43.5/100,000 DOS), a CRKP incidence rate of isolation in blood of 2.46/100,000 days of stay (95% CI 1.89–3.17/100,000 days of stay (DOS) has been registered; significant variability has been registered in facilities providing different levels of care. The network of CRKP patients’ exchange was correlated to that of the healthcare organization, with some inequalities and the identification of bridges in CRKP transfers. More than 73% of isolates were closely related. Patients’ exchange was an important route of spread of antimicrobial resistance, highlighting the pivotal role played by the hub, and selected institution to be used in prioritizing infection control efforts.

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

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

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

Regional Spread of an Outbreak of Carbapenem-Resistant Enterobacteriaceae Through an Ego Network of Healthcare Facilities

Background

In 2013, New Delhi metallo-β-lactamase (NDM)-producing Escherichia coli, a type of carbapenem-resistant Enterobacteriaceae uncommon in the United States, was identified in a tertiary care hospital (hospital A) in northeastern Illinois. The outbreak was traced to a contaminated duodenoscope. Patient-sharing patterns can be described through social network analysis and ego networks, which could be used to identify hospitals most likely to accept patients from a hospital with an outbreak.

Methods

Using Illinois' hospital discharge data and the Illinois extensively drug-resistant organism (XDRO) registry, we constructed an ego network around hospital A. We identified which facilities NDM outbreak patients subsequently visited and whether the facilities reported NDM cases.

Results

Of the 31 outbreak cases entered into the XDRO registry who visited hospital A, 19 (61%) were subsequently admitted to 13 other hospitals during the following 12 months. Of the 13 hospitals, the majority (n = 9; 69%) were in our defined ego network, and 5 of those 9 hospitals consequently reported at least 1 additional NDM case. Ego network facilities were more likely to identify cases compared to a geographically defined group of facilities (9/22 vs 10/66; P = .01); only 1 reported case fell outside of the ego network.

Conclusions

The outbreak hospital's ego network accurately predicted which hospitals the outbreak patients would visit. Many of these hospitals reported additional NDM cases. Prior knowledge of this ego network could have efficiently focused public health resources on these high-risk facilities.

Network analysis of intra-hospital transfers and hospital onset clostridium difficile infection

OBJECTIVES

To explore how social network analysis (SNA) can be used to analyse intra-hospital patient networks of individuals with a hospital acquired infection (HAI) for further analysis in a geographical information systems (GIS) environment.

METHODS

A case and control study design was used to select 2008 patients. We retrieved locational data for the patients, which was then translated into a network with the SNA software and then GIS software. Overall metrics were calculated for the SNA based on three datasets and further analysed with a GIS.

RESULTS

The SNA analysis compared cases to control indicating significant differences in the overall structure of the networks. A GIS visual representation of these metrics was developed, showing spatial variation across the example hospital floor.

DISCUSSION

This study confirmed the importance that intra-hospital patient networks play in the transmission of HAIs, highlighting opportunities for interventions utilising these data. Due to spatial variation differences, further research is necessary to confirm this is not a localised phenomenon, but instead a common situation occurring within many hospitals.

CONCLUSION

Utilising SNA and GIS analysis in conjunction with one another provided a data-rich environment in which the risk inherent in intra-hospital transfer networks was quantified, visualised and interpreted for potential interventions.

Using hospital network-based surveillance for antimicrobial resistance as a more robust alternative to self-reporting

Hospital performance is often measured using self-reported statistics, such as the incidence of hospital-transmitted micro-organisms or those exhibiting antimicrobial resistance (AMR), encouraging hospitals with high levels to improve their performance. However, hospitals that increase screening efforts will appear to have a higher incidence and perform poorly, undermining comparison between hospitals and disincentivising testing, thus hampering infection control. We propose a surveillance system in which hospitals test patients previously discharged from other hospitals and report observed cases. Using English National Health Service (NHS) Hospital Episode Statistics data, we analysed patient movements across England and assessed the number of hospitals required to participate in such a reporting scheme to deliver robust estimates of incidence. With over 1.2 million admissions to English hospitals previously discharged from other hospitals annually, even when only a fraction of hospitals (41/155) participate (each screening at least 1000 of these admissions), the proposed surveillance system can estimate incidence across all hospitals. By reporting on other hospitals, the reporting of incidence is separated from the task of improving own performance. Therefore the incentives for increasing performance can be aligned to increase (rather than decrease) screening efforts, thus delivering both more comparable figures on the AMR problems across hospitals and improving infection control efforts.

Reduction in Clostridium difficile infection rates following a multifacility prevention initiative in Orange County, California: A controlled interrupted time series evaluation

OBJECTIVE

To evaluate the Orange County Clostridium difficile infection (CDI) prevention collaborative's effect on rates of CDI in acute-care hospitals (ACHs) in Orange County, California.

DESIGN

Controlled interrupted time series.

METHODS

We convened a CDI prevention collaborative with healthcare facilities in Orange County to reduce CDI incidence in the region. Collaborative participants received onsite infection control and antimicrobial stewardship assessments, interactive learning and discussion sessions, and an interfacility transfer communication improvement initiative during June 2015-June 2016. We used segmented regression to evaluate changes in monthly hospital-onset (HO) and community-onset (CO) CDI rates for ACHs. The baseline period comprised 17 months (January 2014-June 2015) and the follow-up period comprised 28 months (September 2015-December 2017). All 25 Orange County ACHs were included in the CO-CDI model to account for direct and indirect effects of the collaborative. For comparison, we assessed HO-CDI and CO-CDI rates among 27 ACHs in 3 San Francisco Bay Area counties.

RESULTS

HO-CDI rates in the 15 participating Orange County ACHs decreased 4% per month (incidence rate ratio [IRR], 0.96; 95% CI, 0.95-0.97; P &lt; .0001) during the follow-up period compared with the baseline period and 3% (IRR, 0.97; 95% CI, 0.95-0.99; P = .002) per month compared to the San Francisco Bay Area nonparticipant ACHs. Orange County CO-CDI rates declined 2% per month (IRR, 0.98; 95% CI, 0.96-1.00; P = .03) between the baseline and follow-up periods. This decline was not statistically different from the San Francisco Bay Area ACHs (IRR, 0.97; 95% CI, 0.95-1.00; P = .09).

CONCLUSIONS

Our analysis of ACHs in Orange County provides evidence that coordinated, regional multifacility initiatives can reduce CDI incidence.

Transitions of care across hospital settings in patients with inflammatory bowel disease

BACKGROUND

Inflammatory bowel disease (IBD) is a chronic, inflammatory disorder characterised by both intestinal and extra-intestinal pathology. Patients may receive both emergency and elective care from several providers, often in different hospital settings. Poorly managed transitions of care between providers can lead to inefficiencies in care and patient safety issues. To ensure that the sharing of patient information between providers is appropriate, timely, accurate and secure, effective data-sharing infrastructure needs to be developed. To optimise inter-hospital data-sharing for IBD patients, we need to better understand patterns of hospital encounters in this group.

AIM

To determine the type and location of hospital services accessed by IBD patients in England.

METHODS

This was a retrospective observational study using Hospital Episode Statistics, a large administrative patient data set from the National Health Service in England. Adult patients with a diagnosis of IBD following admission to hospital were followed over a 2-year period to determine the proportion of care accessed at the same hospital providing their outpatient IBD care, defined as their ‘home provider’. Secondary outcome measures included the geographic distribution of patient-sharing, regional and age-related differences in accessing services, and type and frequency of outpatient encounters.

RESULTS

95055 patients accessed hospital services on 1760156 occasions over a 2-year follow-up period. The proportion of these encounters with their identified IBD ‘home provider’ was 73.3%, 87.8% and 83.1% for accident and emergency, inpatient and outpatient encounters respectively. Patients living in metropolitan centres and younger patients were less likely to attend their ‘home provider’ for hospital services. The most commonly attended specialty services were gastroenterology, general surgery and ophthalmology.

CONCLUSION

Transitions of care between secondary care settings are common for patients with IBD. Effective systems of data-sharing and care integration are essential to providing safe and effective care for patients. Geographic and age-related patterns of care transitions identified in this study may be used to guide interventions aimed at improving continuity of care.

An empiric approach to identifying physician peer groups from claims data: An example from breast cancer care

OBJECTIVE

To develop an empiric approach for evaluating the performance of physician peer groups based on patient-sharing in administrative claims data.

DATA SOURCES

Surveillance, Epidemiology and End Results-Medicare linked dataset.

STUDY DESIGN

Applying social network theory, we constructed physician peer groups for patients with breast cancer. Under different assumptions of key parameter values-minimum patient volume for physician inclusion and minimum number of patients shared between physicians for a connection-we compared agreement in group membership between split samples during 2004-2006 (T1) (reliability) and agreement in group membership between T1 and 2007-2009 (T2) (stability). We also compared the results with those derived from randomly generated groups and to hospital affiliation-based groups.

PRINCIPAL FINDINGS

The sample included 142 098 patients treated by 43 174 physicians in T1 and 136 680 patients treated by 51 515 physicians in T2. We identified parameter values that resulted in a median peer group reliability of 85.2 percent (Interquartile range (IQR) [0 percent, 96.2 percent]) and median stability of 73.7 percent (IQR [0 percent, 91.0 percent]). In contrast, stability of randomly assigned peer groups was 6.2 percent (IQR [0 percent, 21.0 percent]). Median overlap of empirical groups with hospital groups was 32.2 percent (IQR [12.1 percent, 59.2 percent]).

CONCLUSIONS

It is feasible to construct physician peer groups that are reliable, stable, and distinct from both randomly generated and hospital-based groups.

Guiding interoperable electronic health records through patient-sharing networks

Effective sharing of clinical information between care providers is a critical component of a safe, efficient health system. National data-sharing systems may be costly, politically contentious and do not reflect local patterns of care delivery. This study examines hospital attendances in England from 2013 to 2015 to identify instances of patient sharing between hospitals. Of 19.6 million patients receiving care from 155 hospital care providers, 130 million presentations were identified. On 14.7 million occasions (12%), patients attended a different hospital to the one they attended on their previous interaction. A network of hospitals was constructed based on the frequency of patient sharing between hospitals which was partitioned using the Louvain algorithm into ten distinct data-sharing communities, improving the continuity of data sharing in such instances from 0 to 65-95%. Locally implemented data-sharing communities of hospitals may achieve effective accessibility of clinical information without a large-scale national interoperable information system.

How Social Networks May Influence Cancer Patients' Situated Identity and Illness-Related Behaviors

Little research is currently available that captures variation in the degree to which individuals who have, or had cancer in the past (but are in remission) integrate their cancer experience into their sense of self or their cancer-associated identity. Such research should cover how those identities shape personal narratives within existing or new social networks so that, ultimately, we understand the implications for treatment choices and health outcomes. Particularly understudied are the social factors influencing the incorporation of cancer into identity, learning, and behavior. Social network analysis captures specific relationships, what they offer, and the structure or constellation of these relationships around someone who has cancer or has had cancer. Some studies point to potential cultural differences in ethnic or social groups in how social influences on the cancer experience play out in terms of individual coping strategies. In some populations, social cohesion or tight networks are common and of particular importance to individuals and include social institutions like church communities. Social status might also generate social pressures not typically noticed or experienced by other groups. We will discuss how social network analysis can be used to elucidate these factors and, conversely, how the specific context of cancer diagnosis can be used through social network analysis to better understand the role of community in helping individuals address situations of severe adversity.

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

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

Exploring the networking behaviors of hospital organizations

Background

Despite an extensive body of knowledge exists on network outcomes and on how hospital network structures may contribute to the creation of outcomes at different levels of analysis, less attention has been paid to understanding how and why hospital organizational networks evolve and change. The aim of this paper is to study the dynamics of networking behaviors of hospital organizations.

Methods

Stochastic actor-based model for network dynamics was used to quantitatively examine data covering six-years of patient transfer relations among 35 hospital organizations. Specifically, the study investigated about determinants of patient transfer evolution modeling partner selection choice as a combination of multiple organizational attributes and endogenous network-based processes.

Results

The results indicate that having overlapping specialties and treating patients with the same case-mix decrease the likelihood of observing network ties between hospitals. Also, results revealed as geographical proximity and membership of the same LHA have a positive impact on the networking behavior of hospitals organizations, there is a propensity in the network to choose larger hospitals as partners, and to transfer patients between hospitals facing similar levels of operational uncertainty.

Conclusions

Organizational attributes (overlapping specialties and case-mix), institutional factors (LHA), and geographical proximity matter in the formation and shaping of hospital networks over time. Managers can benefit from the use of these findings by clearly identifying the role and strategic positioning of their hospital with respect to the entire network. Social network analysis can yield novel information and also aid policy makers in the formation of interventions, encouraging alliances among providers as well as planning health system restructuring.

A systematic review of transmission dynamic studies of methicillin-resistant Staphylococcus aureus in non-hospital residential facilities

BACKGROUND

Non-hospital residential facilities are important reservoirs for MRSA transmission. However, conclusions and public health implications drawn from the many mathematical models depicting nosocomial MRSA transmission may not be applicable to these settings. Therefore, we reviewed the MRSA transmission dynamics studies in defined non-hospital residential facilities to: (1) provide an overview of basic epidemiology which has been addressed; (2) identify future research direction; and (3) improve future model implementation.

METHODS

A review was conducted by searching related keywords in PUBMED without time restriction as well as internet searches via Google search engine. We included only articles describing the epidemiological transmission pathways of MRSA/community-associated MRSA within and between defined non-hospital residential settings.

RESULTS

Among the 10 included articles, nursing homes (NHs) and correctional facilities (CFs) were two settings considered most frequently. Importation of colonized residents was a plausible reason for MRSA outbreaks in NHs, where MRSA was endemic without strict infection control interventions. The importance of NHs over hospitals in increasing nosocomial MRSA prevalence was highlighted. Suggested interventions in NHs included: appropriate staffing level, screening and decolonizing, and hand hygiene. On the other hand, the small population amongst inmates in CFs has no effect on MRSA community transmission. Included models ranged from system-level compartmental models to agent-based models. There was no consensus over the course of disease progression in these models, which were mainly featured with NH residents /CF inmates/ hospital patients as transmission pathways. Some parameters used by these models were outdated or unfit.

CONCLUSIONS

Importance of NHs has been highlighted from these current studies addressing scattered aspects of MRSA epidemiology. However, the wide variety of non-hospital residential settings suggest that more work is needed before robust conclusions can be drawn. Learning from existing work for hospitals, we identified critical future research direction in this area from infection control, ecological and economic perspectives. From current model deficiencies, we suggest more transmission pathways be specified to depict MRSA transmission, and further empirical studies be stressed to support evidence-based mathematical models of MRSA in non-hospital facilities. Future models should be ready to cope with the aging population structure.

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

Objective

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

Materials and Methods

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

Results

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

Discussion

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

Analysis of the U.S. patient referral network

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

Investigating the Temporal Dynamics of Interorganizational Exchange: Patient Transfers among Italian Hospitals

Previous research on interaction behavior among organizations (resource exchange, collaboration, communication) has typically aggregated those behaviors over time as a network of organizational relationships. The authors instead study structural-temporal patterns in organizational exchange, focusing on the dynamics of reciprocation. Applying this lens to a community of Italian hospitals during 2003-7, the authors observe two mechanisms of interorganizational reciprocation: organizational embedding and resource dependence. The authors show how these two mechanisms operate on distinct time horizons: dependence applies to contemporaneous exchange structures, whereas embedding develops through longer-term historical patterns. They also show how these processes operate differently in competitive and non-competitive contexts, operationalized in terms of market differentiation and geographic space. In noncompetitive contexts, the authors observe both logics of reciprocation, dependence in the short term and embedding over the long term, developing into population-level generalized exchange. In competitive contexts, they find no reciprocation and instead observe the microfoundations of status hierarchies.

Spread of hospital-acquired infections: A comparison of healthcare networks

Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the potential scale of HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. Using an exhaustive database of all hospital discharge summaries in France in 2014, we construct and analyze three patient networks based on the following: transfers of patients with HAI (HAI-specific network); patients with suspected HAI (suspected-HAI network); and all patients (general network). All three networks have heterogeneous patient flow and demonstrate small-world and scale-free characteristics. Patient populations that comprise these networks are also heterogeneous in their movement patterns. Ranking of hospitals by centrality measures and comparing community clustering using community detection algorithms shows that despite the differences in patient population, the HAI-specific and suspected-HAI networks rely on the same underlying structure as that of the general network. As a result, the general network may be more reliable in studying potential spread of HAIs. Finally, we identify transfer patterns at both the French regional and departmental (county) levels that are important in the identification of key hospital centers, patient flow trajectories, and regional clusters that may serve as a basis for novel wide-scale infection control strategies.

Hospital-acquired infections (HAIs), including emerging multi-drug resistant organisms, threaten healthcare systems worldwide. Efficient containment measures of HAIs must mobilize the entire healthcare network. Thus, to best understand how to reduce the scale of potential HAI epidemic spread, we explore patient transfer patterns in the French healthcare system. We construct and compare the characteristics of three different patient transfer networks based on data on transfers of patients with diagnosed HAIs, suspected HAIs, or of all patients. Our analyses show that these healthcare networks, the patient populations that comprise them and the patient movement patterns are heterogeneous and centralized. Despite the differences in patient populations, the HAI-specific and suspected-HAI healthcare networks have the same underlying structure as that of the general healthcare network. We identify key hospital centers, patient flow trajectories, at both the regional and department (county) level that may serve as a basis for proposing novel wide-scale infection control strategies.

Bayesian exponential random graph modelling of interhospital patient referral networks

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

Population genetic structuring of methicillin-resistant Staphylococcus aureus clone EMRSA-15 within UK reflects patient referral patterns

Antibiotic resistance forms a serious threat to the health of hospitalised patients, rendering otherwise treatable bacterial infections potentially life-threatening. A thorough understanding of the mechanisms by which resistance spreads between patients in different hospitals is required in order to design effective control strategies. We measured the differences between bacterial populations of 52 hospitals in the United Kingdom and Ireland, using whole-genome sequences from 1085 MRSA clonal complex 22 isolates collected between 1998 and 2012. The genetic differences between bacterial populations were compared with the number of patients transferred between hospitals and their regional structure. The MRSA populations within single hospitals, regions and countries were genetically distinct from the rest of the bacterial population at each of these levels. Hospitals from the same patient referral regions showed more similar MRSA populations, as did hospitals sharing many patients. Furthermore, the bacterial populations from different time-periods within the same hospital were generally more similar to each other than contemporaneous bacterial populations from different hospitals. We conclude that, while a large part of the dispersal and expansion of MRSA takes place among patients seeking care in single hospitals, inter-hospital spread of resistant bacteria is by no means a rare occurrence. Hospitals are exposed to constant introductions of MRSA on a number of levels: (1) most MRSA is received from hospitals that directly transfer large numbers of patients, while (2) fewer introductions happen between regions or (3) across national borders, reflecting lower numbers of transferred patients. A joint coordinated control effort between hospitals, is therefore paramount for the national control of MRSA, antibiotic-resistant bacteria and other hospital-associated pathogens.

Relational event models for longitudinal network data with an application to interhospital patient transfers

The main objective of this paper is to introduce and illustrate relational event models, a new class of statistical models for the analysis of time-stamped data with complex temporal and relational dependencies. We outline the main differences between recently proposed relational event models and more conventional network models based on the graph-theoretic formalism typically adopted in empirical studies of social networks. Our main contribution involves the definition and implementation of a marked point process extension of currently available models. According to this approach, the sequence of events of interest is decomposed into two components: (a) event time and (b) event destination. This decomposition transforms the problem of selection of event destination in relational event models into a conditional multinomial logistic regression problem. The main advantages of this formulation are the possibility of controlling for the effect of event-specific data and a significant reduction in the estimation time of currently available relational event models. We demonstrate the empirical value of the model in an analysis of interhospital patient transfers within a regional community of health care organizations. We conclude with a discussion of how the models we presented help to overcome some the limitations of statistical models for networks that are currently available. Copyright © 2017 John Wiley & Sons, Ltd.