Revenue, relationships and routines: the social organization of acute myocardial infarction patient transfers in the United States

Implications of interhospital patient transfers for emergency medical services transportation systems in the Netherlands: a retrospective study

OBJECTIVES

Interhospital patient transfers have become routine. Known drivers are access to specialty care and non-clinical reasons, such as limited capacity. While emergency medical services (EMS) providers act as main patient transfer operators, the impact of interhospital transfers on EMS service demand and fleet management remains understudied. This study aims to identify patterns in regional interhospital patient transfer volumes and their spatial distribution, and to discuss their potential implications for EMS service demand and fleet management.

DESIGN

A retrospective study was performed analysing EMS transport data from the province of Drenthe in the Netherlands between 2013 and 2019 and public hospital listings. Yearly volume changes in urgent and planned interhospital transfers were quantified. Further network analysis, including geomapping, was used to study how transfer volumes and their spatial distribution relate to hospital specialisation, and servicing multihospital systems. Organisational data were considered for relating transfer patterns to fleet changes.

SETTING

EMS in the province of Drenthe, the Netherlands, 492 167 inhabitants.

PARTICIPANTS

Analyses are based on routinely collected patient data from EMS records, entailing all 248 114 transports (137 168 patients) of the Drenthe EMS provider (2013-2019). From these interhospital transports were selected (24 311 transports).

RESULTS

Interhospital transfers represented a considerable (9.8%) and increasing share of transports (from 8.6% in 2013 to 11.3% in 2019). Most transfers were related to multihospital systems (47.3%, 11 509 transports), resulting in a considerable growth of planned EMS transports (from 2093 in 2013 to 3511 in 2019). Geomapping suggests increasing transfer distances and diminishing resource efficiencies due to lacking follow-up rides. Organisational data clarify how EMS fleets were adjusted by expanding resources and reorganising fleet operation.

CONCLUSIONS

Emerging interhospital network transfers play an important role in EMS service demand. Increased interhospital transport volumes and geographical spread require a redesign of current EMS fleets and management along regional lines.

Racial and ethnic disparities in emergency department transfers to public hospitals

OBJECTIVE

To examine racial/ethnic differences in emergency department (ED) transfers to public hospitals and factors explaining these differences.

DATA SOURCES AND STUDY SETTING

ED and inpatient data from the Healthcare Cost and Utilization Project for Florida (2010-2019); American Hospital Association Annual Survey (2009-2018).

STUDY DESIGN

Logistic regression examined race/ethnicity and payer on the likelihood of transfer to a public hospital among transferred ED patients. The base model was controlled for patient and hospital characteristics and year fixed effects. Models II and III added urbanicity and hospital referral region (HRR), respectively. Model IV used hospital fixed effects, which compares patients within the same hospital. Models V and VI stratified Model IV by payer and condition, respectively. Conditions were classified as emergency care sensitive conditions (ECSCs), where transfer is protocolized, and non-ECSCs. We reported marginal effects at the means.

DATA COLLECTION/EXTRACTION METHODS

We examined 1,265,588 adult ED patients transferred from 187 hospitals.

PRINCIPAL FINDINGS

Black patients were more likely to be transferred to public hospitals compared with White patients in all models except ECSC patients within the same initial hospital (except trauma). Black patients were 0.5-1.3 percentage points (pp) more likely to be transferred to public hospitals than White patients in the same hospital with the same payer. In the base model, Hispanic patients were more likely to be transferred to public hospitals compared with White patients, but this difference reversed after controlling for HRR. Hispanic patients were - 0.6 pp to -1.2 pp less likely to be transferred to public hospitals than White patients in the same hospital with the same payer.

CONCLUSIONS

Large population-level differences in whether ED patients of different races/ethnicities were transferred to public hospitals were largely explained by hospital market and the initial hospital, suggesting that they may play a larger role in explaining differences in transfer to public hospitals, compared with other external factors.

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.

From Ties to Events in the Analysis of Interorganizational Exchange Relations

Relational event models expand the analytical possibilities of existing statistical models for interorganizational networks by: (i) making efficient use of information contained in the sequential ordering of observed events connecting sending and receiving units; (ii) accounting for the intensity of the relation between exchange partners, and (iii) distinguishing between short- and long-term network effects. We introduce a recently developed relational event model (REM) for the analysis of continuously observed interorganizational exchange relations. The combination of efficient sampling algorithms and sender-based stratification makes the models that we present particularly useful for the analysis of very large samples of relational event data generated by interaction among heterogeneous actors. We demonstrate the empirical value of event-oriented network models in two different settings for interorganizational exchange relations-that is, high-frequency overnight transactions among European banks and patient-sharing relations within a community of Italian hospitals. We focus on patterns of direct and generalized reciprocity while accounting for more complex forms of dependence present in the data. Empirical results suggest that distinguishing between degree- and intensity-based network effects, and between short- and long-term effects is crucial to our understanding of the dynamics of interorganizational dependence and exchange relations. We discuss the general implications of these results for the analysis of social interaction data routinely collected in organizational research to examine the evolutionary dynamics of social networks within and between organizations.

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.

Provocations for Reimagining Informatics Approaches to Health Equity

As the informatics community commits to the goal of advancing health equity, it is essential that we openly critique our current approaches and reimagine the ways in which we design, implement, evaluate, and advocate for policies related to informatics interventions. In this paper, we present five provocations as a starting point for building more conscientious informatics practice in service of this goal: 1) Health informatics interventions can create an "illusion of impactful action" without significant material benefits for marginalized patients, families, and communities; 2) Health informatics interventions target the wrong stakeholders, the wrong processes, and the wrong technologies to achieve equity; 3) Informaticians must conceptualize health literacy and other factors shaping patients' experiences as a system-level rather than individual-level characteristic; 4) Informatics interventions wrongly assume that interacting contextual factors can be meaningfully captured by over-simplified structured variables; and 5) Informatics interventions often specify the wrong system boundaries and solution space. We further assert that drastic shifts in our current practices will allow us to honor our claims of valuing patient-centered approaches, especially for marginalized communities.

How to fall into a new routine: factors influencing the implementation of an admission and discharge programme in hospitals and general practices

INTRODUCTION

The VESPEERA programme is a multifaceted programme to enhance information transfer between general practice and hospital across the process of hospital admission, stay and discharge. It was implemented in 7 hospitals and 72 general practices in Southern Germany. Uptake was heterogeneous and overall low. A process evaluation aimed at identifying factors associated with the implementation of the VESPEERA programme.

METHODS

This was a qualitative study using semi-structured interviews in a purposeful sample of health workers in hospitals and general practices in the VESPEERA programme. Qualitative framework analysis using the Consolidated Framework for Implementation Research was performed and revealed the topic of previous and new routines to be protruding. Inductive content analysis was used for in-depth examination of stages in the process of staying in a previous or falling into a new routines.

RESULTS

Thirty-six interviews were conducted with 17 participants from general practices and 19 participants from hospitals. The interviewees were in different stages of the implementation process at the time of the interviews. Four stages were identified: Stage 1,'Previous routine and tension for change', describes the situation in which VESPEERA was to be implemented and the factors leading to the decision to participate. In stage 2,'Adoption of the VESPEERA programme', factors that influenced whether individuals decided to employ the innovation are relevant. Stage 3 comprises 'Determinants for falling into and staying in the new VESPEERA-routine' relates to actual implementation and finally, in stage 4, the participants reflect on the success of the implementation.

CONCLUSIONS

The individuals and organisations participating in the VESPEERA programme were in different stages of a process from the previous to the new routine, which were characterised by different determinants of implementation. In all stages, organisational factors were main determinants of implementation, but different factors emerged in different implementation stages. A low distinction between decision-making power and executive, as well as available resources, were beneficial for the implementation of the innovation.

TRIAL REGISTRATION

DRKS00015183 on DRKS / Universal Trial Number (UTN): U1111-1218-0992.

Influence of Hospital Characteristics on Hospital Transfer Destinations for Patients With Stroke

BACKGROUND

Patients with stroke are frequently transferred between hospitals. This may have implications on the quality of care received by patients; however, it is not well understood how the characteristics of sending and receiving hospitals affect the likelihood of a transfer event. Our objective was to identify hospital characteristics associated with sending and receiving patients with stroke.

METHODS

Using a comprehensive statewide administrative dataset, including all 78 Massachusetts hospitals, we identified all transfers of patients with ischemic stroke between October 2007 and September 2015 for this observational study. Hospital variables included reputation (US News and World Report ranking), capability (stroke center status, annual stroke volume, and trauma center designation), and institutional affiliation. We included network variables to control for the structure of hospital-to-hospital transfers. We used relational event modeling to account for complex temporal and relational dependencies associated with transfers. This method decomposes a series of patient transfers into a sequence of decisions characterized by transfer initiations and destinations, modeling them using a discrete-choice framework.

RESULTS

Among 73 114 ischemic stroke admissions there were 7189 (9.8%) transfers during the study period. After accounting for travel time between hospitals and structural network characteristics, factors associated with increased likelihood of being a receiving hospital (in descending order of relative effect size) included shared hospital affiliation (5.8× higher), teaching hospital status (4.2× higher), stroke center status (4.3× and 3.8× higher when of the same or higher status), and hospitals of the same or higher reputational ranking (1.5× higher).

CONCLUSIONS

After accounting for distance and structural network characteristics, in descending order of importance, shared hospital affiliation, hospital capabilities, and hospital reputation were important factor in determining transfer destination of patients with stroke. This study provides a starting point for future research exploring how relational coordination between hospitals may ensure optimized allocation of patients with stroke for maximal patient benefit.

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.

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.

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.

Association Between Insurance and Transfer of Injured Children From Emergency Departments

OBJECTIVES

To determine if injured children presenting to nondesignated trauma centers are more or less likely to be transferred relative to being admitted based on insurance status.

METHODS

We conducted a cross-sectional study by using the Healthcare Cost and Utilization Project Nationwide Emergency Department Sample. Pediatric trauma patients receiving care in emergency departments (EDs) at nontrauma centers who were either admitted locally or transferred to another hospital were included. We performed logistic regression analysis adjusting for injury severity and other confounders and incorporated nationally representative weights to determine the association between insurance and transfer or admission.

RESULTS

Nine thousand four hundred and sixty-one ED pediatric trauma events at 386 nontrauma centers met inclusion criteria. EDs that treated a higher proportion of patients with Medicaid had higher odds of transfer relative to admission (odds ratio [OR]: 1.2 per 10% increase in Medicaid; 95% confidence interval [CI]: 1.1-1.4), resulting in overall higher odds of transfer among patients with Medicaid compared with patients with private insurance (OR: 1.3; 95% CI: 1.0-1.5). A patient's insurance status was not associated with different odds of transfer relative to admission within individual EDs after adjusting for the ED's proportion of patients with Medicaid (Medicaid OR: 1.0; 95% CI: 0.8-1.1).

CONCLUSIONS

Injured pediatric patients presenting to nondesignated trauma centers are slightly more likely to be transferred than admitted when the ED treats a higher proportion of Medicaid patients. In this study, ongoing concerns about inequities in the delivery of care among hospitals treating high proportions of children with Medicaid are reinforced.

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.

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.

Exploring interhospital transfers and partnerships in the hospital sector in New South Wales, Australia

Objective The aim of the present study was to explore characteristics of interhospital transfers (IHT) and sharing of care among hospitals in New South Wales (NSW), Australia. Methods Data were extracted from patient-level linked hospital administrative datasets for separations from all NSW acute care hospitals from 1 July 2013 to 30 June 2015. Patient discharge and arrival information was used to identify IHTs. Characteristics of patients and related hospitals were then analysed. Results Transfer-in patients accounted for 3.9% of all NSW admitted patients and, overall, 7.3% of NSW admissions were associated with transfers (IHT rate). Patients with injuries and circulatory system diseases had the highest IHT rate, accounting for one-third of all IHTs. Patients were more often transferred to larger than smaller hospitals (61% vs 29%). Compared with private hospitals, public hospitals had a higher IHT rate (8.4% vs 5.1%) and a greater proportion of transfer-out IHTs (52% vs 28%). Larger public hospitals had lower IHT rates (3–8%) compared with smaller public hospitals (13–26%). Larger public hospitals received and retransferred higher proportions of IHT patients (52–58% and 11% respectively) than their smaller counterparts (26–30% and 2–3% respectively). Less than one-quarter of IHTs were between the public and private sectors or between government health regions. The number of interacting hospitals and their interactions varied across hospital peer groups. Conclusion NSW IHTs were often to hospitals with greater speciality services. The patterns of interhospital interactions could be affected by organisational and regional preferences. What is known about the topic? IHTs aim to provide efficient and effective care. Nonetheless, information on transfers and the sharing of care among hospitals in an Australian setting is lacking. Studies of transfers and hospital partnership patterns will inform efforts to improve patient-centred transfers and hospital accountability in terms of end outcomes for patients. What does this paper add? Transfer-in patients accounted for 3.9% of all NSW admissions; they were often (61%) transferred to hospitals with greater speciality services. The number of IHTs and sharing of care among hospitals varied across hospital peer groups, and could have been affected by organisational and regional preferences. What are the implications for practitioners? The findings of the present study suggest that different patterns of IHTs may not only have resulted from clinical priorities, but that organisational and regional preferences are also likely to be influential factors. Patient-centred IHTs and the development of guidelines need to be pursued to enhance the care and functionality of healthcare. Patient sharing should be acknowledged in hospital and regional performance profiling.

Excess mortality and guideline-indicated care following non-ST-elevation myocardial infarction

BACKGROUND

Adherence to guideline-indicated care for the treatment of non-ST-elevation myocardial infarction (NSTEMI) is associated with improved outcomes. We investigated the extent and consequences of non-adherence to guideline-indicated care across a national health system.

METHODS

A cohort study ( ClinicalTrials.gov identifier: NCT02436187) was conducted using data from the Myocardial Ischaemia National Audit Project ( n = 389,057 NSTEMI, n = 247 hospitals, England and Wales, 2003-2013). Accelerated failure time models were used to quantify the impact of non-adherence on survival according to dates of guideline publication.

RESULTS

Over a period of 1,079,044 person-years (median 2.2 years of follow-up), 113,586 (29.2%) NSTEMI patients died. Of those eligible to receive care, 337,881 (86.9%) did not receive one or more guideline-indicated intervention; the most frequently missed were dietary advice ( n = 254,869, 68.1%), smoking cessation advice ( n = 245,357, 87.9%), P2Y12 inhibitors ( n = 192,906, 66.3%) and coronary angiography ( n = 161,853, 43.4%). Missed interventions with the strongest impact on reduced survival were coronary angiography (time ratio: 0.18, 95% confidence interval (CI): 0.17-0.18), cardiac rehabilitation (time ratio: 0.49, 95% CI: 0.48-0.50), smoking cessation advice (time ratio: 0.53, 95% CI: 0.51-0.57) and statins (time ratio: 0.56, 95% CI: 0.55-0.58). If all eligible patients in the study had received optimal care at the time of guideline publication, then 32,765 (28.9%) deaths (95% CI: 30,531-33,509) may have been prevented.

CONCLUSION

The majority of patients hospitalised with NSTEMI missed at least one guideline-indicated intervention for which they were eligible. This was significantly associated with excess mortality. Greater attention to the provision of guideline-indicated care for the management of NSTEMI will reduce premature cardiovascular deaths.

Effect of hospital referral networks on patient readmissions

Previous studies have shown that referral networks encompass important mechanisms of coordination and integration among hospitals, which enhance numerous organizational-level benefits, such as productivity, efficiency, and quality of care. The present study advances previous research by demonstrating how hospital referral networks influence patient readmissions. Data include 360,697 hospitalization events within a regional community of hospitals in the Italian National Health Service. Multilevel hierarchical regression analysis tests the impacts of referral networks' structural characteristics on patient hospital readmissions. The results demonstrate that organizational centrality in the overall referral network and ego-network density have opposing effects on the likelihood of readmission events within hospitals; greater centrality is negatively associated with readmissions, whereas greater ego-network density increases the likelihood of readmission events. Our findings support the (re)organization of healthcare systems and provide important indications for policymakers and practitioners.

Emergency department transfers and transfer relationships in United States hospitals

OBJECTIVES

The objective was to describe transfers out of hospital-based emergency departments (EDs) in the United States and to identify different characteristics of sending and receiving hospitals, travel distance during transfer, disposition on arrival to the second hospital, and median number of transfer partners among sending hospitals.

METHODS

Emergency department records were linked at transferring hospitals to ED and inpatient records at receiving hospitals in nine U.S. states using the 2010 Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases and State Inpatient Databases, the American Hospital Association Annual Survey, and the Trauma Information Exchange Program. Using the Clinical Classification Software (CCS) to categorize conditions, the 50 disease categories with the highest transfer rates were studied, and these were then placed into nine clinical groups. Records were included where both sending and receiving records were available; these data were tabulated to describe ED transfer patterns, hospital-to-hospital distances, final patient disposition, and number of transfer partners.

RESULTS

A total of 97,021 ED transfer encounters were included in the analysis from the 50 highest transfer rate disease categories. Among these, transfer rates ranged from 1% to 13%. Circulatory conditions made up about half of all transfers. Receiving hospitals were more likely to be nonprofit, teaching, trauma, and urban and have more beds with greater specialty coverage and more advanced diagnostic and therapeutic resources. The median transfer distance was 23 miles, with 25% traveling more than 40 to 50 miles. About 8% of transferred encounters were discharged from the second ED, but that varied from 0.6% to 53% across the 50 conditions. Sending hospitals had a median of seven transfer partners across all conditions and between one and four per clinical group.

CONCLUSIONS

Among high-transfer conditions in U.S. EDs, patients are often transferred great distances, more commonly to large teaching hospitals with greater resources. The large number of transfer partners indicates a possible lack of stable transfer relationships between U.S. hospitals.

Factors associated with the disposition of severely injured patients initially seen at non–trauma center emergency departments: disparities by insurance status

IMPORTANCE

Trauma is the leading cause of potential years of life lost before age 65 years in the United States. Timely care in a designated trauma center has been shown to reduce mortality by 25%. However, many severely injured patients are not transferred to trauma centers after initially being seen at non–trauma center emergency departments (EDs).

OBJECTIVES

To determine patient-level and hospital-level factors associated with the decision to admit rather than transfer severely injured patients who are initially seen at non–trauma center EDs and to ascertain whether insured patients are more likely to be admitted than transferred compared with uninsured patients.

DESIGN, SETTING, AND PARTICIPANTS

Retrospective analysis of the 2009 Nationwide Emergency Department Sample. We included all ED encounters for major trauma (Injury Severity Score, >15) seen at non–trauma centers in patients aged 18 to 64 years. We excluded ED discharges and ED deaths. We quantified the absolute risk difference between admission vs transfer by insurance status, while adjusting for age, sex, mechanism of injury, Injury Severity Score, weekend admission and month of visit, and urban vs rural status and median household income of the home zip code, as well as annual ED visit volume and teaching status and US region.

MAIN OUTCOMES AND MEASURES

Inpatient admission vs transfer to another acute care facility.

RESULTS

In 2009, a total of 4513 observations from 636 non–trauma center EDs were available for analysis, representing a nationally weighted population of 19,312 non–trauma center ED encounters for major trauma. Overall, 54.5% in 2009 were admitted to the non–trauma center. Compared with patients without insurance, the adjusted absolute risk of admission vs transfer was 14.3% (95% CI, 9.2%-19.4%) higher for patients with Medicaid and 11.2% (95% CI, 6.9%-15.4%) higher for patients with private insurance. Other factors associated with admission vs transfer included severe abdominal injuries (risk difference, 15.9%; 95% CI, 9.4%-22.3%), urban teaching hospital vs non–teaching hospital (risk difference, 26.2%; 95% CI, 15.2%-37.2%), and annual ED visit volume (risk difference, 3.4%; 95% CI, 1.6%-5.3% higher for every additional 10,000 annual ED visits).

CONCLUSIONS AND RELEVANCE

Patients with severe injuries initially evaluated at non–trauma center EDs were less likely to be transferred if insured and were at risk of receiving suboptimal trauma care. Efforts in monitoring and optimizing trauma interhospital transfers and outcomes at the population level are warranted.

Quality of care and interhospital collaboration: a study of patient transfers in Italy

OBJECTIVES

We examine the dynamics of patient-sharing relations within an Italian regional community of 35 hospitals serving approximately 1,300,000 people. We test whether interorganizational relations provide individual patients access to higher quality providers of care.

RESEARCH DESIGN AND METHODS

We reconstruct the complete temporal sequence of the 3461 consecutive interhospital patient-sharing events observed between each pair of hospitals in the community during 2005-2008. We distinguish between transfers occurring between and within different medical specialties. We estimate newly derived models for relational event sequences that allow us to control for the most common forms of network-like dependencies that are known to characterize collaborative relations between hospitals. We use 45-day risk-adjusted readmission rate as a proxy for hospital quality.

RESULTS

After controls (eg, geographical distance, size, and the existence of prior collaborative relations), we find that patients flow from less to more capable hospitals. We show that this result holds for patient being shared both between as well as within medical specialties. Nonetheless there are strong and persistent other organizational and relational effects driving transfers.

CONCLUSIONS

Decentralized patient-sharing decisions taken by the 35 hospitals give rise to a system of collaborative interorganizational arrangements that allow the patient to access hospitals delivering a higher quality of care. This result is relevant for health care policy because it suggests that collaborative relations between hospitals may produce desirable outcomes both for individual patients, and for regional health care systems.