Association of Geographic Distance and Hospital Characteristics With Use of Interhospital Transfer by Air: A Multicenter Retrospective Study.

OBJECTIVE: Interhospital transfer by air (IHTA) represents the majority of helicopter air ambulance transports in the United States, but the evaluation of what factors are associated with utilization has been limited. We aimed to assess the association of geographic distance and hospital characteristics (including patient volume) with the use of IHTA. METHODS: This was a multicenter, retrospective study of helicopter flight request data from 2018 provided by a convenience sample of 4 critical care transport medicine programs in 3 US census regions. Nonfederal referring hospitals located in the home state of the associated critical care transport medicine program and within 100 miles of the primary receiving facility in the region were included if complete data were available. We fit a Poisson principal component regression model incorporating geographic distance, the number of emergency department visits, the number of hospital discharges, case mix index, the number of intensive care unit beds, and the number of general beds and tested the association of the variables with helicopter emergency medical services utilization. RESULTS: A total of 106 referring hospitals were analyzed, 21 of which were hospitals identified as having a consistent request pattern. Using the hospitals with a consistent referral pattern, geographic distance had a significant positive association with flight request volume. Other variables, including emergency department visit volume, were not associated. Overall, the included variables offered poor explanatory power for the observed variation between referring facilities in the use of IHTA (r2 = 0.09). Predicted flights based on the principal component regression model for all referring hospitals suggested the majority of referring hospitals used multiple flight programs. CONCLUSION: Geographic distance is associated with the use of IHTA. Unexpectedly, most basic hospital characteristics are not associated with the use of IHTA, and the degree of variation between referring facilities that is explained by patient volume is limited. The evaluation of nonhospital factors, such as the density and availability of critical care or advanced life support ground emergency medical services resources, is needed.

FASStR: a framework for ensuring high-quality operational metrics in health care.

OBJECTIVES: Poorly defined measurement impairs interinstitutional comparison, interpretation of results, and process improvement in health care operations. We sought to develop a unifying framework that could be used by administrators, practitioners, and investigators to help define and document operational performance measures that are comparable and reproducible. STUDY DESIGN: Retrospective analysis. METHODS: Health care operations and clinical investigators used an iterative process consisting of (1) literature review, (2) expert assessment and collaborative design, and (3) end-user feedback. We sampled the literature from the medical, health systems research, and health care operations (business and engineering) disciplines to assemble a representative sample of studies in which outpatient health care performance metrics were used to describe the primary or secondary outcome of the research. RESULTS: We identified 2 primary deficiencies in outpatient performance metric definitions: incompletion and inconsistency. From our review of performance metrics, we propose the FASStR framework for the Focus, Activity, Statistic, Scale type, and Reference dimensions of a performance metric. The FASStR framework is a method by which performance metrics can be developed and examined from a multidimensional perspective to evaluate their comprehensiveness and clarity. The framework was tested and revised in an iterative process with both practitioners and investigators. CONCLUSIONS: The FASStR framework can guide the design, development, and implementation of operational metrics in outpatient health care settings. Further, this framework can assist investigators in the evaluation of the metrics that they are using. Overall, the FASStR framework can result in clearer, more consistent use and evaluation of outpatient performance metrics.

Preventable delays to intravenous furosemide administration in the emergency department prolong hospitalization for patients with acute heart failure.

BACKGROUND: We sought to examine whether factors impacting the time to emergency department (ED) administration of intravenous (IV) furosemide were associated with the duration of hospital admission for patients with acute heart failure (AHF). METHODS AND RESULTS: We conducted a single-center, retrospective analysis of patients presenting to the ED and admitted between January 1, 2007 and December 31, 2014 who received a dose of IV furosemide. A Cox proportional hazards model was used to examine the likelihood that a patient would be discharged home alive, adjusting for patient demographics, AHF severity (low, moderate, high), laboratory result timing, and known AHF confounders. We identified 695 patients who met study criteria with 430 (61.9%) in the low-severity group. In the overall model, every 60-minute delay in IV furosemide administration was associated with an 8% lower chance of successful discharge home relative to someone who received early furosemide (aHR 0.93, 95%CI 0.87, 0.98, P = 0.012). Subgroup analysis suggests this association was most impactful in low-acuity patients. Our adjusted analysis suggests delaying furosemide administration until after serum creatinine results resulted in a 41% lower chance of successful discharge home relative to someone who had furosemide administered prior to creatinine results (aHR 1.41, 95%CI 1.07, 1,84). CONCLUSIONS: AHF patients, particularly those with lower severity, may benefit from rapid administration of IV furosemide in the ED. This suggests that a key determinant of hospital visit duration in this low-risk cohort is decongestion, which occurs sooner when IV therapy is begun early in the ED stay regardless of serum creatinine.

Lessons Learned From the Development and Parameterization of a Computer Simulation Model to Evaluate Task Modification for Health Care Providers.

Computer simulation is a highly advantageous method for understanding and improving health care operations with a wide variety of possible applications. Most computer simulation studies in emergency medicine have sought to improve allocation of resources to meet demand or to assess the impact of hospital and other system policies on emergency department (ED) throughput. These models have enabled essential discoveries that can be used to improve the general structure and functioning of EDs. Theoretically, computer simulation could also be used to examine the impact of adding or modifying specific provider tasks. Doing so involves a number of unique considerations, particularly in the complex environment of acute care settings. In this paper, we describe conceptual advances and lessons learned during the design, parameterization, and validation of a computer simulation model constructed to evaluate changes in ED provider activity. We illustrate these concepts using examples from a study focused on the operational effects of HIV screening implementation in the ED. Presentation of our experience should emphasize the potential for application of computer simulation to study changes in health care provider activity and facilitate the progress of future investigators in this field.

Understanding Emergency Care Delivery Through Computer Simulation Modeling.

In 2017, Academic Emergency Medicine convened a consensus conference entitled, "Catalyzing System Change through Health Care Simulation: Systems, Competency, and Outcomes." This article, a product of the breakout session on "understanding complex interactions through systems modeling," explores the role that computer simulation modeling can and should play in research and development of emergency care delivery systems. This article discusses areas central to the use of computer simulation modeling in emergency care research. The four central approaches to computer simulation modeling are described (Monte Carlo simulation, system dynamics modeling, discrete-event simulation, and agent-based simulation), along with problems amenable to their use and relevant examples to emergency care. Also discussed is an introduction to available software modeling platforms and how to explore their use for research, along with a research agenda for computer simulation modeling. Through this article, our goal is to enhance adoption of computer simulation, a set of methods that hold great promise in addressing emergency care organization and design challenges.

Improving the Context Supporting Quality Improvement in a Neonatal Intensive Care Unit Quality Collaborative: An Exploratory Field Study.

Successful quality improvement (QI) requires a supportive context. The goal was to determine whether a structured curriculum could help QI teams improve the context supporting their QI work. An exploratory field study was conducted of 43 teams participating in a neonatal intensive care unit QI collaborative. Using a curriculum based on the Model for Understanding Success in Quality, teams identified gaps in their context and tested interventions to modify context. Surveys and self-reflective journals were analyzed to understand how teams developed changes to modify context. More than half (55%) targeted contextual improvements within the microsystem, focusing on motivation and culture. "Information sharing" interventions to communicate information about the project as a strategy to engage more staff were the most common interventions tested. Further study is needed to determine if efforts to modify context consistently lead to greater outcome improvements.

Monte Carlo Simulation Modeling of a Regional Stroke Team's Use of Telemedicine.

OBJECTIVES: The objective of this study was to evaluate operational policies that may improve the proportion of eligible stroke patients within a population who would receive intravenous recombinant tissue plasminogen activator (rt-PA) and minimize time to treatment in eligible patients. METHODS: In the context of a regional stroke team, the authors examined the effects of staff location and telemedicine deployment policies on the timeliness of thrombolytic treatment, and estimated the efficacy and cost-effectiveness of six different policies. A process map comprising the steps from recognition of stroke symptoms to intravenous administration of rt-PA was constructed using data from published literature combined with expert opinion. Six scenarios were investigated: telemedicine deployment (none, all, or outer-ring hospitals only) and staff location (center of region or anywhere in region). Physician locations were randomly generated based on their zip codes of residence and work. The outcomes of interest were onset-to-treatment (OTT) time, door-to-needle (DTN) time, and the proportion of patients treated within 3 hours. A Monte Carlo simulation of the stroke team care-delivery system was constructed based on a primary data set of 121 ischemic stroke patients who were potentially eligible for treatment with rt-PA. RESULTS: With the physician located randomly in the region, deploying telemedicine at all hospitals in the region (compared with partial or no telemedicine) would result in the highest rates of treatment within 3 hours (80% vs. 75% vs. 70%) and the shortest OTT (148 vs. 164 vs. 176 minutes) and DTN (45 vs. 61 vs. 73 minutes) times. However, locating the on-call physician centrally coupled with partial telemedicine deployment (five of the 17 hospitals) would be most cost-effective with comparable eligibility and treatment times. CONCLUSIONS: Given the potential societal benefits, continued efforts to deploy telemedicine appear warranted. Aligning the incentives between those who would have to fund the up-front technology investments and those who will benefit over time from reduced ongoing health care expenses will be necessary to fully realize the benefits of telemedicine for stroke care.

Effects of Common Data Errors in Electronic Health Records on Emergency Department Operational Performance Metrics: A Monte Carlo Simulation.

OBJECTIVES: The objective was to estimate how data errors in electronic health records (EHRs) can affect the accuracy of common emergency department (ED) operational performance metrics. METHODS: Using a 3-month, 7,348-visit data set of electronic time stamps from a suburban academic ED as a baseline, Monte Carlo simulation was used to introduce four types of data errors (substitution, missing, random, and systematic bias) at three frequency levels (2, 4, and 7%). Three commonly used ED operational metrics (arrival to clinician evaluation, disposition decision to exit for admitted patients, and ED length of stay for admitted patients) were calculated and the proportion of ED visits that achieved each performance goal was determined. RESULTS: Even small data errors have measurable effects on a clinical organization's ability to accurately determine whether it is meeting its operational performance goals. Systematic substitution errors, increased frequency of errors, and the use of shorter-duration metrics resulted in a lower proportion of ED visits reported as meeting the associated performance objectives. However, the presence of other error types mitigated somewhat the effect of the systematic substitution error. Longer time-duration metrics were found to be less sensitive to data errors than shorter time-duration metrics. CONCLUSIONS: Infrequent and small-magnitude data errors in EHR time stamps can compromise a clinical organization's ability to determine accurately if it is meeting performance goals. By understanding the types and frequencies of data errors in an organization's EHR, organizational leaders can use data management best practices to better measure true performance and enhance operational decision-making.

The nature and necessity of operational flexibility in the emergency department.

Hospital-based emergency departments (EDs), given their high cost and major role in allocating care resources, are at the center of the debate about how to maximize value in delivering health care in the United States. To operate effectively and create value, EDs must be flexible, having the ability to rapidly adapt to the highly variable needs of patients. The concept of flexibility has not been well described in the ED literature. We introduce the concept, outline its potential benefits, and provide some illustrative examples to facilitate incorporating flexibility into ED management. We draw on operations research and organizational theory to identify and describe 5 forms of flexibility: physical, human resource, volume, behavioral, and conceptual. Each form of flexibility may be useful individually or in combination with other forms in improving ED performance and enhancing value. We also offer suggestions for measuring operational flexibility in the ED. A better understanding of operational flexibility and its application to the ED may help us move away from reactive approaches of managing variable demand to a more systematic approach. We also address the tension between cost and flexibility and outline how "partial flexibility" may help resolve some challenges. Applying concepts of flexibility from other disciplines may help clinicians and administrators think differently about their workflow and provide new insights into managing issues of cost, flow, and quality in the ED.

Applications of Business Analytics in Healthcare.

The American healthcare system is at a crossroads, and analytics, as an organizational skill, figures to play a pivotal role in its future. As more healthcare systems capture information electronically and as they begin to collect more novel forms of data, such as human DNA, how will we leverage these resources and use them to improve human health at a manageable cost? In this article, we argue that analytics will play a fundamental role in the transformation of the American healthcare system. However, there are numerous challenges to the application and use of analytics, namely the lack of data standards, barriers to the collection of high-quality data, and a shortage of qualified personnel to conduct such analyses. There are also multiple managerial issues, such as how to get end users of electronic data to employ it consistently for improving healthcare delivery, and how to manage the public reporting and sharing of data. In this article, we explore applications of analytics in healthcare, barriers and facilitators to its widespread adoption, and how analytics can help us achieve the goals of the modern healthcare system: high-quality, responsive, affordable, and efficient care.

Communication in diagnostic radiology: meeting the challenges of complexity.

OBJECTIVE: As patients and information flow through the imaging process, value is added step-by-step when information is acquired, interpreted, and communicated back to the referring clinician. However, radiology information systems are often plagued with communication errors and delays. This article presents theories and recommends strategies to continuously improve communication in the complex environment of modern radiology. CONCLUSION: Communication theories, methods, and systems that have proven their effectiveness in other environments can serve as models for radiology.

The flex track: flexible partitioning between low- and high-acuity areas of an emergency department.

STUDY OBJECTIVE: Emergency departments (EDs) with both low- and high-acuity treatment areas often have fixed allocation of resources, regardless of demand. We demonstrate the utility of discrete-event simulation to evaluate flexible partitioning between low- and high-acuity ED areas to identify the best operational strategy for subsequent implementation. METHODS: A discrete-event simulation was used to model patient flow through a 50-bed, urban, teaching ED that handles 85,000 patient visits annually. The ED has historically allocated 10 beds to a fast track for low-acuity patients. We estimated the effect of a flex track policy, which involved switching up to 5 of these fast track beds to serving both low- and high-acuity patients, on patient waiting times. When the high-acuity beds were not at capacity, low-acuity patients were given priority access to flexible beds. Otherwise, high-acuity patients were given priority access to flexible beds. Wait times were estimated for patients by disposition and Emergency Severity Index score. RESULTS: A flex track policy using 3 flexible beds produced the lowest mean patient waiting time of 30.9 minutes (95% confidence interval [CI] 30.6 to 31.2 minutes). The typical fast track approach of rigidly separating high- and low-acuity beds produced a mean patient wait time of 40.6 minutes (95% CI 40.2 to 50.0 minutes), 31% higher than that of the 3-bed flex track. A completely flexible ED, in which all beds can accommodate any patient, produced mean wait times of 35.1 minutes (95% CI 34.8 to 35.4 minutes). The results from the 3-bed flex track scenario were robust, performing well across a range of scenarios involving higher and lower patient volumes and care durations. CONCLUSION: Using discrete-event simulation, we have shown that adding some flexibility into bed allocation between low and high acuity can provide substantial reductions in overall patient waiting and a more efficient ED.

Transient and sustained changes in operational performance, patient evaluation, and medication administration during electronic health record implementation in the emergency department.

STUDY OBJECTIVE: Little is known about the transient and sustained operational effects of electronic health records on emergency department (ED) performance. We quantify how the implementation of a comprehensive electronic health record was associated with metrics of operational performance, test ordering, and medication administration at a single-center ED. METHODS: We performed a longitudinal analysis of electronic data from a single, suburban, academic ED during 28 weeks between May 2011 and November 2011. We assessed length of stay, use of diagnostic testing, medication administration, radiologic imaging, and patient satisfaction during a 4-week baseline measurement period and then tracked changes in these variables during the 24 weeks after implementation of the electronic health record. RESULTS: Median length of stay increased and patient satisfaction was reduced transiently, returning to baseline after 4 to 8 weeks. Rates of laboratory testing, medication administration, overall radiologic imaging, radiographs, computed tomography scans, and ECG ordering all showed sustained increases throughout the 24 weeks after electronic health record implementation. CONCLUSION: Electronic health record implementation in this single-center study was associated with both transient and sustained changes in metrics of ED performance, as well as laboratory and medication ordering. Understanding ways in which an ED can be affected by electronic health record implementation is critical to providing insight about ways to mitigate transient disruption and to maximize potential benefits of the technology.

Operational data integrity during electronic health record implementation in the ED.

OBJECTIVE: Operational data are often used to make systems changes in real time. Inaccurate data, however, transiently, can result in inappropriate operational decision making. Implementing electronic health records (EHRs) is fraught with the possibility of data errors, but the frequency and magnitude of transient errors during this fast-evolving systems upheaval are unknown. This study was done to assess operational data quality in an emergency department (ED) immediately before and after an EHR implementation. METHODS: Direct observations of standard ED timestamps (arrival, bed placement, clinician evaluation, disposition decision, and exit from ED) were conducted in a suburban ED for 4 weeks immediately before and 4 weeks after EHR implementation. Direct observations were compared with electronic timestamps to assess data quality. Differences in proportions and medians with 95% confidence intervals (CIs) were used to estimate the magnitude of effect. RESULTS: There were 260 observations: 122 before and 138 after implementation. We found that more systematic data errors were introduced after EHR implementation. The proportion of discrepancies where the observed and electronic timestamp differed by more than 10 minutes was reduced for the disposition timestamp (29.3% vs 16.1%; difference in proportions, -13.2%; 95% CI, -24.4% to -1.9%). The accuracy of the clinician-evaluation timestamp was reduced after implementation (median difference of 3 minutes earlier than observed; 95% CI, -5.02 to -0.98). Multiple service intervals were less accurate after implementation. CONCLUSION: This single-center study raises questions about operational data quality in the peri-implementation period of EHRs. Using electronic timestamps for operational assessment and decision making following implementation should recognize the magnitude and compounding of errors when computing service times.

An exploratory analysis of the model for understanding success in quality.

BACKGROUND: Experience suggests that differences in context produce variability in the effectiveness of quality improvement (QI) interventions. However, little is known about which contextual factors affect success or how they exert influence. PURPOSE: Using the Model for Understanding Success in Quality (MUSIQ), we perform exploratory quantitative tests of the role of context in QI success. METHODOLOGY: We used a cross-sectional design to survey individuals participating in QI projects in three settings: a pediatric hospital, hospitals affiliated with a state QI collaborative, and organizations sponsoring participants in an improvement advisor training program. Individuals participating in QI projects completed a questionnaire assessing contextual factors included in MUSIQ and measures of perceived success. Path analysis was used to test the direct, indirect, and total effects of context variables on QI success as hypothesized in MUSIQ. FINDINGS: In the 74 projects studied, most contextual factors in MUSIQ were found to be significantly related to at least one QI project performance outcome. Contextual factors exhibiting significant effects on two measures of perceived QI success included resource availability, QI team leadership, team QI skills, microsystem motivation, microsystem QI culture, and microsystem QI capability. There was weaker evidence for effects of senior leader project sponsors, organizational QI culture, QI team decision-making, and microsystem QI leadership. These initial tests add to the validity of MUSIQ as a tool for identifying which contextual factors affect improvement success and understanding how they exert influence. PRACTICE IMPLICATIONS: Using MUSIQ, managers and QI practitioners can begin to identify aspects of context that must be addressed before or during the execution of QI projects and plan strategies to modify context for increased success. Additional work by QI researchers to improve the theory, refine measurement approaches, and validate MUSIQ as a predictive tool in a wider range of QI efforts is necessary.

The Model for Understanding Success in Quality (MUSIQ): building a theory of context in healthcare quality improvement.

UNLABELLED: BACKGROUND Quality improvement (QI) efforts have become widespread in healthcare, however there is significant variability in their success. Differences in context are thought to be responsible for some of the variability seen. OBJECTIVE: To develop a conceptual model that can be used by organisations and QI researchers to understand and optimise contextual factors affecting the success of a QI project. METHODS: 10 QI experts were provided with the results of a systematic literature review and then participated in two rounds of opinion gathering to identify and define important contextual factors. The experts subsequently met in person to identify relationships among factors and to begin to build the model. RESULTS: The Model for Understanding Success in Quality (MUSIQ) is organised based on the level of the healthcare system and identifies 25 contextual factors likely to influence QI success. Contextual factors within microsystems and those related to the QI team are hypothesised to directly shape QI success, whereas factors within the organisation and external environment are believed to influence success indirectly. CONCLUSIONS: The MUSIQ framework has the potential to guide the application of QI methods in healthcare and focus research. The specificity of MUSIQ and the explicit delineation of relationships among factors allows a deeper understanding of the mechanism of action by which context influences QI success. MUSIQ also provides a foundation to support further studies to test and refine the theory and advance the field of QI science.

The influence of context on quality improvement success in health care: a systematic review of the literature.

CONTEXT: The mixed results of success among QI initiatives may be due to differences in the context of these initiatives. METHODS: The business and health care literature was systematically reviewed to identify contextual factors that might influence QI success; to categorize, summarize, and synthesize these factors; and to understand the current stage of development of this research field. FINDINGS: Forty-seven articles were included in the final review. Consistent with current theories of implementation and organization change, leadership from top management, organizational culture, data infrastructure and information systems, and years involved in QI were suggested as important to QI success. Other potentially important factors identified in this review included: physician involvement in QI, microsystem motivation to change, resources for QI, and QI team leadership. Key limitations in the existing literature were the lack of a practical conceptual model, the lack of clear definitions of contextual factors, and the lack of well-specified measures. CONCLUSIONS: Several contextual factors were shown to be important to QI success, although the current body of literature lacks adequate definitions and is characterized by considerable variability in how contextual factors are measured across studies. Future research should focus on identifying and developing measures of context tied to a conceptual model that examines context across all levels of the health care system and explores the relationships among various aspects of context.

Design, implementation, and assessment of a radiology workflow management system.

OBJECTIVE: The objective of this article is to describe the development, launch, and outcomes studies of a paperless workflow management system (WMS) that improves radiology workflow in a filmless and speech-recognition environment. MATERIALS AND METHODS: The WMS prioritizes cases automatically on the basis of medical and operational acuity factors, automatically facilitates communication of critical radiology results, and provides permanent documentation of these results and communications. It runs in parallel with an integrated radiology information system (RIS)-PACS and speech-recognition system. Its effects on operations, staff stress and satisfaction, and patient satisfaction were studied. RESULTS: Despite an increase in caseload volume after the launch of the WMS, case turnaround times, defined as the time between case availability on PACS and signing of the final radiology staff interpretation, decreased for all case types. Median case turnaround time decreased by 33 minutes (22%) for emergency department, 47 minutes (37%) for inpatient, and 22 minutes (38%) for outpatient radiology cases. All reductions were significant at a p value of < 0.05. Interruptions were reduced, consuming an estimated 28% less radiology staff time, after implementation. Patient perceptions of radiology service timeliness showed modest improvement after the WMS was implemented. Staff satisfaction showed no significant change. CONCLUSION: There is room for improvement in radiology workflow even in departments with integrated RIS-PACS and speech-recognition systems. This study has shown that software tools that coordinate decentralized workflow and dynamically balance workloads can increase the efficiency and efficacy of radiologists. Operational benefits, such as reduced reading times, improvements in the timeliness of care (both actual and as perceived by patients), and reduced interruptions to radiologists, further reinforce the benefits of such a system. Secondary benefits, such as documenting communication about a case and facilitating review of results, can also promote more timely and effective care. Although use of the system did not result in a substantial improvement in staff perceptions, neither did it reduce their satisfaction, suggesting that these operational improvements were not achieved as a trade-off against the quality of the work environment.