Evaluating the effects of increasing surgical volume on emergency department patient access

Reducing Crowding in Emergency Departments With Early Prediction of Hospital Admission of Adult Patients Using Biomarkers Collected at Triage: Retrospective Cohort Study

BACKGROUND

Emergency department crowding continues to threaten patient safety and cause poor patient outcomes. Prior models designed to predict hospital admission have had biases. Predictive models that successfully estimate the probability of patient hospital admission would be useful in reducing or preventing emergency department "boarding" and hospital "exit block" and would reduce emergency department crowding by initiating earlier hospital admission and avoiding protracted bed procurement processes.

OBJECTIVE

To develop a model to predict imminent adult patient hospital admission from the emergency department early in the patient visit by utilizing existing clinical descriptors (ie, patient biomarkers) that are routinely collected at triage and captured in the hospital's electronic medical records. Biomarkers are advantageous for modeling due to their early and routine collection at triage; instantaneous availability; standardized definition, measurement, and interpretation; and their freedom from the confines of patient histories (ie, they are not affected by inaccurate patient reports on medical history, unavailable reports, or delayed report retrieval).

METHODS

This retrospective cohort study evaluated 1 year of consecutive data events among adult patients admitted to the emergency department and developed an algorithm that predicted which patients would require imminent hospital admission. Eight predictor variables were evaluated for their roles in the outcome of the patient emergency department visit. Logistic regression was used to model the study data.

RESULTS

The 8-predictor model included the following biomarkers: age, systolic blood pressure, diastolic blood pressure, heart rate, respiration rate, temperature, gender, and acuity level. The model used these biomarkers to identify emergency department patients who required hospital admission. Our model performed well, with good agreement between observed and predicted admissions, indicating a well-fitting and well-calibrated model that showed good ability to discriminate between patients who would and would not be admitted.

CONCLUSIONS

This prediction model based on primary data identified emergency department patients with an increased risk of hospital admission. This actionable information can be used to improve patient care and hospital operations, especially by reducing emergency department crowding by looking ahead to predict which patients are likely to be admitted following triage, thereby providing needed information to initiate the complex admission and bed assignment processes much earlier in the care continuum.

Effect of a Boarding Restriction Protocol on Emergency Department Crowding

PURPOSE

Access block due to the lack of hospital beds causes crowding of emergency departments (ED). We initiated the "boarding restriction protocol" that limits the time of stay in the ED for patients awaiting hospitalization to 24 hours from arrival. The purpose of this study was to determine the effect of the boarding restriction protocol on ED crowding.

MATERIALS AND METHODS

The primary outcome was ED occupancy rate, which was calculated as the ratio of the number of occupying patients to the total number of ED beds. Time factors, such as length of stay (LOS), treatment time, and boarding time, were investigated.

RESULTS

The mean of the ED occupancy rate decreased from 1.532±0.432 prior to implementation of the protocol to 1.273±0.353 after (p<0.001). According to time series analysis, the absolute effect caused by the protocol was -0.189 (-0.277 to -0.110) (p=0.001). The proportion of patients with LOS exceeding 24 hours decreased from 7.6% to 4.0% (p<0.001). Among admitted patients, ED LOS decreased from 770.7 (421.4-1587.1) minutes to 630.2 (398.0-1156.8) minutes (p<0.001); treatment time increased from 319.6 (198.5-482.8) minutes to 344.7 (213.4-519.5) minutes (p<0.001); and boarding time decreased from 298.9 (109.5-1149.0) minutes to 204.1 (98.7-545.7) minutes (p<0.001). In pre-protocol period, boarding patients accumulated in the ED during the weekdays and resolved on Friday, but this pattern was alleviated in post-period.

CONCLUSION

The boarding restriction protocol was effective in alleviating ED crowding by reducing the accumulation of boarding patients in the ED during the weekdays.

[Emergency overcrowding and hospital organization: Causes and solutions]

Emergency Department (ED) overcrowding is a silent killer. Thus, several studies in different countries have described an increase in mortality, a decrease in the quality of care and prolonged hospital stays associated with ED overcrowding. Causes are multiple: input and in particular lack of access to lab test and imaging for general practitioners, throughput and unnecessary or time-consuming tasks, and output, in particular the availability of hospital beds for unscheduled patients. The main cause of overcrowding is waiting time for available beds in hospital wards, also known as boarding. Solutions to resolve the boarding problem are mostly organisational and require the cooperation of all department and administrative levels through efficient bed management. Elderly and polypathological patients wait longer time in ED. Internal Medicine, is the ideal specialty for these complex patients who require time for observation and evaluation. A strong partnership between the ED and the internal medicine department could help to reduce ED overcrowding by improving care pathways.

Factors Influencing Time-Dependent Quality Indicators for Patients With Suspected Acute Coronary Syndrome

OBJECTIVES

Rapid risk stratification and timely treatment are critical to favorable outcomes for patients with acute coronary syndrome (ACS). Our objective was to identify patient and system factors that influence time-dependent quality indicators (QIs) for patients with unstable angina/non-ST elevation myocardial infarction (NSTEMI) in the emergency department (ED).

METHODS

A retrospective, cohort study was conducted during a 42-month period of all patients 24 years or older suspected of having ACS as defined by receiving an electrocardiogram and at least 1 cardiac biomarker test. Cox regression was used to model the effects of patient characteristics, ancillary service use, staffing provisions, equipment availability, and ED and hospital crowding on ACS QIs.

RESULTS

Emergency department adherence rates to national standards for electrocardiogram readout time and biomarker turnaround time were 42% and 37%, respectively. Cox regression models revealed that chief complaints without chest pain and the timing of stress testing and medication administration were associated with the most significant delays.

CONCLUSIONS

Patient and system factors both significantly influenced QI times in this cohort with unstable angina/NSTEMI. These results illustrate both the complexity of diagnosing patients with NSTEMI and the competing effects of clinical and system factors on patient flow through the ED.

Inpatient flow management: a systematic review

Purpose

In hospitals, several patient flows compete for access to shared resources. Failure to manage these flows result in one or more disruptions within a hospital system. To ensure continuous care delivery, solving flow problems must not be limited to one unit, but should be extended to other departments – a prerequisite for solving flow problems in the entire hospital. Since most current studies focus solely on overcrowding in emergency units, additional insights are needed on system-wide patient flow management. The purpose of this paper is to look at the information available in system-wide patient flow management studies, which were also systematically evaluated to demonstrate which interventions improve inpatient flow.

Design/methodology/approach

The authors searched PubMed and Web of Science (Core Collection) literature databases and collected full-text articles using two selection and classification stages. Stage 1 was used to screen articles relating to patient flow management for inpatient settings with typical characteristics. Stage 2 was used to classify the articles selected in Stage 1 according to the interventions and their impact on patient flow within a hospital system.

Findings

In Stage 1, 107 studies were selected. Although a growing trend was observed, there were fewer studies on patient flow management in inpatient than studies in emergency settings. In Stage 2, 61 intervention studies were classified. The authors found that most interventions were about creating and adding supply resources. Since many hospital managers these days cannot easily add capacity owing to cost and resource constraints, using existing capacity efficiently is important – unfortunately not addressed in many studies. Furthermore, arrival variability was the factor most frequently mentioned as affecting flow. Of all interventions addressed in this review, the most prominent for advancing patient access to inpatient units was employing a specialized individual or team to maintain patient flow and bed placement across hospital units.

Originality/value

This study provides the first patient flow management systematic overview within an inpatient setting context.

A systematic literature review of simulation models for non-technical skill training in healthcare logistics

Background

Resource allocation in patient care relies heavily on individual judgements of healthcare professionals. Such professionals perform coordinating functions by managing the timing and execution of a multitude of care processes for multiple patients. Based on advances in simulation, new technologies that could be used for establishing realistic representations have been developed. These simulations can be used to facilitate understanding of various situations, coordination training and education in logistics, decision-making processes, and design aspects of the healthcare system. However, no study in the literature has synthesized the types of simulations models available for non-technical skills training and coordination of care.

Methods

A systematic literature review, following the PRISMA guidelines, was performed to identify simulation models that could be used for training individuals in operative logistical coordination that occurs on a daily basis. This article reviewed papers of simulation in healthcare logistics presented in the Web of Science Core Collections, ACM digital library, and JSTOR databases. We conducted a screening process to gather relevant papers as the knowledge foundation of our literature study. The screening process involved a query-based identification of papers and an assessment of relevance and quality.

Results

Two hundred ninety-four papers met the inclusion criteria. The review showed that different types of simulation models can be used for constructing scenarios for addressing different types of problems, primarily for training and education sessions. The papers identified were classified according to their utilized paradigm and focus areas. (1) Discrete-event simulation in single-category and single-unit scenarios formed the most dominant approach to developing healthcare simulations and dominated all other categories by a large margin. (2) As we approached a systems perspective (cross-departmental and cross-institutional), discrete-event simulation became less popular and is complemented by system dynamics or hybrid modeling. (3) Agent-based simulations and participatory simulations have increased in absolute terms, but the share of these modeling techniques among all simulations in this field remains low.

Conclusions

An extensive study analyzing the literature on simulation in healthcare logistics indicates a growth in the number of examples demonstrating how simulation can be used in healthcare settings. Results show that the majority of studies create situations in which non-technical skills of managers, coordinators, and decision makers can be trained. However, more system-level and complex system-based approaches are limited and use methods other than discrete-event simulation.

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.

Uniting emergency and inpatient clinicians across the ED-inpatient interface: The last frontier?

Unwell patients in the ED requiring inpatient admission must negotiate the interface between the ED and inpatient wards. Despite its importance and scale, this ED-inpatient interface (EDii) is poorly characterised. The aim of this paper is to clearly define the EDii and to describe its importance to (i) the patient: delays to admission and errors in communication across the EDii can increase adverse outcomes; (ii) the hospital: poor EDii function reduces hospital efficiency and effectiveness; and (iii) the healthcare system: half of all hospital inpatient admissions occur via the EDii and so EDii affects system-wide performance. The EDii can be defined as the dynamic, transitional phase of patient care in which responsibility for, and delivery of care, is shared between ED and inpatient hospital services. The EDii is characterised by a complex interplay of patient, hospital and system factors. A clear definition of the EDii and an understanding of its importance will assist future research and interventions to improve patient outcomes.

Trends in the supply of California's emergency departments and inpatient services, 2005-2014: a retrospective analysis

OBJECTIVES

Given increasing demand for emergency care, there is growing concern over the availability of emergency department (ED) and inpatient resources. Existing studies of ED bed supply are dated and often overlook hospital capacity beyond ED settings. We described recent statewide trends in the capacity of ED and inpatient hospital services from 2005 to 2014.

DESIGN

Retrospective analysis.

SETTING

Using California hospital data, we examined the absolute and per admission changes in ED beds and inpatient beds in all hospitals from 2005 to 2014.

PARTICIPANTS

Our sample consisted of all patients inpatient and outpatient) from 501 hospital facilities over 10-year period.

OUTCOME MEASURES

We analysed linear trends in the total annual ED visits, ED beds, licensed and staffed inpatient hospital beds and bed types, ED beds per ED visit, and inpatient beds per admission (ED and non-ED).

RESULTS

Between 2005 and 2014, ED visits increased from 9.8 million to 13.2 million (an increase of 35.0%, p<0.001). ED beds also increased (by 29.8%, p<0.001), with an average annual increase of 195.4 beds. Despite this growth, ED beds per visit decreased by 3.9%, from 6.0 ED beds per 10 000 ED visits in 2005 to 5.8 beds in 2014 (p=0.01). While overall admission numbers declined by 4.9% (p=0.06), inpatient medical/surgical beds per visit grew by 11.3%, from 11.6 medical/surgical beds per 1000 admissions in 2005 to 12.9 beds in 2014 (p<0.001). However, there were reductions in psychiatric and chemical dependency beds per admission, by -15.3% (p<0.001) and -22.4% (p=0.05), respectively.

CONCLUSIONS

These trends suggest that, in its current state, inadequate supply of ED and specific inpatient beds cannot keep pace with growing patient demand for acute care. Analysis of ED and inpatient supply should capture dynamic variations in patient demand. Our novel 'beds pervisit' metric offers improvements over traditional supply measures.

Real-time prediction of inpatient length of stay for discharge prioritization

OBJECTIVE

Hospitals are challenged to provide timely patient care while maintaining high resource utilization. This has prompted hospital initiatives to increase patient flow and minimize nonvalue added care time. Real-time demand capacity management (RTDC) is one such initiative whereby clinicians convene each morning to predict patients able to leave the same day and prioritize their remaining tasks for early discharge. Our objective is to automate and improve these discharge predictions by applying supervised machine learning methods to readily available health information.

MATERIALS AND METHODS

The authors use supervised machine learning methods to predict patients' likelihood of discharge by 2 p.m. and by midnight each day for an inpatient medical unit. Using data collected over 8000 patient stays and 20 000 patient days, the predictive performance of the model is compared to clinicians using sensitivity, specificity, Youden's Index (i.e., sensitivity + specificity - 1), and aggregate accuracy measures.

RESULTS

The model compared to clinician predictions demonstrated significantly higher sensitivity (P < .01), lower specificity (P < .01), and a comparable Youden Index (P > .10). Early discharges were less predictable than midnight discharges. The model was more accurate than clinicians in predicting the total number of daily discharges and capable of ranking patients closest to future discharge.

CONCLUSIONS

There is potential to use readily available health information to predict daily patient discharges with accuracies comparable to clinician predictions. This approach may be used to automate and support daily RTDC predictions aimed at improving patient flow.

Cardiac catheterization laboratory inpatient forecast tool: a prospective evaluation

OBJECTIVE

To develop and prospectively evaluate a web-based tool that forecasts the daily bed need for admissions from the cardiac catheterization laboratory using routinely available clinical data within electronic medical records (EMRs).

METHODS

The forecast model was derived using a 13-month retrospective cohort of 6384 catheterization patients. Predictor variables such as demographics, scheduled procedures, and clinical indicators mined from free-text notes were input to a multivariable logistic regression model that predicted the probability of inpatient admission. The model was embedded into a web-based application connected to the local EMR system and used to support bed management decisions. After implementation, the tool was prospectively evaluated for accuracy on a 13-month test cohort of 7029 catheterization patients.

RESULTS

The forecast model predicted admission with an area under the receiver operating characteristic curve of 0.722. Daily aggregate forecasts were accurate to within one bed for 70.3% of days and within three beds for 97.5% of days during the prospective evaluation period. The web-based application housing the forecast model was used by cardiology providers in practice to estimate daily admissions from the catheterization laboratory.

DISCUSSION

The forecast model identified older age, male gender, invasive procedures, coronary artery bypass grafts, and a history of congestive heart failure as qualities indicating a patient was at increased risk for admission. Diagnostic procedures and less acute clinical indicators decreased patients' risk of admission. Despite the site-specific limitations of the model, these findings were supported by the literature.

CONCLUSION

Data-driven predictive analytics may be used to accurately forecast daily demand for inpatient beds for cardiac catheterization patients. Connecting these analytics to EMR data sources has the potential to provide advanced operational decision support.

Solutions to emergency department 'boarding' and crowding are underused and may need to be legislated

The practice of keeping admitted patients on stretchers in hospital emergency department hallways for hours or days, called "boarding," causes emergency department crowding and can be harmful to patients. Boarding increases patients' morbidity, lengths of hospital stay, and mortality. Strategies that optimize bed management reduce boarding by improving the efficiency of hospital patient flow, but these strategies are grossly underused. Convincing hospital leaders of the value of such solutions, and educating patients to advocate for such changes, may promote improvements. If these strategies do not work, legislation may be required to effect meaningful change.

Real-time forecasting of pediatric intensive care unit length of stay using computerized provider orders

OBJECTIVE

To develop a model to produce real-time, updated forecasts of patients' intensive care unit length of stay using naturally generated provider orders. The model was designed to be integrated within a computerized decision support system to improve patient flow management.

DESIGN

Retrospective cohort study.

SETTING

Twenty-six bed pediatric intensive care unit within an urban, academic children's hospital using a computerized order entry system.

PATIENTS

A total of 2,178 consecutive pediatric intensive care unit admissions during a 16-month time period.

MEASUREMENTS AND MAIN RESULTS

We obtained unit length of stay measurements, time-stamped provider orders, age, admission source, and readmission status. A joint discrete-time logistic regression model was developed to produce probabilistic length of stay forecasts from continuously updated provider orders. Accuracy was assessed by comparing forecasted expected discharge time with observed discharge time, rank probability scoring, and calibration curves. Cross-validation procedures were conducted. The distribution of length of stay was heavily right-skewed with a mean of 3.5 days (95% confidence interval 0.3-19.1). Provider orders were predictive of length of stay in real-time accurately forecasting discharge within a 12-hr window: 46% for patients within 1 day of discharge, 34% for patients within 2 days of discharge, and 27% for patients within 3 days of discharge. The forecast model incorporating predictive orders demonstrated significant improvements in accuracy compared with forecasts based solely on empirical and temporal information. Seventeen predictive orders were found, grouped by medication, ventilation, laboratory, diet, activity, foreign body, and extracorporeal membrane oxygenation.

CONCLUSIONS

Provider orders reflect dynamic changes in patients' conditions, making them useful for real-time length of stay prediction and patient flow management. Patients' length of stay represent a major source of variability in intensive care unit resource utilization and if accurately predicted and communicated, may lead to proactive bed management with more efficient patient flow.