Patient flow in emergency departments: a comprehensive umbrella review of solutions and challenges across the health system.

BACKGROUND: Globally, emergency departments (EDs) are overcrowded and unable to meet an ever-increasing demand for care. The aim of this study is to comprehensively review and synthesise literature on potential solutions and challenges throughout the entire health system, focusing on ED patient flow. METHODS: An umbrella review was conducted to comprehensively summarise and synthesise the available evidence from multiple research syntheses. A comprehensive search strategy was employed in four databases alongside government or organisational websites in March 2023. Gray literature and reports were also searched. Quality was assessed using the JBI critical appraisal checklist for systematic reviews and research syntheses. We summarised and classified findings using qualitative synthesis, the Population-Capacity-Process (PCP) model, and the input/throughput/output (I/T/O) model of ED patient flow and synthesised intervention outcomes based on the Quadruple Aim framework. RESULTS: The search strategy yielded 1263 articles, of which 39 were included in the umbrella review. Patient flow interventions were categorised into human factors, management-organisation interventions, and infrastructure and mapped to the relevant component of the patient journey from pre-ED to post-ED interventions. Most interventions had mixed or quadruple nonsignificant outcomes. The majority of interventions for enhancing ED patient flow were primarily related to the 'within-ED' phase of the patient journey. Fewer interventions were identified for the 'post-ED' phase (acute inpatient transfer, subacute inpatient transfer, hospital at home, discharge home, or residential care) and the 'pre-ED' phase. The intervention outcomes were aligned with the aim (QAIM), which aims to improve patient care experience, enhance population health, optimise efficiency, and enhance staff satisfaction. CONCLUSIONS: This study found that there was a wide range of interventions used to address patient flow, but the effectiveness of these interventions varied, and most interventions were focused on the ED. Interventions for the remainder of the patient journey were largely neglected. The metrics reported were mainly focused on efficiency measures rather than addressing all quadrants of the quadruple aim. Further research is needed to investigate and enhance the effectiveness of interventions outside the ED in improving ED patient flow. It is essential to develop interventions that relate to all three phases of patient flow: pre-ED, within-ED, and post-ED.

Airborne grass pollen and thunderstorms influence emergency department asthma presentations in a subtropical climate.

BACKGROUND: Grass pollen is considered a major outdoor aeroallergen source worldwide. It is proposed as a mechanism for thunderstorm asthma that lightning during thunderstorms promotes electrical rupture of pollen grains that leads to allergic airway inflammation. However, most evidence of associations between grass pollen and asthma comes from temperate regions. The objective of this study was to investigate short-term associations between airborne grass pollen exposure and asthma emergency department presentations in a subtropical population. METHODS: Episode level public hospital presentations for asthma (2016-2020) were extracted for greater Brisbane, Australia, from Queensland Health's Emergency Data Collection. Concentrations of airborne pollen were determined prospectively using a continuous flow volumetric impaction sampler. Daily time series analysis using a generalised additive mixed model were applied to determine associations between airborne grass pollen concentrations, and lightning count data, with asthma presentations. RESULTS: Airborne grass pollen showed an association with asthma presentations in Brisbane; a significant association was detected from same day exposure to three days lag. Grass pollen exposure increased daily asthma presentations up to 48.5% (95% CI: 12%, 85.9%) in female children. Lightning did not modify the effect of grass pollen on asthma presentations, however a positive association was detected between cloud-to-cloud lightning strikes and asthma presentations (P = 0.048). CONCLUSION: Airborne grass pollen exposure may exacerbate symptoms of asthma requiring urgent medical care of children and adults in a subtropical climate. This knowledge indicates an opportunity for targeted management of respiratory allergic disease to reduce patient and health system burden. For the first time, an influence of lightning on asthma was detected in this context. The outcomes support a need for continued pollen monitoring and surveillance of thunderstorm asthma risk in subtropical regions.

Improving preoperative prediction of surgery duration.

BACKGROUND: Operating rooms (ORs) are one of the costliest units in a hospital, therefore the cumulative consequences of any kind of inefficiency in OR management lead to a significant loss of revenue for the hospital, staff dissatisfaction, and patient care disruption. One of the possible solutions to improving OR efficiency is knowing a reliable estimate of the duration of operations. The literature suggests that the current methods used in hospitals, e.g., a surgeon's estimate for the given surgery or taking the average of only five previous records of the same procedure, have room for improvement. METHODS: We used over 4 years of elective surgery records (n = 52,171) from one of the major metropolitan hospitals in Australia. We developed robust Machine Learning (ML) approaches to provide a more accurate prediction of operation duration, especially in the absence of surgeon's estimation. Individual patient characteristics and historic surgery information attributed to medical records were used to train predictive models. A wide range of algorithms such as Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were tested for predicting operation duration. RESULTS: The results show that the XGBoost model provided statistically significantly less error than other compared ML models. The XGBoost model also reduced the total absolute error by 6854 min (i.e., about 114 h) compared to the current hospital methods. CONCLUSION: The results indicate the potential of using ML methods for reaching a more accurate estimation of operation duration compared to current methods used in the hospital. In addition, using a set of realistic features in the ML models that are available at the point of OR scheduling enabled the potential deployment of the proposed approach.

A discrete event simulation for improving operating theatre efficiency.

BACKGROUND: Increasing demand in healthcare services has posed excessive burden on healthcare professionals and hospitals with finite capacity. Operating theatres are critical resources within hospitals that can become bottlenecks in patient flow during high demand conditions. There are substantial costs associated with running operating theatres that include keeping professional staff ready, maintaining operating theatres and equipment, environmental services and cleaning of operating theatres and recovery rooms, and these costs can increase if theatres are not used efficiently. In addition to cost, operating theatre inefficiency can result in surgery cancelations and delays, and eventually, poor patient outcomes, which can be exacerbated under the increase in demand. METHODS: The allocation of surgeries to operating theatres is explored using a simulation model for patients admitted to inpatient beds and sent for surgery. We proposed a discrete event simulation (DES) to model incoming flow to operating theatres of a major metropolitan hospital. We assessed how changing the configuration of surgery at the target hospital affects Key Performance Indicators relating to theatre efficiency. In particular, the model was used to assess impacts of six different scenarios by defining new/hypothetical theatre case-mix, opening and closing times of theatres, turnaround (changeover) time, and repurposing the theatres. Target performance metrics included theatre utilisation, pre-operative length-of-stay, average reclaimable time, the percentage of total theatre time in a year that could be reclaimed, and patient waiting time. A web-based application was developed that allows testing user-defined scenarios and interactive analysis of the results. RESULTS: Extending the opening hours of operating theatres by 1 hour almost halved the number of deferred electives as well as over-run cases but at the expense of reduced theatre utilisation. A one-hour reduction in opening hours resulted in 10 times more deferred elective cases and a negligible increase in theatre utilisation. Reducing turnaround time by 50% had positive effects on theatre management: increased utilisation and less deferred and over-run elective cases. Pooling emergency theatres did not affect theatre utilisation but resulted in a considerable reduction in average wait time and the proportion of the delayed emergency cases. CONCLUSIONS: The developed DES-based simulation model of operating theatres along with the web-based user interface provided a useful interrogation tool for theatre management and hospital executive teams to assess new operational strategies. The next step is to embed simulation as ongoing practices in theatre planning workflow, allowing operational managers to use the model outputs to increase theatre utilisation, and reduce cancellations and schedule changes. This can support hospitals in providing services as efficiently and effectively as possible.

The impact of acute pneumococcal disease on health state utility values: a systematic review.

PURPOSE: Streptococcus pneumoniae infections remain a significant source of morbidity and mortality worldwide. The purpose of this review was to summarize the impact of pneumococcal disease on health state utilities (HSU) in the acute phase of illness. METHODS: We searched MEDLINE, EMBASE, EconLit, the Health Technology Assessment Database, the National Health Economic Evaluation Database, and Tufts Cost-Effectiveness Registry (up to January 2020) for primary studies. Eligible studies elicited HSU estimates using preference-based instruments for the acute phase of infection of pneumococcal syndromes including acute otitis media, pneumonia/lower respiratory tract infections, bacteremia/sepsis, and meningitis. Two reviewers independently conducted screening, data extraction and quality appraisal. RESULTS: We screened 10,178 studies, of which 26 met our inclusion criteria. Cohort sizes ranged from 8 to 2060 respondents. The most frequently studied syndrome was pneumonia (n = 17), followed by acute otitis media (n = 9), meningitis (n = 7) and bacteremia/sepsis (n = 4). Overall, each syndrome was associated with a substantial impact on HSU. Bacteremia/sepsis (range: - 0.331 to 0.992) and meningitis (range: - 0.330 to 0.977) were generally associated with the lowest HSU, followed by pneumonia (range: - 0.054 to 0.998) and acute otitis media (range: 0.064 to 0.970). HSU estimates varied considerably by treatment setting, elicitation method and type of respondent. The only study to compare pneumococcal infections to non-pneumococcal infections in the same population revealed significantly lower HSU estimates among pneumococcal infections. CONCLUSIONS: Pneumococcal syndromes are associated with decreased HSU estimates. Given the considerable heterogeneity in methods and source populations as well as study quality, care should be taken to select the most appropriate estimates.

Daily surgery caseload prediction: towards improving operating theatre efficiency.

BACKGROUND: In many hospitals, operating theatres are not used to their full potential due to the dynamic nature of demand and the complexity of theatre scheduling. Theatre inefficiencies may lead to access block and delays in treating patients requiring critical care. This study aims to employ operating theatre data to provide decision support for improved theatre management. METHOD: Historical observations are used to predict long-term daily surgery caseload in various levels of granularity, from emergency versus elective surgeries to clinical specialty-level demands. A statistical modelling and a machine learning-based approach are developed to estimate daily surgery demand. The statistical model predicts daily demands based on historical observations through weekly rolling windows and calendar variables. The machine learning approach, based on regression algorithms, learns from a combination of temporal and sequential features. A de-identified data extract of elective and emergency surgeries at a major 783-bed metropolitan hospital over four years was used. The first three years of data were used as historical observations for training the models. The models were then evaluated on the final year of data. RESULTS: Daily counts of overall surgery at a hospital-level could be predicted with approximately 90% accuracy, though smaller subgroups of daily demands by medical specialty are less predictable. Predictions were generated on a daily basis a year in advance with consistent predictive performance across the forecast horizon. CONCLUSION: Predicting operating theatre demand is a viable component in theatre management, enabling hospitals to provide services as efficiently and effectively as possible to obtain the best health outcomes. Due to its consistent predictive performance over various forecasting ranges, this approach can inform both short-term staffing choices as well as long-term strategic planning.

The National Emergency Access Target (NEAT) and the 4-hour rule: time to review the target.

OBJECTIVE: We explored the relationship between the National Emergency Access Target (NEAT) compliance rate, defined as the proportion of patients admitted or discharged from emergency departments (EDs) within 4 hours of presentation, and the risk-adjusted in-hospital mortality of patients admitted to hospital acutely from EDs. DESIGN, SETTING AND PARTICIPANTS: Retrospective observational study of all de-identified episodes of care involving patients who presented acutely to the EDs of 59 Australian hospitals between 1 July 2010 and 30 June 2014. MAIN OUTCOME MEASURE: The relationship between the risk-adjusted mortality of inpatients admitted acutely from EDs (the emergency hospital standardised mortality ratio [eHSMR]: the ratio of the numbers of observed to expected deaths) and NEAT compliance rates for all presenting patients (total NEAT) and admitted patients (admitted NEAT). RESULTS: ED and inpatient data were aggregated for 12.5 million ED episodes of care and 11.6 million inpatient episodes of care. A highly significant (P < 0.001) linear, inverse relationship between eHSMR and each of total and admitted NEAT compliance rates was found; eHSMR declined to a nadir of 73 as total and admitted NEAT compliance rates rose to about 83% and 65% respectively. Sensitivity analyses found no confounding by the inclusion of palliative care and/or short-stay patients. CONCLUSION: As NEAT compliance rates increased, in-hospital mortality of emergency admissions declined, although this direct inverse relationship is lost once total and admitted NEAT compliance rates exceed certain levels. This inverse association between NEAT compliance rates and in-hospital mortality should be considered when formulating targets for access to emergency care.

Using capacity alert calls to reduce overcrowding in a major public hospital.

OBJECTIVE: To investigate the efficacy of capacity alert calls in reducing acute hospital overcrowding through addressing rising occupancy, high patient throughput and increased access block. METHODS: Retrospective analysis of 24 months of in-patient, emergency department, and capacity alert call log data from a large metropolitan public hospital in Australia. The analysis explored statistical differences in patient flow parameters between capacity alert call days and other days including a control case set of days with statistically similar levels of occupancy. RESULTS: The study identified a significant (P<0.05) reduction in occupancy, patient throughput and access block on capacity alert call days. Capacity alert call days reversed rising occupancy trends, with 6 out of 7 flow parameters reporting significant improvement (P<0.05) over the 48 h following the call. Only 3 of these 7 flow parameters were significantly improved 48 h after control case days, confirming value in the alert mechanism and that the results are not a regression toward the mean phenomenon. CONCLUSIONS Escalation processes that alert and engage the whole hospital in tackling overcrowding can successfully deliver sustained improvements in occupancy, patient throughput and access block. The findings support and validate the use of capacity alert escalation calls to manage overcrowding, but suggest the need to improve the consistency of trigger mechanisms and the efficiency of the processes initiated by the capacity alert call.

A New Statistical Method to Detect Disease Outbreaks from Hospital Emergency Department Data.

Early detection and prediction of disease outbreaks are crucial for public health service delivery, containment response, saving patient lives, and reducing costs. We propose a new data-driven statistical methodology for outbreak detection and prediction based on routinely collected hospital Emergency Department data. The time between consecutive ED presentations matching a diagnosis of interest forms the basis of a novel index measure to signal that an outbreak has occurred. We validate the method using historical presentations of influenza-like illness made to a large sample of public hospital EDs in 2020 and compare outbreaks identified by the method with the start of the first wave of COVID-19. The method shows promise within the field of disease outbreak detection.

A Step Towards Building Health Digital Twins: Patient Phenotype Representation for Health Outcome Prediction.

Precision medicine aims to provide more effective interventions and preventive options to patients by considering their individual risk factors and by employing available evidence. This proof of concept study presents an approach towards generating holistic virtual representations of patients, a.k.a. health digital twins. The developed virtual representations were applied in two health outcome prediction case studies for readmission and in-hospital mortality predictions. The results demonstrated the effectiveness of the virtual representations to facilitate predictive analysis in practicing precision medicine.

Emergency Department Demand and the First Year of the COVID-19 Pandemic.

We present a retrospective analysis of Emergency Department daily patient flow across 84 hospitals in Queensland, Australia over a four-year period from 2017 - 2020, leading up to and including the start of the COVID-19 pandemic. Daily ED demand significantly increased year-on-year over the study period, though significant increases in 2020 were likely attributed to ED fever screening clinics. Compliance against a four-hour ED Length of Stay target had been slightly decreasing since 2017, and the first year of the pandemic showed significant improvements in target compliance compared to previous years for all patients including the cohort admitted from ED. The length of stay for ED patients was also significantly less in 2020 (mean = 3.1 hours) compared to previous years. As an area of topical interest, a special focus on influenza-like illness presentations to ED helps quantify changes in volume of this cohort. This knowledge assists hospitals in planning and responding to variations in hospital demand.

Automated Diabetic Retinopathy Diagnosis for Improved Clinical Decision Support.

We report on the prediction performance of artificial intelligence components embedded into a telehealth platform underlying a newly established eye screening service connecting metropolitan-based ophthalmologists to patients in remote indigenous communities in Northern Territory and Queensland. Two AI-based components embedded into the telehealth platform were evaluated on retinal images collected from 328 unique patients: an image quality alert system and a diabetic retinopathy detection system. Compared to ophthalmologists, at an individual image level, the image quality detection algorithm was correct 72% of the time, and 85% accurate at a patient level. The retinopathy detection algorithm was correct 85% accurate at an individual image level, and 87% accurate at a patient level. This evaluation provides assurances for future service models using AI to complement and support decisions of eye health assessment teams.

Modelling Planned vs. Actual Start Time to Control the Efficiency of Surgery.

To control the efficiency of surgery, it is ideal to have actual starting times of surgical procedures coincide with their planned start time. This study analysed over 4 years of data from a large metropolitan hospital and identified factors associated with surgery commencing close to the planned starting time via statistical modelling. A web application comprising novel visualisations to complement the statistical analysis was developed to facilitate translational impact by providing theatre administrators and clinical staff with a tool to assist with continuous quality improvement.

New bed configurations and discharge timing policies: A hospital-wide simulation.

OBJECTIVE: Optimising patient flow is becoming an increasingly critical issue as patient demand fluctuates in healthcare systems with finite capacity. Simulation provides a powerful tool to fine-tune policies and investigate their impact before any costly intervention. METHODS: A hospital-wide discrete event simulation is developed to model incoming flow from ED and elective units in a busy metropolitan hospital. The impacts of two different policies are investigated using this simulation model: (i) varying inpatient bed configurations and a load sharing strategy among a cluster of wards within a medical department and (ii) early discharge strategies on inpatient bed access. Several clinically relevant bed configurations and early discharge scenarios are defined and their impact on key performance metrics are quantified. RESULTS: Sharing beds between wards reduced the average and total ED length of stay (LOS) by 21% compared to having patients queue for individual wards. The current baseline performance level could be maintained by using fewer beds when the load sharing approach was imposed. Earlier discharge of inpatients resulted in reducing average patient ED LOS by approximately 16% and average patient waiting time by 75%. Specific time-based discharge targets led to greater improvements in flow compared to blanket approaches of discharging all patients 1 or 2 hours earlier. CONCLUSIONS: ED access performance for admitted patients can be improved by modifying downstream capacity or inpatient discharge times. The simulation model was able to quantify the potential impacts of such policies on patient flow and to provide insights for future strategic planning.

Mortality and readmission differences associated with after-hours hospital admission: A population-based cohort study in Queensland Australia.

Background and Aims: Policy makers and health system managers are seeking evidence on the risks involved for patients associated with after-hours care. This study of approximately 1 million patients who were admitted to the 25 largest public hospitals in Queensland Australia sought to quantify mortality and readmission differences associated with after-hours hospital admission. Methods: Logistic regression was used to assess whether there were any differences in mortality and readmissions based on the time inpatients were admitted to hospital (after-hours versus within hours). Patient and staffing data, including the variation in physician and nursing staff numbers and seniority were included as explicit predictors within patient outcome models. Results: After adjusting for case-mix confounding, statistically significant higher mortality was observed for patients admitted on weekends via the hospital's emergency department compared to within hours. This finding of elevated mortality risk after-hours held true in sensitivity analyses which explored broader definitions of after-hours care: an "Extended" definition comprising a weekend extending into Friday night and early Monday morning; and a "Twilight" definition comprising weekends and weeknights.There were no significant differences in 30-day readmissions for emergency or elective patients admitted after-hours. Increased mortality risks for elective patients was found to be an evening/weekend effect rather than a day-of-week effect. Workforce metrics that played a role in observed outcome differences within hours/after-hours were more a time of day rather than day of week effect, i.e. staffing impacts differ more between day and night than the weekday versus weekend. Conclusion: Patients admitted after-hours have significantly higher mortality than patients admitted within hours. This study confirms an association between mortality differences and the time patients were admitted to hospital, and identifies characteristics of patients and staffing that affect those outcomes.

Predicting emergency department admissions.

OBJECTIVE: To develop and validate models to predict emergency department (ED) presentations and hospital admissions for time and day of the year. METHODS: Initial model development and validation was based on 5 years of historical data from two dissimilar hospitals, followed by subsequent validation on 27 hospitals representing 95% of the ED presentations across the state. Forecast accuracy was assessed using the mean average percentage error (MAPE) between forecasts and observed data. The study also determined a daily sample size threshold for forecasting subgroups within the data. RESULTS: Presentations to the ED and subsequent admissions to hospital beds are not random and can be predicted. Forecast accuracy worsened as the forecast time intervals became smaller: when forecasting monthly admissions, the best MAPE was approximately 2%, for daily admissions, 11%; for 4-hourly admissions, 38%; and for hourly admissions, 50%. Presentations were more easily forecast than admissions (daily MAPE ∼7%). When validating accuracy at additional hospitals, forecasts for urban facilities were generally more accurate than regional forecasts (accuracy is related to sample size). Subgroups within the data with more than 10 admissions or presentations per day had forecast errors statistically similar to the entire dataset. The study also included a software implementation of the models, resulting in a data dashboard for bed managers. CONCLUSIONS: Valid ED prediction tools can be generated from access to de-identified historic data, which may be used to assist elective surgery scheduling and bed management. The paper provides forecasting performance levels to guide similar studies.

Impact of influenza across 27 public emergency departments in Australia: a 5-year descriptive study.

OBJECTIVE: To describe the incidence, characteristics and outcomes of patients with influenza-like symptoms presenting to 27 public hospital emergency departments (EDs) in Queensland, Australia. METHODS: A descriptive retrospective study covering 5 years (2005-9) of historical data from 27 hospital EDs was undertaken. State-wide hospital ED Information System data were analysed. Annual comparisons between influenza and non-influenza cases were made across the southern hemisphere influenza season (June-September) each year. RESULTS: Influenza-related presentations increased significantly over the 5 years from 3.4% in 2005 to 9.4% in 2009, reflecting a 276% relative increase. Differences over time regarding characteristics of patients with influenza-like symptoms, based on the influenza season, occurred for admission rate (decreased over time from 28% in 2005 to 18% in 2009), length of stay (decreased over time from a median of 210 min in 2005 to 164 min in 2009) and access block (increased over time from 33% to 41%). Also, every year there was a significantly (p<0.001) higher percentage of access block in the influenza cohort than in the non-influenza cohort. CONCLUSIONS: Although there was a large increase over time in influenza-related ED presentations, most patients were discharged home from the ED. Special consideration of health service delivery management (eg, establishing an 'influenza clinic border protection and public rollout of vaccination, beginning with those most at risk') for this group of patients is warranted but requires evaluation. These results may inform planning for service delivery models during the influenza season.

Visualising patient flow.

We describe the development of a method to distil routinely collected clinical data into patient flow information to aid hospital bed management. Using data from state-wide emergency department and inpatient clinical information systems, a user-friendly interface was developed to visualise patient flow conditions for a particular hospital. The historical snapshots employ a variable time scale, allowing flow to be visualised across a day, week, month or year. Flow information includes occupancy, arrival and departure rates, length-of-stay and access block observations, which can be filtered by age, departure status, diagnosis, elective status, triage category, and admission unit. The tool may be helpful in supporting hospital bed managers in their daily decision making.

Early discharge and its effect on ED length of stay and access block.

Effecting early discharge is a widely recommended strategy for improving patient flow in acute hospitals. This paper analyses the impact of inpatient discharge timing on Emergency Department (ED) flow parameters such as access block and length of stay, while comparing this to the effect on hospital occupancy, to arrive at an understanding of a 'whole of hospital' response to discharge timing. The impact of hospital size is also investigated. The analysis reveals that, on days when the discharge peak lags the peak in inpatient admissions, hospitals of all sizes exhibit increased levels of occupancy, inpatient and ED length of stay, and access block. The findings corroborate the efficacy of early discharge initiatives and 'whole of hospital' flow improvement initiatives for addressing overcrowding and efficiency issues in hospitals.

Characteristics of patients with COVID-19 hospitalised in South Australia during the first wave of the pandemic.

OBJECTIVE: To describe the first wave of hospitalisations of patients testing positive for COVID-19 in South Australia. METHODS: Pathology test results for COVID-19 between January and June 2020 were matched against state-wide ED and inpatient data sets. RESULTS: The impact of the first wave of COVID-19 on South Australian hospitals was 440 unique patients with COVID-19; median ED, hospital and ICU lengths of stay of 4.7 h, 9.8 days and 4.1 days, respectively; and a crude mortality rate of 0.23 deaths per 100 000 population (four deaths). CONCLUSION: The study sheds light on the characteristics of patients with COVID-19 hospitalised in South Australia.