Exploring emergency department 4-hour target performance and cancelled elective operations: a regression analysis of routinely collected and openly reported NHS trust data

A multivariable analysis to predict variations in hospital mortality using systems-based factors of healthcare delivery to inform improvements to healthcare design within the English NHS

Over the last decade, the strain on the English National Health Service (NHS) has increased. This has been especially felt by acute hospital trusts where the volume of admissions has steadily increased. Patient outcomes, including inpatient mortality, vary between trusts. The extent to which these differences are explained by systems-based factors, and whether they are avoidable, is unclear. Few studies have investigated these relationships. A systems-based methodology recognises the complexity of influences on healthcare outcomes. Rather than clinical interventions alone, the resources supporting a patient's treatment journey have near-equal importance. This paper first identifies suitable metrics of resource and demand within healthcare delivery from routinely collected, publicly available, hospital-level data. Then it proceeds to use univariate and multivariable linear regression to associate such systems-based factors with standardised mortality. Three sequential cross-sectional analyses were performed, spanning the last decade. The results of the univariate regression analyses show clear relationships between five out of the six selected predictor variables and standardised mortality. When these five predicators are included within a multivariable regression analysis, they reliably explain approximately 36% of the variation in standardised mortality between hospital trusts. Three factors are consistently statistically significant: the number of doctors per hospital bed, bed occupancy, and the percentage of patients who are placed in a bed within four hours after a decision to admit them. Of these, the number of doctors per bed had the strongest effect. Linear regression assumption testing and a robustness analysis indicate the observations have internal validity. However, our empirical strategy cannot determine causality and our findings should not be interpreted as established causal relationships. This study provides hypothesis-generating evidence of significant relationships between systems-based factors of healthcare delivery and standardised mortality. These have relevance to clinicians and policymakers alike. While identifying causal relationships between the predictors is left to the future, it establishes an important paradigm for further research.

Waiting times in emergency departments: exploring the factors associated with longer patient waits for emergency care in England using routinely collected daily data

Background

Long lengths of stay (also called waiting times) in emergency departments (EDs) are associated with higher patient mortality and worse outcomes.

Objective

To add to the literature using high-frequency data from a large number of hospitals to analyse factors associated with long waiting times, including exploring non-linearities for 'tipping points'.

Methods

Multivariate ordinary least squares regressions with fixed effects were used to analyse factors associated with the proportion of patients in EDs in England waiting more than 4 hours to be seen, treated and admitted or discharged. Daily situation reports (Sitrep), hospital episode statistics and electronic staffing records data over 90 days between December 2016 and February 2017 were used for all 138 English NHS healthcare providers with a major ED.

Results

Higher inpatient bed occupancy was correlated with longer ED waiting times, with a non-linear association. In a full hospital, with 100% bed occupancy, the proportion of patients who remained in the ED for more than 4 hours was 9 percentage points higher (95% CI 7.5% to 11.1%) than with an 85% occupancy level. For each percentage point change in the following factors, the proportion of ED stays over 4 hours also increased: more inpatients with hospital length of stay over 21 days (0.07%, 95% CI 0.008% to 0.13%); higher emergency admissions (0.08%, 95% CI 0.06% to 0.10%); and lower discharges relative to admissions on the same day (0.04%, 95% CI 0.02% to 0.06%), the following day (0.05%, 95% CI 0.03% to 0.06%) and at 2 days (0.05%, 95% CI 0.04% to 0.07%).

Conclusions

These results suggest that tackling patient flow and capacity in the wider hospital, particularly very high bed occupancy levels and patient discharge, is important to reduce ED waiting times and improve patient outcomes.

A simple prognostic score predicts one-year mortality of alert and calm emergency department patients: A prospective two-center observational study

STUDY OBJECTIVE

To derive and validate a prognostic score to predict 1-year mortality using vital signs, mobility and other variables that are readily available at the bedside at no additional cost.

METHODS

Post hoc analysis of two independent prospective observational studies in two emergency departments, one in Denmark and the other in Switzerland.

PARTICIPANTS

Alert and calm emergency department patients.

MEASUREMENTS

The prediction of mortality from presentation to 365 days by vital signs, mobility and other variables that are readily available at the bedside at no additional cost.

RESULTS

One thousand six hundred and eighteen alert and calm patients were in the Danish cohort and 1331 in the Swiss cohort. Logistic regression identified age >68 years, abnormal vital signs, impaired mobility and the decision to admit as significant predictors of 365-day mortality. A simple prognostic score awarded one point to each of these predictors. Less than two of these predictors were present in 45.6% of patients, and only 0.4% of these patients died within a year. If two or more of these predictors were present, 365-day mortality increased exponentially.

CONCLUSION

Age >68 years, the decision for hospital admission, any vital sign abnormality at presentation and impaired mobility at presentation are equally powerful predictors of 1-year mortality in alert and calm emergency department patients. If validated by others these predictors could be used to discharge patients with confidence since nearly half of these patients had less than two predictors and none of them died within 30 days. However, when two or more predictors were present 365-day mortality increased exponentially.

Why are there long waits at English emergency departments?

A core performance target for the English National Health Service (NHS) concerns waiting times at Emergency Departments (EDs), with the aim of minimising long waits. We investigate the drivers of long waits. We analyse weekly data for all major EDs in England from April 2011 to March 2016. A Poisson model with ED fixed effects is used to explore the impact on long (> 4 h) waits of variations in demand (population need and patient case-mix) and supply (emergency physicians, introduction of a Minor Injury Unit (MIU), inpatient bed occupancy, delayed discharges and long-term care). We assess overall ED waits and waits on a trolley (gurney) before admission. We also investigate variation in performance among EDs. The rate of long overall waits is higher in EDs serving older patients (4.2%), where a higher proportion of attendees leave without being treated (15.1%), in EDs with a higher death rate (3.3%) and in those located in hospitals with greater bed occupancy (1.5%). These factors are also significantly associated with higher rates of long trolley waits. The introduction of a co-located MIU is significantly and positively associated with long overall waits, but not with trolley waits. There is substantial variation in waits among EDs that cannot be explained by observed demand and supply characteristics. The drivers of long waits are only partially understood but addressing them is likely to require a multi-faceted approach. EDs with high rates of unexplained long waits would repay further investigation to ascertain how they might improve.

Can network science reveal structure in a complex healthcare system? A network analysis using data from emergency surgical services

INTRODUCTION

Hospitals are complex systems and optimising their function is critical to the provision of high quality, cost effective healthcare. Metrics of performance have to date focused on the performance of individual elements rather than the whole system. Manipulation of individual elements of a complex system without an integrative understanding of its function is undesirable and may lead to counterintuitive outcomes and a holistic metric of hospital function might help design more efficient services.

OBJECTIVES

We aimed to use network analysis to characterise the structure of the system of perioperative care for emergency surgical admissions in our tertiary care hospital.

DESIGN

We constructed a weighted directional network representation of the emergency surgical services using patient location data from electronic health records.

SETTING

A single-centre tertiary care hospital in the UK.

PARTICIPANTS

We selected data from the retrospective electronic health record data of all unplanned admissions with a surgical intervention during their stay during a 3.5-year period, which resulted in a set of 16 500 individual admissions.

METHODS

We then constructed and analysed the structure of this network using established methods from network science such as degree distribution, betweenness centrality and small-world characteristics.

RESULTS

The analysis showed the service to be a complex system with scale-free, small-world network properties. We also identified such potential hubs and bottlenecks in the system.

CONCLUSIONS

Our holistic, system-wide description of a hospital service may provide tools to inform service improvement initiatives and gives us insights into the architecture of a complex system of care. The implications for the structure and resilience of the service is that while being robust in general, the system may be vulnerable to outages at specific key nodes.

Using problem-based learning to improve patient safety in the emergency department

Pressures from rising patient numbers and overcrowding in emergency departments (EDs) are putting patients' safety at risk. Beyond improved provision of resources, two elements are essential to patient safety in emergency care - work culture and staff training. In traditional training environments, the teacher dispenses knowledge to nursing students in a classroom setting. However, problem-based learning (PBL) and the related concept of team-based learning (TBL) aim to enhance learners' knowledge and skills in non-technical subjects, such as patient safety, as well as their ability to address challenges they encounter in clinical practice. This article explores the theories that underpin PBL and TBL and discusses how they can be used by nurse educators to motivate staff and improve patient safety in the ED.

The impact of delayed transfers of care on emergency departments: common sense arguments, evidence and confounding

OBJECTIVES

There have been claims that Delayed Transfers of Care (DTOCs) of inpatients to home or a less acute setting are related to Emergency Department (ED) crowding. In particular DTOCs were associated with breaches of the UK 4-hour waiting time target in a previously published analysis. However, the analysis has major limitations by not adjusting for the longitudinal trend of the data. The aim of this work is to investigate whether the proposition that DTOCs impact the 4-hour target requires further research.

METHOD

Estimation of an association between two or more variables that are measured over time requires specialised statistical methods. In this study, we performed two separate analyses. First, we created two sets of artificial data with no correlation. We then added an upward trend over time and again assessed for correlation. Second, we reproduced the simple linear regression of the original study using NHS England open data of English trusts between 2010 and 2016, assessing correlation of numbers of DTOCs and ED breaches of the 4-hour target. We then reanalysed the same data using standard time series methods to remove the trend before estimating an association.

RESULTS

After introducing upward trends into the uncorrelated artificial data the correlation between the two data sets increased (R²=0.00 to 0.51 respectively). We found strong evidence of longitudinal trends within the NHS data of ED breaches and DTOCs. After removal of the trends the R² reduced from 0.50 to 0.01.

CONCLUSION

Our reanalysis found weak correlation between numbers of DTOCs and ED 4-hour target breaches. Our study does not indicate that there is no relationship between 4-hour target and DTOCs, it highlights that statistically robust evidence for this relationship does not currently exist. Further work is required to understand the relationship between breaches of the 4-hour target and numbers of DTOCs.

Winter cancellations of elective surgical procedures in the UK: a questionnaire survey of patients on the economic and psychological impact

OBJECTIVES

To quantify the economic and psychological impact of the cancellation of operations due to winter pressures on patients, their families and the economy.

DESIGN

This questionnaire study was designed with the help of patient groups. Data were collected on the economic and financial burden of cancellations. Emotions were also quantified on a 5-point Likert scale.

SETTING

Five NHS Hospital Trusts in the East Midlands region of England.

PARTICIPANTS

We identified 796 participants who had their elective operations cancelled between 1 November 2017 and 31 March 2018 and received responses from 339 (43%) participants.

INTERVENTIONS

Participants were posted a modified version of a validated quality of life questionnaire with a prepaid return envelope.

MAIN OUTCOME MEASURES

The primary outcome measures were the financial and psychological impact of the cancellation of elective surgery on patients and their families.

RESULTS

Of the 339 respondents, 163 (48%) were aged <65 years, with 111 (68%) being in employment. Sixty-six (19%) participants had their operations cancelled on the day. Only 69 (62%) of working adults were able to return to work during the time scheduled for their operation, with a mean loss of 5 working days (SD 10). Additional working days were lost subsequently by 60 (54%) participants (mean 7 days (SD 10)). Family members of 111 (33%) participants required additional time off work (mean 5 days (SD 7)). Over 30% of participants reported extreme levels of sadness, disappointment, anger, frustration and stress. At least moderate concern about continued symptoms was reported by 234 (70%) participants, and 193 (59%) participants reported at least moderate concern about their deteriorating condition.

CONCLUSIONS

The cancellation of elective surgery during the winter had an adverse impact on patients and the economy, including days of work lost and health-related anxiety. We recommend better planning, and provision of more notice and better support to patients.

An exhaustive review and analysis on applications of statistical forecasting in hospital emergency departments

Emergency departments (EDs) provide medical treatment for a broad spectrum of illnesses and injuries to patients who arrive at all hours of the day. The quality and efficient delivery of health care in EDs are associated with a number of factors, such as patient overall length of stay (LOS) and admission, prompt ambulance diversion, quick and accurate triage, nurse and physician assessment, diagnostic and laboratory services, consultations and treatment. One of the most important ways to plan the healthcare delivery efficiently is to make forecasts of ED processes. The aim this study is thus to provide an exhaustive review for ED stakeholders interested in applying forecasting methods to their ED processes. A categorisation, analysis and interpretation of 102 papers is performed for review. This exhaustive review provides an insight for researchers and practitioners about forecasting in EDs in terms of showing current state and potential areas for future attempts.