Patient Characteristics and Clinical Process Predictors of Patients Leaving Without Being Seen from the Emergency Department

What explains differences in average wait time in the emergency department among different racial and ethnic populations: A linear decomposition approach

Objective

Non-Hispanic Black (NHB) and Hispanic/Latino (Hispanic) patients wait longer in the emergency department (ED) to see practitioners when compared with non-Hispanic White (NHW) patients. We investigate factors contributing to longer wait times for NHB and Hispanic patients using a linear decomposition approach.

Methods

This retrospective observational study included patients presenting to one tertiary hospital ED from 2019 to 2021. Median wait times among NHW, NHB, and Hispanic were calculated with multivariable linear regressions. The extent to which demographic, clinical, and hospital factors explained the differences in average wait time among the three groups were analyzed with Blinder‒Oaxaca post-linear decomposition model.

Results

There were 310,253 total patients including 34.7% of NHW, 34.7% of NHB, and 30.6% of Hispanic patients. The median wait time in NHW was 9 min (interquartile range [IQR] 4‒47 min), in NHB was 13 min (IQR 4‒59 min), and in Hispanic was 19 min (IQR 5‒78 min, p < 0.001). The top two contributors of average wait time difference were mode of arrival and triage acuity level. Post-linear decomposition analysis showed that 72.96% of the NHB‒NHW and 87.77% of the Hispanic‒NHW average wait time difference were explained by variables analyzed.

Conclusion

Compared to NHW patients, NHB and Hispanic patients typically experience longer ED wait times, primarily influenced by their mode of arrival and triaged acuity levels. Despite these recognized factors, there remains 12%‒27% unexplained factors at work, such as social determinants of health (including implicit bias and systemic racism) and many other unmeasured confounders, yet to be discovered.

Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study

A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes extended waiting times and increased patient dissatisfaction, both of which are immediately linked to a greater number of patients who leave the ED early, without any evaluation by a healthcare provider (Leave Without Being Seen, LWBS). This has an impact on the hospital in terms of missing income from lost opportunities to offer treatment and, in general, of negative outcomes from the ED process. Consequently, healthcare managers must be able to forecast and control patients who leave the ED without being evaluated in advance. This study is a retrospective analysis of patients registered at the ED of the "San Giovanni di Dio e Ruggi d'Aragona" University Hospital of Salerno (Italy) during the years 2014-2021. The goal was firstly to analyze factors that lead to patients abandoning the ED without being examined, taking into account the features related to patient characteristics such as age, gender, arrival mode, triage color, day of week of arrival, time of arrival, waiting time for take-over and year. These factors were used as process measures to perform a correlation analysis with the LWBS status. Then, Machine Learning (ML) techniques are exploited to develop and compare several LWBS prediction algorithms, with the purpose of providing a useful support model for the administration and management of EDs in the healthcare institutions. During the examined period, 688,870 patients were registered and 39188 (5.68%) left without being seen. Of the total LWBS patients, 59.6% were male and 40.4% were female. Moreover, from the statistical analysis emerged that the parameter that most influence the abandonment rate is the waiting time for take-over. The final ML classification model achieved an Area Under the Curve (AUC) of 0.97, indicating high performance in estimating LWBS for the years considered in this study. Various patient and ED process characteristics are related to patients who LWBS. The possibility of predicting LWBS rates in advance could be a valid tool quickly identifying and addressing "bottlenecks" in the hospital organization, thereby improving efficiency.

The association between nurse staffing and quality of care in emergency departments: A systematic review

BACKGROUND

The relationship between nurse staffing, skill-mix and quality of care has been well-established in medical and surgical settings, however, there is relatively limited evidence of this relationship in emergency departments. Those that have been published identified that lower nurse staffing levels in emergency departments are generally associated with worse outcomes with the conclusion that the evidence in emergency settings was, at best, weak.

METHODS

We searched thirteen electronic databases for potentially eligible papers published in English up to December 2023. Studies were included if they reported on patient outcomes associated with nurse staffing within emergency departments. Observational, cross-sectional, prospective, retrospective, interrupted time-series designs, difference-in-difference, randomised control trials or quasi-experimental studies and controlled before and after studies study designs were considered for inclusion. Team members independently screened titles and abstracts. Data was synthesised using a narrative approach.

RESULTS

We identified 16 papers for inclusion; the majority of the studies (n = 10/16) were observational. The evidence reviewed identified that poorer staffing levels within emergency departments are associated with increased patient wait times, a higher proportion of patients who leave without being seen and an increased length of stay. Lower levels of nurse staffing are also associated with an increase in time to medications and therapeutic interventions, and increased risk of cardiac arrest within the emergency department.

CONCLUSION

Overall, there remains limited high-quality empirical evidence addressing the association between emergency department nurse staffing and patient outcomes. However, it is evident that lower levels of nurse staffing are associated with adverse events that can result in delays to the provision of care and serious outcomes for patients. There is a need for longitudinal studies coupled with research that considers the relationship with skill-mix, other staffing grades and patient outcomes as well as a wider range of geographical settings.

TWEETABLE ABSTRACT

Lower levels of nurse staffing in emergency departments are associated with delays in patients receiving treatments and poor quality care including an increase in leaving without being seen, delay in accessing treatments and medications and cardiac arrest.

Improving Outcomes in Patients Sent to the Emergency Department from Outpatient Providers: A Receiver-Driven Handoff Process Improvement

BACKGROUND

Outpatient providers refer to emergency departments (EDs) due to findings requiring assessment beyond existing capabilities. However, poor communication surrounding these transitions may hinder safety and timeliness of emergency care. Receiver-driven handoff (RDH) is a process that helps ensure that all pertinent information is shared. This quality improvement project aimed to (1) improve knowledge of RDH, (2) increase satisfaction and perceptions surrounding RDH, (3) modify behaviors in relation to RDH, and (4) decrease referred patients leaving without being seen (LWBS).

METHODS

The Iowa Model and Implementation Framework guided this evidence-based quality improvement project. A multidisciplinary team developed and implemented a standardized RDH process consisting of screening to determine whether a patient was referred to the ED, review of electronic health record (EHR), and use of EHR documentation. Process measures were collected via questionnaire pre- and postimplementation and were analyzed quantitatively. Outcome measures were trended by a statistical process control p-chart, which was developed to demonstrate changes in the percentage of patients who were referred to the ED from the outpatient setting and LWBS.

RESULTS

The average response for the question "How satisfied are you with the handoff of patient information from referring clinic providers to the ED?" increased from 1.51 preintervention to 2.04 postintervention (p = 0.005). Respondents rated the information received during handoff higher postintervention (2.12 vs. 2.52, p = 0.04). Compliance with screening for referral to the ED was 84.0%. The proportion of patients LWBS after referral decreased by 6.2 percentage points (p < 0.001).

CONCLUSION

Using RDH in conjunction with a standardized triage screening may improve quality of information shared during this vulnerable transition and may assist in reduction of referred patients LWBS. The RDH process should be adapted into everyday workflow to ensure sustainability and effectiveness.

Racial and Ethnic Disparities in Emergency Department Wait Times for Headache

Headache is a common complaint of individuals seeking treatment in the emergency department (ED). Because pain is subjective, medical evaluation is susceptible to implicit bias that can lead to disparities in wait times. The aim of this study was to determine whether there are racial and ethnic disparities in ED wait times for headache. Our study used the 2015-2018 National Hospital Ambulatory Care Surveys (NHAMCS), a nationally representative sample of ambulatory care visits to EDs. Our sample consisted of visits made by adults for headaches, which were identified using ICD-10 diagnosis codes and NHAMCS reason for visit codes. There were 12,301,655 ED visits for headache represented by our sample. The mean wait time for headache visits was 38.1 min (95%CI: 31.1, 45.0). The mean wait time for Non-Hispanic White patients, non-Hispanic Black patients, Hispanic patients, and the other race and ethnicity groups were 34.7 min (95%CI: 27.5, 42.0), 46.4 min (95%CI: 26.5, 66.4), 37.9 min (95%CI: 19.4, 56.3), and 21.0 min (95%CI: 6.3, 35.7) respectively. After controlling for patient- and hospital-level covariates, visits by non-Hispanic Black patients had 40% (95%CI: -0.01, 0.81, p = 0.056) longer wait times and visits by Hispanic patients had 39% (95%CI: -0.03, 0.80, p = 0.068) longer wait times than visits by non-Hispanic White patients. While our findings suggest that there may be longer wait times for visits by non-Hispanic Black and Hispanic patients compared to visits by non-Hispanic White patients, further research is needed to confirm these findings and determine causes of wait times disparities in the ED.

The effect of having a physician in the triage area on the rate of patients leaving without being seen: A quality improvement initiative at King Fahad Specialist hospital

OBJECTIVES

To evaluate the effect of the presence of a physician in the triage area on the number of patients who leave without being seen (LWBS) and some of the factors affecting emergency department (ED) crowding.

METHODS

This was a pre-post study carried out at King Fahad Specialist Hospital, Dammam, Saudi Arabia. The 3-month study, consisting of 7826 patients, was split into pre-physician and post-physician periods. Variables compared across these periods were the number of LWBS patients, length of hospital stay, time to physician, and time to disposition decision. Statistical analysis was carried out using R version 4.3.0.

RESULTS

Our results showed that the presence of a triage physician significantly decreased the number of LWBS patients (p<0.001) and the time taken to encounter an ED physician (p<0.001). However, it did not have any significant impact on the length of hospital stay (p=0.5) or time to disposition decision (p=0.9).

CONCLUSION

The appointment of a triage physician has streamlined patient flow and decreased LWBS rates in the ED, demonstrating the need for more thorough research in this area.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Patients who leave the Emergency Department without being seen. Has COVID-19 affected this phenomenon?

BACKGROUND AND AIM

Patients who present to an Emergency Department (ED) and leave without being seen by a physician represent a safety concern because they may become severely ill and experience adverse events as a result of lacking or delayed ED treatment. Prior to the COVID-19 outbreak, the increasing number of patients accessing care through the ED in Italy and throughout the world has had implications for health policies.

METHODS

A retrospective cohort study that included all ED visits from 1st January 2013 to 31st December 2018 in the Perugia University Hospital has been carried out.

RESULTS

During the 5 years investigated 26,344 out of 300,372 (8.77%) patients who attended the ED left the triage area before being seen with an average of 439 patients per month. The same phenomenon has been analysed from February to October 2020. During these 9 months there were a total of 1,824 out of 30,990 (5.88%) patients who left the ED without being seen with an average of 202 per month. The latter value is one third lower than the one related to the period investigated prior to the COVID-19 outbreak.

CONCLUSIONS

Such investigation could help to differentiate actual essential demand from non-essential demand within the ED, which could inform quality-improvement policies. Several strategies could be implemented to lower the proportion of patients who leave the department without being seen. Reorganising the activities in the ED with different paths should be implemented with the aim of reducing waiting times and in turn patients' satisfaction.