patient flow

Improving emergency department flow by introducing a simple time out moment (The TRAFFIC LIGHT study)

Background and importance: Long waiting times in the emergency department (ED) is an increasing problem in the recent years and is expected to become an even bigger problem in the future
Objective: We aimed to test the hypothesis whether increasing awareness of the time lapse with the treating physician, 2 hours after patient arrival, can reduce long patient turnaround time (TAT).
Method: In this prospective single-center cohort study we compared and analyzed patient TAT in the ED before and after implementation of a so called ‘traffic light’ moment 2 hours after patient arrival. At this ‘traffic light” moment a team member contacted the treating physician to increased awareness over the time lapse. Difference in percentage of patients who stayed more than 4 hours in the ED before and after intervention was the primary outcome
Results: Between October 2nd 2021 and January 2nd,2022 1494 patients were included for primary outcome analysis. A total of 419 patients (n=740, 56.6%) had a TAT of less than 4 hour in the ED before intervention, compared to 497 (n=754, 65.9%) after intervention (p <0.001). Median time spent in de ED before intervention was 3:40 (IQR 2:24 – 5:04) compared to 3:15 (IQR 2:03 – 4:38) after intervention (p<0.001). Conclusion: This simple and low-cost intervention reduces the ED length of stay significantly. Although multiple interventions will be required to ensure less patients spending more than 4-hours in the ED, a ‘traffic light’ moment can be a simple and an effective tool.

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Patient Characteristics and Variables Influencing Acute Medical Flow

Waiting times are the most widely used indicator of patient flow. This project aims to analyse 24-hour variation in referrals and waiting times for patients referred to the Acute Medical Service (AMS). A retrospective cohort study was conducted at the AMS of Wales’ largest hospital. Collected data included patient characteristics, referral times, waiting times and adherence to Clinical Quality Indicators (CQIs). Peak referral times were found between 11:00-19:00. Peak waiting times occurred between 17:00–01:00, which was longer on weekdays in comparison to weekends. Referrals between 17:00-21:00 had the longest waiting times with > 40% of patients failing both junior and senior CQIs. Mean and median age and NEWS were higher between 17:00-09:00. Weekday evening and nights are problematic for acute medical patient flow. Interventions, including workforce, should be targeted towards these findings.

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Predicting length of stay for acute medical admissions using the ALICE score: a simple bedside tool

Background: Early identification of patients likely to have a short admission permits best use of limited resources to facilitate rapid discharge where possible. The ALICE score is a simple bedside tool developed in one hospital as a decision aid. This study sought to confirm its widespread applicability.

Method: Retrospective review of 250 consecutive admissions at five acute hospitals. Clinical records were reviewed for a total of 1003 patients. ALICE score was calculated for each patient and compared to LoS data.

Results: There was a statistically significant positive correlation between rising ALICE scores and increasing length of stay irrespective of final diagnoses.

Conclusion: The ALICE score provides a simple bedside tool to predict length of stay.

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Tracking the take – Using patient flow data to improve AMU performance and safety

Abstract

Aims: To create a system to co-ordinate the medical take, bed management and track patient flow.
To use the system to continuously audit against Society for Acute Medicine Quality Indicators.
To use the data to model patient flow and optimize working patterns to improve waiting times.

Method: An online whiteboard and underlying database system were designed, tested and implemented.
Data from this system were used to audit against SAM Quality Indicators and then analysed to optimise both trainee and consultant working patterns.

Results: The online whiteboard proved effective and popular as a working tool.
Data collection improved using the electronic system.
Optimising junior doctor working patterns to match demand led to a reduction of average waiting time to see a doctor from 190 minutes to 71 minutes (p < 0.0001), and a reduction in the proportion of patients waiting over 4 hours from 40% to 10% (p < 0.0001). Optimising consultant working patterns did not produced significant changes in waiting times. Conclusions: The online whiteboard improved day-to-day working and data collection, when compared to the previous paper-based system. Better data facilitated analysis of working patterns leading to a significant improvement in patient waiting times.

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An Effective System to Measure and Report Quality Indicators in Acute Medical Units

Abstract

The Society for Acute Medicine has developed a number of clinical quality indicators by which all UK Acute Medicine Units can bench mark their activity. These will help to ensure high quality care for patients, inform the continuing development of acute medical services and demonstrate the positive impact of this new speciality. Prospective collection of these data may be a challenge for many busy units. This paper describes a local solution developed in house in a North East hospital. It demonstrates how the data collected can be analysed to assess the effect of changes in consultant presence on the unit and also time taken for patients to be seen by a doctor. The limitations of the system and potential for future development are considered.

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