Improving Emergency Department radiology transportation time: a successful implementation of lean methodology

Ufford²,

2019

Int J Emerg Med

Objective The objective of this study is to assess the impact of a multimodal intervention on emergency department (ED) crowding and patient flow in a Dutch level 1 trauma center. Methods In this cross-sectional study, we compare ED crowding and patient flow between a 9-month pre-intervention period and a 9-month intervention period, during peak hours and overall (24/7). The multimodal intervention included (1) adding an emergency nurse practitioner (ENP) and (2) five medical specialists during peak hours to the 24/7 available emergency physicians (EPs), (3) a Lean programme to improve radiology turnaround times, and (4) extending the admission offices’ openings hours. Crowding is measured with the modified National ED OverCrowding Score (mNEDOCS). Furthermore, radiology turnaround times, patients’ length of stay (LOS), proportion of patients leaving without being seen (LWBS) by a medical provider, and unscheduled representations are assessed. Results The number of ED visits were grossly similar in the two periods during peak hours (15,558 ED visits in the pre-intervention period and 15,550 in the intervention period) and overall (31,891 ED visits in the pre-intervention period vs. 32,121 in the intervention period). During peak hours, ED crowding fell from 18.6% (pre-intervention period) to 3.5% (intervention period), radiology turnaround times decreased from an average of 91 min (interquartile range 45–256 min) to 50 min (IQR 30–106 min., p < 0.001) and LOS reduced with 13 min per patient from 167 to 154 min ( p < 0.001). For surgery, neurology and cardiology patients, LOS reduced significantly (with 17 min, 25 min, and 8 min. respectively), while not changing for internal medicine patients. Overall, crowding, radiology turnaround times and LOS also decreased. Less patients LWBS in the intervention period (270 patients vs. 348 patients, p < 0.001) and less patients represented unscheduled within 1 week after the initial ED visit: 864 (2.7%) in the pre-intervention period vs. 645 (2.0%) patients in the intervention period, p < 0.001. Conclusions In this hospital, a multimodal intervention successfully reduces crowding, radiology turnaround times, patients’ LOS, number of patients LWBS and the number of unscheduled return visits, suggesting improved ED processes. Further research is required on total costs of care and long-term effects.

Section: Discussionsupporting

confidence: 75%

“…Unfortunately, this reduction in waiting time for radiology results is only partially translated into reduced overall LOS. This is not surprising, since LOS is affected by factors beyond the ED process [20].…”

Section: Discussionmentioning

confidence: 99%

The impact of a multimodal intervention on emergency department crowding and patient flow

Ufford²,

2019

Int J Emerg Med

“…This finding is consistent with other research. 115 Studies also reported other frequent tools used such as standard work (which is considered one prerequisite for flow); 93 the 5 steps of sort, set in order, shine, standardize, and sustain (the 5S, which are used to eliminate clutter and organize workstations); 33 and Kaizen (which is used for intensive team-based improvement projects 95 and for the engagement of key stakeholders 12 ). Such findings are consistent with those reported in other studies 19,44 and support the claim that most LH applications focus more on assessment and improvement tools and less on process-monitoring tools after LH interventions.…”

Section: Discussionmentioning

confidence: 99%

“…8 Hospitals provide many types of services in their outpatient departments, including emergency and clinic visits, imaging and other diagnostic services, laboratory tests, and ambulatory surgery. 9 Common indicators of patient flow in ambulatory care include patient waiting time 10,11 and length of stay (LOS) 12,13 ; however, time frames for patient throughput, and metrics used to monitor throughput, vary widely in both literature and practice. 14 Since the 1990s, in an effort to deal with both quality and cost issues, healthcare providers have looked outside the healthcare sector for inspiration and guidance.…”

Section: Introductionmentioning

confidence: 99%

Effects of Lean Healthcare on Patient Flow: A Systematic Review

et al. 2020

Objectives: To assess the effects of lean healthcare (LH) on patient flow in ambulatory care and determine whether waiting time and length of stay (LOS) decrease after LH interventions. Methods: A systematic review was performed with close adherence to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We searched for studies of healthcare organizations applying LH interventions within ambulatory care published between 2002 and 2018. Six databases and grey literature sources were used. Two reviewers independently screened and assessed each study. When consensus was difficult to reach, a third reviewer intervened. Finally, a summary of findings was generated. Results: Out of 5627 studies, 40 were included. Regarding LOS for all patients, 19 out of 22 studies reported a decrease. LOS for discharged patients decreased in 11 out of 13 studies, whereas LOS for admitted patients was reduced in 6 out of 7 studies. Waiting time for patients before seeing a healthcare professional decreased in 24 out of 26 studies. Waiting time to treatment and waiting time for appointments were minimized in 4 and 2 studies, respectively. Patients who left without being seen by a doctor decreased in 9 out of 12 studies. Finally, patient and staff satisfaction were measured in 8 and 2 studies, respectively, with each reporting improvements. Conclusions: According to our findings, LH helped to reduce waiting time and LOS in ambulatory care, mainly owing to its focus on identifying and minimizing non-value added (NVA) activities. Nevertheless, evidence of the impact of LH on patient/ staff satisfaction and the translation of the obtained benefits into savings is scarce among studies.

“…ETT position is typically assessed using a frontal chest radiograph. However, due to high clinical volume, the delay in average turnaround time (TAT) for emergent chest radiographs is approximately 22.89 min [5]. Thus, an objective and accurate tool for characterization of ETT location on chest radiographs would be an invaluable asset in the acute care setting.…”

Section: Introductionmentioning

confidence: 99%

Identification and Localization of Endotracheal Tube on Chest Radiographs Using a Cascaded Convolutional Neural Network Approach

Kara¹,

Akers

Chang

2021

J Digit Imaging

Rapid and accurate assessment of endotracheal tube (ETT) location is essential in the intensive care unit (ICU) setting, where timely identification of a mispositioned support device may prevent significant patient morbidity and mortality. This study proposes a series of deep learning-based algorithms which together iteratively identify and localize the position of an ETT relative to the carina on chest radiographs. Using the open-source MIMIC Chest X-Ray (MIMIC-CXR) dataset, a total of 16,000 patients were identified (8000 patients with an ETT and 8000 patients without an ETT). Three different convolutional neural network (CNN) algorithms were created. First, a regression loss function CNN was trained to estimate the coordinate location of the carina, which was then used to crop the original radiograph to the distal trachea and proximal bronchi. Second, a classifier CNN was trained using the cropped inputs to determine the presence or absence of an ETT. Finally, for radiographs containing an ETT, a third regression CNN was trained to both refine the coordinate location of the carina and identify the location of the distal ETT tip. Model accuracy was assessed by comparing the absolute distance of prediction and ground-truth coordinates as well as CNN predictions relative to measurements documented in original radiologic reports. Upon five-fold cross validation, binary classification for the presence or absence of ETT demonstrated an accuracy, sensitivity, specificity, PPV, NPV, and AUC of 97.14%, 97.37%, 96.89%, 97.12%, 97.15%, and 99.58% respectively. CNN predicted coordinate location of the carina, and distal ETT tip was estimated within a median error of 0.46 cm and 0.60 cm from ground-truth annotations respectively. Overall final CNN assessment of distance between the carina and distal ETT tip was predicted within a median error of 0.60 cm from manual ground-truth annotations, and a median error of 0.66 cm from measurements documented in the original radiology reports. A serial cascaded CNN approach demonstrates high accuracy for both identification and localization of ETT tip and carina on chest radiographs. High performance of the proposed multi-step strategy is in part related to iterative refinement of coordinate localization as well as explicit image cropping which focuses algorithm attention to key anatomic regions of interest.