ObjectiveRapid Emergency Medicine Score (REMS) is an attenuated version of the Acute Physiology and Chronic Health Evaluation (APACHE) II score and has utility in predicting mortality in non-surgical patients, but has yet to be tested among the trauma population. The objective was to evaluate REMS as a risk stratification tool for predicting in-hospital mortality in traumatically injured patients and to compare REMS accuracy in predicting mortality to existing trauma scores, including the Revised Trauma Score (RTS), Injury Severity Score (ISS) and Shock Index (SI).Design and settingRetrospective chart review of the trauma registry from an urban academic American College of Surgeons (ACS) level 1 trauma centre.Participants3680 patients with trauma aged 14 years and older admitted to the hospital over a 4-year period. Patients transferred from other hospitals were excluded from the study as were those who suffered from burn or drowning-related injuries. Patients with vital sign documentation insufficient to calculate an REMS score were also excluded.Primary outcome measuresThe predictive ability of REMS was evaluated using ORs for in-hospital mortality. The discriminate power of REMS, RTS, ISS and SI was compared using the area under the receiver operating characteristic curve.ResultsHigher REMS was associated with increased mortality (p<0.0001). An increase of 1 point in the 26-point REMS scale was associated with an OR of 1.51 for in-hospital death (95% CI 1.45 to 1.58). REMS (area under the curve (AUC) 0.91±0.02) was found to be similar to RTS (AUC 0.89±0.04) and superior to ISS (AUC 0.87±0.01) and SI (AUC 0.55±0.31) in predicting in-hospital mortality.ConclusionsIn the trauma population, REMS appears to be a simple, accurate predictor of in-hospital mortality. While REMS performed similarly to RTS in predicting mortality, it did outperform other traditionally used trauma scoring systems, specifically ISS and SI.
Objectives
Queuing theory suggests that signing up for multiple patients at once (batching) can negatively affect patients’ length of stay (LOS). At academic centers, resident assignment adds a second layer to this effect. In this study, we measured the rate of batched patient assignment by resident physicians, examined the effect on patient in‐room LOS, and surveyed residents on underlying drivers and perceptions of batching.
Methods
This was a retrospective study of discharged patients from August 1, 2020 to October 27, 2020, supplemented with survey data conducted at a large, urban, academic hospital with an emergency medicine training program in which residents self‐assign to patients. Time stamps were extracted from the electronic health record and a definition of batching was set based on findings of a published time and motion study.
Results
A total of 3794 patients were seen by 28 residents and ultimately discharged during the study period. Overall, residents batched 23.7% of patients, with a greater rate of batching associated with increasing resident seniority and during the first hour of resident shifts. In‐room LOS for batched assignment patients was 15.9 minutes longer than single assignment patients (
P
value < 0.01). Residents’ predictions of their rates of batching closely approximated actual rates; however, they underestimated the effect of batching on LOS.
Conclusions
Emergency residents often batch patients during signup with negative consequences to LOS. Moreover, residents significantly underestimate this negative effect.
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