Emergency Departments (ED) are trying to alleviate crowding using various interventions. We assessed the effect of an alternative model of care, the Medical Team Evaluation (MTE) concept, encompassing team triage, quick registration, redesign of triage rooms and electronic medical records (EMR) on door-to-doctor (waiting) time and ED length of stay (LOS). We conducted an observational, before-and-after study at an urban academic tertiary care centre. On July 17th 2014, MTE was initiated from 9:00 a.m. to 10 p.m., 7 days a week. A registered triage nurse was teamed with an additional senior ED physician. Data of the 5-month pre-MTE and the 5-month MTE period were analysed. A matched comparison of waiting times and ED LOS of discharged and admitted patients pertaining to various Emergency Severity Index (ESI) triage categories was performed based on propensity scores. With MTE, the median waiting times improved from 41.2 (24.8–66.6) to 10.2 (5.7–18.1) minutes (min; P < 0.01). Though being beneficial for all strata, the improvement was somewhat greater for discharged, than for admitted patients. With a reduction from 54.3 (34.2–84.7) to 10.5 (5.9–18.4) min (P < 0.01), in terms of waiting times, MTE was most advantageous for ESI4 patients. The overall median ED LOS increased for about 15 min (P < 0.01), increasing from 3.4 (2.1–5.3) to 3.7 (2.3–5.6) hours. A significant increase was observed for all the strata, except for ESI5 patients. Their median ED LOS dropped by 73% from 1.2 (0.8–1.8) to 0.3 (0.2–0.5) hours (P < 0.01). In the same period the total orders for diagnostic radiology increased by 1,178 (11%) from 10,924 to 12,102 orders, with more imaging tests being ordered for ESI 2, 3 and 4 patients. Despite improved waiting times a decrease of ED LOS was only seen in ESI level 5 patients, whereas in all the other strata ED LOS increased. We speculate that this was brought about by the tendency of triage physicians to order more diagnostic radiology, anticipating that it may be better for the downstream physician to have more information rather than less.

We analysed the effects of Swiss national lockdown due to the COVID-19 pandemic on the glycaemic control in patients with diabetes mellitus. In a retrospective observational cohort study with observation period 16 December 2018–27 July 2020, we included tertiary care patients with diabetes and at least one glycated haemoglobin A1c (HbA1c) measurement before and after the lockdown beginning. Main outcome measure was change in HbA1c after the lockdown. We included 1078 patients (86% diabetes type 2) with a mean HbA1c of 55.63 mmol/mol (7.24%). Glycaemic control was susceptible to seasonal changes with higher mean HbA1c in winter as compared to spring (57.49 mmol/mol (7.41%) vs. 55.52 mmol/mol (7.23%), p = 0.013). The lockdown did not affect the mean HbA1c values of all patients. However, we found a higher proportion of type 2 diabetes patients with a worsening HbA1c after the lockdown as compared to the year before (32% vs. 22.9%, p = 0.02). In a mixed-model regression multivariable analysis, inappropriate alcohol intake and hypothyroidism were associated with an increase in HbA1c after the lockdown. In conclusion, the national lockdown had no effect on overall mean HbA1c values but affected a proportion of type 2 diabetes patients with worsening HbA1c, whose individual risk factors were identified.

Purpose The purpose of the study was to develop and implement an institution-specific trigger tool based on the Institute for Healthcare Improvement medication module trigger tool (IHI MMTT) in order to detect and monitor ADEs. Methods We performed an investigator-driven, single-center study using retrospective and prospective patient data to develop (“development phase”) and implement (“implementation phase”) an efficient, institution-specific trigger tool based on the IHI MMTT. Complete medical data from 1008 patients hospitalized in 2018 were used in the development phase. ADEs were identified by chart review. The performance of two versions of the tool was assessed by comparing their sensitivities and specificities. Tool A employed only digitally extracted triggers (“e-trigger-tool”) while Tool B employed an additional manually extracted trigger. The superior tool – taking efficiency into account – was applied prospectively to 19–22 randomly chosen charts per month for 26 months during the implementation phase. Results In the development phase, 189 (19%) patients had ≥1 ADE (total 277 ADEs). The time needed to identify these ADEs was 15 minutes/chart. A total of 203 patients had ≥1 trigger (total 273 triggers – Tool B). The sensitivities and specificities of Tools A and B were 0.41 and 0.86, and 0.43 and 0.86, respectively. Tool A was more time-efficient than Tool B (4 vs 9 minutes/chart) and was therefore used in the implementation phase. During the 26-month implementation phase, 22 patients experienced trigger-identified ADEs and 529 did not. The median number of ADEs per 1000 patient days was 6 (range 0–13). Patients with at least one ADE had a mean hospital stay of 22.3 ± 19.7 days, compared to 8.0 ± 7.6 days for those without an ADE (p = 2.7×10 −14 ). Conclusion We developed and implemented an e-trigger tool that was specific and moderately sensitive, gave consistent results and required minimal resources.

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