American Journal of Medical Quality最新文献

Diabetic ketoacidosis (DKA) is the leading cause of morbidity and mortality in pediatric type 1 diabetes mellitus (T1D). Baseline data showed 139 of 182 DKA readmissions (76.4%) were due to missed basal insulin dosing. The team used quality improvement tools to implement a process change around basal insulin. The project utilized insulin degludec and school-based nurses when missed basal insulin was noted as a main driver for readmission. The DKA readmission rate averaged 5.25 per month from January 2017 to April 2019. The rate decreased to 3.64 per month during the intervention from May 2019 to March 2020, a 31% reduction over 11 months. This standardized approach for patients with T1D readmitted with DKA, using a school-based intervention and insulin degludec, reduced the number of DKA readmissions. This method is safe and effective for lowering DKA readmissions due to missed basal insulin in areas with reliable school nursing.

Neutropenic fever (NF) is an oncologic emergency for which expert consensus recommends that anti-pseudomonas antibiotics be administered within 60 minutes of detection. This study investigated whether delays in time to antibiotics (TTA) impacted overall survival (OS) for patients with hematological malignancies who developed inpatient NF via a retrospective cohort study of 187 de novo NF cases categorized by TTA (<1, 1-2, 2-3, 3-4 and >4 hours). OS at 180 days post-NF episode was compared using Kaplan-Meier estimates and multivariable Cox proportional hazards model. TTA did not significantly affect OS (P = 0.420). Patients with Charleston Comorbidity Indexes ≥3, a measure of overall health, had higher hazard (hazard ratio [HR] = 2.728, 95% confidence interval, 1.265-5.882, P = 0.010). TTA delays in the hospital may not be long enough to cause significant patient harm. Larger studies may be needed to detect small, but significant mortality differences.

The development of vaccines for SARS-CoV-2 has offered game-changing protection from severe disease and death from COVID-19. Despite efforts to vaccinate individuals in the ambulatory setting, a sizable minority of the US population remains unvaccinated for COVID-19. For unvaccinated patients, hospitalization for non-COVID-19 illness offers another opportunity for vaccination. In the summer of 2021, the authors noted that COVID-19 vaccination rate for medicine inpatients at their hospital had fallen to 5.3 vaccine doses administered per 4-week block. In response, they created Vax the Max, a gamification program of COVID-19 vaccination tasks where internal medicine resident teams were awarded points for completing these tasks. Residents were anonymously surveyed after participation. The hospital demonstrated higher rates of administering the initial COVID-19 vaccine dose and completing the vaccine series in the inpatient setting per 4-week plan-do-study-act cycle after implementation of Vax the Max (5.3 versus 8.8 doses per plan-do-study-act cycle). Among residents, 76.8% reported that Vax the Max spurred their COVID-19 task engagement, and 66% reported that a similar gamification model could be utilized for a different clinical task in the future. An increase was observed in the COVID-19 vaccination rate for medicine inpatients after launching the Vax the Max competition. This occurred in the setting of resident turnover every 4 weeks, which normally makes practice sustainment more challenging. Despite this, a high degree of engagement was produced by itinerant residents. There is potential to explore similar gamification approaches involving resident physicians in areas of quality improvement and patient safety.

Delirium is known to be underdiagnosed and underdocumented. Delirium detection in retrospective studies occurs mostly by clinician diagnosis or nursing documentation. This study aims to assess the effectiveness of natural language processing-confusion assessment method (NLP-CAM) algorithm when compared to conventional modalities of delirium detection. A multicenter retrospective study analyzed 4351 COVID-19 hospitalized patient records to identify delirium occurrence utilizing three different delirium detection modalities namely clinician diagnosis, nursing documentation, and the NLP-CAM algorithm. Delirium detection by any of the 3 methods is considered positive for delirium occurrence as a comparison. NLP-CAM captured 80% of overall delirium, followed by clinician diagnosis at 55%, and nursing flowsheet documentation at 43%. Increase in age, Charlson comorbidity score, and length of hospitalization had increased delirium detection odds regardless of the detection method. Artificial intelligence-based NLP-CAM algorithm, compared to conventional methods, improved delirium detection from electronic health records and holds promise in delirium diagnostics.