Journal of hospital medicine最新文献

Background: Various strategies have attempted to address increased patient lengths of stay (LOS), but effectiveness varies. Factors related to work design and workforce experience may also play significant roles.

Objective: Utilizing data from the Discharge in the A.M. trial, we aimed to validate an electronic measure of workload (i.e., note count) and assess the relationship of workload, patient complexity, and physician years of experience to LOS.

Methods: Retrospective observational study at three large academic hospitals with hospital medicine physicians and patients they care for during the study.

Measures: Workload as measured by electronic note count and physician years of experience; patient LOS.

Results: From February 9, 2021 to July 31, 2021, 59 physicians completed daily surveys for 93% of 2318 scheduled shifts. We observed a moderate correlation (r: .56) between starting morning census and note counts but no association with LOS. We observed an effect modification between note count and the Charlson Comorbidity Index (CCI), with LOS increasing by 2.3% (95% CI: 0.3%, 4.3%; p = .02) and 3.9% (95% CI: 2.0%, 5.9%; p < .0001) per patient for every 1 unit increase in note count for patients with a moderate CCI or severe CCI, respectively. Years since training was associated with a 0.7% decrease in LOS (95% CI: -1.3%, -0.1%, p = .03).

Conclusion: Physician workload, as measured by note count, was associated with longer LOS with higher CCI, while more years of experience was associated with shorter LOS. Original Clinical Trial Registration: ClinicalTrials.gov number, NCT05370638.

Diuresis for hospitalized patients with acute decompensated heart failure is a routine clinical practice but one that remains reliant on error-prone and resource-intensive intake and output and weight measurements and is subject to wide provider variation. We sought to use quality improvement approaches to implement a data-driven predictive diuresis protocol based on natriuresis using the electronic health record to titrate dosing. Our initiative did not result in significant reductions in length of stay but did demonstrate a significant increase in the use of urine studies to guide diuresis and signals toward more aggressive diuretic dosing without an increase in adverse outcomes.

Background: Treatment of alcohol withdrawal syndrome (AWS) in hospitals is inconsistent. Electronic health record (EHR) order sets protocolize care.

Objective: We examined variation in AWS order sets across hospital organizations and their concordance with AWS guidelines.

Methods: We conducted a cross-sectional study of hospital organization user-created EHR order sets for AWS extracted from the December 2021 Epic® userweb community library. Hospital organizations with an acute care hospital and $\ge$ 1 AWS order set were included. We measured the proportion of guideline-concordant care practices within four categories: (1) laboratory assessment, (2) risk assessment for severe AWS and associated management changes, (3) symptom assessment and treatment of AWS, and identification and management of complications and (4) screening, diagnosis, and treatment of unhealthy alcohol use and AUD including medications for alcohol use disorder (MAUD).

Results: Ninety-five organizations with 289 order sets were included. The proportion of organizations with guideline-concordant laboratory assessments included testing of electrolytes (83%), hepatic function (75%), substance use (83%), and screening for infections (33%). Guidance for assessing risk of severe AWS (34%) and indications for care escalation (63%) used inconsistent definitions. Use of guideline-concordant medications for AWS (99%) and AWS symptom scores (91%) were nearly universal. MAUD was included by two organizations (2%). A common templated order set was used by 26% of organizations in EHR order sets.

Conclusions: We observed frequent organizational inclusion of guideline-concordant medications and symptom scores but rare and/or poorly defined guidance for evaluating risk of severe AWS, escalation of care, and MAUD.