Bartelt, Jennifer M.

An ST-segment elevation myocardial infarction is a life-threatening condition within the spectrum of acute coronary syndrome, with well-recognized implications for short-term and long-term health outcomes. Retrospective review of our institutional electronic health records identified patterns of inaccurate acute coronary syndrome diagnostic assignment, including the presence of erroneous ST-segment elevation myocardial infarction diagnoses in discharge records. Such erroneous diagnoses in the durable health record may increase risk to patients through inadequate risk assessment and inappropriate follow-up care. Misapplication of a shortcut in our electronic health record system for rapidly ordering emergent cases was identified as a potential cause of the quality gap. To address this issue, a nurse-led quality improvement project was developed by using the define, measure, analyze, improve, and control framework. The project intervention decreased the percentage of patients with erroneous diagnoses from 24% to 7% and marginally improved nursing staff satisfaction. Our project successfully addressed an institutional quality concern. Our goal of reducing erroneous discharge diagnoses with minimal disruption to our existing practice was achieved, positively affecting our patients. Including both nurse leaders and frontline health care workers ensured the success of our project.

Introduction: This study evaluated the impact of parallel-level patient transfers on bed utilization efficiency within the Mayo Clinic Health System in Southern Minnesota, focusing on optimizing resources across tertiary and critical access hospitals. Methods: A retrospective analysis of 179,066 Emergency Department visits (2018–2022) was conducted, with ~2% involving parallel-level transfers for observation or admission. Machine learning was utilized to identify patients suitable for parallel transfers based on demographics, comorbidities, and clinical factors. A Random Forest model with an AUROC of 0.87 guided transfer decisions. Saved patient days were calculated as the difference between the actual LOS and the benchmark LOS based on Diagnosis-Related Groups (DRGs). Generalized estimating equations analyzed length of stay (LOS) differences, adjusted for confounders, with 95% confidence intervals (CI). Statistical analyses were conducted using SPSS (v.26). Results: The mean patient age was 56 years (SD = 17.2), with 51.4% being female. Saved patient days increased from ~600 to 5200 days over the study period. Transferred patients had a 5.7% longer unadjusted LOS compared to non-transferred patients (95% CI: 2.9–8.6%, p < 0.001). After adjustment for demographics and comorbidities, the LOS difference was not significant (adjusted mean difference: 0.4%, 95% CI: −1.7–2.5%, p = 0.51). Conclusions: Parallel-level transfers increased saved patient days, reflecting enhanced resource utilization. However, the adjusted LOS differences were not significant, highlighting the need for robust transfer protocols and controlled studies to confirm these findings.