BackgroundInfection prevention and control (IPC) is a prioritised task for healthcare workers in emergency department (ED). Here, we examined compliance with admission screening (AS) and additional precautions (AP) measures for patients at risk of infection with multidrug-resistant organisms (MDROs) by using a two-stage, multifaceted educational intervention, also comparing the cost of a developed automated indicator for AS and AP compliance and clinical audits to sustain observed findings.MethodsIn the first stage, staff in the ED of the University Hospitals of Geneva, Switzerland, were briefed on IPC measures (AS and AP). A cross-sectional survey was then conducted to assess barriers to IPC measures. In the second stage, healthcare workers underwent training sessions, and an electronic patient record ‘order-set’ including AS and AP compliance indicators was designed. We compared the cost–benefit of the audits and the automated indicators for AS and AP compliance.ResultsCompliance significantly improved after training, from 36.2% (95% CI 23.6% to 48.8%) to 78.8% (95% CI 67.1% to 90.3%) for AS (n=100, p=0.0050) and from 50.2% (95% CI 45.3% to 55.1%) to 68.5% (95% CI 60.1% to 76.9%) for AP (n=125, p=0.0092). Healthcare workers recognised MDRO screening as an ED task (70.2%), with greater acknowledgment of risk factors at AS considered an ED duty. The monthly cost was higher for clinical audits than the automated indicator, with a reported yearly cost of US$120 203. The initial cost of developing the automated indicator was US$18 290 and its return on investment US$3.44 per US$1 invested.ConclusionTraining ED staff increased compliance with IPC measures when accompanied by team discussions for optimal effectiveness. An automated indicator of compliance is cheaper and closer to real-time than a clinical audit.
In hospitalized populations, there is significant heterogeneity in patients’ characteristics, disease severity, and treatment responses, which translates into different related outcomes and costs. Identifying inpatient clusters with similar clinical profiles could lead to better quality and personalized care while improving clinical resources used. Super-utilizers (SUs) are one such a group, who contribute a substantial proportion of health care costs and utilize a disproportionately high share of health care resources. This study uses cost, utilization metrics and clinical information to segment the population of patients (N=32,759) admitted to the University Hospitals of Geneva per year in 2017 - 2019. Using Latent Class Analysis it identifies 8 subgroups with highly similar patients demographics, medical conditions, types of service and costs within groups and which are highly different between groups. As such 82% of all SU patients, 99% of all patients less than 20 years old and 78% of all orthopedics patients are clustered into only 3 separate groups while one group contain only adult women 90% of them 20 to 40 years of age.
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