Conectar
Selection of antiseizure medications (ASMs) for newly diagnosed epilepsy remains largely a trial-and-error process. We have developed a machine learning (ML) model using retrospective data collected from five international cohorts that predicts response to different ASMs as the initial treatment for individual adults with new-onset epilepsy. This study aims to prospectively evaluate this model in Australia using a randomised controlled trial design.
At least 234 adult patients with newly diagnosed epilepsy will be recruited from 14 centres in Australia. Patients will be randomised 1:1 to the ML group or usual care group. The ML group will receive the ASM recommended by the model unless it is considered contraindicated by the neurologist. The usual care group will receive the ASM selected by the neurologist alone. Both the patient and neurologists conducting the follow-up will be blinded to the group assignment. Both groups will be followed up for 52 weeks to assess treatment outcomes. Additional information on adverse events, quality of life, mood and use of healthcare services and productivity will be collected using validated questionnaires. Acceptability of the model will also be assessed.
The primary outcome will be the proportion of participants who achieve seizure-freedom (defined as no seizures during the 12-month follow-up period) while taking the initially prescribed ASM. Secondary outcomes include time to treatment failure, time to first seizure after randomisation, changes in mood assessment score and quality of life score, direct healthcare costs, and loss of productivity during the treatment period.
This trial will provide class I evidence for the effectiveness of a ML model as a decision support tool for neurologists to select the first ASM for adults with newly diagnosed epilepsy.
This study is approved by the Alfred Health Human Research Ethics Committee (Project 130/23). Findings will be presented in academic conferences and submitted to peer-reviewed journals for publication.
ACTRN12623000209695.
Dashboards display hospital quality and patient safety measures aimed to improve patient outcomes. Although literature establishes dashboards aid quality and performance improvement initiatives, research is limited from the frontline nurse manager's perspective. This study characterizes factors influencing hospital nurse managers' use of dashboards for unit-level quality and performance improvement with suggestions for dashboard design. Using a descriptive qualitative design, semistructured interviews were conducted with 11 hospital nurse managers from a health system in the Midwestern United States. Thematic analysis was used to describe four perceived factors influencing dashboard use: external, data, technology features, and personal. External factors included regulatory standards, professional standards of care, organizational expectations, and organizational resources. Data factors included dashboard data quality and usefulness. Technology features included preference for simple, interactive, and customizable visual displays. Personal factors included inherent nurse manager qualities and knowledge. Guidelines for dashboard design involve display of required relevant quality measures that are accurate, timely, useful, and usable. Future research should involve hospital nurse managers in user-centered design to ensure dashboards are favorable for use. Further, opportunities exist for nurse manager informatics training and education on dashboard use in preparation for their role and responsibilities in unit-level quality and performance improvement. 
FreshRSS 