Related Experiment Video
Updated: May 24, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Health-care staff perspectives in optimising delirium prevention using data-driven interventions
Swapna Gokhale1,2, Belinda Garth1, Melinda Webb-St Mart2
1Monash Centre for Health Research and Implementation, Faculty of Medicine, Nursing, and Health Sciences, Monash University, Clayton, Victoria, Australia.
Objectives:
This study aimed to identify factors influencing delirium prevention (risk identification and screening), from the perspective of health service staff, in order to ascertain the characteristics and implementation strategies critical for the clinical adoption of data-driven optimisations for delirium prevention. This pre-implementation study used the Monash Learning Health System (LHS) paradigm to visualise iterative integrated assimilation of delirium prevention in routine care.
Methods:
A qualitative study was conducted in a large metropolitan public health network in Australia. Following consultation with organisational leaders, a purposive sample of clinical/non-clinical participants with expertise in delirium care delivery was recruited. Interviews were inductively analysed using a framework approach. The Consolidated Framework for Implementation Research (CFIR) domains underpinned interview questions and guided thematic mapping and analysis of responses.
Results:
Semi-structured interviews were conducted with 18 participants (clinical [n = 14] and non-clinical [n = 4]). Key themes included challenges in consistently integrating delirium risk identification and screening processes into clinical workflows, infrastructure-related obstacles hindering the digitisation of decision support, and the need to engage caregivers and staff in designing optimisations to enable appropriate and timely delirium prevention.
Conclusions:
This study generated insights into key factors influencing delirium prevention, focusing on the development and implementation of optimisations such as automated delirium risk prediction. Improving hospital information technology infrastructure, supporting workforce digital literacy and ensuring accountability in all professional groups are crucial for implementing automated delirium risk prediction models in clinical practice. Future research should examine the feasibility and efficacy of optimised delirium prevention interventions in pragmatic clinical trials.
More Related Videos
09:52Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
Published on: January 15, 2017
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
Related Concept Videos
Patient-centered Care
Documentation in Long-Term and Home Healthcare Setting
Long-Term Care Facilities
Interdisciplinary Care: The Health Care Team-I
Physicians
The physician's primary responsibility is to diagnose illness and direct the medical or surgical treatment of the condition. The authority to admit patients to a healthcare agency or institution and practice care within that setting is granted to physicians by the healthcare agency or institution...
Current Trends in Nursing I
Methods of Documentation IV: Focus Charting
It typically involves three columns for recording information:
Alzheimer's Disease: Treatment