Accepted for/Published in: JMIR Human Factors
Date Submitted: Jan 22, 2024
Open Peer Review Period: Jan 26, 2024 - Mar 22, 2024
Date Accepted: Nov 20, 2024
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The meaning of a CVA outcome prediction model for patients, family and health care providers: a Qualitative Evaluation
ABSTRACT
Background:
CVA patients should be involved setting the rehabilitation goals. A personalized prediction on CVA outcomes would allow care professionals to better inform patients and informal caregivers. Several accurate prediction models have been created, but acceptance and proper implementation of the models is a prerequisite for model adoption.
Objective:
This study aims to assess the added value of a prediction model for long-term outcomes of rehabilitation after CVA, and how it can best be displayed, implemented and integrated in the care process.
Methods:
We designed a mockup including visualizations, based on our recently developed prediction model. We conducted focus groups with CVA patients and informal care givers, and focus groups with health care professionals (HCPs). Their opinions on the current information management and the model were analyzed using a thematic analysis approach. Lastly, a MIDI questionnaire was used to collect quantified insights of the prediction model and visualizations with HCPs.
Results:
The analysis of the six focus groups, with nine patients, four informal caregivers, and eight HCPs, resulted in 10 themes in 3 categories: evaluation of current care process (information absorption, expectations of rehabilitation, prediction of outcomes, and decision aid), content of the prediction model (reliability, relevance, influence on care process), and accessibility of the model (ease of understanding, model type preference, moment of use). We extracted recommendations for the prediction model and visualizations. The results of the questionnaire (9 responses, 56% response rate) underscored the themes of the focus groups.
Conclusions:
There is a need for a prediction model on CVA outcomes, as shown by the general approval of participants in both from the focus groups and the questionnaire. We recommend that the prediction model be geared towards HCPs, as they can provide the context necessary for the patients and informal caregivers. Good reliability and relevance of the prediction model will be essential for wide adoption of the prediction model.
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Copyright
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