Accepted for/Published in: JMIR Medical Education
Date Submitted: Oct 31, 2023
Open Peer Review Period: Oct 31, 2023 - Dec 26, 2023
Date Accepted: Apr 26, 2024
(closed for review but you can still tweet)
Evaluation of an interdisciplinary education program to foster learning health systems
ABSTRACT
Background:
Learning Health Systems (LHS) have the potential to utilise health data in real-time through rapid and continuous cycles of data interrogation, implementing insights to practice, feedback, and practice change. However, there is a lack of an appropriately skilled interprofessional informatics workforce that can leverage knowledge to design innovative solutions. Therefore, there is a need to develop tailored professional development training in digital health, to foster skilled interprofessional learning communities in the healthcare workforce in Australia.
Objective:
This study aimed to explore participants’ experiences and perspectives from participating in an interprofessional education program over 13 weeks online. The evaluation also aimed to assess the benefits, barriers, and opportunities for improvements, and identify future applications of the course materials.
Methods:
We developed a wholly online short course open to interdisciplinary professionals working in digital health in the healthcare sector. In a flipped classroom model, participants (N=400) undertook 2 hours of pre-class learning online and then attended 2.5 hours of live synchronous learning in interactive weekly Zoom workshops for 13 weeks. Throughout the course, they worked in small, simulated learning communities (N=5-8) working through various activities and problems, contributing their unique perspectives and diverse expertise. To evaluate the utility of the program, we undertook a mixed methods evaluation consisting of pre- and post-surveys rating scales for usefulness, engagement, value and applicability for various aspects of the course. Participants also completed identical measures of self-efficacy pre- and post (N=200), with scales mapped to specific skills and tasks that should have been achievable following each of the topics covered. Further, they undertook voluntary weekly surveys to provide feedback on which aspects to continue and recommendations for improvements, via free text responses.
Results:
From the evaluation, it was evident that participants found the teaching model engaging, useful, valuable and applicable to their work. In the self-efficacy component, we observed a significant increase (P<.0001) in perceived confidence for all topics, when comparing pre-and post-course ratings. Overall, it was evident that the program gave participants a framework to organise their knowledge and a common understanding and shared language to converse with other disciplines; changed the way they perceived their role and the possibilities of data and technologies; and provided a toolkit through the LHS framework, that they could apply in their workplaces.
Conclusions:
We present one program and means of educating the health workforce to adopt the LHS model into standard practice. Interprofessional collaborative learning was a major component of the value of the program. This evaluation shed light on the multifaceted challenges and expectations of individuals embarking on a digital health program. Understanding the barriers and facilitators of the audience is crucial for creating an inclusive and supportive learning environment. Addressing these challenges will not only enhance participant engagement but also contribute to the overall success of the program and, by extension, the broader integration of digital health solutions in healthcare practice and, ultimately patient outcomes.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.