Exploration of current challenges in rehabilitation from the perspective of healthcare professionals: Switzerland as a case in point

Moving from Interest to Engagement-Understanding the Research Capacity Building Needs of Rehabilitation Doctors

INTRODUCTION

Doctors working in rehabilitation settings have specialized clinical skills and experience, but research activity may be constrained by time pressures and inadequate current skills. This means missed opportunities to contribute to the evidence-base for better clinical practice and outcomes for people living with disabling, chronic complex conditions. This research aimed to understand rehabilitation doctors' research needs, experience, and aspirations to enable future training initiatives that are tailored to their practice context.

METHODS

This exploratory sequential mixed-method study comprised a survey followed by focus groups. Rehabilitation doctors from three health services in south-east Queensland, Australia completed an online survey incorporating the Research Spider to explore their research experience, confidence, interest, opportunity, and intent. Focus groups explored the survey results and participants' views regarding strategies to build research capacity.

RESULTS

The major findings were the gap between high research interest and low experience, confidence and opportunity; and fundamental research skills were identified as priority training topics. These findings support previous research. However, rehabilitation doctors may also have a self-expectation that, as competent clinicians, they should necessarily also be research-competent, and hold misperceptions regarding the shared nature of health services research.

DISCUSSION

Protected time and funding may enhance engagement with research to generate specialty-relevant evidence for practice. To this end, a research capacity building initiative in the form of a series of self-directed learning packages has been developed and implemented. A tailored workshop to strengthen rehabilitation doctors' research skills and engagement has also been developed for implementation.

AI Technologies for Machine Supervision and Help in a Rehabilitation Scenario

We consider, evaluate, and develop methods for home rehabilitation scenarios. We show the required modules for this scenario. Due to the large number of modules, the framework falls into the category of Composite AI. Our work is based on collected videos with high-quality execution and samples of typical errors. They are augmented by sample dialogues about the exercise to be executed and the assumed errors. We study and discuss body pose estimation technology, dialogue systems of different kinds and the emerging constraints of verbal communication. We demonstrate that the optimization of the camera and the body pose allows high-precision recording and requires the following components: (1) optimization needs a 3D representation of the environment, (2) a navigation dialogue to guide the patient to the optimal pose, (3) semantic and instance maps are necessary for verbal instructions about the navigation. We put forth different communication methods, from video-based presentation to chit-chat-like dialogues through rule-based methods. We discuss the methods for different aspects of the challenges that can improve the performance of the individual components. Due to the emerging solutions, we claim that the range of applications will drastically grow in the very near future.