Purpose: The aim of this study was to assess physiotherapists’ clinical use and acceptance of a novel telemonitoring platform to facilitate the recording of measurements during rehabilitation of patients following anterior cruciate ligament reconstruction. Additionally, suggestions for platform improvement were explored. Methods: Physiotherapists from seven Dutch private physiotherapy practices participated in the study. Data were collected through log files, a technology acceptance questionnaire and focus group meetings using the “buy a feature” method. Data regarding platform use and acceptance (7-point/11-point numeric rating scale) were descriptively analysed. Total scores were calculated for the features suggested to improve the platform, based on the priority rating (1 = nice to have, 2 = should have, 3 = must have). Results: Participating physiotherapists (N = 15, mean [SD] age 33.1 [9.1] years) together treated 52 patients during the study period. Platform use by the therapists was generally limited, with the number of log-ins per patient varying from 3 to 73. Overall, therapists’ acceptance of the platform was low to moderate, with average (SD) scores ranging from 2.5 (1.1) to 4.9 (1.5) on the 7-point Likert scale. The three most important suggestions for platform improvement were: (1) development of a native app, (2) system interoperability, and (3) flexibility regarding type and frequency of measurements. Conclusions: Even though health care professionals were involved in the design of the telemonitoring platform, use in routine care was limited. Physiotherapists recognized the relevance of using health technology, but there are still barriers to overcome in order to successfully implement eHealth in routine care.
Background: Clinical reasoning skills are considered to be among the key competencies a physiotherapist should possess. Yet, we know little about how physiotherapy students actually learn these skills in the workplace. A better understanding will benefit physiotherapy education.Objectives: To explore how undergraduate physiotherapy students learn clinical reasoning skills during placements.Design: A qualitative research design using focus groups and semi-structured interviews.Setting: European School of Physiotherapy, Amsterdam, the Netherlands.Participants: Twenty-two undergraduate physiotherapy students and eight clinical teachers participated in this study.Main outcome measures: Thematic analysis of focus groups and semi-structured interviews.Results: Three overarching factors appeared to influence the process of learning clinical reasoning skills: the learning environment, the clinical teacher and the student. Preclinical training failed to adequately prepare students for clinical practice, which expected them to integrate physiotherapeutic knowledge and skills into a cyclic reasoning process. Students’ basic knowledge and assessment structure therefore required further development during the placements. Clinical teachers expected a holistic, multifactorial problem-solving approach from their students. Both students and teachers considered feedback and reflection essential to clinical learning. Barriers to learning experienced by students included time constraints, limited patient exposure and patient communication.Conclusions: Undergraduate physiotherapy students develop clinical reasoning skills through comparison of and reflection on different reasoning approaches observed in professional therapists. Over time, students learn to synthesise these different approaches into their own individual approach. Physiotherapy programme developers should aim to include a wide variety of multidisciplinary settings and patient categories in their clinical placements.
From diagnosis to patient scheduling, AI is increasingly being considered across different clinical applications. Despite increasingly powerful clinical AI, uptake into actual clinical workflows remains limited. One of the major challenges is developing appropriate trust with clinicians. In this paper, we investigate trust in clinical AI in a wider perspective beyond user interactions with the AI. We offer several points in the clinical AI development, usage, and monitoring process that can have a significant impact on trust. We argue that the calibration of trust in AI should go beyond explainable AI and focus on the entire process of clinical AI deployment. We illustrate our argument with case studies from practitioners implementing clinical AI in practice to show how trust can be affected by different stages in the deployment cycle.
