Potential of digitalization within physiotherapy: a comparative survey

BACKGROUND Due to the global digitalization, implementation of digital elements into daily work can support physiotherapists' work but may also pose some challenges. Only little is known about physiotherapists' attitude towards digitalization. This study primarily aimed to analyze physiotherapists' attitude towards digitalization and to what extend digital tools have been implemented into their daily work. In second analysis, participants' characteristics such as age, working place, gender and mode of survey participation were assessed. METHODS A 12-main-item survey amongst voluntary course participants of one physiotherapeutic training center was conducted via paper-based as well as online questionnaires between July 2018 and June 2019 including questions on participants' general as well as particular attitude towards digitalization, the use of (mobile) applications and possible advantages and disadvantages of the ongoing digital transformation. Sub-analysis was performed for age (≤40 years versus > 40 years), gender, mode of participation (paper vs. online) and working place (practice vs. hospital). RESULTS Overall, 488 physiotherapists participated in the survey. In comparison of the age groups, younger participants had more concerns about data security (p = 0.042) and insufficient financial remuneration (p < 0.001). Younger participants stated higher satisfaction with data literacy than their counterparts (p = 0.0001). Physiotherapists working in the outpatient sector, rather than in hospitals, expected digitalization to increase more in relevance (p < 0.001). The online respondents (OG) indicated that they had more knowledge about key aspects of the current legal situation regarding digitalization than participants completing the paper-based survey (p = 0.002). 50.4% of the considered digitalization as useful for their job. CONCLUSIONS The majority of participants saw high potential for digitalization in the physiotherapy sector. Younger physiotherapists seem to be more concerned about data security and insufficient financial remuneration. Physiotherapists in the outpatient sector seem to see more potential in digital transformations. General concerns like missing reimbursement, lack of data security or knowledge on legal frameworks should be addressed in the future. Further studies should focus on identifying specific digital tools which can support physiotherapists

Use of artificial intelligence in sports medicine: a report of 5 fictional cases

Background Artificial intelligence (AI) is one of the most promising areas in medicine with many possibilities for improving health and wellness. Already today, diagnostic decision support systems may help patients to estimate the severity of their complaints. This fictional case study aimed to test the diagnostic potential of an AI algorithm for common sports injuries and pathologies. Methods Based on a literature review and clinical expert experience, five fictional “common” cases of acute, and subacute injuries or chronic sport-related pathologies were created: Concussion, ankle sprain, muscle pain, chronic knee instability (after ACL rupture) and tennis elbow. The symptoms of these cases were entered into a freely available chatbot-guided AI app and its diagnoses were compared to the pre-defined injuries and pathologies. Results A mean of 25–36 questions were asked by the app per patient, with optional explanations of certain questions or illustrative photos on demand. It was stressed, that the symptom analysis would not replace a doctor’s consultation. A 23-yr-old male patient case with a mild concussion was correctly diagnosed. An ankle sprain of a 27-yr-old female without ligament or bony lesions was also detected and an ER visit was suggested. Muscle pain in the thigh of a 19-yr-old male was correctly diagnosed. In the case of a 26-yr-old male with chronic ACL instability, the algorithm did not sufficiently cover the chronic aspect of the pathology, but the given recommendation of seeing a doctor would have helped the patient. Finally, the condition of the chronic epicondylitis in a 41-yr-old male was correctly detected. Conclusions All chosen injuries and pathologies were either correctly diagnosed or at least tagged with the right advice of when it is urgent for seeking a medical specialist. However, the quality of AI-based results could presumably depend on the data-driven experience of these programs as well as on the understanding of their users. Further studies should compare existing AI programs and their diagnostic accuracy for medical injuries and pathologies.Peer Reviewe