Patient perspectives on data sharing regarding implementing and using artificial intelligence in general practice - a qualitative study.

BACKGROUND: Due to more elderly and patients with complex illnesses, there is an increasing pressure on the healthcare system. General practice especially feels this pressure as being the first point of contact for the patients. Developments in digitalization have undergone fast progress and data-driven artificial intelligence (AI) has shown great potential for use in general practice. To develop AI as a support tool for general practitioners (GPs), access to patients' health data is needed, but patients have concerns regarding data sharing. Furthermore, studies show that trust is important regarding the patient-GP relationship, data sharing, and AI. The aim of this paper is to uncover patient perspectives on trust regarding the patient-GP relationship, data sharing and AI in general practice. METHOD: This study investigated 10 patients' perspectives through qualitative interviews and written vignettes were chosen to elicit the patients (interviewees) perspectives on topics that they were not familiar with prior to the interviews. The study specifically investigated perspectives on 1) The patient-GP relationship, 2) data sharing regarding developing AI for general practice, and 3) implementation and use of AI in general practice using thematic analysis. The study took place in the North Denmark Region and the interviewees included had to be registered in general practice and be above 18 years in age. We included four men between 25 to 74 years in age and six women between 27 to 46 years in age. RESULTS: The interviewees expressed a high level of trust towards their GP and were willing to share their health data with their GP. The interviewees believed that AI could be a great help to GPs if used as a support tool in general practice. However, it was important for the interviewees that the GP would still be the primary decision maker. CONCLUSION: Patients may be willing to share health data to help implement and use AI in general practice. If AI is implemented in a way that preserves the patient-GP relationship and used as a support tool for the GP, our results indicate that patients may be positive towards the use of AI in general practice.

Machine learning in general practice: scoping review of administrative task support and automation.

BACKGROUND: Artificial intelligence (AI) is increasingly used to support general practice in the early detection of disease and treatment recommendations. However, AI systems aimed at alleviating time-consuming administrative tasks currently appear limited. This scoping review thus aims to summarize the research that has been carried out in methods of machine learning applied to the support and automation of administrative tasks in general practice. METHODS: Databases covering the fields of health care and engineering sciences (PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEE Xplore) were searched. Screening for eligible studies was completed using Covidence, and data was extracted along nine research-based attributes concerning general practice, administrative tasks, and machine learning. The search and screening processes were completed during the period of April to June 2022. RESULTS: 1439 records were identified and 1158 were screened for eligibility criteria. A total of 12 studies were included. The extracted attributes indicate that most studies concern various scheduling tasks using supervised machine learning methods with relatively low general practitioner (GP) involvement. Importantly, four studies employed the latest available machine learning methods and the data used frequently varied in terms of setting, type, and availability. CONCLUSION: The limited field of research developing in the application of machine learning to administrative tasks in general practice indicates that there is a great need and high potential for such methods. However, there is currently a lack of research likely due to the unavailability of open-source data and a prioritization of diagnostic-based tasks. Future research would benefit from open-source data, cutting-edge methods of machine learning, and clearly stated GP involvement, so that improved and replicable scientific research can be done.

Implementing patient-reported outcomes in clinical decision-making within knee and hip osteoarthritis: an explorative review.

BACKGROUND: In the past few decades, there has been an increasing focus on the importance of patient involvement in the health care system. Patient participation executed through patient-reported outcomes (PROs) and the integration of such into clinical practice has been framed as positive for patients, care providers, and the health care system as a whole. This review aims to elucidate and discuss the current and future use of PROs in clinical practice and to identify the most common types of PRO measures (PROMs) used for patients with hip or knee osteoarthritis in different treatment settings. METHODS: The following databases were searched: PubMed, Embase, CINAHL, Scopus, the Cochrane Library, and EconLit. For inclusion in the study, studies had to cover either knee or hip osteoarthritis and report on PROs. The type of PROM, treatment setting, and study design of each included study were extracted from their respective abstracts. Additionally, the full text of studies concerning PROs as an integrated part of clinical practice was examined and information on the year of publication, study design, topic, and use of PROMs was extracted. RESULTS: It was found that only two pilot studies reported on the use of PROs as an integrated part of patient treatment within hip or knee osteoarthritis. In 349 studies, a total of 38 different PROMs relevant for patients with either hip or knee osteoarthritis were identified. The EQ-5D, WOMAC, and VAS questionnaires were the most commonly reported generic, disease-specific, and domain-specific PROMs, respectively. However, a large variation in the use of different PROMs both within and between surgical and nonsurgical settings was found. CONCLUSION: Limited evidence on the use of PROs as an integrated part of clinical practice for patients with hip and knee osteoarthritis was found. Further research is necessary to clarify the effects on patient outcomes of using PROs in clinical practice. In addition, there is limited agreement on a joint standard for the use of PROMs both within and across the sectorial boarders. Further exploration of PROMs to generate future standardisation is suggested.