Background: Burnout among clinicians, including physicians, is a growing concern in healthcare. An overwhelming burden of clinical documentation is a significant contributor. While medical scribes have been employed to mitigate this burden, they have limitations such as cost, training needs, and high turnover rates. Artificial intelligence (AI) scribe systems can transcribe, summarize, and even interpret clinical conversations, offering a potential solution for improving clinician well-being. We aimed to evaluate the effectiveness of AI scribes in streamlining clinical documentation, with a focus on clinician experience, healthcare system efficiency, and patient engagement. Methods: We conducted a systematic review following Cochrane methods and PRISMA guidelines. Two reviewers conducted the selection process independently. Eligible intervention studies included quantitative and mixed-methods studies evaluating AI scribe systems. We summarized the data narratively. Results: Eight studies were included. AI scribes demonstrated positive effects on healthcare provider engagement, with users reporting increased involvement in their workflows. The documentation burden showed signs of improvement, as AI scribes helped alleviate the workload for some participants. Many clinicians have found AI systems to be user-friendly and intuitive, although some have expressed concerns about scribe training and documentation quality. A limited impact on reducing burnout was found, although documentation time improved in some studies. Conclusions: Most of the studies reported in this review involved small sample sizes and specific healthcare settings, limiting the generalizability of the findings to other contexts. Accuracy and consistency can vary significantly depending on the specific technology, model training data, and implementation approach. AI scribes show promise in improving documentation efficiency and clinician workflow, although the evidence remains limited and heterogeneous. Broader and real-world evaluations are needed to confirm their effectiveness and inform responsible implementations.

Background: People with chronic diseases can benefit from wearable devices in managing their health and encouraging healthy lifestyle habits. Wearables such as activity trackers or blood glucose monitoring devices can lead to positive health impacts, including improved physical activity adherence or better management of type 2 diabetes. Few literature reviews have focused on the intersection of various chronic diseases, the wearable devices used, and the outcomes evaluated in intervention studies, particularly in the context of primary health care. Objective: This study aims to identify and describe (1) the chronic diseases represented in intervention studies, (2) the types or combinations of wearables used, and (3) the health or health care outcomes assessed and measured. Methods: We conducted a scoping review following the Joanna Briggs Institute guidelines, searching the MEDLINE and Web of Science databases for studies published between 2012 and 2022. Pairs of reviewers independently screened titles and abstracts, applied the selection criteria, and performed full-text screening. We included interventions using wearables that automatically collected and transmitted data to adult populations with at least one chronic disease. We excluded studies with participants with only a predisposition to develop a chronic disease, hospitalized patients, patients with acute diseases, patients with active cancer, and cancer survivors. We included randomized controlled trials and cohort, pretest-posttest, observational, mixed methods, and qualitative studies. Results: After the removal of 1987 duplicates, we screened 4540 titles and abstracts. Of the remaining 304 articles after exclusions, we excluded 215 (70.7%) full texts and included 89 (29.3%). Of these 89 texts, 10 (11%) were related to the same interventions as those in the included studies, resulting in 79 studies being included. We structured the results according to chronic disease clusters: (1) diabetes, (2) heart failure, (3) other cardiovascular conditions, (4) hypertension, (5) multimorbidity and other combinations of chronic conditions, (6) chronic obstructive pulmonary disease, (7) chronic pain, (8) musculoskeletal conditions, and (9) asthma. Diabetes was the most frequent health condition (18/79, 23% of the studies), and wearable activity trackers were the most used (42/79, 53% of the studies). In the 79 included studies, 74 clinical, 73 behavioral, 36 patient technology experience, 28 health care system, and 25 holistic or biopsychosocial outcomes were reported. Conclusions: This scoping review provides an overview of the wearable devices used in chronic disease self-management intervention studies, revealing disparities in both the range of chronic diseases studied and the variety of wearable devices used. These findings offer researchers valuable insights to further explore health care outcomes, validate the impact of concomitant device use, and expand their use to other chronic diseases. Trial Registration: Open Science Framework Registries (OSF) s4wfm; https://osf.io/s4wfm

Background: Artificial intelligence (AI) predictive models in primary health care have the potential to enhance population health by rapidly and accurately identifying individuals who should receive care and health services. However, these models also carry the risk of perpetuating or amplifying existing biases toward diverse groups. We identified a gap in the current understanding of strategies used to assess and mitigate bias in primary health care algorithms related to individuals’ personal or protected attributes. Objective: This study aimed to describe the attempts, strategies, and methods used to mitigate bias in AI models within primary health care, to identify the diverse groups or protected attributes considered, and to evaluate the results of these approaches on both bias reduction and AI model performance. Methods: We conducted a scoping review following Joanna Briggs Institute (JBI) guidelines, searching Medline (Ovid), CINAHL (EBSCO), PsycINFO (Ovid), and Web of Science databases for studies published between January 1, 2017, and November 15, 2022. Pairs of reviewers independently screened titles and abstracts, applied selection criteria, and performed full-text screening. Discrepancies regarding study inclusion were resolved by consensus. Following reporting standards for AI in health care, we extracted data on study objectives, model features, targeted diverse groups, mitigation strategies used, and results. Using the mixed methods appraisal tool, we appraised the quality of the studies. Results: After removing 585 duplicates, we screened 1018 titles and abstracts. From the remaining 189 full-text articles, we included 17 studies. The most frequently investigated protected attributes were race (or ethnicity), examined in 12 of the 17 studies, and sex (often identified as gender), typically classified as “male versus female” in 10 of the studies. We categorized bias mitigation approaches into four clusters: (1) modifying existing AI models or datasets, (2) sourcing data from electronic health records, (3) developing tools with a “human-in-the-loop” approach, and (4) identifying ethical principles for informed decision-making. Algorithmic preprocessing methods, such as relabeling and reweighing data, along with natural language processing techniques that extract data from unstructured notes, showed the greatest potential for bias mitigation. Other methods aimed at enhancing model fairness included group recalibration and the application of the equalized odds metric. However, these approaches sometimes exacerbated prediction errors across groups or led to overall model miscalibrations. Conclusions: The results suggest that biases toward diverse groups are more easily mitigated when data are open-sourced, multiple stakeholders are engaged, and during the algorithm’s preprocessing stage. Further empirical studies that include a broader range of groups, such as Indigenous peoples in Canada, are needed to validate and expand upon these findings. Trial Registration: OSF Registry osf.io/9ngz5/; https://osf.io/9ngz5/ International Registered Report Identifier (IRRID): RR2-10.2196/46684

Background: Managing chronic diseases remains a critical challenge in primary health care (PHC) across the Organization for Economic Co-operation and Development countries. Electronic patient-reported outcome measures (ePROMs) are emerging as valuable tools for enhancing patient engagement, facilitating clinical decision-making, and improving health outcomes. However, their implementation in PHC remains limited, with significant variability in effectiveness and adoption. Objective: This systematic review aimed to assess the implementation and effectiveness of ePROMs in chronic disease management within PHC settings and to identify key barriers and facilitators influencing their integration. Methods: A mixed methods systematic review was conducted following the Cochrane Methods and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We included studies that implemented ePROMs among adults for chronic disease management in PHC. The extracted data included patient health outcomes, provider workflow implications, implementation factors, and cost considerations. The reach, effectiveness, adoption, implementation, and maintenance framework was used. Results: Our search yielded 12,525 references, from which 22 (0.18%) studies were included after screening and exclusions. These studies, primarily conducted in the United States (n=9, 41%) and Canada (n=8, 36%), covered various chronic diseases and used diverse ePROM tools, predominantly mobile apps (n=9, 41%). While some studies (n=10, 45%) reported improvements in patient health outcomes and self-management, others (n=12, 55%) indicated no significant change. Key barriers included digital literacy gaps, integration challenges within clinical workflows, and increased provider workload. Facilitators included strong patient-provider relationships, personalized interventions, and technical support for users. While some studies (n=10, 45%) demonstrated improved patient engagement and self-management, long-term cost-effectiveness and sustainability remain uncertain. Conclusions: Success in implementing ePROMs in PHC appears to hinge on addressing digital literacy, ensuring personalization and meaningful patient-provider interactions, carefully integrating technology into clinical workflows, and conducting thorough research on their long-term impacts and cost-effectiveness. Future efforts should focus on these areas to fully realize the benefits of digital health technologies for patients, providers, and health care systems. Trial Registration: PROSPERO CRD42022333513; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022333513 International Registered Report Identifier (IRRID): RR2-10.2196/48155

Background: Chronic diseases represent a significant global burden of mortality, exacerbated by behavioral risk factors. Artificial intelligence (AI) has transformed health promotion and disease reduction through improved early detection, encouraging healthy lifestyle modifications, and mitigating the economic strain on health systems. Objective: The aim of this study is to investigate how AI contributes to health promotion and disease reduction among Organization for Economic Co-operation and Development countries. Methods: We conducted a rapid review of the literature to identify the latest evidence on how AI is used in health promotion and disease reduction. We applied comprehensive search strategies formulated for MEDLINE (OVID) and CINAHL to locate studies published between 2019 and 2024. A pair of reviewers independently applied the inclusion and exclusion criteria to screen the titles and abstracts, assess the full texts, and extract the data. We synthesized extracted data from the study characteristics, intervention characteristics, and intervention purpose using structured narrative summaries of main themes, giving a portrait of the current scope of available AI initiatives used in promoting healthy activities and preventing disease. Results: We included 22 studies in this review (out of 3442 publications screened), most of which were conducted in the United States (10/22, 45%) and focused on health promotion by targeting lifestyle dimensions, such as dietary behavior (10/22, 45%), smoking cessation (6/22, 27%), physical activity (4/22, 18%), and mental health (3/22, 14%). Three studies targeted disease reduction related to metabolic health (eg, obesity, diabetes, hypertension). Most AI initiatives were AI-powered mobile apps. Overall, positive results were reported for process outcomes (eg, acceptability, engagement), cognitive and behavioral outcomes (eg, confidence, step count), and health outcomes (eg, glycemia, blood pressure). We categorized the challenges, benefits, and suggestions identified in the studies using a Strengths, Weaknesses, Opportunities, and Threats analysis to inform future developments. Key recommendations include conducting further investigations, taking into account the needs of end users, improving the technical aspect of the technology, and allocating resources. Conclusions: These findings offer critical insights into the effective implementation of AI for health promotion and disease prevention, potentially guiding policymakers and health care practitioners in optimizing the use of AI technologies in supporting health promotion and disease reduction.

Background: The current literature identifies several potential benefits of artificial intelligence models for populations’ health and health care systems' efficiency. However, there is a lack of understanding on how the risk of bias is considered in the development of primary health care and community health service artificial intelligence algorithms and to what extent they perpetuate or introduce potential biases toward groups that could be considered vulnerable in terms of their characteristics. To the best of our knowledge, no reviews are currently available to identify relevant methods to assess the risk of bias in these algorithms. The primary research question of this review is which strategies can assess the risk of bias in primary health care algorithms toward vulnerable or diverse groups? Objective: This review aims to identify relevant methods to assess the risk of bias toward vulnerable or diverse groups in the development or deployment of algorithms in community-based primary health care and mitigation interventions deployed to promote and increase equity, diversity, and inclusion. This review looks at what attempts to mitigate bias have been documented and which vulnerable or diverse groups have been considered. Methods: A rapid systematic review of the scientific literature will be conducted. In November 2022, an information specialist developed a specific search strategy based on the main concepts of our primary review question in 4 relevant databases in the last 5 years. We completed the search strategy in December 2022, and 1022 sources were identified. Since February 2023, two reviewers independently screened the titles and abstracts on the Covidence systematic review software. Conflicts are solved through consensus and discussion with a senior researcher. We include all studies on methods developed or tested to assess the risk of bias in algorithms that are relevant in community-based primary health care. Results: In early May 2023, almost 47% (479/1022) of the titles and abstracts have been screened. We completed this first stage in May 2023. In June and July 2023, two reviewers will independently apply the same criteria to full texts, and all exclusion motives will be recorded. Data from selected studies will be extracted using a validated grid in August and analyzed in September 2023. Results will be presented using structured qualitative narrative summaries and submitted for publication by the end of 2023. Conclusions: The approach to identifying methods and target populations of this review is primarily qualitative. However, we will consider a meta-analysis if quantitative data and results are sufficient. This review will develop structured qualitative summaries of strategies to mitigate bias toward vulnerable populations and diverse groups in artificial intelligence models. This could be useful to researchers and other stakeholders to identify potential sources of bias in algorithms and try to reduce or eliminate them. Trial Registration: OSF Registries qbph8; https://osf.io/qbph8 International Registered Report Identifier (IRRID): DERR1-10.2196/46684