Journal of Medical Internet Research最新文献

Background: Living evidence (LE) synthesis refers to the method of continuously updating systematic evidence reviews to incorporate new evidence. It has emerged to address the limitations of the traditional systematic review process, particularly the absence of or delays in publication updates. The emergence of COVID-19 accelerated the progress in the field of LE synthesis, and currently, the applications of artificial intelligence (AI) in LE synthesis are expanding rapidly. However, in which phases of LE synthesis should AI be used remains an unanswered question.

Objective: This study aims to (1) document the phases of LE synthesis where AI is used and (2) investigate whether AI improves the efficiency, accuracy, or utility of LE synthesis.

Methods: We searched Web of Science, PubMed, the Cochrane Library, Epistemonikos, the Campbell Library, IEEE Xplore, medRxiv, COVID-19 Evidence Network to support Decision-making, and McMaster Health Forum. We used Covidence to facilitate the monthly screening and extraction processes to maintain the LE synthesis process. Studies that used or developed AI or semiautomated tools in the phases of LE synthesis were included.

Results: A total of 24 studies were included, including 17 on LE syntheses, with 4 involving tool development, and 7 on living meta-analyses, with 3 involving tool development. First, a total of 34 AI or semiautomated tools were involved, comprising 12 AI tools and 22 semiautomated tools. The most frequently used AI or semiautomated tools were machine learning classifiers (n=5) and the Living Interactive Evidence synthesis platform (n=3). Second, 20 AI or semiautomated tools were used for the data extraction or collection and risk of bias assessment phase, and only 1 AI tool was used for the publication update phase. Third, 3 studies demonstrated the improvement in efficiency achieved based on time, workload, and conflict rate metrics. Nine studies applied AI or semiautomated tools in LE synthesis, obtaining a mean recall rate of 96.24%, and 6 studies achieved a mean F1-score of 92.17%. Additionally, 8 studies reported precision values ranging from 0.2% to 100%.

Conclusions: AI and semiautomated tools primarily facilitate data extraction or collection and risk of bias assessment. The use of AI or semiautomated tools in LE synthesis improves efficiency, leading to high accuracy, recall, and F1-scores, while precision varies across tools.

Unlabelled: Trauma-informed approaches can promote the creation of systems that prioritize safety and empowerment to improve patient well-being. These approaches are especially important in sexual and reproductive health care, where patients are often asked to disclose sensitive and personal information. This disclosure is particularly relevant in the context of endometriosis, a condition that affects 10% of reproductive-aged women and causes debilitating pelvic pain. Our team led a trauma-informed social media campaign to raise awareness and improve the understanding of endometriosis by sharing research findings from a photovoice study focusing on Asian women's experiences of endometriosis during the COVID-19 pandemic in Canada (EndoPhoto Study). In this paper, we describe how we adapted and applied trauma-informed approaches to the development and implementation of the social media campaign. To do this, we followed five adapted trauma-informed principles: (1) support and collaboration, (2) trustworthiness and transparency, (3) safety, (4) empowerment and voice, and (5) cultural and gender sensitivity, and four steps: (1) frame the campaign, (2) create content and manage the campaign, (3) measure campaign impact, and (4) conduct postcampaign reflections. We co-designed this campaign with patient partners having lived experience of endometriosis to facilitate support and collaboration. Additionally, we shared details about the funders of this study to increase trust and transparency, moderated comments and deidentified images to promote participant safety, chose safer platforms to enhance empowerment and voice, avoided stereotypes, and shared authentic experiences of Asian women with endometriosis to support cultural and gender sensitivity. The campaign launched on Instagram and Pinterest in March 2025 to coincide with Endometriosis Awareness Month. The social media campaign received 8,540,528 total impressions over the course of the month and had engagement rates of 6.23% and 1.4% on Instagram and Pinterest, respectively.

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Background: Artificial intelligence (AI)-enabled clinical decision support systems (CDSSs) are increasingly embedded within electronic health record (EHR) environments; however, their introduction can disrupt existing workflows and raise patient safety concerns, particularly in high-stakes settings such as surgical transfusion. Limited qualitative evidence exists regarding how frontline professionals anticipate the clinical, organizational, and workflow implications of such systems before wider deployment.

Objective: This study aims to qualitatively examine the anticipated clinical, organizational, and workflow-level implications of implementing personalized Maximum Surgical Blood Order Schedule-Thoracic Surgery (pMSBOS-TS), an AI-enabled CDSS for personalized surgical blood ordering, before large-scale deployment.

Methods: We conducted a consensual qualitative study with 14 multidisciplinary health care professionals involved in transfusion-related tasks at a large tertiary hospital. Following 1 pilot focus group to refine the interview guide and workflow diagram, 2 semistructured focus group discussions were held with 14 participants (5 physicians, 6 nurses, and 3 blood bank staff). Transcripts were analyzed using the Systems Engineering Initiative for Patient Safety (SEIPS) 101 framework, focusing on People, Environment, Tools, and Tasks, and were supported by task- and workflow-based analyses of transfusion processes. Member checking was conducted with participants and external clinicians to enhance validity.

Results: A total of 189 semantic units and 61 core ideas were identified across 18 subdomains and 7 overarching domains. Participants anticipated that pMSBOS-TS could reduce unwarranted variation in blood ordering and planning, provided that algorithmic performance is reliable and the interface is tightly integrated into existing EHR workflows. At the same time, they expressed concerns regarding increased verification burden, system limitations in unexpected clinical scenarios, and potential communication bottlenecks between clinical units and the blood bank. Organizational culture, governance structures, and local transfusion logistics were viewed as critical determinants of whether the system would reduce or inadvertently increase workload and blood product waste.

Conclusions: This preimplementation, SEIPS-based qualitative evaluation suggests that the successful adoption of an AI-enabled transfusion CDSS depends not only on predictive performance but also on sociotechnical readiness, including user trust, workflow fit, and organizational support. These findings provide practice-based insights to inform staged implementation, training, and governance strategies aimed at safely integrating predictive transfusion CDSSs into EHR-supported surgical workflows.