Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
JMIR cardio
Experiences of Patients With Atrial Fibrillation Using Technology to Personalize Self-Care Decision-Making: Interpretive Description Study
BACKGROUND: Atrial fibrillation (AF) is the most common sustained heart rhythm disorder and is a challenging chronic disease to manage. Patients' daily self-care decisions are associated with improved AF outcomes, quality of life, and decreased hospital use and cost. However, many patients find these real-world or naturalistic decisions difficult, often because of their inherent complexity and ambiguity, coupled with the uncertainty of AF. Intervention research using technology to support AF self-care has largely emphasized making decisions with clinicians. Patients with AF are increasingly using consumer technology; yet, little is known about the use of technology by patients with AF in independent self-care decision-making. Addressing this gap will facilitate developing interventions that better leverage technology to enhance patients' naturalistic decision-making. OBJECTIVE: This study aimed to explore the experiences of older adult patients in using technology to support self-care decision-making. METHODS: Following an interpretive descriptive qualitative approach, older adult patients with AF were recruited from 3 specialty heart function clinics in a Western Canadian province to participate in 1 of 6 facilitated virtual focus groups for 1.5 hours. Patients were asked about their self-care decision-making since AF diagnosis, their AF-specific technology use and its use in making self-care decisions, their technology motivations, benefits, constraints, and other possibilities for use. Inductive thematic analysis was used to code the transcribed data, moving from open coding to clustering of common codes into categories, looking for patterns of meaning between and across categories to iteratively arrive at main themes and subthemes. RESULTS: Thirty patients (n=15, 50% women) with AF (mean age, 73, SD 5.7 years; range 63-85 years) participated in the focus groups. Participants' experiences of using technology to make daily self-care decisions were highly variable but centered on its personalized use to meet their individualized needs, preferences, and life context. The personalizing process of technology use in decision-making was characterized by three themes: (1) beginning technology use in their own times and ways, during their AF trajectory-pre-, at the time, at some point AF post diagnosis, and could be either self-initiated and/or provider recommended or influenced; (2) developing patterns of AF self-care decision-making using technology, including establishing their personal baseline, keeping out of the danger zone, watchful waiting, and seeking decision-making support; and (3) finding the place for technology in normalizing daily life, either settling on or limiting its use to normalize life. CONCLUSIONS: Findings expand understandings of naturalistic decision-making by elucidating the personalized process of technology use in AF self-care.
13 June 2026
Read appraisal →Journal of medical Internet research
Human-AI Interaction in Kidney Transplant Decision Support Systems: Qualitative Study of Patient and Support Person Expectations
BACKGROUND: Artificial intelligence (AI) is increasingly applied in medicine, including clinical decision-making. AI-based decision support systems (DSS) can enhance early risk detection and treatment optimization. However, the perspectives of patients and their support persons on AI-assisted DSS in clinical care, particularly regarding shared decision-making (SDM), remain underexplored. OBJECTIVE: This study investigates the expectations, informational needs, and perceptions of patients who underwent kidney transplantation and their support persons regarding AI-assisted DSS and its influence on SDM in posttransplant care. METHODS: In a longitudinal qualitative study, 36 semistructured interviews were conducted with patients who underwent kidney transplantation and their support persons at a German kidney transplant center. Participants were asked about their views on AI's role in follow-up care, its impact on communication, trust, and decision-making, as well as their informational needs regarding AI-assisted DSS. Interviews were transcribed, pseudonymized, and analyzed using framework analysis. RESULTS: Participants recognized AI's potential to support clinicians by identifying risks of transplant loss, rejection, and infection, and by providing data-driven treatment recommendations. However, they emphasized that final decisions should remain with physicians. A majority of participants (n=28, 78%) expressed concern that AI might depersonalize care and diminish physician-patient communication due to a lack of "human touch." Participants demonstrated limited understanding of AI-based DSS functionality and highlighted the need for simple, accessible educational materials (eg, leaflets) explaining AI operations. While most doubted AI could replicate human empathy, some acknowledged that AI might be perceived as more attentive than time-pressured physicians, offering consistent monitoring and support. Participants consistently stressed that AI should augment, not replace, clinical decision-making. CONCLUSIONS: Patients who underwent kidney transplantation and support persons endorse the integration of AI in follow-up care when it enhances clinical decision-making without supplanting the physician's role. Acceptance and trust depend on transparency, accountability, and preserving the "human touch" in care. The development of educational tools to communicate AI functions and limitations is crucial to empower patients and support persons in SDM processes and to ensure AI complements, rather than undermines, patient-centered care.
23 May 2026
Read appraisal →Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation
Health-related quality of life domains relevant to people in Europe undergoing cancer treatment: a systematic review of qualitative research
PURPOSE: To identify and synthesize evidence from European qualitative studies on cancer-related quality of life outcomes, needs, experiences, preferences, and concerns of people undergoing cancer treatment in the last decade. METHODS: Systematic review ( https://www.crd.york.ac.uk/PROSPERO , CRD42024575065) of European studies using qualitative methodology, assessing constructs related to HRQoL, and involving adults receiving cancer treatment. The search was performed in PubMed and Scopus from January 2013 to July 2024. Titles, abstracts, and full texts screening, data extraction and risk of bias assessment were conducted independently by two researchers. The main outcomes were the themes reported in each study. The thematic analysis was performed by organizing the themes of the studies into categories. RESULTS: Out of 18,256 articles initially identified, 36 met the inclusion criteria: 21 with generic and 15 with specific objectives. Five categories encompassing 110 themes were identified from the generic studies: Psychological Function (n = 41), Clinical Management (n = 26), Symptoms and Physical Function (n = 18), Social Function (n = 16), and Life Disruption (n = 9). Eleven studies with specific objectives focused on clinical management with all their themes fitting within the categories identified in the generic studies. CONCLUSIONS: Results showed the predominance of psychological function and clinical management themes. Symptoms and physical function, social function, and life disruption maintained their importance within the classical HRQoL framework. The emergence of clinical management is consistent with the growing patient-centered care approach, suggesting the need to integrate this content into the evaluation of patients undergoing cancer treatment. LIMITATIONS: most European countries were not represented, and publication bias could hide traditional domains.
15 May 2026
Read appraisal →Journal of medical Internet research
Exploring Patient Perspectives on the Use of Artificial Intelligence to Inform Joint Decision-Making for Patients With Multiple Conditions in Primary Care in the United Kingdom: Qualitative Study
BACKGROUND: Multimorbidity, living with 2 or more long-term health conditions, is increasing globally and now affects over one-quarter of adults in England. People with multiple long-term conditions (MLTC) face complex health and treatment challenges, often experiencing fragmented care within systems oriented toward single-disease management. Artificial intelligence (AI) has the potential to support clinicians and patients by analyzing complex health data, optimizing treatment strategies, and predicting disease trajectories. OBJECTIVE: The OPTIMAL (Optimizing Therapies, Disease Trajectories, and AI-Assisted Clinical Management for Patients Living with Complex Multimorbidity) project aims to develop AI-enabled tools to support shared decision-making in primary care. This study explored how patients with MLTC perceive the use of AI to inform joint decision-making in primary care. METHODS: Semistructured interviews were conducted via telephone or video call with 29 adults living with MLTC between July and November 2023. Participants were recruited through general practitioner practices via the Clinical Practice Research Datalink and community-based organizations across the West Midlands. Interviews were transcribed verbatim and analyzed thematically using an inductive approach. Members of a patient advisory group were involved in developing study materials, refining the interview guide, and reviewing emerging findings to ensure relevance and authenticity. RESULTS: Participants identified potential benefits of AI in enhancing consultation efficiency and accuracy, improving access to information for patients and clinicians, promoting early detection of health changes, and reducing health care inequalities. However, concerns were raised about the loss of human interaction, data privacy and security, transparency of algorithms, and the potential for bias and inequity in AI systems. Trust and acceptance varied by age and familiarity with technology. Some participants expressed uncertainty about what AI entails and how it could be used in primary care. CONCLUSIONS: Patients with MLTC viewed AI-assisted decision-making in primary care with cautious optimism. While many recognized potential benefits for coordination and personalization of care, others expressed reservations about privacy, fairness, and the risk of diminished human connection.
24 Apr 2026
Read appraisal →Acta orthopaedica
Effect of an in-consult patient decision aid on decisional quality and involvement for patients with severe hip or knee osteoarthritis: a multicenter, cluster randomized controlled trial
BACKGROUND AND PURPOSE: Shared decision-making (SDM) is important in managing patients with severe hip and knee osteoarthritis, where treatment decisions often depend on patient preferences and values. We aimed to evaluate the effectiveness of an in-consult patient decision aid in improving decision quality and patient involvement compared with usual care. METHODS: In a cluster-randomized controlled trial, surgeons from 2 Danish orthopedic departments were randomized to use in-consult patient decision aids or provide usual care. The primary outcome was decision quality assessed using the Hip/Knee OA Decision Quality Instrument (HK-DQI). Patients who were well informed and received their preferred treatment received an Informed Patient-Centered (IPC) score of 1, indicating good decisional quality; all others scored 0. Secondary outcomes included patient-experienced engagement and involvement in decision-making, assessed using CollaboRATE and the decision-process domain of the Hip/Knee OA Decision Quality Instrument and consultation duration. Relative risks (RR) and mean differences (MD) with 95% confidence intervals (CIs) were estimated using multilevel regression models. RESULTS: 20 surgeons were randomized to either use in-consult patient decision aids (n = 10) or provide usual care (n = 10). Of the 1,038 patients screened for severe hip or knee osteoarthritis, 581 were included in the study. Decision quality was similar between groups, with a score of 43 (36-51) in the intervention group and 44 (37-52) in the usual care group (RR 0.9, CI 0.7-1.2; P = 0.8), likewise, patient-experienced engagement assessed with CollaboRATE (RR 1.1, CI 0.8-1.4; P = 0.6). Consultations were longer in the intervention group, with a mean difference of 3.9 minutes (CI 0.1-7.7; P = 0.04). CONCLUSION: Decision quality and patient-reported engagement were similar between groups, while consultation time was longer with in-consult patient decision aids use. In the absence of prespecified minimal important differences, the clinical relevance of these findings remains uncertain.
10 Apr 2026
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