Background: The shift in medical care toward prediction and prevention has led to the emergence of digital health care as a valuable tool for managing health issues. Aiding long-term follow-up care for cancer survivors and contributing to improved survival rates. However, potential barriers to mobile health usage, including age-related disparities and challenges in user retention for commercial health apps, highlight the need to assess the impact of patients’ abilities and health status on the adoption of these interventions. Objective: This study aims to investigate the app adherence and user experience of commercial health care apps among cancer survivors using an extended technology acceptance model (TAM). Methods: The study enrolled 264 cancer survivors. We collected survey results from May to August 2022 and app usage records from the app companies. The survey questions were created based on the TAM. Results: We categorized 264 participants into 3 clusters based on their app usage behavior: short use (n=77), medium use (n=101), and long use (n=86). The mean usage days were 9 (SD 11) days, 58 (SD 20) days, and 84 (SD 176) days, respectively. Analysis revealed significant differences in perceived usefulness (P=.01), interface satisfaction (P<.01), equity (P<.01), and utility (P=.01) among the clusters. Structural equation modeling indicated that perceived ease-of-use significantly influenced perceived usefulness (β=0.387, P<.01), and both perceived usefulness and attitude significantly affected behavioral intention and actual usage. Conclusions: This study showed the importance of positive user experience and clinician recommendations in facilitating the effective usage of digital health care tools among cancer survivors and contributing to the evolving landscape of medical care.

Park YE, et al. J Korean Med Sci. 2022 Nov;37(43):e316. https://doi.org/10.3346/jkms.2022.37.e316

Patient-generated health data (PGHD) enhance traditional healthcare by enabling continuous monitoring and supporting personalized care, yet concerns over privacy, security, and integration into existing systems hinder broader adoption. This study examined the perceptions, awareness, and concerns of patients and caregivers regarding PGHD and assessed their willingness to share such data for clinical, research, and commercial purposes. A cross-sectional survey was conducted from 6 to 12 November 2023, involving 400 individuals with experience using PGHD. Participants completed structured questionnaires addressing health information management, PGHD usage, and attitudes toward its application. PGHD was most commonly used by patients with chronic conditions and guardians of minors, with tethered personal health record apps frequently utilized. Respondents identified improved self-management and better access to information as key benefits. However, significant concerns about data privacy and security emerged, especially regarding non-clinical use. Younger adults, particularly those in their 20s, showed lower willingness to engage with PGHD due to heightened privacy concerns. These findings suggest that, while support for clinical use of PGHD is strong, barriers related to trust and consent remain. Addressing privacy concerns and simplifying consent processes will be essential to promote equitable and responsible PGHD utilization across diverse patient populations.

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.

BACKGROUND The shift in medical care toward prediction and prevention has led to the emergence of digital health care as a valuable tool for managing health issues. Aiding long-term follow-up care for cancer survivors and contributing to improved survival rates. However, potential barriers to mobile health usage, including age-related disparities and challenges in user retention for commercial health apps, highlight the need to assess the impact of patients’ abilities and health status on the adoption of these interventions. OBJECTIVE This study aims to investigate the app adherence and user experience of commercial health care apps among cancer survivors using an extended technology acceptance model (TAM). METHODS The study enrolled 264 cancer survivors. We collected survey results from May to August 2022 and app usage records from the app companies. The survey questions were created based on the TAM. RESULTS We categorized 264 participants into 3 clusters based on their app usage behavior: short use (n=77), medium use (n=101), and long use (n=86). The mean usage days were 9 (SD 11) days, 58 (SD 20) days, and 84 (SD 176) days, respectively. Analysis revealed significant differences in perceived usefulness (P=.01), interface satisfaction (P<.01), equity (P<.01), and utility (P=.01) among the clusters. Structural equation modeling indicated that perceived ease-of-use significantly influenced perceived usefulness (β=0.387, P<.01), and both perceived usefulness and attitude significantly affected behavioral intention and actual usage. CONCLUSIONS This study showed the importance of positive user experience and clinician recommendations in facilitating the effective usage of digital health care tools among cancer survivors and contributing to the evolving landscape of medical care.

Patient education is crucial for understanding medical conditions, treatment options, and self-management techniques. Digital technology has revolutionised patient education, providing efficient and cost-effective alternatives to traditional methods. However, challenges persist in customising educational materials to specific clinical settings. We investigated the relationship between healthcare terminology standards and the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT) dictionary. Our aim was to provide practical mapping guidance and case examples to help users better understand how to map patient education materials (PEM) to SNOMED CT to generate consistent results across different institutions. We mapped the titles of PEM in a single hospital setting using SNOMED CT terms. We focused on materials made between October 1, 2018, and November 30, 2022, and found 1,271 matchable PEM titles. Keywords were identified and sorted into categories like Target, Time, Condition, and Procedure. Two researchers worked independently to match these keywords with SNOMED CT, and any differences were resolved through discussion or third-party review. Out of 1,271 PEM titles that were assigned to categories, only 3.9% remained unmapped. Most of the mapped titles were related to Procedure (93.3%), Condition (56.8%), Time (17.5%), and Target (12%); 2,688 keywords were mapped, with 62.4% single-mapped and 36.9% were multi-mapped. Among the keywords mapped to various categories, Procedures accounted for the highest proportion (49.4%). Examining the keywords mapped to each SNOMED CT concept in different categories provided valuable insights into how terms are distributed and improved our understanding of the specific terms used in PEM. When educational materials are mapped to standard clinical terminologies and integrated into the Electronic Health Record (EHR), patients receive personalised and relevant health information, potentially improving engagement and health outcomes. This approach may reduce the workload of healthcare personnel, reduce miscommunication, and improve overall healthcare efficiency.