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Development of a Predictive Dashboard With Prescriptive Decision Support for Falls Prevention in Residential Aged Care: User-Centered Design Approach

Development of a Predictive Dashboard With Prescriptive Decision Support for Falls Prevention in Residential Aged Care: User-Centered Design Approach

We followed an iterative process to co-design the dashboard, with intended dashboard end users (ie, RACF staff, general practitioners, and consumers at RACFs), along with domain experts in the field of falls prevention and management. Figure 1 highlights the dashboard development process followed during the study. The stakeholders were involved continuously in Stage A and Stage B through regular discussions, interviews, and workshops, which will be described in detail elsewhere.

S Sandun Malpriya Silva, Nasir Wabe, Amy D Nguyen, Karla Seaman, Guogui Huang, Laura Dodds, Isabelle Meulenbroeks, Crisostomo Ibarra Mercado, Johanna I Westbrook

JMIR Aging 2025;8:e63609

Patient and Clinician Perspectives on Alert-Based Remote Monitoring–First Care for Cardiovascular Implantable Electronic Devices: Semistructured Interview Study Within the Veterans Health Administration

Patient and Clinician Perspectives on Alert-Based Remote Monitoring–First Care for Cardiovascular Implantable Electronic Devices: Semistructured Interview Study Within the Veterans Health Administration

Field notes were taken during both sets of interviews to summarize key points and supplemented with transcribed interview recordings to ensure accuracy. There were no repeat interviews. Reflexive thematic analysis [17,18] of interview field notes and transcripts was used to elucidate veteran and clinician views about RM-first care.

Allison Kratka, Thomas L Rotering, Scott Munson, Merritt H Raitt, Mary A Whooley, Sanket S Dhruva

JMIR Cardio 2025;9:e66215

Considering Theory-Based Gamification in the Co-Design and Development of a Virtual Reality Cognitive Remediation Intervention for Depression (bWell-D): Mixed Methods Study

Considering Theory-Based Gamification in the Co-Design and Development of a Virtual Reality Cognitive Remediation Intervention for Depression (bWell-D): Mixed Methods Study

The application of gamification to mental health domains is still a new research field with limited studies [46] that tend to be heterogeneous [47]. The results are inconsistent; for example, it was observed that its application to web-based interventions did not lead to a significant increase in adherence [48]. There is also a lack of comparison between gamified and nongamified interventions, which makes it difficult to evaluate the impact of such features on adherence and engagement.

Mark Hewko, Vincent Gagnon Shaigetz, Michael S Smith, Elicia Kohlenberg, Pooria Ahmadi, Maria Elena Hernandez Hernandez, Catherine Proulx, Anne Cabral, Melanie Segado, Trisha Chakrabarty, Nusrat Choudhury

JMIR Serious Games 2025;13:e59514

Impact of Bottom-Up Cocreation of Nursing Technological Innovations: Explorative Interview Study Among Hospital Nurses and Managers

Impact of Bottom-Up Cocreation of Nursing Technological Innovations: Explorative Interview Study Among Hospital Nurses and Managers

A pilot test was conducted to refine the interview guide and involved expert assessment and field testing with one participant. After the interviews, the first author created memos recording thoughts that arose due to the interviews [19]. The interviews were audio recorded and conducted by Sv S, a nurse with no prior experience at the hospital where the research took place. Sv S was trained in conducting interviews.

Saskia van Steenis, Onno Helder, Helianthe S M Kort, Thijs van Houwelingen

JMIR Hum Factors 2025;12:e60543

Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review

Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review

Characteristics of studies included in the review. a LLM: large language model. b NER: named entity recognition. c Bi-LSTM-CRF: bidirectional-long short term memory-conditional random field. d AUROC: area under the receiver operating characteristic. e AUPRC: area under the precision-recall curve. f APHE: hyperintense enhancement in the arterial phase. g PDPH: hypointense in the portal and delayed phases. h PMK-EN: Prior Medical Knowledge-English Prompt i ROC: receiver operating characteristic.

David Chen, Saif Addeen Alnassar, Kate Elizabeth Avison, Ryan S Huang, Srinivas Raman

JMIR Cancer 2025;11:e65984