Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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JMIR research protocols
An AI-Enabled Predictive Harm Response Management Algorithmic Tool to Reduce Adverse Events in Health Care Settings (PreHaRM): Protocol for a Three-Phase Model Development and Pilot-Testing Study
BACKGROUND: Current data analysis and coordination methods do not effectively support nurses and midwives in risk reduction, as retrospective reporting does not allow real-time insights and precludes proactive, preventive care. Analysis of administrative data within Australia's health care sector to predict risk may help address this shortcoming. Predictive analytics can transform these data into meaningful insights, identifying harm risk profiles that benefit the performance of Australian and international clinical programs. Importantly, these tools may support nurses and midwives in preventing adverse events and predicting high-risk situations. Researchers, in collaboration with local health network staff, will develop a proof-of-concept predictive risk algorithm. The "predictive harm response management algorithmic tool to reduce adverse events in healthcare settings" program (project DHCRC-0156) will provide real-time insights via an interactive dashboard, enabling nurses, midwives, and health administration to assess risks and optimize resources in health care settings. This protocol details the algorithm development activities for subproject 1a, predictive risk model development, which aims to develop and pilot-test a predictive harm algorithm across 2 South Australian local health networks. OBJECTIVE: This study aims to identify the clinical harm outcome of interest and relevant data sources per site and build a suitable data solution to model predictors of harm risk and identify actionable clinical, workforce, and environmental factors that affect the harm outcome of interest. METHODS: This study design includes three phases: (1) model generation, (2) model evaluation, and (3) prototype development. Data linkage by SA-NT DataLink can only proceed following approval from each of the following: the South Australia Department for Health and Wellbeing Human Research Ethics Committee, the University of South Australia Human Research Ethics Committee, and hospital governance committees. The clinical dataset will be split into a training set, a validation set, and a test set. Exploratory data analysis will be undertaken to ascertain features and classify outcomes from the raw dataset. Derived features will be computed, feature correlations will be estimated, and initial feature selection will be performed. Iterative model development will occur over 3 stages, and a dashboard to display these results will be developed. RESULTS: The study commenced on July 19, 2021, and will conclude on December 31, 2025. Finalized results are expected in December 2025. CONCLUSIONS: This research will conclude with the development of the algorithm for transferability to health care environments. Research activities will be detailed in publicly available reports and manuscripts prepared for peer-reviewed journals that will be drafted in accordance with existing and appropriate checklists. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/75474.
29 May 2026
Read appraisal →JMIR human factors
Development and Psychometric Validation of a Usability Instrument Based on ISO 25010 for Electronic Health Record Systems in Peruvian Health Care Settings: Methodological Study
BACKGROUND: The usability of electronic health records influences patient safety, clinical efficiency, and the sustainable digital transformation of health care. While tools such as the System Usability Scale exist, few are based on structured software quality models or align with local regulatory frameworks, particularly in low- and middle-income countries. OBJECTIVE: This study aims to develop and psychometrically validate an instrument to assess electronic health record usability, based on the ISO 25010 (International Organization for Standardization) quality model, and in accordance with the Peruvian Technical Health Standard (Norma Técnica de Salud [NTS]). METHODS: A 3-stage methodological study was conducted following COSMIN (Consensus-based Standards for the Selection of Health Measurement Instruments) guidelines. First, a 23-item instrument was developed, grouped into 7 usability dimensions based on ISO 25010. Second, content validity was assessed using a modified Delphi method with 10 experts, using Aiken's V coefficient. Third, structural validity and reliability were evaluated in 115 participants (physicians, nurses, and administrative staff) using confirmatory factor analysis and Cronbach α. Additionally, the module's functional compliance with the NTS was verified. RESULTS: The instrument demonstrated excellent psychometric properties among 115 participants (53 physicians, 48 nurses, and 14 administrative staff); high content validity (Aiken's V=0.95; 95% CI 0.84-0.98), excellent internal consistency (α=.968), and optimal factor fit (Comparative Fit Index=1.000; Tucker-Lewis Index=1.001). Overall usability was perceived as very high by 94 of 115 users (81.7%), and high by 21 of 115 users (18.3%), with no perceptions at lower levels. The module met 28 of 35 requirements (80%) of the Peruvian Technical Health Standard. Administrative staff reported higher satisfaction (13/14, 93% rated it as very high) compared with clinical professionals (81/101, 80% rated it as very high), particularly in the dimensions of error protection and operability. CONCLUSIONS: This study provides the first fully psychometrically validated instrument in Spanish based on ISO 25010, System Usability Scale, and other theoretical foundations for evaluating the usability of information systems. The results highlight the importance of considering both international standards and local regulatory requirements (NTS) in the design of digital health systems. The instrument is applicable in Spanish-speaking contexts and can serve as a reference for future usability evaluations in the region as well as in low- and middle-income countries.
23 May 2026
Read appraisal →PloS one
Unpacking the dual psychological paths of employee-AI collaboration on creativity: The role of proactive behavior
The deep integration of artificial intelligence (AI) into organizational settings has significantly transformed employees' work patterns, underscoring the need to investigate the mechanisms through which employee-AI collaboration influences creativity. Grounded in social cognitive theory, this study examines how employee-AI collaboration affects creativity via internal (self-efficacy) and external (performance pressure) mechanisms, and explores the moderating role of proactive behavior. Using convenience sampling, 733 valid responses were collected from six technology-driven enterprises. The theoretical model was tested through confirmatory factor analysis and regression analyses using AMOS and the Process macro for SPSS. Our results reveal that employee-AI collaboration is positively associated with creativity through two statistically supported indirect pathways: parallel indirect effects via self-efficacy (internal pathway) and performance pressure (external pathway), and a sequential indirect association in which higher self-efficacy is related to higher performance pressure, which in turn is positively related to creativity. Moreover, proactive behavior appears to moderate these relationships, such that the association between employee-AI collaboration and self-efficacy is more pronounced at higher levels of proactive behavior, whereas the association between employee-AI collaboration and performance pressure is less pronounced at higher levels of proactive behavior, yielding a pattern consistent with moderated indirect effects on creativity through both routes. These findings provide theoretical insights into the complex psychological processes underlying creativity in AI-enabled work environments and offer practical implications for optimizing AI applications and developing targeted interventions to foster employee creativity.
28 Apr 2026
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