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A Novel Framework to Assess Clinical Information in Digital Health Technologies: Cross-Sectional Survey Study

A Novel Framework to Assess Clinical Information in Digital Health Technologies: Cross-Sectional Survey Study

The cross-sectional survey approach allowed the assessment of the applicability, internal consistency, and construct validity of the CLIQ framework. The web-based approach offered a convenient, affordable, and pragmatic way of conducting a study and collecting data.

Kayode Philip Fadahunsi, Petra A Wark, Nikolaos Mastellos, Ana Luisa Neves, Joseph Gallagher, Azeem Majeed, Josip Car

JMIR Med Inform 2025;13:e58125

The Color of Drinking Survey Questionnaire for Measuring the Secondhand Impacts of High-Risk Drinking in College Settings: Validation Study

The Color of Drinking Survey Questionnaire for Measuring the Secondhand Impacts of High-Risk Drinking in College Settings: Validation Study

This property is a measure of the intercorrelation among items and hence the consistency in the measurement of the intended construct [19]. The internal consistency was evaluated in the following sets of items: (1) impact of alcohol consumption on academics (Q8. How often have you experienced the following during the current semester? items: 8.1 to 8.5), (2) impact of microaggressions (Q17. How much did the microaggressions impact your… items: 17.1 to 17.3?), (3) witnessed microaggressions (Q25.

Agustina Marconi, Reonda Washington, Amanda Jovaag, Courtney Blomme, Ashley Knobeloch, Vilma Irazola, Carolina Muros Cortés, Laura Gutierrez, Natalia Elorriaga

Interact J Med Res 2025;14:e64720

Diagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design

Diagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design

This advanced analytical capability enables AI to identify signs of glaucoma with consistency and accuracy, often surpassing that of human optometrists [6,7]. For example, in Akter et al [8], researchers found that AI algorithms could detect glaucoma with up to 96% accuracy, markedly higher than the 80% accuracy rate commonly associated with optometrists. Similarly, in another study [9], an accuracy of 83.4% is reported in identifying glaucomatous optic neuropathy using AI.

Faisal Ghaffar, Nadine M. Furtado, Imad Ali, Catherine Burns

JMIR Med Inform 2025;13:e63109

Effect of Performance-Based Nonfinancial Incentives on Data Quality in Individual Medical Records of Institutional Births: Quasi-Experimental Study

Effect of Performance-Based Nonfinancial Incentives on Data Quality in Individual Medical Records of Institutional Births: Quasi-Experimental Study

According to previous research [69], consistency is a measure of data accuracy (the extent to which data elements accurately represent the true numbers), commonly assessed in RHIS through data verification (agreement of data among data sources). This research crosschecked the agreement between the data elements in the source documents to assess consistency.

Biniam Kefiyalew Taye, Lemma Derseh Gezie, Asmamaw Atnafu, Shegaw Anagaw Mengiste, Jens Kaasbøll, Monika Knudsen Gullslett, Binyam Tilahun

JMIR Med Inform 2024;12:e54278

Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models

Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models

Building upon prior research by Wang et al [28] and Li et al [29], we hypothesized that using a diverse range of prompts can reveal distinct reasoning paths while maintaining consistency in the correct responses regardless of the variations. When using multiple-choice prompts for the DC3 cases, we presented the same options available in the original web-based polls to the models, but on the MIMIC-III data set, we generated random wrong answers that were closely related to the correct diagnosis.

Tassallah Abdullahi, Ritambhara Singh, Carsten Eickhoff

JMIR Med Educ 2024;10:e51391