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How to Design Electronic Case Report Form (eCRF) Questions to Maximize Semantic Interoperability in Clinical Research

How to Design Electronic Case Report Form (eCRF) Questions to Maximize Semantic Interoperability in Clinical Research

Ambiguous or complex wording found in the CRF variables needed to be evaluated as part of this mapping activity as well. Based on our experience of being involved in large-scale national and international research projects, we believe that apart from ensuring accuracy and quality of collected data, CRF design should also maximize semantic interoperability. That way, time- and cost-efficient merging, analysis, and sharing of data can be facilitated.

Eugenia Rinaldi, Caroline Stellmach, Sylvia Thun

Interact J Med Res 2025;14:e51598

Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping

Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping

Data from patients enrolled in a study are commonly collected using a case report form (CRF) [5]. The International Conference on Harmonization Guidelines for Good Clinical Practice defines a CRF as a “printed, optical or electronic document designed to record all of the protocol-required information to be reported to the sponsor on each trial subject” [6].

Caroline Stellmach, Sina Marie Hopff, Thomas Jaenisch, Susana Marina Nunes de Miranda, Eugenia Rinaldi, The NAPKON, LEOSS, ORCHESTRA, and ReCoDID Working Groups

J Med Internet Res 2024;26:e50049

Use of Wearable Devices for Peak Oxygen Consumption Measurement in Clinical Cardiology: Case Report and Literature Review

Use of Wearable Devices for Peak Oxygen Consumption Measurement in Clinical Cardiology: Case Report and Literature Review

It is a parameter used to evaluate cardiorespiratory fitness (CRF), thus named peak oxygen consumption (VO2), representing an individual’s largest volume of oxygen extracted from inhaled air during efforts [1]. This measurement is affected by age, gender, genetics, underlying medical conditions, and physical activity, especially high-intensity training [2-4].

Gabriella Bayshtok, Shmuel Tiosano, Ariel Furer

Interact J Med Res 2023;12:e45504

An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation

An Efficient Method for Deidentifying Protected Health Information in Chinese Electronic Health Records: Algorithm Development and Validation

For example, Jian et al [11] designed a set of regular expressions based on the characteristics of Chinese EHRs to filter sentences with sparse PHI, then used the filtered sentences to train a conditional random field (CRF) model for PHI recognition. Du et al [12] manually extracted lexical and dictionary features of PHI from Chinese EHRs to train a CRF model and utilized regular expressions to capture missed features using the CRF.

Peng Wang, Yong Li, Liang Yang, Simin Li, Linfeng Li, Zehan Zhao, Shaopei Long, Fei Wang, Hongqian Wang, Ying Li, Chengliang Wang

JMIR Med Inform 2022;10(8):e38154