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Assessing the Effect of Electronic Health Record Data Quality on Identifying Patients With Type 2 Diabetes: Cross-Sectional Study

Assessing the Effect of Electronic Health Record Data Quality on Identifying Patients With Type 2 Diabetes: Cross-Sectional Study

Currently, evidence is lacking on the effect of data quality issues (eg, completeness, accuracy, and timeliness) and the identification of T2 D populations in large clinical data sources such as EHRs. This gap in evidence is further amplified given the variations in characteristics of published T2 D phenotypes and potential discrepancies in underlying data types (ie, diagnosis, medication, and laboratory) in EHRs.

Priyanka Dua Sood, Star Liu, Harold Lehmann, Hadi Kharrazi

JMIR Med Inform 2024;12:e56734

Data-Driven Identification of Potentially Successful Intervention Implementations Using 5 Years of Opioid Prescribing Data: Retrospective Database Study

Data-Driven Identification of Potentially Successful Intervention Implementations Using 5 Years of Opioid Prescribing Data: Retrospective Database Study

Note that these CCGs meet the criteria for identification, that is, their prescribing rate immediately before the reduction was in the top 38 (20%) CCGs. The total OME measure shows a gradual reduction over time in all 3 CCGs, with the algorithm identifying a reduction of up to 31%.

Lisa EM Hopcroft, Helen J Curtis, Richard Croker, Felix Pretis, Peter Inglesby, David Evans, Sebastian Bacon, Ben Goldacre, Alex J Walker, Brian MacKenna

JMIR Public Health Surveill 2024;10:e51323

The Costs of Anonymization: Case Study Using Clinical Data

The Costs of Anonymization: Case Study Using Clinical Data

Reference 14: R-U policy frontiers for health data de-identification Reference 21: Use and understanding of anonymization and de-identification in the biomedical literature Reference 23: A systematic review of re-identification attacks on health data Reference 43: Managing re-identification risks while providing access to the All of Us research program Reference 50: A globally optimal k-anonymity method for the de-identification of health dataidentification

Lisa Pilgram, Thierry Meurers, Bradley Malin, Elke Schaeffner, Kai-Uwe Eckardt, Fabian Prasser, GCKD Investigators

J Med Internet Res 2024;26:e49445

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review

Use of Artificial Intelligence in the Identification and Diagnosis of Frailty Syndrome in Older Adults: Scoping Review

Nonetheless, it is agreed that frailty is a condition preceding disability and that strategies must be set for an early classification and identification of older adults into nonfrail, prefrail, and frail individuals [10].

Daniel Velazquez-Diaz, Juan E Arco, Andres Ortiz, Verónica Pérez-Cabezas, David Lucena-Anton, Jose A Moral-Munoz, Alejandro Galán-Mercant

J Med Internet Res 2023;25:e47346

A Web-Based Instrument for Infantile Atopic Dermatitis Identification (Electronic Version of the Modified Child Eczema Questionnaire): Development and Implementation

A Web-Based Instrument for Infantile Atopic Dermatitis Identification (Electronic Version of the Modified Child Eczema Questionnaire): Development and Implementation

Table 3 shows the different e CEQ rules and the identification values in phase 1 (the identification values of separate questions from Q1 to Q7 are summarized in Table S1 in Multimedia Appendix 4). Although rule 2 showed better identification values than rule 1, both of them showed unsatisfactory NPVs of 58% and 60.6%, respectively. Further analysis focused on the NPV of rule 2 showed that it obtained 28 false-negative questionnaires, in which Q4 (12/28, 43%) and Q6 (9/28, 32%) were the main causes.

Heping Fang, Lin Chen, Juan Li, Luo Ren, Yu Yin, Danleng Chen, Huaying Yin, Enmei Liu, Yan Hu, Xiaoyan Luo

J Med Internet Res 2023;25:e44614

Automated Identification of Aspirin-Exacerbated Respiratory Disease Using Natural Language Processing and Machine Learning: Algorithm Development and Evaluation Study

Automated Identification of Aspirin-Exacerbated Respiratory Disease Using Natural Language Processing and Machine Learning: Algorithm Development and Evaluation Study

Reference 3: Automated identification of an aspirin-exacerbated respiratory disease cohort Reference 10: Natural language processing of clinical notes for identification of critical limb ischemia gaps in electronic health records by using natural language processing: gynecologic surgery history identificationidentificationAutomated Identification of Aspirin-Exacerbated Respiratory Disease Using Natural Language Processing

Thanai Pongdee, Nicholas B Larson, Rohit Divekar, Suzette J Bielinski, Hongfang Liu, Sungrim Moon

JMIR AI 2023;2:e44191

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study

These preliminary results identify a novel, easily measured physiological metric that may aid in the tracking and identification of SARS-Co V-2 infections. Current means to control COVID-19 spread rely on case isolation and contact tracing, which have played major roles in the successful containment of prior infectious disease outbreaks [18-20].

Robert P Hirten, Matteo Danieletto, Lewis Tomalin, Katie Hyewon Choi, Micol Zweig, Eddye Golden, Sparshdeep Kaur, Drew Helmus, Anthony Biello, Renata Pyzik, Alexander Charney, Riccardo Miotto, Benjamin S Glicksberg, Matthew Levin, Ismail Nabeel, Judith Aberg, David Reich, Dennis Charney, Erwin P Bottinger, Laurie Keefer, Mayte Suarez-Farinas, Girish N Nadkarni, Zahi A Fayad

J Med Internet Res 2021;23(2):e26107