Search Articles

View query in Help articles search

Search Results (1 to 10 of 41 Results)

Download search results: CSV END BibTex RIS


Applying Robotic Process Automation to Monitor Business Processes in Hospital Information Systems: Mixed Method Approach

Applying Robotic Process Automation to Monitor Business Processes in Hospital Information Systems: Mixed Method Approach

This study explored the potential of RPA in complex EMR systems with its role as “a canary in a coal mine” [11], presenting generalizable findings from a 3-year project at Seoul National University Bundang Hospital (SNUBH). The paper posits RPA as a means to bridge the gap between the limitations of both component-level system monitoring and application-level monitoring.

Adam Park, Se Young Jung, Ilha Yune, Ho-Young Lee

JMIR Med Inform 2025;13:e59801

Deep Learning Models to Predict Diagnostic and Billing Codes Following Visits to a Family Medicine Practice: Development and Validation Study

Deep Learning Models to Predict Diagnostic and Billing Codes Following Visits to a Family Medicine Practice: Development and Validation Study

Following a patient visit, physicians document their note in an EMR often in the SOAP (subjective, objective, assessment, plan) format. To submit an invoice, physicians must select 1 or more diagnostic codes and 1 or more billing codes. Invoices are compiled electronically in the EMR, reviewed by FHT billing personnel, and subsequently submitted to the provincial health insurance plan for payment every month.

Akshay Rajaram, Michael Judd, David Barber

JMIR AI 2025;4:e64279

25 Years of Electronic Health Record Implementation Processes: Scoping Review

25 Years of Electronic Health Record Implementation Processes: Scoping Review

Although the terms “EHR” and “EMR” are conceptually distinct, they are often used interchangeably in the literature. We recognize that the definitions we used for EHRs and EMRs in our review are not universally observed, and the terminology used in the literature and in practice often reflects the contexts in which these systems are implemented rather than the strict definitional boundaries placed upon them.

Harriet Finnegan, Nicola Mountford

J Med Internet Res 2025;27:e60077

Chinese Clinical Named Entity Recognition With Segmentation Synonym Sentence Synthesis Mechanism: Algorithm Development and Validation

Chinese Clinical Named Entity Recognition With Segmentation Synonym Sentence Synthesis Mechanism: Algorithm Development and Validation

For example, in the EMR text “依据头颅 CT:多发脑梗死,故多发脑梗死诊断明确 (Based on cranial CT: multiple cerebral infarctions, hence the diagnosis of multiple cerebral infarctions is clear),” the disease entity “多发脑梗死 (multiple cerebral infarctions)” and the treatment entity “单硝酸异山梨酯扩冠 (isosorbide mononitrate vasodilation)” in the phrase “单硝酸异山梨酯扩冠改善心肌缺血 (isosorbide mononitrate vasodilation to improve myocardial ischemia)” appeared only once in the original dataset and they were not recognized by the baseline model.

Jian Tang, Zikun Huang, Hongzhen Xu, Hao Zhang, Hailing Huang, Minqiong Tang, Pengsheng Luo, Dong Qin

JMIR Med Inform 2024;12:e60334

An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation

An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation

Development of a robust, interpretable prediction model that can estimate the risk of inpatient falls using solely routinely recorded electronic medical record (EMR) data to guide the assignment of effective inpatient fall prevention strategies remains a priority.

Rex Parsons, Robin Blythe, Susanna Cramb, Ahmad Abdel-Hafez, Steven McPhail

J Med Internet Res 2024;26:e59634

Claims-Based Algorithm to Identify Pre-Exposure Prophylaxis Indications for Tenofovir Disoproxil Fumarate and Emtricitabine Prescriptions (2012-2014): Validation Study

Claims-Based Algorithm to Identify Pre-Exposure Prophylaxis Indications for Tenofovir Disoproxil Fumarate and Emtricitabine Prescriptions (2012-2014): Validation Study

The algorithm was validated using both (1) electronic data from a cohort of patients from a large EMR system and (2) medical record abstraction data from all TDF/FTC-treated patients from a large urban sexual health clinic.

Patrick Sean Sullivan, Robertino M Mera-Giler, Staci Bush, Valentina Shvachko, Eleanor Sarkodie, Daniel O'Farrell, Stephanie Dubose, David Magnuson

JMIR Form Res 2024;8:e55614