Related Experiment Videos
AI-Assisted Clinical Data Abstraction From Electronic Health Records: Retrospective Concordance Study.
Camille Sarah Schwartz1, Michael John Anderson2, Kelsey Nicole Moakler2
1University of Nevada, Reno, 1664 N Virginia St, Reno, NV, 89557, United States, 1 7027529240.
JMIR Formative Research
|July 7, 2026
Summary
Artificial intelligence (AI) significantly improves the efficiency and accuracy of extracting patient-reported outcomes from electronic health records. This AI-assisted approach reduced data abstraction time by over 90% with high concordance to human review.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Clinical Outcomes Research
Background:
- Manual chart abstraction from electronic health records (EHRs) is crucial but time-consuming and error-prone.
- Artificial intelligence (AI), especially large language models, offers potential for automating data extraction from clinical notes.
- AI can improve efficiency and consistency in obtaining structured data from unstructured clinical documentation.
Purpose of the Study:
- To evaluate the accuracy and efficiency of an AI-assisted method for extracting patient-reported outcomes from clinical notes.
- To compare AI-assisted abstraction with traditional manual abstraction by human reviewers.
Main Methods:
- A retrospective study involving 26 patients treated with low-dose radiation therapy for osteoarthritis.
- Human reviewers abstracted Numeric Rating Scale (NRS) pain scores and von Pannewitz Score (VPS) improvement.
- A HIPAA-compliant generative pretrained transformer AI system extracted the same data points; concordance and time were assessed.
Main Results:
- The AI system showed high concordance with human abstraction: 92% exact match for NRS (ICC=0.96) and 94% for VPS (κ=0.91).
- AI abstraction reduced average patient abstraction time from ~30 minutes to 2 minutes (>90% time savings).
- The AI identified one clinically relevant data point missed by manual review; no spurious values were generated.
Conclusions:
- AI-assisted data abstraction demonstrates high concordance with human review and significantly reduces time requirements.
- Findings support the feasibility of AI-assisted abstraction workflows in clinical research.
- Further validation across larger, diverse datasets is necessary to confirm generalizability.
Related Concept Videos
01:30Methods of Documentation VII: EMR
Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare settings,...
01:24Data Reporting and Recording
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
01:30Data Collection I
Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...
01:11Purpose of Health Records I
The vital purpose of health records is to provide a complete and accurate account of a patient's medical history, including communication, diagnostic and therapeutic orders, care planning, research, and quality review.
Here's a breakdown of how health records serve these purposes:
Here's a breakdown of how health records serve these purposes:
01:19Purpose of Health Records II
Health records serve various essential purposes in the healthcare system. Here are some key purposes:
01:23Methods of Documentation V: CBE
Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...