Related Experiment Video
Updated: Apr 4, 2026
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping
Wei-Qi Wei1, Pedro L Teixeira1, Huan Mo1
1Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
Insights
Combining electronic health record components improves patient phenotyping accuracy. Using multiple data sources, such as diagnosis codes and clinical notes, yields better results than relying on a single source for disease identification.
Area of Science:
- Health Informatics
- Clinical Data Analysis
- Electronic Health Records (EHR)
Background:
- Accurate patient phenotyping is crucial for clinical research and decision-making.
- Electronic health records (EHR) offer a rich source of patient data.
- Evaluating the performance of different EHR components for phenotyping is essential.
Purpose of the Study:
- To assess the phenotyping performance of three key EHR components: International Classification of Disease (ICD) diagnosis codes, primary notes, and specific medications.
- To compare the accuracy of individual EHR components versus combinations of components for phenotyping ten selected diseases.
Main Methods:
- Utilized de-identified Vanderbilt EHR data for ten distinct diseases.
- Classified patients based on evidence from diagnosis codes, notes, and medications.
- Randomly selected patients for manual chart review to estimate positive predictive value (PPV), sensitivity, and F-score.
Main Results:
- Single EHR components showed inconsistent and inadequate phenotyping performance (PPV: 0.06-0.71).
- Combining two or more components significantly improved accuracy (mean F-score: 0.70 ± 0.12) and PPV (0.91 ± 0.08).
- Primary notes offered the highest sensitivity (0.77), while combined components provided stable and high sensitivity (0.59 ± 0.16).
Conclusions:
- Multiple EHR components yield more consistent and higher performance for phenotyping than single components.
- Future phenotyping strategies should integrate multiple EHR data sources for optimal results.
Objective:
To evaluate the phenotyping performance of three major electronic health record (EHR) components: International Classification of Disease (ICD) diagnosis codes, primary notes, and specific medications.
Materials And Methods:
We conducted the evaluation using de-identified Vanderbilt EHR data. We preselected ten diseases: atrial fibrillation, Alzheimer's disease, breast cancer, gout, human immunodeficiency virus infection, multiple sclerosis, Parkinson's disease, rheumatoid arthritis, and types 1 and 2 diabetes mellitus. For each disease, patients were classified into seven categories based on the presence of evidence in diagnosis codes, primary notes, and specific medications. Twenty-five patients per disease category (a total number of 175 patients for each disease, 1750 patients for all ten diseases) were randomly selected for manual chart review. Review results were used to estimate the positive predictive value (PPV), sensitivity, andF-score for each EHR component alone and in combination.
Results:
The PPVs of single components were inconsistent and inadequate for accurately phenotyping (0.06-0.71). Using two or more ICD codes improved the average PPV to 0.84. We observed a more stable and higher accuracy when using at least two components (mean ± standard deviation: 0.91 ± 0.08). Primary notes offered the best sensitivity (0.77). The sensitivity of ICD codes was 0.67. Again, two or more components provided a reasonably high and stable sensitivity (0.59 ± 0.16). Overall, the best performance (Fscore: 0.70 ± 0.12) was achieved by using two or more components. Although the overall performance of using ICD codes (0.67 ± 0.14) was only slightly lower than using two or more components, its PPV (0.71 ± 0.13) is substantially worse (0.91 ± 0.08).
Conclusion:
Multiple EHR components provide a more consistent and higher performance than a single one for the selected phenotypes. We suggest considering multiple EHR components for future phenotyping design in order to obtain an ideal result.
More Related Videos
07:31Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
Published on: May 15, 2020
07:50A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Related Concept Videos
01:30Methods of Documentation VII: EMR
01:30Health Information Technology and Healthcare Information System
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
01:29Issues And Trends In Healthcare Delivery System
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
01:23Methods of Documentation V: CBE
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...
01:21Methods of Documentation III: PIE
01:10Documentation of Nursing Diagnosis
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...