TY  - JOUR
AU  - Rajaram, Akshay
AU  - Judd, Michael
AU  - Barber, David
PY  - 2025
DA  - 2025/3/7
TI  - Deep Learning Models to Predict Diagnostic and Billing Codes Following Visits to a Family Medicine Practice: Development and Validation Study
JO  - JMIR AI
SP  - e64279
VL  - 4
KW  - machine learning
KW  - ML
KW  - artificial intelligence
KW  - algorithm
KW  - predictive model
KW  - predictive analytics
KW  - predictive system
KW  - family medicine
KW  - primary care
KW  - family doctor
KW  - family physician
KW  - income
KW  - billing code
KW  - electronic notes
KW  - electronic health record
KW  - electronic medical record
KW  - EMR
KW  - patient record
KW  - health record
KW  - personal health record
AB  - Background: Despite significant time spent on billing, family physicians routinely make errors and miss billing opportunities. In other disciplines, machine learning models have predicted Current Procedural Terminology codes with high accuracy. Objective: Our objective was to derive machine learning models capable of predicting diagnostic and billing codes from notes recorded in the electronic medical record. Methods: We conducted a retrospective algorithm development and validation study involving an academic family medicine practice. Visits between July 1, 2015, and June 30, 2020, containing a physician-authored note and an invoice in the electronic medical record were eligible for inclusion. We trained 2 deep learning models and compared their predictions to codes submitted for reimbursement. We calculated accuracy, recall, precision, F1-score, and area under the receiver operating characteristic curve. Results: Of the 245,045 visits eligible for inclusion, 198,802 (81%) were included in model development. Accuracy was 99.8% and 99.5% for the diagnostic and billing code models, respectively. Recall was 49.4% and 70.3% for the diagnostic and billing code models, respectively. Precision was 55.3% and 76.7% for the diagnostic and billing code models, respectively. The area under the receiver operating characteristic curve was 0.983 for the diagnostic code model and 0.993 for the billing code model. Conclusions: We developed models capable of predicting diagnostic and billing codes from electronic notes following visits to a family medicine practice. The billing code model outperformed the diagnostic code model in terms of recall and precision, likely due to fewer codes being predicted. Work is underway to further enhance model performance and assess the generalizability of these models to other family medicine practices. 
SN  - 2817-1705
UR  - https://ai.jmir.org/2025/1/e64279
UR  - https://doi.org/10.2196/64279
DO  - 10.2196/64279
ID  - info:doi/10.2196/64279
ER  -