PainRelief.com Interview with:

Ismail Nabeel MD, MPH
Associate Professor
Public Health and General Preventive Medicine
Mount Sinai Medical Center

PainRelief.com:  What is the background for this study?

Response: Acute and chronic low back pain (LBP) are different conditions with different treatments. However, they are coded in electronic health records with the same International Classification of Diseases, 10th revision (ICD-10) code (M54.5) and can be differentiated only by retrospective chart reviews. This prevents an efficient definition of data-driven guidelines for billing and therapy recommendations, such as return-to-work options, etc.

In this feasibility study, we evaluated if Artificial intelligence can automatically distinguish the quality of Low Back Pain (LBP) episodes by analyzing free-text clinical notes from the treating providers. 

These clinical notes were collected during a previous pilot study evaluating an RTW tool based on EHR data that included nearly 40,000 encounters for 15,715 patients spanning from 2016 to 2018 and clinical notes written by 81 different providers. We used a dataset of 17,409 clinical notes from different primary care practices; of these, 891 documents were manually annotated as “acute low back pain” and 2,973 were generally associated with LBP via the recorded ICD-10 code.