Using artificial intelligence to develop a measure of orthopaedic treatment success from clinical notes
Our results suggest that text classifiers applied to clinical notes are capable of differentiating patients with successful treatment outcomes with high levels of accuracy. This finding is encouraging, signaling that routinely collected clinical note content may serve as a data source to develop an outcome measure for orthopaedic patients.
Authors
- Benjamin L. Judkins (ORCID: https://orcid.org/0000-0002-1119-116X)
- Jihad S. Obeid (ORCID: https://orcid.org/0000-0002-7193-7779)
- Zachary Cole Reynolds
- Sarah B. Floyd (ORCID: https://orcid.org/0000-0001-7412-8136)
- D. Hudson Smith (ORCID: https://orcid.org/0000-0003-3041-4602)
- Kyle J. Jeray (ORCID: https://orcid.org/0000-0002-3603-3344)
- Ahmed G Almeldien
- Claire E Krohn
Institutions
- Prisma Health (US)
- Medical University of South Carolina (US)
- Clemson University (US)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2025-04-24
- DOI
- https://doi.org/10.3389/fdgth.2025.1523953
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.31
Funders
- Agency for Healthcare Research and Quality