Leveraging Machine Learning and Robotic Process Automation to Identify and Convert Unstructured Colonoscopy Results Into Actionable Data: Proof-of-Concept Study
Implementation of an automated workflow to extract and update CRC screening follow-up dates from colonoscopy reports is feasible and has the potential to improve accuracy in patient recall while reducing documentation burden. By standardizing data ingestion, extending this approach to various unstructured data types can address deficiencies in structured EHR documentation and solve for a lack of data integration and reporting for quality measures. Automated workflows leveraging ML and RPA offer practical solutions to overcome interoperability challenges and the use of unstructured data within health care systems.
Authors
- Paul Testa (ORCID: https://orcid.org/0000-0002-1512-9638)
- Adam Szerencsy (ORCID: https://orcid.org/0000-0002-0066-4400)
- Elizabeth R. Stevens (ORCID: https://orcid.org/0000-0001-6063-1523)
- Deepa Prabhu (ORCID: https://orcid.org/0000-0002-0257-2200)
- Anne-Marie Côté (ORCID: https://orcid.org/0000-0002-8728-0548)
- David Ranson (ORCID: https://orcid.org/0000-0002-6301-7593)
- John Hartman (ORCID: https://orcid.org/0000-0002-8667-107X)
- Amelia Shunk (ORCID: https://orcid.org/0009-0007-6131-7721)
- Casey Monina
- Ajay Mansukhani (ORCID: https://orcid.org/0009-0001-0796-5302)
- Yana Imberg
Institutions
- Tulane University (US)
- NYU Langone Health (US)
- New York University (US)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-10-29
- DOI
- https://doi.org/10.2196/73504
- Primary Topic
- Colorectal Cancer Screening and Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00