Computationally assisted patient finding for navigation to optimize pancreatic cancer care access
This study provides promising evidence that an NLP-assisted identification workflow can improve care delivery and investigation in a larger study is warranted to validate these findings.
Authors
- Shama Khan (ORCID: https://orcid.org/0000-0002-3173-9815)
- Kristen Beyer
- Anthony Carvino
- Kristen M. John (ORCID: https://orcid.org/0000-0002-1200-3386)
- Tiffany Zavadsky
- Sandeep Nadella (ORCID: https://orcid.org/0000-0002-3290-4018)
- Bernadette Bingham (ORCID: https://orcid.org/0000-0002-6294-8855)
- Tara McEvoy
- J Tenner
- Matthew A. Barish (ORCID: https://orcid.org/0000-0002-7521-8878)
- Richard D. Carvajal (ORCID: https://orcid.org/0000-0002-3796-1118)
- Rolando Croocks
- Rita Mercieca
- Cristina C Valente
- Elizabeth G Cohn
- David A Tuveson
- Daniel A King
- Amber N Habowski
Institutions
- Northwell Health (US)
- Feinstein Institute for Medical Research (US)
- Cold Spring Harbor Laboratory (US)
- Donald & Barbara Zucker School of Medicine at Hofstra/Northwell (US)
- North Shore Diabetes and Endocrine Associates (US)
Publication Details
- Journal
- The Oncologist
- Published
- 2026-02-13
- DOI
- https://doi.org/10.1093/oncolo/oyag037
- Primary Topic
- Global Cancer Incidence and Screening
- Type
- article
- Field-Weighted Citation Impact
- 0.00