| Title: |
Computationally assisted patient finding for navigation to optimize pancreatic cancer care access |
| Authors: |
King, Daniel A; John, Kristen M; Tenner, Joseph; Nadella, Sandeep; Zavadsky, Tiffany; Carvino, Anthony; Khan, Shama; Croocks, Rolando; McEvoy, Tara; Beyer, Kristen; Mercieca, Rita; Valente, Cristina; Bingham, Bernadette; Cohn, Elizabeth G; Habowski, Amber N; Tuveson, David A; Barish, Matthew A; Carvajal, Richard D |
| Contributors: |
National Institutes of Health; Northwell Health Tissue Donation Program |
| Source: |
The Oncologist ; volume 31, issue 4 ; ISSN 1083-7159 1549-490X |
| Publisher Information: |
Oxford University Press (OUP) |
| Publication Year: |
2026 |
| Description: |
Background Patient navigators are increasingly utilized in cancer care but ensuring patients are properly identified and referred to navigators is a significant challenge. The primary objective was to compare time from radiographic report to biopsy, oncology visit, and treatment before versus after implementation of a computationally assisted navigation referral stream. Secondary objectives included evaluating care delivery across demographic groups and assessing survival outcomes. Materials and Methods A quality initiative at Northwell Health compared care delivery metrics between 2 cohorts of patients with suspected pancreatic cancer: those identified retrospectively using computational methods in January 2023 and those identified and navigated prospectively in June 2023. Radiology reports from a centralized health information exchange were analyzed by an ML-based natural language processing (NLP) model to detect findings suspicious of pancreatic cancer. Participants deemed eligible for navigation were contacted by a navigator to improve the likelihood and expediency of follow-up care. Results Seventy-one patients were included, with 38 patients in the retrospective cohort and 33 patients in the prospective cohort. The prospective cohort showed numeric reduction in time to biopsy (12-6 days, P = 0.173), oncology appointment (27-17 days, P = 0.192), and treatment (56-35 days, P = 0.136), though these results were not statistically significant. These metrics showed a significant reduction in standard deviation (P < 0.001), including among racial and ethnic minorities. The survival of patients in both cohorts was comparable (hazard ratio [HR] = 0.82, P = 0.66) Conclusion 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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1093/oncolo/oyag037 |
| DOI: |
10.1093/oncolo/oyag037/67107481/oyag037.pdf |
| Availability: |
https://doi.org/10.1093/oncolo/oyag037; https://academic.oup.com/oncolo/advance-article-pdf/doi/10.1093/oncolo/oyag037/67107481/oyag037.pdf; https://academic.oup.com/oncolo/article-pdf/31/4/oyag037/67107481/oyag037.pdf |
| Rights: |
https://creativecommons.org/licenses/by-nc/4.0/ |
| Accession Number: |
edsbas.A527419B |
| Database: |
BASE |