Computationally assisted patient finding for navigation to optimize pancreatic cancer care access.

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.