Closing the loop: automatically identifying abnormal imaging results in scanned documents.

ABSTRACT:

Objectives

Scanned documents (SDs), while common in electronic health records and potentially rich in clinically relevant information, rarely fit well with clinician workflow. Here, we identify scanned imaging reports requiring follow-up with high recall and practically useful precision.

Materials and methods

We focused on identifying imaging findings for 3 common causes of malpractice claims: (1) potentially malignant breast (mammography) and (2) lung (chest computed tomography [CT]) lesions and (3) long-bone fracture (X-ray) reports. We train our ClinicalBERT-based pipeline on existing typed/dictated reports classified manually or using ICD-10 codes, evaluate using a test set of manually classified SDs, and compare against string-matching (baseline approach).

Results

SUBMITTER: Kumar A 

PROVIDER: S-EPMC9714594 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature