Identifying Information Gaps in Electronic Health Records by Using Natural Language Processing: Gynecologic Surgery History Identification.

ABSTRACT:

Background

Electronic health records (EHRs) are a rich source of longitudinal patient data. However, missing information due to clinical care that predated the implementation of EHR system(s) or care that occurred at different medical institutions impedes complete ascertainment of a patient's medical history.

Objective

This study aimed to investigate information discrepancies and to quantify information gaps by comparing the gynecological surgical history extracted from an EHR of a single institution by using natural language processing (NLP) techniques with the manually curated surgical history information through chart review of records from multiple independent regional health care institutions.

Methods

To facilitate high-throughput evaluation, we developed a rule-

SUBMITTER: Moon S 

PROVIDER: S-EPMC8838563 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature