An NLP Solution to Foster the Use of Information in Electronic Health Records for Efficiency in Decision-Making in Hospital Care

TL;DR

This paper presents an NLP-based system that automatically extracts structured information from Portuguese clinical records to improve decision-making efficiency in hospital care.

Contribution

It introduces a novel NLP solution for automatically identifying key attributes in free-text clinical records, enabling structured summaries for clinical decision support.

Findings

Automated extraction of clinical attributes from free-text records.

Improved access and comprehensiveness of patient history.

Time savings in clinical decision-making processes.

Abstract

The project aimed to define the rules and develop a technological solution to automatically identify a set of attributes within free-text clinical records written in Portuguese. The first application developed and implemented on this basis was a structured summary of a patient's clinical history, including previous diagnoses and procedures, usual medication, and relevant characteristics or conditions for clinical decisions, such as allergies, being under anticoagulant therapy, etc. The project's goal was achieved by a multidisciplinary team that included clinicians, epidemiologists, computational linguists, machine learning researchers and software engineers, bringing together the expertise and perspectives of a public hospital, the university and the private sector. Relevant benefits to users and patients are related with facilitated access to the patient's history, which translates into exhaustiveness in apprehending the patient's clinical past and efficiency due to time saving.