An NLP Solution to Foster the Use of Information in Electronic Health Records for Efficiency in Decision-Making in Hospital Care
This addresses efficiency in hospital decision-making by automating data extraction from electronic health records, though it is incremental as it applies existing NLP methods to a specific domain and language.
The project developed an NLP system to automatically extract structured patient information from Portuguese free-text clinical records, creating a summary of medical history, diagnoses, medications, and conditions like allergies, which improved access and saved time for users.
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.