In the medical world, high quality digital registration in an Electronic Patient Dossier (EPD) of symptoms, diagnoses, treatments, test results, images, interpretations, and outcomes becomes commonplace. Together with a shortage of medical professionals, means that they experience pressure at the expense of actual `hands on the bed'. On the other hand, EPDs contain a wealth of largely unused, unstructured textual information. Clinicians primarily communicate with each other through letters and reports. Our main question is: Can Natural Language Processing (NLP) exploit this wealth? By extracting structured data and using it as features for machine learning, a wide variety of process improvements become possible. Furthermore, it may contribute to the desire of government and health stakeholders to simplify registration and relieve pressure. This paper sketches a few prominent process improvements that we plan to research.
Introduction
Tests
First Imaging symptoms Consults
Long term outcome now score malignant Years of letters and reports about the course of a disease of a patient time data window findings findings
Co rrelate outcome label (a) Di erent data windows have di erent prediction performance (b) BIRADS score model may be unobtrusively incorporated in existing EPD software warning the clinician of substantial risk for uncommon conditions like GCA.
As another example, chemo and radiation therapy in cancer patients damage
heart and bloodvessels. It is important to identify those patients who are at high
risk for long term complications. They need tailored medical therapy to prevent
cardiovascular diseases. Machine learning may aid in identi cation, avoiding or
suggesting such measures [
Dynamic Shortening of Care Processes. Actions like tests, imaging,
treatments, etc. are preceded by consults. Patterns may be discovered between data
in letters and reports from before a consult, that su ciently predict a subsequent
action. In such cases, one may dynamically adapt the process to already plan
this action before the consult, at the limited expense of some cancellations due
to mispredictions, or one may even decide to omit the consult altogether.
Quality assurance. Hospitals often enforce strict protocols as a means for
quality assurance. NLP techniques may aid in checking adherence to protocols
and supporting clinicians in upholding the standards. For example, the
radiology diagnostic reports for breast cancer in the Hospital Group Twente contain
among other things a standard risk classi cation, called \Breast Imaging
Reporting and Data System" (BIRADS; see Figure 1(b)). Whether or not these
risk scores correlate well with actual outcomes is largely unknown. Being able to
check this is important for quality assurance. Moreover, discovering under which
circumstances deviating risk assessments are given, may aid in improving care
processes with better-informed decisions [