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Using NLP to Improve Radiologist Productivity & Reduce Burnout

Radiology is a key diagnostic tool for many diseases and has an important role in monitoring treatment and predicting outcomes which have led to an increase in diagnostic imaging demands.

More than 80% of all hospital and health system visits include at least one imaging test and estimates suggest that, overall, hospitals and health systems spend $65 billion each year on imaging. However, the availability of diagnostic radiologists isn’t proportional to demand, which impacts the quality of diagnosis, causes delays in patient care, and leads to a high rate of burnout among radiologists.

Radiologists are moving out from traditional RIS setups to digital solutions. AI & Cloud platforms can reduce the administrative burden and enable them to focus on faster and better clinical diagnoses.

Radiology systems and departments are focusing on optimizing workflows to handle heavy workloads. In this talk, we will focus on one subfield of AI – Natural Language Processing, and its ability to improve radiologist productivity and reduce burnout.

We will discuss 2 areas of excellence via the application of healthcare interoperability and NLP: Clinical excellence which involves the extraction of unstructured clinical information from health systems, reconciliation with medical images, and supporting intelligent clinical decision support to enable faster & accurate diagnosis.

Operational excellence involves, information augmentation, worklist prioritization, and workflow optimization thereby reducing administrative burden and enabling effective workload management.

Building a Better Patient Chart: Combining structured, unstructured, and missing data

Diameter Health has deep expertise in doing structural and semantic normalization on structured medical data. However, this is only part of the...