Room: AAPM ePoster Library
As part of the routine clinical practice, large amounts of information is entered into our electronic healthcare records (EMR), radiation oncology information systems (ROIS) and treatment planning systems (TPS). With the aim to extract data for big data initiatives, this information needs to be in a structured and discrete format but much of it is stored as free text, often in Word documents. Here we present a method to create Word templates with macros so that discrete data elements can be programmatically extracted for clinical data analysis.
From notes completed between Dec 2018 to Feb 2020, 3671 OTV notes for 1218 unique patients were programmatically extracted using this system. Unique patients per primary disease site was lung (n=263), prostate (n=254), breast (n=287), H&N (n=223). We were able to capture seven to nine disease site specific toxicity scores per patient and the total number of data elements collected from the OTV templates ranged 35 to 55 (median: 42 elements).
Data needs to be captured as part of the routine clinical workflow using structure clinical templates for meaningful big data repositories to be created. In addition, standard ontologies and data dictionaries needs to be created to enable sharing of the knowledge acquired from the collected datasets.
Not Applicable / None Entered.