Impact of curated data on electronic quality measure capture rates within CancerLinQ.

Abstract

307 Background: Accurate calculation of key quality measures is critical for informing high-quality, value-based cancer care that is consistent with clinical guidelines. The American Society of Clinical Oncology (ASCO)’s CancerLinQ enables oncology organizations around the US to view near-real time quality measure dashboards sourced from structured electronic medical record (EMR) data; however, use of structured data in key fields is highly variable. Unstructured content, such as progress notes, contains important clinical information on treatment and disease status, which can then undergo curation. This process involves trained data abstractors searching for key data elements through a combination of manual review and natural language processing (NLP) to extract structured data from unstructured content. We hypothesize inclusion of curated data substantially augments structured data alone by more accurately representing the patient journey, thus improving validity of quality measures across EMRs. Methods: A total of 96,399 records across 57,232 patients from 4 EMRs vendors were analyzed from 2018-2019 across structured EMR and curated data. Each record represents 1 of 7 key data elements used to calculate the Staging Documented within One Month of First Office Visit quality measure. Structured documentation of these data elements determines if a patient is concordant with the measure, meaning they were staged within 31 days of their first visit after diagnosis, or non-concordant, meaning they were not staged within the appropriate window. Results: More than a quarter of records from patients concordant or non-concordant with the measure (28.85%) had key data elements sourced from curation. In total, 33% of all records among concordant patients were sourced from curation. Relying on structured data alone would show only 67% concordance versus 97.5% concordance among curated records. This demonstrates that appropriate care may often be delivered but documentation may be missing in a significant fraction of structured EMR data, thus limiting accurate reporting capabilities. Conclusions: NLP-assisted curation can meaningfully supplement structured EMR data by providing a more accurate picture of care rendered, which can have substantial impacts on clinical care, quality reporting, and business operations. [Table: see text]