Overcoming the Challenges of Unstructured Data in Multisite, Electronic Medical Record-based Abstraction
Dados Bibliográficos
BACKGROUND: Unstructured data encountered during retrospective electronic medical record (EMR) abstraction has routinely been identified as challenging to reliably abstract, as these data are often recorded as free text, without limitations to format or structure. There is increased interest in reliably abstracting this type of data given its prominent role in care coordination and communication, yet limited methodological guidance exists. OBJECTIVES: As standard abstraction approaches resulted in substandard data reliability for unstructured data elements collected as part of a multisite, retrospective EMR study of hospital discharge communication quality, our goal was to develop, apply and examine the utility of a phase-based approach to reliably abstract unstructured data. This approach is examined using the specific example of discharge communication for warfarin management. RESEARCH DESIGN: We adopted a "fit-for-use" framework to guide the development and evaluation of abstraction methods using a 4-step, phase-based approach including (1) team building; (2) identification of challenges; (3) adaptation of abstraction methods; and (4) systematic data quality monitoring. MEASURES: Unstructured data elements were the focus of this study, including elements communicating steps in warfarin management (eg, warfarin initiation) and medical follow-up (eg, timeframe for follow-up). RESULTS: After implementation of the phase-based approach, interrater reliability for all unstructured data elements demonstrated κ's of ≥0.89-an average increase of +0.25 for each unstructured data element. CONCLUSIONS: As compared with standard abstraction methodologies, this phase-based approach was more time intensive, but did markedly increase abstraction reliability for unstructured data elements within multisite EMR documentation
Abstraction · Adaptation (eye) · Big data · Data mining · Documentation · Medical record · Reliability (semiconductor) · Unstructured data · Computer Science · Electronic Health Records Systems · Hospital Admissions and Outcomes · Machine Learning in Healthcare · Medicine · Psychology · Surgery
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