Electronic medical records for discovery research in rheumatoid arthritis

Abstract

Objective. Electronic medical records (EMRs) are a rich data source for discovery research but are underutilized due to the difficulty of extracting highly accurate clinical data. We assessed whether a classification algorithm incorporating narrative EMR data (typed physician notes) more accurately classifies subjects with rheumatoid arthritis (RA) compared with an algorithm using codified EMR data alone. Methods. Subjects with >1 International Classification of Diseases, Ninth Revision RA code (714.xx) or who had anti-cyclic citrullinated peptide (anti-CCP) checked in the EMR of 2 large academic centers were included in an "RA Mart" (n ‫؍‬ 29,432). For all 29,432 subjects, we extracted narrative (using natural language processing) and codified RA clinical information. In a training set of 96 RA and 404 non-RA cases from the RA Mart classified by medical record review, we used narrative and codified data to develop classification algorithms using logistic regression. These algorithms were applied to the entire RA Mart. We calculated and compared the positive predictive value (PPV) of these algorithms by reviewing the records of an additional 400 subjects classified as having RA by the algorithms. Results. A complete algorithm (narrative and codified data) classified RA subjects with a significantly higher PPV of 94% than an algorithm with codified data alone (PPV of 88%). Characteristics of the RA cohort identified by the complete algorithm were comparable to existing RA cohorts (80% women, 63% anti-CCP positive, and 59% positive for erosions). Conclusion. We demonstrate the ability to utilize complete EMR data to define an RA cohort with a PPV of 94%, which was superior to an algorithm using codified data alone.

Processing of Electronic Medical Records for Health Services Research in an Academic Medical Center: Methods and Validation (Preprint)

“…However, most have focused only on partial aspects of data processing [11][12][13][15][16][17][18] or processing related to a specific disease [6,11,19]. Designing an efficient and structured way to standardize records, process features, link data, and select cohorts for analysis is urgently needed given the increasing emphasis on big data and analytics to improve patient care and reduce healthcare expenditure [20,21].…”

Large-Scale Simultaneous Testing of Cross-Covariance Matrices with Applications to PheWAS

“…Anonymized patient level phenotype data linked with genotype data can be extracted and stored for clinical research (Liao et al, 2015). Through such a process, a cohort of 1837 rheumatoid arthritis (RA) patients with their genomic and phenotypic information available has been established at PHS for discovery research (Liao et al, 2010;Kurreeman et al, 2011;Liao et al, 2013).…”

“…Our proposed procedures allow clinical investigators to make use of the counts of ICD9 codes, which could be more powerful and more robust since it is difficult to choose the appropriate threshold for each disease as the ICD9 codes have varying degree of accuracy (Liao et al, 2010).…”

“…We applied our proposed procedure to the aforementioned EMR cohort of 1837 RA subjects (Liao et al, 2010). Blood samples were collected for these RA cases using the BWH Specimen…”

Negation scope and spelling variation for text-mining of Danish electronic patient records

“…Previously unstructured data has been used for a range of purposes such as diagnosis detection (e.g. Meyste, 2006;Suzuki, 2008;Liao, 2010), decision support (Tremblay, 2009), and temporal investigation of adverse drug reactions (Eriksson, to appear 2014). Structured EPR data will primarily contain diagnoses relevant to the current hospitalization, whereas free text will contain additional information about adverse drug reactions and the general health status of the patient.…”