Pharmacovigilance Using Clinical Notes

Phase IV surveillance is a critical component of drug safety because not all safety issues associated with drugs are detected before market approval. Each year, drug-related events account for up to 50% of adverse events occurring in hospital stays,1 significantly increasing costs and length of stay in hospitals.2 As much as 30% of all drug reactions result from concomitant use—with an estimated 29.4% of elderly patients on six or more drugs.3 Efforts such as the Sentinel Initiative and the Observational Medical Outcomes Partnership4 envision the use of electronic health records (EHRs) for active pharmacovigilance.5–7 Complementing the current state of the art—based on reports of suspected adverse drug reactions—active surveillance aims to monitor drugs in near real time and potentially shorten the time that patients are at risk. Coded discharge diagnoses and insurance claims data from EHRs have already been used for detecting safety signals.8–10 However, some experts argue that methods that rely on coded data could be missing >90% of the adverse events that actually occur, in part because of the nature of billing and claims data.1 Researchers have used discharge summaries (which summarize information from a care episode, including the final diagnosis and follow-up plan) for detecting a range of adverse events11 and for demonstrating the feasibility of using the EHR for pharmacovigilance by identifying known adverse events associated with seven drugs using 25,074 notes from 2004.12 Therefore, the clinical text can potentially play an important role in future pharmacovigilance, 13,14 particularly if we can transform notes taken daily by doctors, nurses, and other practitioners into more accessible data-mining inputs.15–17 Two key barriers to using clinical notes are privacy and accessibility. 16 Clinical notes contain identifying information, such as names, dates, and locations, that are difficult to redact automatically, so care organizations are reluctant to share clinical notes. We describe an approach that computationally processes clinical text rapidly and accurately enough to serve use cases such as drug safety surveillance. Like other terminology-based systems, it deidentifies the data as part of the process.18 We trade the “unreasonable effectiveness”24 of large data sets in exchange for sacrificing some individual note-level accuracy in the text processing. Given the large volumes of clinical notes, our method produces a patient–feature matrix encoded using standardized medical terminologies. We demonstrate the use of the resulting patient–feature matrix as a substrate for signal detection algorithms for drug–adverse event associations and drug–drug interactions.