Measurement of selected preventable adverse drug events in electronic health records: Toward developing a complexity score. (15th November 2017) - Jeon, Nakyung; Sorokina, Magarita; Henriksen, Carl; Staley, Ben; Lipori, Gloria Pflugfelder; Winterstein, Almut G.
Abstract: Purpose: The defining of a select number of high-priority preventable adverse drug events (pADEs) for measurement in the electronic health record (EHR) and the estimation of pADE incidences in two tertiary care facilities are described. Methods: This study was part of a larger effort aimed at developing an automated electronic health record (EHR)-based complexity-score (C-score) that ranks hospitalized patients according to their risk for pADEs for clinical intervention. We developed measures for 16 high-priority pADEs often deemed preventable using discrete clinical and administrative EHR data. For each pADE we specified inclusion and exclusion criteria that were used to define risk populations for each specific pADE. The incidence of each type of pADE was then measured during a designated follow-up period considering all adult admissions to 2 large academic tertiary care hospitals, who were eligible for the pADE-specific risk populations during any of their first 5 hospital days. Results: Utilizing the data from 83, 787 admissions who were at risk for at least one pADE during at least one of their first five hospital days, we found that 27, 193 admissions (32.5%) developed at least one pADE. Uncontrolled postsurgical pain, uncontrolled pneumonia, and drug-associated hypotension had the highest incidences with the following number of days with pADE per number of patients at risk: 13, 484 of 19, 640; 527 of 1, 530; and 13, 394 of 43, 630, while drug-associated falls (446 of 75, 036), drug-associated acute mental status changes (262 of 66, 875) and venous thromboembolism (214 of 74, 283) had the lowest incidence rates. Conclusion: EHR-based definitions of clinically important pADEs were developed, and the incidence of the pADEs was estimated. These definitions will be advanced for the creation of prediction models to develop a C-score for identifying patients at risk for pADEs to prioritize pharmacist intervention.