BACKGROUND: @entity1 with @entity8 are at risk for adverse events due to improper medication prescribing. Few randomized controlled trials of @entity922 ( @entity922 ) utilizing dynamic assessment of @entity1 ' kidney function to improve prescribing for @entity1 with @entity8 have been published. METHODS: We developed a @entity922 tool for 20 medications within a commercial electronic health record. Our system detected scenarios in which drug discontinuation or dosage adjustment was recommended for adult @entity1 with @entity8 in the ambulatory and acute settings - both at the time of the initial prescription ("prospective" alerts) and by monitoring changes in renal function for @entity1 already receiving one of the study medications ("look-back" alerts). We performed a prospective, cluster randomized controlled trial of physicians receiving @entity922 for renal dosage adjustments versus those performing their usual workflow. The primary endpoint was the proportion of study prescriptions that were appropriately adjusted for @entity1 ' kidney function at the time that @entity1 ' conditions warranted a change according to the alert logic. We employed multivariable logistic regression modeling to adjust for glomerular filtration rate, gender, age, hospitalized status, length of stay, type of alert, time from start of study, and clustering within the prescribing physician on the primary endpoint. RESULTS: A total of 4068 triggering conditions occurred in 1278 unique @entity1 ; 1579 of these triggering conditions generated alerts seen by physicians in the intervention arm and 2489 of these triggering conditions were captured but suppressed, so as not to generate alerts for physicians in the control arm. Prescribing orders were appropriate adjusted in 17% of the time vs 5.7% of the time in the intervention and control arms, respectively (odds ratio: 1.89, 95% confidence interval, 1.45-2.47, P   <   .0001). Prospective alerts had a greater impact than look-back alerts (55.6% vs 10.3%, in the intervention arm). CONCLUSIONS: The rate of appropriate drug prescribing in @entity8 is low and remains a @entity1 safety concern. Our results suggest that @entity922 improves drug prescribing, particularly when providing guidance on new prescriptions.

The impact of real-time alerting on appropriate prescribing in XXXX : a cluster randomized controlled trial.

@entity1 :: ('9606', 'Species') :: ['Patients', 'patients', 'patient'] @entity8 :: ('MESH:D007674', 'Disease') :: ['kidney impairment', 'kidney disease', 'impaired renal function'] @entity922 :: ('MESH:D013568', 'Disease') :: ['CDS', 'clinical decision support']
@entity8 :: (MESH:D007674,Disease) :: ['kidney disease']
