Background: Deprescribing interventions delivered through the electronic medical record have not significantly reduced the use of high-risk anticholinergics in prior trials. Pharmacists have been identified as ideal practitioners to conduct deprescribing; however, little experience beyond collaborative consult models has been published.Objective: To evaluate the impact of two pilot pharmacist-based advanced practice models nested within primary care. Methods: Pilot studies of a collaborative clinic-based pharmacist deprescribing intervention and a telephone-based pharmacist deprescribing intervention were conducted. Patients receiving the clinic-based pharmacy model were aged 55 years and older and referred for deprescribing at a specialty clinic. Patients receiving the telephone-based pharmacy model were aged 65 years and older and called by a clinical pharmacist for deprescribing without referral. Deprescribing was defined as a discontinuation or dose reduction reported either in clinical records or through self-reporting. Results:The 18 patients receiving clinic-based deprescribing had a mean age of 68 years and 78% were female. Among 24 medications deemed eligible for deprescribing, 23 (96%) were deprescribed. The clinic-based deprescribing model resulted in a 93% reduction in median annualized total standardized dose (TSD), 56% lowered their annualized exposure below a cognitive risk threshold, and 4 (17%) of medications were represcribed within 6 months. The 24 patients receiving telephone-based deprescribing had a mean age of 73 years and 92% were female. Among 24 medications deemed eligible for deprescribing, 12 (50%) were deprescribed. There was no change in the median annualized TSD, the annualized TSD was lowered below a cognitive risk threshold in 46%, and no medications were represcribed within 6 months.Few withdrawal symptoms or adverse events were reported in both groups.

Any program tasked with the evaluation and acquisition of algorithms for use in deployed scenarios must have an impartial, repeatable, and auditable means of benchmarking both candidate and fielded algorithms. Success in this endeavor requires a body of representative sensor data, data labels indicating the proper algorithmic response to the data as adjudicated by subject matter experts, a means of executing algorithms under review against the data, and the ability to automatically score and report algorithm performance. Each of these capabilities should be constructed in support of program and mission goals. By curating and maintaining data, labels, tests, and scoring methodology, a program can understand and continually improve the relationship between benchmarked and fielded performance of acquired algorithms. A system supporting these program needs, deployed in an environment with sufficient computational power and necessary security controls is a powerful tool for ensuring due diligence in evaluation and acquisition of mission critical algorithms. This paper describes the Seascape system and its place in such a process.

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