A facility-level self-assessment of Autonomous Pharmacy Framework levels.

PURPOSE: The objective of this study was to understand at what level of the Autonomous Pharmacy Framework facilities are operating, in terms of the current state of data collection and analysis in the medication-use process, and to gather insights about systems integration and automation use. METHODS: The Autonomous Pharmacy Advisory Board, a group of chief pharmacy officers and operational leaders, developed a self-assessment instrument based on the previously published Autonomous Pharmacy Framework, made the self-assessment instrument available via the internet, and reviewed respondents' self-reported results. The data collection period for the survey started in March of 2021 and ended in January of 2023. RESULTS: A total of 119 facility-level self-assessments were completed and analyzed. On a scale of 1 to 5, where 1 represented little or no data-driven automation with lots of manual tasks and 5 represented the utmost data-driven automation with few manual tasks, the average overall facility-level score was 2.77 (range, 1.38-4.41). Results revealed slight variance by facility bed capacity. Much more variation was found in the degrees to which individual facilities have automated core processes like inventory management, intravenous medication preparation, and financial reporting. CONCLUSION: As a baseline, this automation-focused facility self-assessment suggests that for essentially all health-system pharmacy facilities and their larger organizations, a substantial body of work needs to be done to further develop and upgrade technology and practice in tandem, greatly expand data collection and analysis, and thereby achieve better operational, financial, and clinical outcomes. Significant advancements are needed to arrive at the highly reliable, highly automated, data-driven medication-use process involving few repetitive manual tasks envisioned in the Autonomous Pharmacy Framework.

Analytical precision assessment for microplastic analyses.

The need for improved microplastic (MP) data accuracy has been widely reported, but MP precision issues have been investigated less thoroughly. This work demonstrates how initial and continuing assessments of a laboratory's analytical precision can be used for establishing laboratory repeatability for MP analyses. These precision estimates can be reported along with MP results to ensure their quality and compare them meaningfully to other data. Re-analyses of reference MP samples can be used to assess and compare precision between different laboratories. A multi-lab precision exercise was demonstrated using infrared (IR) standard test methods performed on reference samples consisting of low-concentration MP spikes in both clean water and wastewater matrices. Each lab repeated their IR analyses 7 times and calculated relative standard deviations (RSD) for each detected polymer type using a standardized template. All labs' MP methods yielded generally repeatable results, though RSDs were consistently higher for lower MP counts. The reported range of total MP counts per sample was 8-33 particles, and the observed RSDs were 0.1-0.6. These RSDs were the same or lower than the expected imprecision due to random (Poisson) counting error alone, suggesting that these automated methods did not contribute any additional variability, and had slightly better reproducibility than expected for independent recounts. The wastewater matrix exhibited numerous interfering particles but did not yield more variability than the clean water matrix. The low-count design is a worst case for precision but is appropriate for some real-world sample concentrations. In practice, labs could generate separate references for precision assessment at multiple MP ranges (e.g., high, medium, and low.) The RSDs obtained from this data can be used to generate QC charts, detect changes in analyst performance, compare to Poisson error to identify additional sources of imprecision, and determine target filtration and instrumental parameters for MP analyses.