Optimizing Medication Distribution in Automated Dispensing Cabinets: Dashboard Implementation and Evaluation.

Purpose: To determine the impact of a business intelligence dashboard tool to optimize automated dispensing cabinets (ADCs). Methods: A pre-post implementation design was used to evaluate key performance indicators (KPI) before and after the implementation of a dashboard tool to optimize ADCs. Eleven ADCs were optimized in 2 phases according to dashboard recommendations: (1) removal of unused medications over 90 days, (2) adjusting periodic automatic replenishment (PAR) levels, and (3) addition of commonly dispensed medications. The KPI measures that were assessed included inventory cost, no. of stocked medications, stockout percentage, vend to refill ratio, and missing dose messages from nursing. An interrupted-time-series regression was used to quantify the impact of ADCs on the means of measured KPIs. Results: Differences in mean distribution of all KPIs, except missing dose, between the pre- and post-ADC periods during the Phase 1 period were statistically significant: inventory cost (54.2 vs 56), stockout percentage (1.55 vs 1.12), vend to refill ratio (6.83 vs 6.14), and missing dose messages (221 vs 229). Only the mean ADC utilization (57.3 vs 64) and missing dose (228 vs 179) were statistically different between the pre- and post-ADC periods in Phase 2. The interrupted-time-series analysis showed that Phase 1 optimization significantly reduced the cost of inventory (ß = -$1.238.00, P < .01), no. Stocked medications (ß = -8.2, P < .01), percent stockout (ß = -.49%, P < .01), vend-to-refill ratio (ß = -1.29%, P<.01) and ADC utilization (ß = -.2, P < .01). Conclusion: Automated dispensing cabinets optimization, through the use of a dashboard tool, had a positive impact on almost all measured KPIs.

Morphine Equivalent Total Dosage as Predictor of Adverse Outcomes in Opioid Prescribing.

OBJECTIVE: The objective of this work was to develop a risk prediction model for opioid overdose and opioid use disorder for patients at first opioid prescription and compare the predictive accuracy of morphine equivalent total dosage with the predictive accuracy of daily dosage . DESIGN: Records from patients 18-79 years of age with opioid prescriptions between January 1, 2016, and June 30, 2019, no prior history of adverse outcomes, and no malignant cancer diagnoses were collected from the electronic health record system of a medium-sized central Ohio health care system (n = 219,276). A Cox proportional-hazards model was developed to predict the adverse outcomes of opioid overdose and opioid use disorder from patient sociodemographic, pharmacological, and clinical diagnosis factors. RESULTS: During the study time frame, 573 patients experienced overdoses, and 2,571 patients were diagnosed with opioid use disorder. Morphine equivalent total dosage of opioid prescriptions was identified as a stronger predictor of adverse outcomes (C = 0.797) than morphine equivalent daily dosage (C = 0.792), with the best predictions coming from a model that includes both predictors (C = 0.803). In the model with both daily and total dosage predictors, patients receiving a high total / low daily dosage experienced a higher risk (hazard ratio [HR] = 2.17) than those receiving a low total / high daily dosage (HR = 2.02). Those receiving a high total / high daily dosage experienced the greatest risk of all (HR = 3.09). CONCLUSIONS: These findings demonstrate the value of including morphine equivalent total dosage as a predictor of adverse opioid outcomes and suggest that total dosage may be more strongly correlated with increased risk than daily dosage.