Impact of Automated Dispensing Cabinet Optimization on the Incidence of Medication Stockouts
| dc.contributor.advisor | Varkey, Divya A. | |
| dc.contributor.committeeMember | Desai, Avani | |
| dc.contributor.committeeMember | Egwim, Oliver U. | |
| dc.contributor.committeeMember | Iwuorie, Martina | |
| dc.creator | Amaka, Ogechukwu | |
| dc.date.accessioned | 2020-08-26T23:50:08Z | |
| dc.date.available | 2020-08-26T23:50:08Z | |
| dc.date.created | May 2020 | |
| dc.date.issued | 2020-05 | |
| dc.date.submitted | May 2020 | |
| dc.date.updated | 2020-08-26T23:50:09Z | |
| dc.description.abstract | PURPOSE: Assess the change in the incidence of medication stockouts and median stockout duration, and to determine the amount of drug wastage before and after an ADC optimization project. METHODS: Par levels were adjusted for several medications in all ADCs hospital wide based on the BD Pyxis® Enterprise Server Stockout Summary Report suggested par levels during the initial static intervention. There are six profile ADCs located on the 4th floor that will be included in this study as the control group. After a one week washout period, a second dynamic intervention was performed on medications in six profile ADCs located on the third floor of the hospital, which make up the study group. This intervention involved a second par level adjustment that was completed utilizing Epic® predictive analytics. This intervention incorporated current patient census data, active medication orders, and frequency of administration to determine the number of medication units necessary to care for each patient. Par levels for the study group were adjusted frequently based on each unit’s patient census. The primary endpoint is to compare the stockout percentage at one, two, and three months after two, unique ADC optimization interventions and determine the cost of the impact of each intervention. Secondary endpoints include the change in median stockout duration and the amount of expired or wasted medications after optimization compared to baseline data. RESULTS: The dynamic intervention contributed to a greater reduction in stockouts in the study group when compared to the static intervention, but was associated with a higher labor cost to perform during the implementation phase. The median stockout duration improved in each study group for two months after the respective optimizations. The interventions also contributed to a reduction in the amount of medication wasted from the ADCs. CONCLUSION: ADC optimization led to an overall reduction in the incidence of stockouts, and contributed to a reduction in median stockout duration and medication waste. The labor costs to carry out the interventions should be considered when performing ADC optimization. | |
| dc.description.department | Pharmacy Practice and Translational Research, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | https://hdl.handle.net/10657/6981 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | automated dispensing cabinet, ADC, stockout | |
| dc.title | Impact of Automated Dispensing Cabinet Optimization on the Incidence of Medication Stockouts | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| thesis.degree.college | College of Pharmacy | |
| thesis.degree.department | Pharmacy Practice and Translational Research, Department of | |
| thesis.degree.discipline | Pharmacy Leadership & Administration | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science |
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