An Automated Metrics System to Measure and Improve the Success of Laboratory Automation Implementation
| Autor: | Fabrice Turlais, Victoria Clark, Neil Benn, Mike Jones, Stephen Clulow |
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| Rok vydání: | 2006 |
| Předmět: |
SQL
Engineering Workstation Computer science Bar chart Clinical Biochemistry Real-time computing computer.software_genre 01 natural sciences Usage data Plot (graphics) law.invention Automation law Humans computer.programming_language Measure (data warehouse) Database Clinical Laboratory Techniques business.industry Biochemistry (medical) 010401 analytical chemistry Purchasing 0104 chemical sciences Computer Science Applications Systems Integration 010404 medicinal & biomolecular chemistry Medical Laboratory Technology Laboratory automation Laboratories business computer |
| Zdroj: | JALA: Journal of the Association for Laboratory Automation. 11:16-22 |
| ISSN: | 1535-5535 |
| DOI: | 10.1016/j.jala.2005.09.001 |
| Popis: | We describe a system for collecting usage metrics from widely distributed automation systems. An application that records and stores usage data centrally, calculates run times, and charts the data was developed. Data were collected over 20 months from at least 28 workstations. The application was used to plot bar charts of date versus run time for individual workstations, the automation in a specific laboratory, or automation of a specified type. The height of one bar represented the length of one run, and its position on the x-axis indicated the date the run started. Graphs were visually inspected and few bars on the graph(s) indicated infrequent use. Quantitative data were obtained by running custom SQL queries. Analyses showed that 90% of the automation was used frequently (>8 runs per week). Where usage was less, or limited to part of the day, the causes were investigated by interviewing users. We show that revised user training, redeployment of equipment, and running complimentary processes on one workstation can increase the average number of runs by up to 20-fold and run times by up to 450%. Active monitoring of usage leads to more effective use of automation. Usage data#could be used to determine whether purchasing particular automation was a good investment. (JALA 2006; 11: 16-22) |
| Databáze: | OpenAIRE |
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