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- Title
Key Performance Indicators to Measure Improvement After Implementation of Total Laboratory Automation Abbott Accelerator a3600.
- Authors
Miler, Marijana; Nikolac Gabaj, Nora; Dukic, Lora; Simundic, Ana-Maria
- Abstract
The aim of the study was to estimate improvement of work efficiency in the laboratory after implementation of total laboratory automation (TLA) by Abbott Accelerator a3600 in the laboratory with measuring different key performance indicators (KPIs) before and after TLA implementation. The objective was also to recommend steps for defining KPIs in other laboratories. For evaluation of improvement 10 organizational and/or technical KPIs were defined for all phases of laboratory work and measured before (November 2013) and after (from 2015 to 2017) TLA implementation. Out of 10 defined KPIs, 9 were successfully measured and significantly improved. Waiting time for registration of samples in the LIS was significantly reduced from 16 (9–28) to 9 (6–16) minutes after TLA (<italic>P</italic> < 0.001). After TLA all tests were performed at core biochemistry analyzers which significantly reduced walking distance for sample management (for more than 800 m per worker) and number of tube touches (for almost 50%). Analyzers downtime and engagement time for analyzers maintenance was reduced for 50 h and 28 h per month, respectively. TLA eliminated manual dilution of samples with extreme results with sigma values increment from 3.4 to >6 after TLA. Although median turnaround time TAT for potassium and troponin was higher (for approximately 20 min), number of outliers with TAT >60 min expressed as sigma values were satisfying (>3). Implementation of the TLA improved the most of the processes in our laboratory with 9 out of 10 properly defined and measured KPIs. With proper planning and defining of KPIs, every laboratory could measure changes in daily workflow.
- Subjects
CROATIA; AUTOMATION; CLINICAL medicine; LABOR productivity; PATHOLOGICAL laboratories; QUALITY assurance; TIME; WORKFLOW; KEY performance indicators (Management); DATA analysis software; ROUTINE diagnostic tests
- Publication
Journal of Medical Systems, 2018, Vol 42, Issue 2, p0
- ISSN
0148-5598
- Publication type
Article
- DOI
10.1007/s10916-017-0878-1