1. Software for tracking and prediction of performances of MDs
Software for tracking and prediction of performances of MDs. Planning maintenance
Almir Badnjević, Phd
Medical Device Inspection Laboratory Verlab Ltd, Sarajevo, Bosnia and Herzegovina
International Burch University Sarajevo, Bosnia and Herzegovina
Different software solutions in healthcare are extremely necessary especially because of big data issues. Management of Medical Devices (MD) is one of the most important challenges too.
Figure. Sign up for eVerlab
Figure. Inspection of MD in Legal Metrology Framework
eVerlab, software solution, for tracking of inspection processes is an easy accessible and reliable online program updated on Verlab Inspection Laboratory's website and comprises imported records of performance statuses based on inspection processes of MD’s in all country healthcare institutions. This database is allowing application of Machine Learning techniques on existing data for prediction of performances of MD’s and also for planning the maintenance. 1 2
DATA ANALYSIS, EXTRACTION AND MD MAINTENANCE PLANNING
SEARCH AND DATA MANAGEMENT
Performance analysis is enabled.
Both inspection laboratory staff and technical unit in healthcare institution have insight in accuracy of MDs through adjustable time period by each group of MD.
Search in enabled by healthcare institution, device group, manufacturer, serial number, date of inspection.
Statistical overview is enabled as well as more detailed reporting with all performance characteristics of each MD.
Information about MD inspections are presented by conducted and planned inspections.
Based on inspection result healthcare institution can plan maintenance program for MD.
MD with performance failure are marked with blue, and inspections with expired date are marked with red colour.
Data can be extracted from eVerlab in Excel file and then used for further analysis. 3
EXPERT SYSTEMS BASED ON MACHINE LEARNING ALGORITHMS
Automated expert systems based on machine learning techniques can be developed to predict performance of medical devices and possible failures which can affect performance. To develop accurate prediction algorithms, data of safety and performance measurements (collected during periodical inspections) can be used. Developed automated systems can be reliable and beneficial for healthcare institutions, for management of medical devices and planning of replacements and preventive/corrective maintenance, therefore for decreasing financial cost of MD management.
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