Moving towards total health data integration including quality management: insights from the SIBioC Working Group “Big Data and Artifcial Intelligence” survey

AUTORI

ABSTRACT

Introduction: recently the Italian Society of Clinical Biochemistry and Clinical Molecular Biology (SIBioC) Big Data and Artificial Intelligence Working Group (BAI-WG) conducted a survey to examine technological and information technology readiness for developing BAI applications and to investigate laboratory professionals’ knowledge and skills. This article examines specific survey questions related to supporting data. These data are visible only by laboratory professionals for the purpose of supporting all laboratory processes and, ultimately for the validation of results and they can be generally ascribed under the quality management system (QMS).
Methods: the questionnaire, designed by the BAI-WG, was sent to 1351 SIBioC members. The responses were evaluated using Survey-Monkey software and Google Sheets.
Results: over 90% of respondents work in laboratories with a QMS in place. The participants consider digitisation of QMS as highly advantageous (93%). Nevertheless, computerisation of the QMS is actually often incomplete, and the connection between QMS and Laboratory Information Systems (LIS) is usually lacking or missing. Consequently, alternative systems, separate from the LIS, have been developed to record various QMS data essential for monitoring processes.
Discussion: the integration of medical data sources is crucial for developing BAI applications. The issue of integration is relevant and strongly linked to digitisation. However, algorithms may often consider only reported data, but integration should also be extended to supporting data, which could be correlated with clinical and process outcomes. Current LIS lack the necessary features for BAI applications and QMS digitisation is still too far behind to allow for real-time control. Software vendors should move towards the total integration of health data.

BIBLIOGRAFIA

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