Defining and Managing the Preanalytical Phase With FMECA: Automation and/or "Human" Control. (February 2020)

• Record Type: Journal Article
• Authors: Bellini, Claudia; Guerranti, Roberto; Cinci, Francesca; Milletti, Eva; Scapellato, Carlo
• Other Names: Toccafondi Giulio guest-editor.; Parlangeli Oronzo guest-editor.
• Is Part Of: Human factors. Volume 62:Number 1(2020)
• Page Start: 20
• Page End: 36
• Publication Date: 2020-02
• Subjects: patient safety -- medical devices and technologies -- process control -- human systems integration -- safety culture and behavior change -- health-information technology (HIT); Human engineering -- Periodicals; 620.82
• DOI: 10.1177/0018720819874906
• Languages: English
• ISSNs: 0018-7208

Abstract

Objective: Our scope is to provide methodological elements on how to manage effectively the preanalytical phase in the laboratory testing process, by objectively measuring the risk connected to the phases handled by man with respect to those managed by machines. Background: Preanalytical errors account for most of the mistakes related to laboratory testing and can affect patient care. Hence, it is necessary to manage the risk connected to the preanalytical phase, as required by certification and accreditation bodies. The risk assessment discloses the steps at greater risk and gives indications to make decisions. Method: We have reviewed the state of art in the automation of the preanalytical phase, addressing needs and problems. We have used the proactive risk assessment methodology FMECA (Failure Mode, Effects, and Criticality Analysis) to identify the most critical phases in our preanalytical process and have calculated the risk associated. Results: The most critical phases were the human controlled ones. In particular, the highest risk indexes were associated to manual acceptance of test orders, identification of the patients, tube labeling, and sample collection. Conclusion: Automation in the preanalytical phase is fundamental to replace, support, or extend the human contribution. Nevertheless each organization is different about workloads and competencies, so the most suitable management must be tailor-made in each context. Application: We present a method by which each organization is able to find its best balance between automation and human contribution in the control of the preanalytical phase.