Failure Mode and Effects Analysis (FMEA) at the preanalytical phase for POCT blood gas analysis: proposal for a shared proactive risk analysis model.

OBJECTIVES: Proposal of a risk analysis model to diminish negative impact on patient care by preanalytical errors in blood gas analysis (BGA). METHODS: Here we designed a Failure Mode and Effects Analysis (FMEA) risk assessment template for BGA, based on literature references and expertise of an international team of laboratory and clinical health care professionals. RESULTS: The FMEA identifies pre-analytical process steps, errors that may occur whilst performing BGA (potential failure mode), possible consequences (potential failure effect) and preventive/corrective actions (current controls). Probability of failure occurrence (OCC), severity of failure (SEV) and probability of failure detection (DET) are scored per potential failure mode. OCC and DET depend on test setting and patient population e.g., they differ in primary community health centres as compared to secondary community hospitals and third line university or specialized hospitals. OCC and DET also differ between stand-alone and networked instruments, manual and automated patient identification, and whether results are automatically transmitted to the patient's electronic health record. The risk priority number (RPN = SEV × OCC × DET) can be applied to determine the sequence in which risks are addressed. RPN can be recalculated after implementing changes to decrease OCC and/or increase DET. Key performance indicators are also proposed to evaluate changes. CONCLUSIONS: This FMEA model will help health care professionals manage and minimize the risk of preanalytical errors in BGA.

A survey of practice in the management of haemolysis, icterus and lipaemia in blood specimens in the United Kingdom and Republic of Ireland.

BACKGROUND: Haemolysis, icterus and lipaemia (HIL) are common interferants in laboratory medicine, potentially impacting patient care. This survey investigates HIL management in medical laboratories across the UK and Republic of Ireland (ROI). METHODS: A survey was sent to members of key professional organisations for laboratory medicine in the UK and ROI. Questions related to the detection, monitoring, quality control, and management of HIL. RESULTS: In total, responses from 124 laboratories were analysed, predominantly from England (52%) and ROI (36%). Most responses were from public hospitals with biochemistry services (90%), serving primary care (91%), inpatients (91%), and outpatients (89%). Most laboratories monitored H (98%), I (88%), and L (96%) using automated indices (93%), alone or in combination with visual inspection.Manufacturer-stated cut-offs were used by 83% and were applied to general chemistries in 79%, and immunoassays in 50%. Where HIL cut-offs are breached, 64% withheld results, while 96% reported interference to users. HIL were defined using numeric scales (70%) and ordinal scales (26%). HIL targets exist in 35% of laboratories, and 54% have attempted to reduce HIL. Internal Quality Control for HIL was lacking in 62% of laboratories, and just 18% of respondents have participated in External Quality Assurance. Laboratories agree manufacturers should: standardise HIL reporting (94%), ensure comparability between platforms (94%), and provide information on HIL cross-reactivity (99%). Respondents (99%) showed interest in evidence-based, standardised HIL cut-offs. CONCLUSIONS: Most respondents monitor HIL, although the wide variation in practice may differentially affect clinical care. Laboratories seem receptive to education and advice on HIL management.

Preanalytical errors in medical laboratories: a review of the available methodologies of data collection and analysis.

Preanalytical errors have previously been shown to contribute a significant proportion of errors in laboratory processes and contribute to a number of patient safety risks. Accreditation against ISO 15189:2012 requires that laboratory Quality Management Systems consider the impact of preanalytical processes in areas such as the identification and control of non-conformances, continual improvement, internal audit and quality indicators. Previous studies have shown that there is a wide variation in the definition, repertoire and collection methods for preanalytical quality indicators. The International Federation of Clinical Chemistry Working Group on Laboratory Errors and Patient Safety has defined a number of quality indicators for the preanalytical stage, and the adoption of harmonized definitions will support interlaboratory comparisons and continual improvement. There are a variety of data collection methods, including audit, manual recording processes, incident reporting mechanisms and laboratory information systems. Quality management processes such as benchmarking, statistical process control, Pareto analysis and failure mode and effect analysis can be used to review data and should be incorporated into clinical governance mechanisms. In this paper, The Association for Clinical Biochemistry and Laboratory Medicine PreAnalytical Specialist Interest Group review the various data collection methods available. Our recommendation is the use of the laboratory information management systems as a recording mechanism for preanalytical errors as this provides the easiest and most standardized mechanism of data capture.