Practical aspects of the use of FMEA tool in clinical laboratory risk management

Lean and Six Sigma as continuous quality improvement frameworks in the clinical diagnostic laboratory

Processes to enhance customer-related services in healthcare organizations are complex and it can be difficult to achieve efficient patient-focused services. Laboratories make an integral part of the healthcare service industry where healthcare providers deal with critical patient results. Errors in these processes may cost a human life, create a negative impact on an organization's reputation, cause revenue loss, and open doors for expensive lawsuits. To overcome these complexities, healthcare organizations must implement an approach that helps healthcare service providers to reduce waste, variation, and work imbalance in the service processes. Lean and Six Sigma are used as continuous process improvement frameworks in laboratory medicine. Six Sigma uses an approach that involves problem-solving, continuous improvement and quantitative statistical process control. Six Sigma is a technique based on the DMAIC process (Define, Measure, Analyze, Improve, and Control) to improve quality performance. Application of DMAIC in a healthcare organization provides guidance on how to handle quality that is directed toward patient satisfaction in a healthcare service industry. The Lean process is a technique for process management in which waste reduction is the primary purpose; this is accomplished by implementing waste mitigation practices and methodologies for quality improvement. Overall, this article outlines the frameworks for continuous quality and process improvement in healthcare organizations, with a focus on the impacts of Lean and Six Sigma on the performance and quality service delivery system in clinical laboratories. It also examines the role of utilization management and challenges that impact the implementation of Lean and Six Sigma in clinical laboratories.

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.

Failure mode and effects analysis for proactive healthcare risk evaluation: A systematic literature review

RATIONALE, AIMS, AND OBJECTIVES

Failure mode and effects analysis (FMEA) is a valuable reliability management tool that can preemptively identify the potential failures of a system and assess their causes and effects, thereby preventing them from occurring. The use of FMEA in the healthcare setting has become increasingly popular over the last decade, being applied to a multitude of different areas. The objective of this study is to review comprehensively the literature regarding the application of FMEA for healthcare risk analysis.

METHODS

An extensive search was carried out in the scholarly databases of Scopus and PubMed, and we only chose the academic articles which used the FMEA technique to solve healthcare risk analysis problems. Furthermore, a bibliometric analysis was performed based on the number of citations, publication year, appeared journals, authors, and country of origin.

RESULTS

A total of 158 journal papers published over the period of 1998 to 2018 were extracted and reviewed. These publications were classified into four categories (ie, healthcare process, hospital management, hospital informatization, and medical equipment and production) according to the healthcare issues to be solved, and analyzed regarding the application fields and the utilized FMEA methods.

CONCLUSION

FMEA has high practicality for healthcare quality improvement and error reduction and has been prevalently employed to improve healthcare processes in hospitals. This research supports academics and practitioners in effectively adopting the FMEA tool to proactively reduce healthcare risks and increase patient safety, and provides an insight into its state-of-the-art.