Failure mode and effects analysis of witnessing protocols for ensuring traceability during PGD/PGS cycles

Applying the healthcare failure mode and effects analysis approach to improve the quality of an organised colorectal cancer screening programme

OBJECTIVE

The first level of a colorectal cancer (CRC) screening process was systematically analysed using the Healthcare Failure Mode and Effects Analysis (HFMEA) approach by a multidisciplinary team aiming to improve the programme quality.

SETTING

The study was conducted at the Local Health Authority of Bologna, Northern Italy.

METHODS

Seven brainstorming sessions were conducted and all the activities performed were recorded on a FMEA worksheet consisting of individual records reporting the specific phases of the analysed process along with associated activities, possible failure modes, their causes and effects, the obtained risk priority numbers (RPNs) and the control measures to plan.

RESULTS

Twenty-three failure modes, 14 effects and 12 possible causes were identified. Nine failure modes were prioritised according to the RPN obtained; most resulted in possible false-negative faecal immunochemical test (FIT) results (66.7%), followed by sample loss (22.2%) and not reaching the entire target population (11.1%). This leads to 66.7% of corrective/preventive actions being applied to the phase of returning the stool sample by the citizen. For this phase reorganisation, the local pharmacies were involved not only as FIT kit delivery points but also as specimen collection and sending points to the laboratory. These organisational changes allowed the introduction of complete traceability of kits and specimens flow, as well as temperature control. A re-evaluation of the prioritised failure modes 6 months after launching the implemented screening process showed that HFMEA application decreased the risk of potential errors by 75.9%.

CONCLUSION

HFMEA application in CRC screening programme is a useful tool to reduce potential errors.

Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study

PURPOSE

To determine whether convolutional neural networks (CNN) can be used to accurately ascertain the patient identity (ID) of cleavage and blastocyst stage embryos based on image data alone.

METHODS

A CNN model was trained and validated over three replicates on a retrospective cohort of 4889 time-lapse embryo images. The algorithm processed embryo images for each patient and produced a unique identification key that was associated with the patient ID at a timepoint on day 3 (~ 65 hours post-insemination (hpi)) and day 5 (~ 105 hpi) forming our data library. When the algorithm evaluated embryos at a later timepoint on day 3 (~ 70 hpi) and day 5 (~ 110 hpi), it generates another key that was matched with the patient's unique key available in the library. This approach was tested using 400 patient embryo cohorts on day 3 and day 5 and number of correct embryo identifications with the CNN algorithm was measured.

RESULTS

CNN technology matched the patient identification within random pools of 8 patient embryo cohorts on day 3 with 100% accuracy (n = 400 patients; 3 replicates). For day 5 embryo cohorts, the accuracy within random pools of 8 patients was 100% (n = 400 patients; 3 replicates).

CONCLUSIONS

This study describes an artificial intelligence-based approach for embryo identification. This technology offers a robust witnessing step based on unique morphological features of each embryo. This technology can be integrated with existing imaging systems and laboratory protocols to improve specimen tracking.

A Customized Tool of Incident Reporting for the Detection of Nonconformances at a Single IVF Center: Development, Application, and Efficacy

In IVF centers, risk assessment applies to complex processes potentially accounting for adverse events and reactions that undergo well-established legislative oversight, and nonconformances (NCs), that lack of established tracking systems. NCs account for an integral part of the quality management system, so that their documentation is important. The study evaluated the performance of a customized tool for incident reporting (IR) to track and characterize NCs in a public IVF center. IVF operators used the IVF-customized IR tool to record NCs at the moment of detection or subsequently, and in a time-saving manner during daily practice. From February 2015 to February 2020, 635 NCs were reported leading to the implementation of 10 operative instructions and 3 procedures with corrective strategies. NCs referred to the IVF laboratory were the most numerically meaningful (454/635, 71.5%). The majority (352/454, 77.5%) accounted for NCs related to procedures of sample management; considering the analytical phase as all the procedures involving sample treatment, the intra-analytical phase (176/352, 50%) has always been more subject to NCs compared to pre- (102/352, 29%) and postanalytical (74/352, 21%) phases. Our experience showed that the IVF-customized IR tool is suitable for application in IVF with regard to NC reports and documentation, as it identifies the most vulnerable steps of treatments. It manages NCs over the time, but it requires a contextual understanding of its application in order to avoid NC underestimates that could negatively influence the safety and quality aspects of IVF treatments.

Preimplantation genetic testing as a component of root cause analysis of errors and reassignment of embryos in IVF

The risks of embryo/gamete mix-up are a threat to the integrity of the IVF process, with significant implications for affected families. The use of preimplantation genetic testing through single-nucleotide polymorphism array or next-generation sequencing technology can help to identify, characterize and ultimately help, in some cases, to find the root cause, and to mitigate the extent of these errors for a given patient or laboratory.

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.

Assessment and management of the risk of SARS-CoV-2 infection in an IVF laboratory

Research question

The study set out to identify corrective measures aimed at reducing the risk of aerosol-mediated viral infection within an IVF laboratory.

Design

A failure modes and effect analysis (FMEA) was conducted by a multidisciplinary IVF team. A schematic representation of new protocols and procedures adopted during COVID-19 emergency has been defined, including directives about the behaviour to adopt when entering the clinic and the laboratory, in case of face-to-face contact with patients and between staff members. In addition, the risk of cross-contamination between samples belonging to different patients during cell handling and manipulation has been evaluated. Potential failure modes for each phase of the emergency have been analysed, focusing on possible sources of error. Risk priority numbers have been calculated as products of Occurrence × Severity × Detection scores.

Results

Except for cell–cell contamination, which was considered highly unlikely, failure modes during patient–staff, staff–staff and staff–cell interactions were estimated as carrrying a moderate to high risk of infection. The main corrective measures entailed precautionary logistic measures, the implementation of additional personal protective equipment and changes in the IVF laboratory procedures and scheduling of the daily routine. Some procedures were also revised, aiming to increase staff's awareness and caution.

Conclusions

Standard laboratory protocols are insufficient to face a virus whose transmission is aerosol mediated. The measures outlined in this FMEA should thus be considered not only for facing this pandemic, but also for the future to promptly manage any aerosol-mediated virus infection, whose impact on the management of an IVF laboratory might be less severe than COVID-19 although not completely negligible.

Which key performance indicators are most effective in evaluating and managing an in vitro fertilization laboratory?

The laboratory is the heart of an in vitro fertilization (IVF) clinic, and a quality management system is critical for its administration. We review the main structural, process, and outcome key performance indicators (KPIs) to provide laboratory managers with concrete tools aimed at enhancing the quality of their work. Three concepts must be stressed when dealing with KPIs in IVF: [1] always consider the three types of indicators (structural, process, and outcome related), [2] carefully adapt the control chart to either promptly identify issues and adopt corrective measures, or redefine the control limits in a process called "progress building," [3] consider that achieving a healthy live birth is a multidisciplinary effort that is subject to several confounders, which must be recognized and accounted for in the analyses. In this regard, future KPIs shared among clinicians and embryologists are desirable to enhance the quality of infertility care for IVF patients.

Failure modes and effects analysis for testicular sperm extraction management process

Failure modes and effects analysis (FMEA) is a proactive risk evaluation to identify and reduce potential failures that may occur during a procedure within a quality management programme. One of the procedures performed in assisted reproduction technology centres is testicular sperm extraction (TESE) as treatment of azoospermic patients. To examine the risks associated with the 'TESE management' process, we applied the FMEA method, before and after implementation of corrective measures defined in a standard operative procedure (SOP). A multidisciplinary team was formed. Possible causes of failures and their potential effects were identified, and risk priority number (RPN) for each failure was calculated. The FMEA team identified 4 process activities, 19 process steps and 19 potential failure modes. The re-evaluation after the corrective measures disclosed a reduction in the number of phases with high/moderate risk (pre-SOP: n = 13; post-SOP: n = 3). Improvements in the traceability system removed 11 out of 13 (85%) steps with a low risk of occurrence. In our experience, FMEA is efficient in helping multidisciplinary groups to strengthen knowledge and awareness on routine processes, identifying critical steps and planning practical improvements for a better compliance with criteria of traceability and conformity of biological samples and patients.

Comprehensive protocol of traceability during IVF: the result of a multicentre failure mode and effect analysis

STUDY QUESTION

Can traceability of gametes and embryos be ensured during IVF?

SUMMARY ANSWER

The use of a simple and comprehensive traceability system that includes the most susceptible phases during the IVF process minimizes the risk of mismatches.

WHAT IS KNOWN ALREADY

Mismatches in IVF are very rare but unfortunately possible with dramatic consequences for both patients and health care professionals. Traceability is thus a fundamental aspect of the treatment. A clear process of patient and cell identification involving witnessing protocols has to be in place in every unit. To identify potential failures in the traceability process and to develop strategies to mitigate the risk of mismatches, previously failure mode and effects analysis (FMEA) has been used effectively. The FMEA approach is however a subjective analysis, strictly related to specific protocols and thus the results are not always widely applicable. To reduce subjectivity and to obtain a widespread comprehensive protocol of traceability, a multicentre centrally coordinated FMEA was performed.

STUDY DESIGN, SIZE, DURATION

Seven representative Italian centres (three public and four private) were selected. The study had a duration of 21 months (from April 2015 to December 2016) and was centrally coordinated by a team of experts: a risk analysis specialist, an expert embryologist and a specialist in human factor. Principal investigators of each centre were first instructed about proactive risk assessment and FMEA methodology. A multidisciplinary team to perform the FMEA analysis was then formed in each centre. After mapping the traceability process, each team identified the possible causes of mistakes in their protocol. A risk priority number (RPN) for each identified potential failure mode was calculated. The results of the FMEA analyses were centrally investigated and consistent corrective measures suggested. The teams performed new FMEA analyses after the recommended implementations.

PARTICIPANTS/MATERIALS, SETTING, METHODS

In each centre, this study involved: the laboratory director, the Quality Control & Quality Assurance responsible, Embryologist(s), Gynaecologist(s), Nurse(s) and Administration. The FMEA analyses were performed according to the Joint Commission International.

MAIN RESULTS AND THE ROLE OF CHANCE

The FMEA teams identified seven main process phases: oocyte collection, sperm collection, gamete processing, insemination, embryo culture, embryo transfer and gamete/embryo cryopreservation. A mean of 19.3 (SD ± 5.8) associated process steps and 41.9 (SD ± 12.4) possible failure modes were recognized per centre. A RPN ≥15 was calculated in a mean of 6.4 steps (range 2-12, SD ± 3.60). A total of 293 failure modes were centrally analysed 45 of which were considered at medium/high risk. After consistent corrective measures implementation and re-evaluation, a significant reduction in the RPNs in all centres (RPN <15 for all steps) was observed. A simple and comprehensive traceability system was designed as the result of the seven FMEA analyses.

LIMITATIONS, REASONS FOR CAUTION

The validity of FMEA is in general questionable due to the subjectivity of the judgments. The design of this study has however minimized this risk by introducing external experts for the analysis of the FMEA results. Specific situations such as sperm/oocyte donation, import/export and pre-implantation genetic testing were not taken into consideration. Finally, this study is only limited to the analysis of failure modes that may lead to mismatches, other possible procedural mistakes are not accounted for.

WIDER IMPLICATIONS OF THE FINDINGS

Every single IVF centre should have a clear and reliable protocol for identification of patients and traceability of cells during manipulation. The results of this study can support IVF groups in better recognizing critical steps in their protocols, understanding identification and witnessing process, and in turn enhancing safety by introducing validated corrective measures.

STUDY FUNDING/COMPETING INTEREST(S)

This study was designed by the Italian Society of Embryology Reproduction and Research (SIERR) and funded by the Italian National Transplant Centre (CNT) of the Italian National Institute of Health (ISS). The authors have no conflicts of interest.

TRIAL REGISTRATION NUMBER

N/A.