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Errors in IVF laboratories: risks assessments and mitigations



Assisted reproductive technology (ART) has positively impacted the field of human reproduction. Over the years, ART procedures have evolved to include several complex techniques, associated with various risks of errors and failure opportunities, especially in laboratories. IVF-associated errors, although rare, have significant implications. Patients may be psychologically affected, given the emotional attachment associated with IVF treatments. Most of these errors are associated with humans and/or systemic failure.


In this article, we used the Google Scholar database to search for related peer-reviewed original and review articles. Search keywords were “IVF laboratory”, “the embryologist”, “laboratory error”, “IVF laboratory errors”, “IVF error prevalence”, “risks of IVF error”, “consequences/ repercussion, IVF errors”, “risk assessment”, and “risk mitigation”. All studies were extensively evaluated.


There is a need for an effective approach toward improving existing risk management systems and, possibly, developing better risk management models that can eliminate these errors. Because laboratory resources (equipment and human expertise) are finite and are dependent on systemic policies, error mitigation must involve a multidimensional approach. This review includes several of these error-mitigating strategies as well as innovative technologies that may provide practical approaches to improve error surveillance, reporting, management, and potentially, eradication. Furthermore, errors in IVF laboratories threaten the integrity of the IVF processes and pose significant implications, which are often negative.


This review highlights those errors and the stages they occur during the IVF process.


Understanding the risks of errors in IVF laboratories can help embryologists develop better approaches to identify, evaluate the causes, and prevent errors in the laboratory. Essentially, the practice of effective risks assessment and management can help reinforce patients’ trust in the clinics and prevent repercussions such as litigations and many more. More laboratories can also begin reporting even minor errors to curb the scarce data in this subject.


The concept of errors in human-assisted reproductive technology (ART) is a poorly discussed subject, even though several techniques used in the retrieval, manipulation, assessment, and culture of gametes and embryos in IVF laboratories are associated with diverse risks profiles and failure tendencies [1]. Therefore, this may be responsible for the limited data reporting the rate of errors in IVF laboratories. These may be due to reluctance in reporting and disclosing data of laboratory errors and their frequency to avoid blames, stigma, and/ or disciplinary actions [2]. It is quite logical to state that the odds of errors occurring increase with the complexity involved in executing a task. The decision to embark on an infertility treatment journey is a major milestone for many patients. This journey is associated with various emotional attachments since it deals with the creation of new human lives and strengthening family ties. Therefore, the occurrence of certain errors may attract severe consequences (litigations and payment of large compensation fees) for clinics [3]. Courts in certain regions ruled that the birth of a healthy child as the outcome of an IVF error is not regarded as compensation [4]. Therefore, the common idiom “To Err is Human” may not apply in IVF. Nevertheless, the reality remains that problems do occur and the outcomes can vary from minimal inconvenience to extreme harm [5].

Harm to patients due to medical errors also arise from laboratories [6]. The definition of error in laboratory medicine is the failure to execute a planned task as intended, or adopting an incorrect plan to achieve an aim, which can occur at any phase in the laboratory cycle—from the period when an examination (or procedure) is ordered to reporting results, interpreting and reacting to them appropriately [7]. These phases in the laboratory cycle can be explicitly categorized into the pre-analytical (e.g. patient identification, labelling, the rapport between the clinical and laboratory personnel of a unit), analytical (actual laboratory procedural; oocyte retrieval, insemination, cryopreservation etc.), and post-analytical phases (e.g. recording, reporting results) [2]. In IVF laboratories, these deviations from the accepted standard or stipulated protocol are called non-conformances. It may also be due to poor training and problems with staffing [5].

Besides the advanced technological procedures utilized in IVF laboratories, the rapidly growing patient population resorting to IVF treatment has increased the responsibilities of embryologists in the laboratory. The intending parents alone do not make up this large population. It also includes gametes from oocytes and sperm donors and gestational surrogates [5]. Thus, the work in the IVF laboratory may be demanding and stressful, which may result in burnout and depression in the embryologists, thereby compromising the quality of care and contributing to errors [8, 9]. To this effect, regulatory bodies of human-assisted reproduction technologies (ARTs), including the Human Fertilization and Embryology Authority (HFEA) [10, 11] and the European Society of Human Reproduction and Embryology (ESHRE) [12] have set up good practice guidelines to minimizes these errors. Humans are fallible, even experienced clinical embryologists can make mistakes. However, understanding the risks of errors in IVF laboratories may help embryologists develop better approaches to identify, evaluate the causes, and prevent errors in the laboratory [5]. There is still need to curb the scarcity of data reporting errors in IVF laboratories. In addition, there is need to provide updates on the strategies for minimizing and preventing reoccurrence of errors in IVF laboratories. Therefore, we aim to delineate the types of IVF laboratory errors, their associated risk of occurrence during each laboratory procedural process. We also aim to highlight the several error-mitigating strategies, which may serve as practical approaches to improve error surveillance, report, management, and potentially eradication.

Type of errors in IVF laboratory

From literature, it is evident that major errors in IVF laboratories are rare [13]. In the IVF laboratories, errors or non-conformances are categorized and graded based on the impact they may elicit on the outcome or progress of a treatment cycle [5, 14, 15]. These gradings include:

Minimal grade

A minimal grade non-conformance refers to problems (usually near-miss cases, which typically elicit no harm) that do not significantly reduce the chances of success in a treatment cycle. However, these problems can necessitate the rescheduling of a cycle.

Moderate grade

Moderate grade non-conformances are problems that adversely affect a cycle (decrease the possibilities of success in the cycle or even the next), but the cycle is not severely jeopardized or lost.

Significant grade

This class of non-conformances are problems that can significantly jeopardize a cycle or result in its loss.

Major grade

This class of non-conformances are rare forms of problems that can cause extreme harm to patients.

On a broad basis, two types of error can occur in the IVF laboratory: active and latent errors.

Active errors

These are unsafe acts that can cause immediate harm to a patient or a system. They are usually unpredictable errors committed by those in direct contact with the patients or systems (de Ziegler et al., 2013). In IVF laboratories, active errors can come in the following forms:

  1. (i)

    Human error

    Traditionally, human errors are viewed as procedural violations and mistakes committed by a person due to poor mental processes like inattention, forgetfulness, carelessness, recklessness, negligence, and poor motivation [2, 16,17,18]. Human errors can be prevented as they are errors committed due to oversight or when procedures are carried out without conforming to documented protocols [5, 19].

    Given that human errors are typically associated with specific behavioural traits, they can be monitored by measuring an operator’s skills, knowledge, and conformance to rules [19, 20]. As shown in Fig. 1, skill-based human errors due to aberrant fine motor coordination and poor whole-body movement are responsible for slips and tripping respectively. For example, dropping dishes containing gametes or embryos or accidentally knocking off pipettes when handling oocytes or embryos. Skill-based human error can also be due to a lack of training [21]. Next, a knowledge-based error can occur when an operator cannot improvise by utilizing existing knowledge to tackle new challenges. For example, failure to inseminate or inject oocytes. Lastly, as a rule in the medical domain, an operator is expected to possess the right qualification, be a team player, and be competent in carrying out designated procedures according to stipulated protocols (that is, to first verify patient identity and materials required before the procedure, correctly execute the procedure, and monitor the process during or after the procedure) [14, 21]. Examples of rule-based human error in IVF laboratories include; wrong labelling, wrong documentation, shutting down a piece of equipment mid cycle, the wrong operation of equipment, skipping an embryo during assessments, carrying dishes inappropriately, poor thawing, record-keeping errors, gametes and embryos mishandling, and misidentification. The impacts of these errors can range from minimal to significant in grade [5, 11].

    According to Toft et al, the contributing factors associated with human errors include stress, conscious automaticity (Intentionally giving less attention to a present task due to familiarity), involuntary automaticity (inadvertently reducing the attention a procedure deserves), and ambiguous accountability (when two people responsible for a similar task fail to reinforce safety and reduce risk of failure) [22, 23].

  2. (ii)

    Communication failure

    An ideal ART clinic operates like a single multicellular organism made up of different functional units including clinicians, nurses, clinical embryologists, ultrasonographers, paramedics, and cleaning and maintenance staff working together as a team, through effective communication and cooperation, to achieve a common goal—optimize patients’ outcomes [13, 24]. The impacts of poor inter-departmental communication may significantly prolong the time taken to identify and solve problems and may create an uncomfortable work environment [6].

  3. (iii)

    Patient-related problems

    These are problems outside the staff purview but are rather associated with a patient’s characteristics or conditions and may compromise treatment. It can also be complaints from patients [25, 26]

Fig. 1
figure 1

Categories of human error. Human errors may arise due to poor skills required to execute tasks (skill-based), lack of relevant knowledge required to tackle new challenges (knowledge-based), and non-conformance to rules (rule-based)

Latent errors

These are errors that arise due to system inefficiencies. For instance, understaffing, staff micromanagement, time pressure, insufficient and poorly maintained equipment, inaccurate, or overly complicated protocols, fatigue, and burnout. They are easy to identify and prevent [14, 19, 20, 27]. Typically, the system inefficiencies responsible for latent errors arise due to poor and non-strategic decisions made by top-level management (like the IVF clinical and laboratory directors), builders, and designers. They decide the conditions under which humans work. Therefore, poor decisions from the top-level management leading to understaffing, time pressure, insufficient and poorly maintained equipment, staff micromanagement, instituting unrealistic, inaccurate, or overly complicated protocols, fatigue, and burnout are triggers for latent errors within the workplace [5, 13, 21]. Similarly, some non-strategic decisions from designers and builders, such as installations of unreliable alarm systems and indicators, as well as infrastructural construction and design blunders, create longer-lasting weaknesses in the system’s defences against errors. Like a time bomb, these latent conditions can remain dormant within the system for years, awaiting a trigger (usually an active error) to elicit an adverse event [16, 28]. The system’s defences are countermeasures kept in place to prevent errors from eliciting adverse events [27]. For example, a laboratory director employed the services of a local IVF clinic construction and design agent to design a cheap cryostorage unit. A substandard alarm system was installed to monitor the capacity of the cryotank. In addition, the alarm system was powered with the same power supply as the liquid nitrogen autofill system. This power supply is expected to remain on always. Unfortunately, a maintenance staff who was not familiar with this unit’s new layout came in, as usual, to clean the lights and assume the power supply was also a light switch. Therefore, this staff always turns off the switch to clean. This latent condition remained undetected for many years. In this situation, the latent errors including non-conformance to a standard policy, poor planning, and design combined with ineffective communication will eventually result in an adverse event that will compromise the quality of patient care. In the famous Reason’s Swiss cheese theory of error, these latent conditions are weaknesses (or gaps/ holes) in the system’s defence layers. When all the holes from each layer (i.e. policy, planning, design, communication) align, they will permit an error to cause harm [16, 27,28,29]. See Fig. 2.

Fig. 2
figure 2

Reason’s Swiss Cheese Theory of Error [27]. The bars represent an organization’s defences against failures and the holes represent weaknesses in the defences. A patient experiences an adverse event only when all the holes in the defensive layers align to allow these errors trajectory to eventually reach the patient. These weaknesses in the clinic’s policy, planning, building design, and inter-departmental communication are responsible for the adverse event experienced by the patient.

Phases of IVF laboratory cycle and associated potential risks of error

As previously highlighted, there are three phases in the laboratory cycle; the pre-analytical, analytical, and post-analytical phases [17, 21, 30, 31]. The pre-analytical phase entails the prerequisite processes carried out before the main laboratory procedural (analytical phase). Typically, the main laboratory procedures in a routine IVF laboratory, include oocyte collection, gamete processing, sperm donation, insemination, embryo culture, embryo transfer, and cryopreservation [25]. The post-analytical phase mostly involves double-checking by a witness and recording critical data on patients’ files [2, 32].

Oocyte collection

The contribution of the laboratory to patients’ IVF treatment journey begins with oocyte retrieval or collection [33]. The pre-analytical steps taken by embryologists intending to perform this procedure include personally ascertaining the identity of the patient in the operating theatre (OT), labelling and assigning tubes and dishes to be used for follicular fluid screening, oocyte washing and culture. The post-analytical processes after the procedure, which should be carried out by a second operator (the witness), include the following: crosschecking whether the patient’s identity corresponds with laboratory records and with the information of labels present on the tubes and dishes, validating the number of labels used versus numbers of supplies/ consumables used, and verifying that all supplies, consumables, and sharps that need to be discarded are trashed properly and tubes and/or dishes are incubated appropriately. Lastly, any important data collected during the procedure should be properly documented in the patient’s file [1, 2, 17, 34].

Sperm collection

Similarly, the pre-analytical steps required before carrying out the procedure first involve the identification of the male partner or donor by the embryologist or andrologist. Next, the same laboratory staff labels the sperm collection container and hands it to the patient or donor for ejaculation [35, 36]. If it is a procedure of surgical sperm retrieval [37, 38], then the patient is identified in the OT and tubes and dishes are also labelled therein. The post-analytical processes remain a witness crosschecking the identity of patients with records of the laboratory and label information present on the sperm collection container, tubes, and dishes, corresponding the number of labels used against the consumed supplies, and verifying the appropriate disposal or incubation of materials and supplies used. Furthermore, any important data collected during the procedure should be properly documented in the patient’s file [1, 34, 39].

Gamete processing

This is a term used to collectively group procedures such as sperm and oocyte preparations (such as denudation) [33, 35, 36]. Before this procedure, the pre-analytical steps taken involve the principal embryologist ensuring that at every step during the main procedure, the label information present on tubes and dishes matches patient records. After the procedure, the post-analytical steps taken involved a witness double-checking the information on labels against records of the laboratory and confirming all material or supplies are either correctly trashed or incubated [1, 2, 34, 40].


Before carrying out insemination either for IVF or intracytoplasmic sperm injection (ICSI), an embryologist first pre-labels the tubes and dishes required for the procedure and ensures that the sperm and oocytes for insemination match the identity of the patients and their partners. After the procedure, the witness double-checks label information against records of the laboratory [34, 41].

Embryo culture

This phase is associated with identifying the pre-labelled culture dishes and managing the uniformity between the labels present on the dishes each time embryos are transferred from one dish to another. As always, a witness double-checks to confirm whether labels on all dishes used for the procedure match laboratory records and if all materials used are either discarded or incubated appropriately [1, 2, 31, 34, 40]. What about the incubators?

Embryo transfer (ET)

This is one of the most crucial determinants of success in the IVF/ICSI treatment cycle [42]. The essential prerequisite steps required for this procedure are patient identification, verification to establish that patient identity corresponds with laboratory records and label information present on the ET dish, and verification that the number of an embryo(s) positioned in the ET dish for either transfer, extended culture, or cryopreservation are correct and matches the right patient, and crosschecking that all supplies and materials used are either disposed or incubated appropriately. After the procedure, important data are recorded on the patient’s file for ET [2, 34, 43].


For vitrification or freezing, patient identities are first matched with the laboratory records and label information. This is followed by pre-labelling all dishes, tubes, straws, or vials for sperm freezing and oocyte and embryo vitrification. At each step during the procedure, the main embryologist ensures that labels correspond with each step. After the procedure, a witness verifies that the label matches with laboratory records and that all supplies utilized are either properly trashed or stored in the appropriate cryotank written in the patients’ file. Lastly, important data are recorded in the patients’ files for freezing or vitrification. On the other hand, for thawing or warming, the principal embryologist ensures the position of the cryo-container in the cryotank corresponds with the information indicated in patients’ records and pre-labels all materials required for the procedure. The straws or vials are identified and a witness double-checks to confirm whether labels match patients’ records. After the procedure, critical data are recorded in patients’ files for thawing [1, 2, 34, 44].

Meanwhile, like other medical laboratories, the potential sources of errors when operating in IVF laboratories are most likely associated with the pre- and post-analytical processes than the main procedural [45]. Rienzi et al. (2015, 2017) in their recent study showed the failure modes associated with common IVF laboratory procedural and their possible consequences. The initial identification of gametes during gamete retrieval and the identification of gamete and embryo during thawing are the pre-analytical steps most vulnerable to failures. Meanwhile, the possible causes of failures during these phases are associated with factors including poor inter-departmental communication, intense clinical workload and distraction, utilization of inappropriate labelling system, and equipment malfunction (see Table 1) [1]. This technique of identifying the potential causes of failure associated with the processes involved in the principal laboratory procedural and their possible adverse effect is referred to as failure mode and effect analysis (FMEA) [46]. This will be discussed further in the subsequent section.

Table 1 Phases of the IVF laboratory cycle and associated causes and consequences of failures

Risk assessment in IVF laboratories

A study showed that the most frequent causes of settlement claims in ART are errors from the IVF laboratory [48]. The absolute eradication of errors in IVF laboratory is impossible; however, there are means currently available to assess and minimize their risks. The risk of errors in IVF laboratory can be assessed either reactively or proactively [29].

Reactive assessment of risks

This is a well-established means of risk assessment that entails reporting adverse incidents after they had occurred. However, a major pitfall of this method is the tendency of the reporter to embellish or underestimate the true extent of the incident when reporting [29]. Nevertheless, this method of risk assessment can only be effective in a setting that runs an open and fair culture, devoid of myths such as everyone can always execute a task with 100 % efficiency and punishment after failure is necessary to improve performance [49]. Only then can an embryologist who by mistake transferred the embryos of patient A to patient B be encouraged to immediately report this incident and not silently hope for an implantation failure or any other outcome apart from pregnancy. Interestingly, in 2005, the Human Fertilization and Embryology Authority (HFEA) of the UK, created a national reporting system for ART clinics to report the occurrence of adverse incidents. The goal of establishing this reactive reporting scheme was to identify common areas of failures in assisted conception units in the UK and develop national practicable corrective measures from the problem patterns [50]. To effectively evaluate failures reactively, conducting a root cause analysis is critical [29, 51]

Root cause analysis

Root cause analysis is a systematic investigative tool for reactive evaluation of risk, usually carried out immediately after the occurrence of an error [52]. As shown in Fig. 3, it typically involves designing a timeline that shows the timing and sequence of events of interest, conducting interviews, analysing data obtained from other sources, creating a schematic illustration of causes and effects, and finally deciding how to respond to the uncovered root cause [53]. The strength of the final decision or recommendation determines how effective and sustainable they will become as a countermeasure against the reoccurrence of such adverse incidents. A strong recommendation is considered possibly the most effective and sustainable countermeasure that depends less on human behaviours. It includes steps like simplifying process, standardizing equipment or procedure, and engineering control. On the other hand, a weak recommendation will necessitate a change in human behaviours. It usually involves changes in policy and training of staff, which are important to improve proficiency but less effective for a sustainable improvement in patients’ safety against future adverse events [53,54,55].

Fig. 3
figure 3

Steps of root cause analysis. This schematic representation shows the 4 stages of root cause analysis (after low pregnancy rate with FET cycles). The process begins with creating a chronological time map to track the sequence of the event. It is followed by a personnel interview and investigation of contributory factors. The third stage involves making a cause and effect flowchart that will help in fabricating recommendations to tackle the root cause

For instance, assuming W is an assisted conception unit. During one of their monthly key performance indicator (KPI) review meetings, the clinical and laboratory directors notice a poor pregnancy rate from frozen-thawed embryo transfer. They unanimously decided to investigate the cause of this failure by root cause analysis. The process is illustrated in Fig. 3.

Proactive assessment of risk

The proactive risk assessment is a probabilistic method of risk assessment that relies on predicting the possibility of risk, and then developing countermeasures to prevent any foreseeable adverse incidents [56,57,58]. The outcome after the analysis of failure incident reports of a unit(s) over time is the establishment of the patterns and prevalence of common failures that occur. Proactive risk assessments or predicting foreseeable risks can be made by understanding these patterns [57]. The failure mode and effect analysis (FMEA) is an example of a proactive technique for risk assessment [59].

Failure mode and effect analysis (FMEA)

In conducting a FMEA, it is important to first identify what could go wrong during the IVF processes, then determine what would have been the cause of such failure, and, lastly, map out the possible consequences of each failure [29] (See Table 1). Studies have shown that this is an effective technique for risk evaluation, which can be applied to identify both known and possible problems that may occur during laboratory procedural processes. As a result, these failures can be prevented from happening eventually [46, 59, 60].

Rienzi et al (2017) came up with a comprehensive system for gametes’ and embryos’ traceability during the IVF process after a 12-month failure mode and effect analysis (FMEA) study from seven assisted conception centres in Italy. The outcome of this study was aimed at supporting clinics to design a standardized and objective system to recognize the mode of failure risks at each step during IVF and assign scores to each failure mode based on their severity (S), frequency of occurrence (O), and probability of detection (D). The score of each parameter is determined on a scale of 1 to 5 (see Rienzi et al (2017) for description) and a risk priority number (RPN) is derived by multiplying the scores of all three parameters. For example; if S = 5, O = 1, and D = 5, then RPN = (5 × 1 × 5) = 25. The failure mode with the highest RPN is prioritized for corrective intervention [34]. I would add some final remarks here about this methodology, probably some conclusions or thoughts on whether it would be recommendable to set up such a system in your labs or whether there are other similar systems out there. Additionally, it would be good to state potential differences with other systems like the ones from Mortimer.

Risk mitigation in IVF laboratories

As earlier stated, IVF is a complex process and as such opportunities for errors increases. The total eradication of error in the laboratory may be impossible but with the aid of several innovative risk management programmes and technologies, the risk of errors can be minimized [61]. See Fig. 4.

Fig. 4
figure 4

Risk management programmes and technologies for tackling errors in the IVF laboratory. Artificial intelligence and electronic witnessing system are innovative technologies introduced in IVF to help optimize procedural efficiency, reduced complacency, and minimize errors. Root cause analysis, failure mode and effect analysis, total quality management activities in the laboratory, and stress reduction (minimizing workload) are essential programmes for minimizing the risk of errors in IVF laboratories

Electronic witnessing system

Sample mismatching errors (simply referred to as mix-ups) in IVF are considered the most dreaded adverse event because, besides both the embryologist involved and the clinic facing serious litigations as a consequence, it may seriously affect patients psychologically and damage their trust in the clinic. The ripple effect of the consequences of mix-ups also tarnishes the reputation of the clinic [62, 63]. Interestingly, the global occurrence of mix-ups in IVF laboratories is considered a rare incident. However, there have been some reports of mix-ups across ART clinics worldwide [3, 64, 65]. Therefore, it became necessary to introduce a reliable safety system to manage the traceability of gametes and embryos during the key steps in the laboratory [12, 66]. To this effect, manual-double witnessing by a second embryologist was first recommended (both by HFEA and ESHRE) as a key strategy for preventing mix-ups in the laboratory [10, 12, 66]. But a major flaw of this traceability control mechanism lies in human fallibility. Experienced embryologists can also make mistakes [67], thus, the justification for introducing the electronic witnessing system (EWS). EWS is a new generation traceability control system that compels an embryologist to conform to the standard operating procedure by following the appropriate sequence of a process and ensuring only samples of one patient is handled at a time [68, 69]. In addition, the EWS minimizes the chances of mix-ups by controlling the correspondence of gametes and embryo change-over between dishes or tubes during all procedural steps. Currently, the use of EWS is recommended by ESHRE in its recent guidelines for good laboratory practices [70].

EWS uses a radiofrequency identification (RFID) or unique barcoded adhesive tags (plastered on all tubes, dishes, and straws) that can be captured by special readers, which are connected to a computer [71,72,73]. There have been concerns raised over the safety of electromagnetic radiation (radiofrequency) on gametes and embryos. Interestingly, a recent study showed that the EWS is safe, as there was no significant difference observed with respect to maturation-, fertilization-, cleavage-, and embryo development- rates when compared with a control [62]. Nevertheless, an alternative exists. The oocytes and embryos (but not sperm) can be directly tagged by attaching polysilicon barcodes on their outer surfaces. Embryologists can read these barcodes under a stereo-zoom microscope [71, 72, 74, 75].

A new frontier in electronic traceability systems for cryostorage was recently unveiled by TMRW Life Sciences [76]. The TMRW technology is a cryostorage efficiency solution. It essentially comprises five features, which include a software programme (that control the correspondence of oocytes and embryos to optimize traceability), a RFID technology (that controls the correspondence between patient IDs and specimen to prevent mix-ups), a robotic automation system (to optimize specimen retrieval and ensure other specimens remain at optimal temperature), a military-grade security system (to ensure patients’ data are secured), and a sensor-integrated monitoring system (that monitors the environment of the specimens, proactively assesses any possible risk of error, and alert the clinic of such risk—thereby preventing the occurrence of adverse events). This technology promises to bring peace of my mind to the embryologists and patients by automating error-prone manual tasks like measuring the level of liquid nitrogen, labelling, and managing patients and specimen correspondences [76,77,78].

Total quality management system

The introduction of quality management system in ART is a concept for optimizing the quality of patient care, quality of care for gametes and embryos, treatment efficiency, the welfare of offspring, and minimizing the financial burden on patients to achieve the desired outcome [79]. Mortimer and Mortimer (2015) describe quality management as the integration of three critical quality processes including quality control (QC), quality assurance (QA), and quality improvement (QI) [28, 29]. Quality control in IVF laboratories is a term used to describe activities done in the laboratory to make certain that either a specific procedure or a piece of equipment within the laboratory runs or functions appropriately. It helps to check that one specific element (e.g. equipment) in the laboratory will consistently yield similar results [80] Meanwhile, quality assurance is a comprehensive approach for monitoring and evaluating the whole process. It is a programme that tries to improve the entire laboratory process. A typical QA programme in an IVF laboratory encompasses all QC activities, a comprehensive standard operating procedure (SOP) manual, continuous educational activity for embryologists, a safety programme to protect both embryologists and patients, and an evaluation programme for staff. QA activities done in IVF laboratories can help in detecting errors as well as correcting them. Finally, unlike the QA programme, which primarily attempts to detect and correct errors, the QI programme is designed to enhance laboratory performance throughout the entire process. QI programmes are considered to be procedural add-ons aimed at enhancing a specific aspect of the laboratory. The combination of these three programmes (QC, QA, and QI) forms the total quality management system of the laboratory. To achieve these via total quality management, then process mapping, process control, and benchmarking are necessary [28, 79, 81].

Process mapping

Process mapping is simply a schematic representation of how tasks should be executed (workflow) [82]. Typically (as shown in Fig. 5), the representation of tasks on a process map follows a pattern that comprises input (material and supplies needed complete a task), work (or labour), output (outcomes), and possible error risks associated with such task [83, 84]. The outcomes entail more than only achieving clinical pregnancy, rather, they encompass all measurable outcomes that signify the achievement of quality objectives (increased patient and staff satisfaction, improved financial performance, improved clinical and laboratory operations). The well-mapped processes become the reference for writing the standard operating procedures (SOPs), and the expected outcome will be a basis for establishing performance indicators [29, 79, 81].

Fig. 5
figure 5

A typical process map. This depicts the input, labour or work, possible error risk, and the outcome. Supplies and materials required to execute the procedural processes (work) are the inputs. Effective procedural process execution and mitigation of the risk of error will result in desired outputs or outcomes

Process control

Process control is a necessary tool that shows if, at any moment, the procedural processes of a unit have deviated from the standard parameters of its operation. This can be seen as a decrease in performance level after comparison with the unit’s historical performance levels (especially data from recent preceding months) [28]. In IVF laboratories, process control involves, first, identifying those contributory factors affecting performance and then developing corrective measures [29]. One corrective measure is writing good SOPs that comprehensively describe the ‘how’, ‘why’ and ‘why not’ of the procedural processes. In addition to controlling the process, a comprehensive and detailed SOP minimizes the risk of failure [28, 29].


Benchmarking is a means of setting up best practice goals for a unit [85]. It is a good tool for tracking the success rate of a centre (cumulation of laboratory and clinical outcomes). Benchmarking has been proven to be a good agent, which helps to prevent complacency within a unit. It also helps a unit in developing better processes and improving outcomes to meet widely accepted best practice standards (e.g. with those stipulated in the Vienna consensus report. See [85]) [28].

Artificial intelligence (AI)

Identifying the sources of problems responsible for unsatisfactory pregnancy outcomes in an ART clinic may be daunting. Total quality management activities in IVF laboratories can help embryologists quickly detect potential problems and correct them in time [81]. The documentation of QC, QA, and QI activities in IVF laboratories through the conventional manual means may be impeded by inaccurate recording, duration of manual data entry, length of time to analysis, time to clinical pregnancy, and subjectivity. The performance of total quality management in IVF laboratories can be automated using a programmed AI platform (convoluted neural network) to collect, store, retrieve, and analyse QC and QA data. The use of AI in total quality management can enhance QC and QA better than the routine monthly quick review [86, 87]. Interestingly, Bormann and colleagues recently demonstrated that AI can automatically monitor an embryologist’s competence in performing ICSI and the embryo culture conditions. The AI system can detect adverse outcomes early and relevant clinical changes in pregnancy rates [86].

Stress alleviation

Stress and fatigue are considered fuel that causes human error to thrive in the medical industry [23]. Heavy workload, understaffing, distractions, and interruptions are precipitating factors of stress in the laboratory. Correcting or removing these stress-inducing factors will help reduce the likelihood of human errors [15, 28]. It is necessary for IVF laboratories to rightly determine their staffing needs, establish considerable work hours for staff with compulsory breaks, and appropriately apportion work volume [13].


Following the recent rapid growth in IVF patient population worldwide, the responsibilities of IVF laboratories increase simultaneously. IVF laboratory operators (clinical embryologists) are required to execute the complex processes involved in IVF while ensuring the patients, their gametes and embryos, as well as donors and surrogates receive the best quality of care. Despite the inevitability of human fallibility in every field, it is however, necessary that the occurrence of errors or adverse events in IVF laboratories is minimal or non-existent. Although the occurrence of adverse incidents in IVF is rare, the impacts on both patients and staff of the clinic may be devastating. Therefore, the critical need for clinics to introduce error-mitigating systems to prevent the reoccurrence of errors cannot be overemphasized. Over the years and to date, several risk management and error mitigation approaches have been introduced to help IVF laboratories improve in areas such as traceability of gametes and embryos, quality management, and strategic adverse event prevention. Similarly, effective root cause analysis after an adverse incident can help laboratories establish effective and sustainable countermeasures against the reoccurrence of such adverse incidents. Nevertheless, some limitations and reason for caution do exist. One primary limitation is the insufficient data on this subject, which necessitate more investigations to shed more insights and transparency. Similarly, Key Performance Indicators (KPIs) are mainly based on expert opinion. Thus, future research may warrant an update of the recommended KPIs, their definition, and the competence level and benchmark values.

Availability of data and materials

Not applicable



Assisted reproductive technology


European Society of Human Reproduction and Embryology


Embryo transfer


Electronic witnessing system


Failure mode and effect analysis


Human Fertilization and Embryology Authority


In vitro fertilization


Operating theatre


Quality assurance


Quality control


Quality improvement


Radiofrequency identification


Risk priority number


Standard operating procedures


  1. Rienzi L, Bariani F, Dalla Zorza M, Romano S, Scarica C, Maggiulli R et al (2015) Failure mode and effects analysis of witnessing protocols for ensuring traceability during IVF. Reprod Biomed Online. 31(4):516–22

    Article  PubMed  Google Scholar 

  2. Plebani M (2010) The detection and prevention of errors in laboratory medicine. Ann Clin Biochem. 47(2):101–10

    Article  PubMed  Google Scholar 

  3. Bender L (2005) To err is human-ART mix-ups: a labor-based, relational proposal. J Gend Race Just. 9:443

    Google Scholar 

  4. Kantsa V, Zanini G, Papadopoulou L (Eds.) (2015) (In)Fertile citizens: Anthropological and legal challenges of assisted reproduction technologies. Retrieved from

  5. Sakkas D, Barrett CB, Alper MM (2018) Types and frequency of non-conformances in an IVF laboratory. Hum Reprod. 33(12):2196–204

    Article  PubMed  Google Scholar 

  6. Choucair F, Younis N, Hourani A (2021) The value of the modern embryologist to a successful IVF system: revisiting an age-old question. Middle East Fertil Soc J. 26(1):1–6

    Article  Google Scholar 

  7. International Organisation for Standardisation. Medical laboratories—reduction of error through risk management and continual improvement. ISO/TS. 2008;22367.

  8. Centola G (2018) Stress in the workplace: results from a perceived stress survey of ART laboratory professionals. Reprod Biomed Online. 37:e3

    Article  Google Scholar 

  9. López-Lería B, Jimena P, Clavero A, Gonzalvo M, Carrillo S, Serrano M et al (2014) Embryologists’ health: a nationwide online questionnaire. J Assist Reprod Genet. 31(12):1587–97

    Article  PubMed  PubMed Central  Google Scholar 

  10. Brison D, Hooper M, Critchlow J, Hunter H, Arnesen R, Lloyd A et al (2004) Reducing risk in the IVF laboratory: implementation of a double witnessing system. Clin Risk. 10(5):176–80

    Article  Google Scholar 

  11. Human Fertilization and Embryology Authority. Adverse incidents in fertility clinics: lessons to learn. 2014;

  12. Magli MC, Van den Abbeel E, Lundin K, Royere D, Van der Elst J, Gianaroli L (2008) Revised guidelines for good practice in IVF laboratories. Hum Reprod. 23(6):1253–62

    Article  PubMed  Google Scholar 

  13. Madeira JL, Lindheim MD SR, Trolice MP (2020) IVF errors-is this only the tip of the iceberg? Fertility and Sterility Dialog. Available from:

  14. Nesbit C, Porter MB, Esfandiari N (2022) Catastrophic human error in assisted reproductive technologies: a systematic review. J Patient Saf. 18(1):e267-74

    Article  PubMed  Google Scholar 

  15. Sakkas D, Gardner DK (2020) The IVF Cycle to Come: Laboratory Innovations. Patient-Centered Assisted Reproduction: How to Integrate Exceptional Care with Cutting-Edge Technology, 54

  16. Reason J (2000) Human error: models and management. Bmj. 320(7237):768–70

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Hawkins R (2012) Managing the pre-and post-analytical phases of the total testing process. Ann Lab Med. 32(1):5–16

    Article  PubMed  Google Scholar 

  18. Plebani M (2018) Clinical laboratory: bigger is not always better. Diagnosis. 5(2):41–6

    Article  PubMed  Google Scholar 

  19. Tilleman K, Tolpe A 2021 From traceability embryo identification during to ensure culture the to safety witnessing of the. Man Embryo Cult Hum Assist Reprod. 61.

  20. Yoe C (2019) Principles of risk analysis: decision making under uncertainty. CRC press, New York

  21. Morini D, Daolio J, Nicoli A, De Feo G, Valli B, Melli B, et al (2021) A customized tool of incident reporting for the detection of nonconformances at a single IVF center: development, application, and efficacy. Todorov P, editor. BioMed Res Int. 2021:1126270

  22. Toft B, Gooderham P (2009) Involuntary automaticity: a potential legal defence against an allegation of clinical negligence? BMJ Qual Saf. 18(1):69–73

    Article  CAS  Google Scholar 

  23. Toft B, Mascie-Taylor H (2005) Involuntary automaticity: a work-system induced risk to safe health care. Health Serv Manage Res. 18(4):211–6

    Article  PubMed  Google Scholar 

  24. Flin R 2014 Improving decision making in the clinic and laboratory. the importance of non-technical skills. In Oxforsd Univ Press Great Clarendon st, Oxford ox2 6DP, England; . p. 83–83.

  25. Balaban B, Sakkas D, Gardner DK (2014) Laboratory procedures for human in vitro fertilization. Thieme Medical Publishers, p. 272–282

  26. Williams PM (2001) Techniques for root cause analysis. In Taylor & Francis, pp 154–157

  27. de Ziegler D, Gambone JC, Meldrum DR, Chapron C (2013) Risk and safety management in infertility and assisted reproductive technology (ART): from the doctor’s office to the ART procedure. Fertil Steril. 100(6):1509–17

    Article  PubMed  Google Scholar 

  28. Mortimer ST, Mortimer D 2015 Quality and risk management in the IVF laboratory. Cambridge University Press;

  29. Kennedy C, Mortimer D (2007) Risk management in IVF. Best Pract Res Clin Obstet Gynaecol. 21(4):691–712

    Article  CAS  PubMed  Google Scholar 

  30. Lippi G, Becan-McBride K, Behúlová D, Bowen RA, Church S, Delanghe J et al (2013) Preanalytical quality improvement: in quality we trust. Clin Chem Lab Med. 51(1):229–41

    Article  CAS  PubMed  Google Scholar 

  31. Plebani M (2013) Harmonization in laboratory medicine: the complete picture. Clin Chem Lab Med. 51(4):741–51

    Article  CAS  PubMed  Google Scholar 

  32. Laposata M, Dighe A (2007) “Pre-pre” and “post-post” analytical error: high-incidence patient safety hazards involving the clinical laboratory. Clin Chem Lab Med. 45(6):712–719.

  33. Leung AS, Dahan MH, Tan SL (2016) Techniques and technology for human oocyte collection. Expert Rev Med Devices. 13(8):701–3

    Article  CAS  PubMed  Google Scholar 

  34. Rienzi L, Bariani F, Dalla Zorza M, Albani E, Benini F, Chamayou S et al (2017) Comprehensive protocol of traceability during IVF: the result of a multicentre failure mode and effect analysis. Hum Reprod. 32(8):1612–20

    Article  CAS  PubMed  Google Scholar 

  35. Agarwal A, Sharma R, Gupta S, Finelli R, Parekh N, Selvam MKP et al (2022) Standardized laboratory procedures, quality control and quality assurance are key requirements for accurate semen analysis in the evaluation of infertile male. World J Mens Health. 40(1):52

    Article  PubMed  Google Scholar 

  36. Bourne H, Archer J (2017) Sperm techniques preparation 5. Textbook of Assisted Reproductive Techniques: Volume 1: Laboratory Perspectives. CRC Press, p. 92–106

  37. Alkandari MH, Moryousef J, Phillips S, Zini A (2021) Testicular sperm aspiration (TESA) or microdissection testicular sperm extraction (Micro–tese): which approach is better in men with cryptozoospermia and severe oligozoospermia? Urology. 154:164–9

    Article  PubMed  Google Scholar 

  38. Verza S, Esteves SC (2019) ESA/MESA/TESA/TESE sperm processing. In: In Vitro Fertilization, Springer, p p 313-34

    Google Scholar 

  39. Fabozzi G, Cimadomo D, Maggiulli R, Vaiarelli A, Ubaldi FM, Rienzi L (2020) Which key performance indicators are most effective in evaluating and managing an in vitro fertilization laboratory? Fertil Steril. 114(1):9–15

    Article  PubMed  Google Scholar 

  40. Swain JE (2015) Optimal human embryo culture. Semin Reprod Med. 33:103–117

  41. Palermo G, O’Neill C, Chow S, Cheung S, Parrella A, Pereira N et al (2017) Intracytoplasmic sperm injection: state of the art in humans. Reproduction. 154(6):F93-110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Arora P, Mishra V (2018) Difficult embryo transfer: a systematic review. J Hum Reprod Sci. 11(3):229

    Article  PubMed  PubMed Central  Google Scholar 

  43. Penzias A, Bendikson K, Butts S, Coutifaris C, Falcone T, Fossum G et al (2017) ASRM standard embryo transfer protocol template: a committee opinion. Fertil Steril. 107(4):897–900

    Article  PubMed  Google Scholar 

  44. Sparks AE (2015) Human embryo cryopreservation—methods, timing, and other considerations for optimizing an embryo cryopreservation program. Semin Reprod Med. p. 128–44

  45. Sakkas D, Pool T, Barrett C (2015) Analyzing IVF laboratory error rates: highlight or hide? Reprod Biomed Online. 31(4):447–8

    Article  CAS  PubMed  Google Scholar 

  46. Liu H, Zhang L, Ping Y, Wang L (2020) Failure mode and effects analysis for proactive healthcare risk evaluation: a systematic literature review. J Eval Clin Pract. 26(4):1320–37

    Article  CAS  PubMed  Google Scholar 

  47. Practice Committee of the American Society for Reproductive Medicine. Performing the embryo transfer: a guideline. Fertil Steril. 2017;107(4):882–96.

  48. Letterie G (2017) Outcomes of medical malpractice claims in assisted reproductive technology over a 10-year period from a single carrier. J Assist Reprod Genet. 34(4):459

    Article  PubMed  PubMed Central  Google Scholar 

  49. Carthey J (2013) Understanding safety in healthcare: the system evolution, erosion and enhancement model. J Public Health Res. 2(3):2

  50. (2003) Human Fertilization and Embryology Authority. Report of clinical incident reporting pilot. Lond HFEA

  51. Woodward S, Hill K, Adams S (2004). Carrying out internal reviews of serious incidents, in: D. DUFFY and T. RYAN (Eds) New Approaches to Preventing Suicide: A Manual for Practitioners. Jessica Kingsley Publishers, London, pp. 227–244.

  52. Latino RJ (2015) How is the effectiveness of root cause analysis measured in healthcare? J Healthc Risk Manag. 35(2):21–30

    Article  PubMed  Google Scholar 

  53. Hibbert PD, Thomas MJ, Deakin A, Runciman WB, Braithwaite J, Lomax S et al (2018) Are root cause analyses recommendations effective and sustainable? an observational study. Int J Qual Health Care. 30(2):124–31

    Article  PubMed  Google Scholar 

  54. Bagian JP, King BJ, Mills PD, McKnight SD (2011) Improving RCA performance: the cornerstone award and the power of positive reinforcement. BMJ Qual Saf. 20(11):974–82

    Article  PubMed  Google Scholar 

  55. National Patient Safety Foundation. RCA2: improving root cause analyses and actions to prevent harm. 2015;

  56. Joint Commission Resources I, Joint Commission on Accreditation of Healthcare Organizations. Patient safety: Essentials for health care. Joint Commission Resources; 2005.

  57. Mitchell I, Schuster A, Smith K, Pronovost P, Wu A (2016) Patient safety incident reporting: a qualitative study of thoughts and perceptions of experts 15 years after ‘To Err is Human.’ BMJ Qual Saf. 25(2):92–9

  58. Torabi SA, Giahi R, Sahebjamnia N (2016) An enhanced risk assessment framework for business continuity management systems. Saf Sci. 89:201–18

    Article  Google Scholar 

  59. Intra G, Alteri A, Corti L, Rabellotti E, Papaleo E, Restelli L, et al (2016) Application of failure mode and effect analysis in an assisted reproduction technology laboratory. Reprod Biomed Online. 33(2):132–9

  60. Jost MT, Branco A, Araujo BR, Viegas K, Caregnato RCA (2021) Tools to organize the work process in patient safety. Esc Anna Nery. 25

  61. Oyewumi A 2020 Risk management in medically assisted reproduction. In: Textbook of Assisted Reproduction. Springer; .p. 715–23.

  62. Gupta S, Fauzdar A, Singh V, Srivastava A, Sharma K, Singh S (2020) A preliminary experience of integration of an electronic witness system, its validation, efficacy on lab performance, and staff satisfaction assessment in a busy Indian in vitro fertilization laboratory. J Hum Reprod Sci. 13(4):333–339

  63. Toft B (2004) Independent review of the circumstances surrounding four adverse events that occurred in the Reproductive Medicine Units at The Leeds Teaching Hospitals NHS Trust, West Yorkshire. Department of Health, London

  64. Go KJ (2015) ‘By the work, one knows the workman’: the practice and profession of the embryologist and its translation to quality in the embryology laboratory. Reprod Biomed Online. 31(4):449–58

    Article  PubMed  Google Scholar 

  65. Spriggs M (2003) IVF mixup: white couple have black babies. J Med Ethics. 29(2):65–65

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Holmes R, Wirka KA, Catherino AB, Hayward B, Swain JE (2021) Comparison of electronic versus manual witnessing of procedures within the in vitro fertilization laboratory: impact on timing and efficiency. FS Rep. 2(2):181–8

    Google Scholar 

  67. Thornhill AR, Brunetti X, Bird S, Bennett K, Rios LM, Taylor J (2011) Reducing human error in IVF with electronic witnessing. Fertil Steril. 96(3):S179

    Article  Google Scholar 

  68. Forte M, Faustini F, Maggiulli R, Scarica C, Romano S, Ottolini C et al (2016) Electronic witness system in IVF—patients perspective. J Assist Reprod Genet. 33(9):1215–22

    Article  PubMed  PubMed Central  Google Scholar 

  69. Glew AM, Hoha K, Graves J, Lawrence H, Read S, Ah-Moye M (2006) P-108: Radio frequency identity tags ‘RFID’for electronic witnessing of IVF laboratory procedures. Fertil Steril. 86(3):S170

  70. De los Santos MJ, Apter S, Coticchio G Debrock S, Lundin K, Plancha CE, Prados F, Rienzi L, Verheyen G, Woodward B, et al (2016) ESHRE Guideline Group on Good Practice in IVF Labs. Revised guidelines for good practice in IVF laboratories (2015). Hum Reprod. 31:685–686

  71. Novo S, Nogués C, Penon O, Barrios L, Santaló J, Gómez-Martínez R et al (2014) Barcode tagging of human oocytes and embryos to prevent mix-ups in assisted reproduction technologies. Hum Reprod. 29(1):18–28

    Article  PubMed  Google Scholar 

  72. Novo S, Mora-Espí I, Gómez-Martínez R, Barrios L, Ibáñez E, Such X et al (2015) Traceability of human sperm samples by direct tagging with polysilicon microbarcodes. Reprod Biomed Online. 31(2):162–70

    Article  CAS  PubMed  Google Scholar 

  73. Trujillo LM, García JA, Lizcano D, Mejías M (2019) Traceability management of systems of systems: a systematic review in the assisted reproduction domain. J Web Eng. 18(4):409–46

    Article  Google Scholar 

  74. Novo S, Penon O, Barrios L, Nogués C, Santaló J, Durán S et al (2013) Direct embryo tagging and identification system by attachment of biofunctionalized polysilicon barcodes to the zona pellucida of mouse embryos. Hum Reprod. 28(6):1519–27

    Article  PubMed  Google Scholar 

  75. Penon O, Novo S, Durán S, Ibañez E, Nogués C, Samitier J et al (2012) Efficient biofunctionalization of polysilicon barcodes for adhesion to the zona pellucida of mouse embryos. Bioconjug Chem. 23(12):2392–402

    Article  CAS  PubMed  Google Scholar 

  76. Points for the TMRW platform [Internet]. 2018 [cited 2021 Oct 30]. Available from:

  77. Logsdon DM, Grimm CK, Schoolcraft WB, McCormick S, Swain JE, Krisher RL et al (2021) Assessment of complete end to end vapor phase nitrogen shipping and storage on gamete and blastocyst quality. Fertil Steril. 116(1):e40

    Article  Google Scholar 

  78. Sharp TA, Garbarini WN, Johnson CA, Watson A, Greenberg R, Go KJ (2019) Initial validation of an automated cryostorage and inventory management system. Fertil Steril. 112(3):e116

    Article  Google Scholar 

  79. Olofsson JI, Banker MR, Sjoblom LP (2013) Quality management systems for your in vitro fertilization clinic’s laboratory: why bother? J Hum Reprod Sci. 6(1):3

    Article  PubMed  PubMed Central  Google Scholar 

  80. Boone WR, Higdon HL III, Johnson JE (2010) Quality management issues in the assisted reproduction laboratory. J Reprod Stem Cell Biotechnol. 1(1):30–107

  81. Stidston O (2018) The ABCs of quality management in IVF. In Practical Problems in Assisted Conception. Cambridge University Press, United Kingdom, 174–180

  82. Parker PA (2004) Process mapping as a tool for managing risk in assisted reproduction. Clin Risk. 10(5):181–3

  83. Bento FC, Esteves SC 2016 Establishing a quality management system in a fertility center: experience with ISO 9001. MedicalExpress. 3.

  84. de los Santos MJ, Ruiz A (2013) Protocols for tracking and witnessing samples and patients in assisted reproductive technology. Fertil Steril 100(6):1499–502

    Article  PubMed  Google Scholar 

  85. ESHRE Special Interest Group of Embryology, Alpha Scientists in Reproductive Medicine. The Vienna consensus: report of an expert meeting on the development of art laboratory performance indicators. Hum Reprod Open. 2017;2017(2):hox011.

  86. Bormann CL, Curchoe CL, Thirumalaraju P, Kanakasabapathy MK, Gupta R, Pooniwala R et al (2021) Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory. J Assist Reprod Genet. 38(7):1641–6

    Article  PubMed  PubMed Central  Google Scholar 

  87. Curchoe CL, Bormann CL (2019) Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J Assist Reprod Genet. 36(4):591–600

    Article  PubMed  PubMed Central  Google Scholar 

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We acknowledge the European Society of Human Reproduction and Embryology (ESHRE) for the Travelling Fellowship Award. CWI acknowledges the Clinical Director (Dr Jaideep Malhotra) and the Laboratory Director (Dr Keshav Malhotra) of Malhotra Embryology Training Academy (META), Rainbow IVF, India, for accepting and hosting him in their Centre in fulfilment of his travelling fellowship.


The authors did not receive any funding for this study.

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Conception, design and drafting of the manuscript—CWI. All authors (CWI, CM, and KM) interpreted search strategy results, reviewed the manuscript and provided substantial advice through the data analysis work, and contributed intellectually to the writing or revising of the manuscript and approval of the final version. The author(s) read and approved the final manuscript.

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Correspondence to Chibuzor Williams Ifenatuoha.

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Ifenatuoha, C.W., Mohammed, C. & Malhotra, K. Errors in IVF laboratories: risks assessments and mitigations. Middle East Fertil Soc J 28, 5 (2023).

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