Introduction

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)

Full size image

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.

Full size image

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].

Insemination

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].

Cryopreservation

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
Full size table

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

Full size image

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

Full size image

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

Full size image

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

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].

Conclusion

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.