Patient safety (PS) (Supplemental material: Appendix B1, Abbreviations) must be prioritised by health care organizations that seek excellence in care. Today, establishing strategies to guarantee PS through the implementation of risk management plans and safe practices aimed at preventing adverse events (AEs) is considered a duty.1

The Study of Health Care-Related Adverse Events in Emergency Departments in Spanish Hospitals (EVADUR),2 sponsored by the Sociedad Española de Medicina de Urgencias y Emergencias (Spanish Society of Emergency Medicine and Urgent Care, SEMES) provided a perspective of PS in hospital emergency departments (HEDs) throughout Spain. Based on its results, 12% of patients managed in HEDs experienced at least 1 AE, and 70% of experienced AEs were preventable.

The particular characteristics of HEDs (diversity and complexity of health care processes, concurrent care of a number of patients with no defined limit presenting with illnesses of varying nature and complexity that may have underlying disease and usually not known to the care team, time constraints, scarcity of clinical information for decision-making, working conditions and organizational aspects) make them one of the care settings where PS is most at risk.1,3–5

On the other hand, paediatric patients have particular characteristics that make them more vulnerable to AEs, such as age-related variations in vital signs, anatomical, physiological or developmental characteristics of each age group, or body weight-based medication dosing.5

Since 2014, our paediatric emergency department (PED) operates within an integrated quality and risk management system for patient safety, certified as adhering to official Spanish standards UNE-EN-ISO-9001:2015 and UNE-179003. Our risk management system is based on two complementary strategies: reactive management of voluntarily reported events through a systems-based methodology focused on the development of barriers to prevent recurrence of the event, and proactive management with analysis of risks and the potential or observed harm to patients in the care delivery process,1,6,7 identifying critical points for PS with the aim of anticipating problems and developing and implementing safe practices to minimise the probability of an AE.

The risk map (RM) is a proactive tool that, through descriptive data and appropriate indicators, allows the periodic evaluation of the risks in a system and to assess the effectiveness of developed interventions once they are implemented.1 The safety representatives of the unit had previous experience in the development of RMs and had applied Failure Mode and Effects Analysis (FMEA)8 at a small scale to review some subprocesses with the multidisciplinary participation of professionals working in other departments (“Online prescription” with the Pharmacy Department and “Urgent microbiological testing” with the Microbiology Department).

The objectives of our project were to identify and manage the risks in the PED of a tertiary care hospital through the development of a RM, and to assess the impact on PS of the implemented improvement actions.

To design the RM, we used FMEA applied to the PED Process Map (Supplemental Material, Appendix B2). We reviewed all operational processes by asking three questions:

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  What could fail? Failure modes (FMs) or risks to PS.

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  What impact could the failure have on the patient? Effects.

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  What could cause this failure? Root causes (Contributory factors classification framework of the National Patient Safety Agency9).

Subsequently, we rated each of the identified FMs by grading their severity (S), frequency of occurrence (O) and ease of detection (D) on a scale of 1–5 (Table 1). Using these scores, we calculated the risk priority number (RPN = S × O × D), a value that allows a theoretical estimation of the level of risk associated with each FM (Table 2), in order to prioritise the management of risks with the greatest impact on patient safety.

Table 2.

Stratification of risk level: RPN = S × O × D.

D, ease of detection; O, frequency of occurrence; RPN, risk priority number; S, severity.a

aNIvel de riesgo needs to be translated to "Risk level" in the table.

Table 3 shows the count of FMs, overall and by process, and the level of risk, overall and by process, in the 2017 and 2019 RMs. In 2017, 106 FM were identified, of which 54.7% (58) were classified as high risk (57) or very high risk (1) (Table 4). Managing this volume of risks was not feasible, so it was necessary to apply the prioritization criteria described in the methods section to plan improvement strategies. Finally, 19 IAs were planned (Table 5) to address 46 FM prioritised on account of their risk level. In 2019, 110 FMs were identified, of which 48.2% (53) were classified as high risk (Table 4).

When we analysed the changes in our RM, we found that the total number of FMs had increased by 3.8% in 2019 (110 FMs compared to 106 in 2017). However, as can be seen in the risk stratification table (Table 4), this increase consisted mainly of FMs classified as moderate and low risk, with a 6.5% reduction in the proportion of FMs classified as high or very high risk (from 54.7% in 2017 to 48.2% in 2019).

Overall, the level of risk, estimated with the median RPN, decreased by 20% in the 2019 RM. In the analysis by process, we found a reduction in the level of risk for all processes except “Registration/Triage” and “Management of life-threatening emergencies” (Table 3).

All IAs planned based on the risks identified in 2017 had been implemented. To assess the impact of the 19 implemented IAs, we specifically analysed the 46 prioritised risks (FMs) that these strategies were meant to address. Sixty percent of these FMs went from being classified as high risk in 2017 to being classified as moderate risk in 2019. Table 6 reflects the impact of improvement actions in the 46 FMs they addressed, showing a reduction in the level of risk, both overall (−27.8%) and in the analysis broken down by process.

Numerous publications support the usefulness of FMEA as a method to systematically assess complex processes to identify risk areas and provide the foundation for the development of interventions that minimise the probability of an AE occurring, thus contributing to improving patient safety in HEDs.1,4,12 Some publications have described experiences with the development of RMs in the field of paediatric emergency care.5,7

However, as noted by Orrego,12 while there may be an enthusiastic investment in the development of methods of analysis at the outset, these should not be the core of the safety strategy of an institution, neglecting the development of improvement strategies and the redesign of processes to improve their safety. Several studies13–16 have highlighted the importance of reviewing RPN scores to assess the effectiveness of the corrective actions planned based on a FMEA.13–16 However, we only found one study conducted in Spain that used FMEA to assess the impact of a set of actions developed to reduce prescription errors in a high-performance one-stop hospital.17 Thus, ours is the first study that has applied FMEA as a follow-up method to monitor changes in overall risk in a complex clinical care setting.

In the framework of certification in patient safety risk management (Spanish standard UNE-179003), the development and periodic revision of RMs is a proactive strategy that complements reactive management based on the analysis of the incidents reported in the unit. In fact, there is a parallelism in the distribution by processes of the FMs identified in the RM (with the largest proportion corresponding to treatment, followed by diagnostic testing) and the pattern of the incidents reported in the unit14 (distribution by processes of notifications of Period 1 of the study: predominance of treatment [35.8%] followed by diagnostic testing [32.1%]). This distribution of FMs is similar to the distribution in RMs designed in other PEDs in Spain5,7 and previous studies in the literature, in which medication errors are the most frequent incidents or AEs reported in paediatric care settings.18,19

The periodic update of our RM with the FMEA method allowed us to establish the impact of planned IAs and monitor the changes in the level of risk of the unit, overall and in each operational process. The 2019 RM evinced an increase in the total number of risks compared to 2017, probably due to the redesign of some processes through the introduction of new software applications (electronic triage, health records and prescription) that gave rise to new FMs, and also the training of the working group in the methodology, which honed the ability of its members to detect risks, especially those of moderate and low level, and allowed an increasingly thorough review of the processes. Despite this increase in the total number of FMs, we found that the overall level of risk in the PED had decreased considerably in 2019, as evinced by the risk stratification profile (reduction of 6.5% in the FMs classified as high or very high risk) and the level of risk estimated by the median RPN, which decreased by 20% in the 2019 RM. This overall reduction in risk was probably due to the fact that the implementation of corrective measures to address very high risks can indirectly reduce other lower risks.1 In the analysis by process, we only found an increase in risk in the registration/triage and management of life-threatening emergencies processes. In the case of registration/triage, the new map included a FM that had not been contemplated in the 2017 RM (“delay in administrative registration”, RPN 48). In the case of management of life-threatening emergencies, one of the FMs included in the 2017 RM (“lack of training in the stabilization of critical patients”, RPN 60) was split into 2 FMs in the 2019 RM to reflect the involvement of different contributing factors (“lack of knowledge/training in critical patient stabilization algorithms” [RPN 60] and “problems in coordination/lack of training in teamwork skills” [RPN 45], of which the latter had been the one addressed by planned IAs). The total number of FMs in this process remained the same because in the 2019 RM, one FM that was present in the 2017 RM disappeared (“error in sample identification”, as it was considered to duplicate the risks identified in the diagnostic testing process). We believe that these differences explain the increase in the level of risk in these processes.

Most of the IAs planned based on the risks identified in the 2017 RM could be grouped into 5 areas: medication safety, diagnostic test safety, stabilization of critically ill patients, communication/transfer of clinical information and supervision of medical residents. Other general safe practices were also developed that affected every process, such as the protocol to guarantee correct patient identification, the safe transport protocol and the written contingency plan to handle information technology emergencies. We believe that the key factor that allowed the successful implantation of all planned IAs was the previous application of prioritization criteria, with selection of strategies considered feasible, in line with the strategic plans of the institution and that would have the highest potential impact on the level of risk, in addition to the appointment of individuals in charge of the IAs and carrying out follow-up evaluations in the medium term.

The analysis of the FMs that had been addressed with these IAs evinced a reduction in the level of risk, overall and by process. This reduction in the level of risk was achieved through two types of strategies: some aimed at reducing the probability of a given FM occurring (multidisciplinary teamwork simulation to improve the ability of professionals to work in a coordinated fashion during the stabilization of a critically ill patient or to use electronic prescription protocols with automatic dose calculation and maximum dose restrictions that reduce the risk of prescription errors) and others that, starting from the assumption that errors are an inescapable aspect of clinical practice, aim at improving the ability to detect the error before it reaches the patient (a closed-loop system of verbal medication orders in the resuscitation bay, or the role of the observation manager to improve the supervision of medical residents).

The strengths of this study include the use of the FMEA approach, not only for diagnostic purposes (RM 2017) but also as a follow-up tool that, driven by a commitment to ongoing improvement, allowed monitoring the level of risk of a complex clinical care setting, thus contributing to improving PS. We believe that it may be useful for other HEDs, as it proposes strategies for improvement as well as a risk management model that could be extrapolated to other facilities, adapting them to the particularities of each department.

Among its limitations, we ought to highlight that FMEA is a theoretical exercise that is somewhat subjective, and this subjectivity may have an effect on the analysis of root causes and the assessment of the level of risk.16 On the other hand, the development of a RM requires a significant investment of time and resources, and its success largely depends on the commitment of the team members and the facility to hold regular and productive meetings20 (in our case, each RM required 5 full working days of each of the members of the working group). Lastly, since the study was conducted in a single centre, the risks identified in the RM are associated with the particular setting and circumstances of the PED in which it was developed. It would be interesting to carry out similar studies in other HEDs with established risk management systems to compare outcomes.

The development of a RM allows detection of critical points for PS during health care delivery. Failure mode effect analysis is a useful tool to identify threats to PS in health care settings, assess the impact of improvement strategies developed based on the identified risks and monitor the level of risk in a complex care setting. The development of improvement actions has had a significant impact in reducing the risk of health care processes in our department, contributing to improving PS.