Predictors of Unscheduled Return-to-EMS Visits within 72 Hours: A Retrospective Case-Control Study from Saudi Arabia Open Access

Emergency departments (EDs) are crucial for delivering round-the-clock emergency care as an essential public service mission [1]. Return visits impose additional pressure on the ED because return patients have a significantly longer LOS at the ED [2].There are many reasons behind the patients’ revisiting Emergency Medical Services (EMS) within 72 h post-discharge, and such return cases can be classified into illness-related, physician-related, and patient-related factors [3], indicating perceived shortcomings in treatment and/or evaluation [4]. Illness-related return cases occur when the patient initially received the appropriate emergency medical care but the subsequent progression of their condition which necessitated a return. Physician-related EMS returns are instances wherein the patients’ return is linked to a missed or incorrect condition diagnosis by the treating EMS physician or the consulted specialty physician during the index visit. Finally, patient-related EMS returns are predominantly driven by decisions initiated by the patients or their families.

ED revisits within a “72-h timeframe” have been increasingly used as a hospital quality metric, although no clear empirical basis exists for the 72 h as the most appropriate period of focus [5]. Pham et al. [6] demonstrated that the 72-h timeframe for EMS returns is a critical measure for this quality metric that indicates premature discharge from the first EMS visit. It may also suggest inadequate patient assessment, treatment, or even discharge care instructions [7]. Several studies have examined the factors that contribute to unscheduled return ED visits within preidentified timeframes since the onset of discharge from the first (index) visit, with timeframes ranging from 48 h up to 1 week [3, 5‒14].

Chan et al. [8] found that male patients, the ones over 81 years of age, ethnic Chinese patients compared with Malay patients, patients arriving to the first EMS visit by ambulance, and patients triaged as critically ill, which is equivalent to Australasian Triage Scale (ATS) level II, and patients diagnosed with heart disease or those diagnosed with abdominal pain compared with those diagnosed with general symptoms had a relatively higher risk of returning to EMS within 72 h. Tangkulpanich et al. [9] reached similar conclusions, finding that the most important and independent predictive factors of ED revisit within 48 h of discharge were patients ≥60 years of age, Emergency Severity Index (ESI) triage level II, ED length of stay ≥4 h, a temperature ≥37.5°C, and pulse rates of less than 60 or more than 100 beats/minute on discharge. Hiti et al. [10] demonstrated that patients who had at least one handover during their first EMS stay (e.g., during shifts overlap) had higher odds of returning to EMS within 72 h. The findings also indicated that EMS cases discharged with digestive system disorders, infectious and parasitic diseases, or kidney and urinary tract disorders; those who presented during off-hour shifts; and those who had been hospitalized in the past 30 days are more likely to return to the ED within 72 h. Linden et al. [11] examined the ED of Medical Center Haaglanden in The Hague, Netherlands to identify factors that contribute to unscheduled return visits to the ED within a week. The findings indicated that patients who were assigned to an urgent triage level, those who arrived during the night shift, and those presenting with a wound or localized infection, abdominal pain, or urinary system issues were more likely to make unscheduled return visits. Ahmed et al. [12] determined that adults with chronic cardiovascular and genitourinary issues were at a higher risk for EMS return visits, highlighting additional factors such as older age (≥60 years), being female, insurance status, weekend ED arrivals, and being a new patient as being linked to increased frequency of return visits to the ED within 72 h. Similarly, Nasradeen et al. [13] demonstrated that adult female patients, those who initially presented with abdominal pain, and patients with comorbidities like hypertension or diabetes had a higher chance of ED return visits. Lin et al. [14] focused on the predictors of 72-h unscheduled return visits with admission in patients presenting to the ED with abdominal pain during the first visit, concluding that elderly patients (aged > 40 years) who needed laboratory workup, had level I or II triage scores, and received ≥2 doses of analgesics during the first visit to the ED had a higher risk of unscheduled return visits with admissions. In summary, predictors of patients’ unscheduled EMS return visits include being elderly [8, 9, 12, 14], being female [12, 13], being triaged as critically ill (ATS triage level II or higher) [8, 9, 11, 14], presenting with abdominal pain or digestive system disorders [8, 10, 11, 13, 14], presenting with genitourinary symptoms [10‒12], and presenting with cardiovascular ailments [8, 9, 11].

The aim of this study is to examine the effect of various clinical and operational factors on the risk of unscheduled return to EMS within 72 h of discharge from the first (index) visit. This study includes factors that had already been addressed in similar previous research and introduces new factors for EMS revisits that had not been previously examined. Our newly explored study-specific predictors include abnormal subacute noncritical echocardiogram (ECG) test results, abnormal noncritical subacute imaging test results, abnormal subacute noncritical blood test results across various body systems, the number of EMS visits in the past 6 months before the first EMS visit, whether or not new medications were prescribed on discharge from EMS and the type of medications (if prescribed), and diagnosis uncertainty upon discharge from the first EMS visit. We found no relevant research that has addressed these factors at the time this study was submitted for publication. Therefore, these newly explored predictors can be considered a contribution of this study in response to the current knowledge gap concerning the factors that influence the potential for patients’ unscheduled EMS return visits. We anticipate that the results will provide guidance and valuable insights for EMS services inside the Kingdom of Saudi Arabia (KSA) and in the regional countries regarding the potential factors that may influence patients’ EMS return within 72 h. In particular, this is the first-of-kind study to be conducted in the Eastern region of the KSA, and it covers a broad spectrum of 19 predictors, ranging from patient-, staff-, illness-, and operational-related factors, making it more comprehensive and inclusive when compared to other similar studies.

We conducted a retrospective case-control study between October 2022 and March 2023 at Johns Hopkins Aramco Healthcare (JHAH) Company’s Emergency Medical Services (EMS) Department to investigate predictors of unscheduled return visits within 72 h of discharge from an initial EMS encounter. JHAH is a distinguished company-owned hospital that has facilities situated in the Eastern region of the KSA and operates a comprehensive care network that includes five EMS facilities. Dhahran Health Center (DHC) EMS and Al Hasa Health Center (AHHC) EMS are both integrated within acute care hospitals, while Udhailiyah (UDU), Ras Tanura (RT), and Abqaiq (ABQ) EMSs function as standalone facilities. As per 2022 operational data, the annual volume of cases that visited the five EMSs is roughly 135,000 patients with an average monthly rate of 11,300 cases and an average daily rate of 376 cases. The baseline return-to-EMS crude rate across JHAH five EMS settings was around 7.5% in 2022. Overall, around 6.4% of the patients that accessed the EMSs were admitted for further care either at DHC or at AHHC.

At JHAH’s EMS Department, any attending patient is initially visually triaged by the assigned registered nurse before being assigned a specific priority level based on the presenting condition and clinical urgency utilizing the ATS system. The ATS system includes five priority levels ranging from level 1 (immediate: life threatening) to level 5 (nonurgent: needs treatment when time permits) [15]. Furthermore, the patient’s medical diagnosis is coded according to the International Classification of Diseases, Tenth Revision (ICD-10). The ICD-10 is a standardized system used to code diseases and medical conditions (morbidity) data [16]. For the purposes of this study, we established a customized list of categories for the following two variables: “type/nature of key complaint” and “key body system affected.”

In this research, the included patients were organized into two groups for comparative purposes: those who revisited the EMS within 72 h of discharge from the first visit and those who did not return within same timeframe. During the study period, a total of 1,420 cases had 72-h return visits to the five JHAH EMSs, and 338 (23.8%) of those return cases were included in the study group after being confirmed as valid return visits and after excluding the remaining 1,082 return cases referring to exclusion criteria in Table 1. The control group cases were a randomly selected sample of 247 patients who visited EMS once without a subsequent return visit during the 6-month study period. To minimize selection bias, control cases were initially selected using a simple random sampling technique in SPSS software. In addition, to ensure that the proportions of nonreturn visits in the control group across the individual EMS facilities were matched to the distribution of the cases in the study group, a subsequent stratification adjustment was made in the control group population to maintain similar proportions across the five EMS facilities. This adjustment reduced the control group size in relation to the study group (ratio was retrospectively adjusted from 1:1 to 4:3). This retrospective change was permissible and is not expected to affect the validity of the study results as all participants had an equal chance of being assigned.

The STORBE flowchart (Fig. 1) shows cases that were excluded from both the study and control groups. Table 1 presents the list of 26 excluded categories, cases that were excluded from the study group, and the rationale behind the exclusion of each category. These exclusion criteria were established by the Risk Management Division (RMD) team of the Quality and Patient Safety Department (QPSD) and were agreed to by the EMS Department. This list was developed to reduce the number of the return-to-EMS morbidity cases, which are subject to further in-depth review, and to ensure that our review efforts were focused on fewer worthwhile return cases. On the other hand, only the pediatric population (i.e., patients less than 15 years old) was excluded from the control group. Furthermore, category I ATS cases were excluded from both the study and control groups because such cases usually present with conditions that are life threatening (or pose imminent risk of deterioration) and require immediate aggressive intervention [15].

Data were obtained for the study group case and the control group case from JHAH’s Electronic Health Record (EHR; Epic) and Incident Reporting System (IRS; RLDatix) covering October 1, 2022 to March 31, 2023. We developed a data collection template to compile data on potential predictor variables for the study and control groups. The variables included demographics, clinical presentations, diagnoses, comorbidities, and utilization measures. We aimed to explore 19 different characteristics, each of which can be classified as patient-, illness-, staff-, or operational-related, which may show a higher risk for patient revisits to EMS. Those characteristics (predictors) encompassed gender, age (in years), stay duration during first visit (in hours), triage level during first visit (ATS categories), one or more body systems with abnormal noncritical subacute imaging test/s findings during first visit, abnormal subacute noncritical ECG results during first visit, abnormal noncritical test results in one or more body system/s during first visit, type/nature of key complaint, key body system affected, primary diagnosis (on discharge from first visit), diagnosis description upon discharge from first visit (provisional, differential, definite/final, or complaint-similar diagnosis), diagnosis being changed/updated during the first visit, EMS shift time during the first visit, day (of the week) of the first visit, history of admission in the last 30 days prior to the first visit, number of EMS visits during the preceding 6 months, whether new medications were prescribed on discharge from EMS at the end of the first visit (and the type of medications: therapeutic, symptomatic, or mixed), number of patient’s comorbidities, and if the patient was present during shifts overlap during the first EMS visit.

We next compared the demographic and clinical characteristics of the study cases and the control ones using descriptive statistics. Continuous variables were reported as means and standard deviations and compared using independent sample t tests. Categorical variables were reported as frequencies and percentages, and associations were assessed using Chi-square tests.

Binary logistic regression analysis was then performed to examine the associations between potential factors and return to EMS status. A backward stepwise selection approach was used, removing variables with p > 0.05. Goodness-of-fit was assessed using the Hosmer-Lemeshow test, and model discrimination was evaluated using the area under the receiver-operating characteristic curve. We used SPSS for statistical analysis, and a p value of <0.05 was considered statistically significant.

A total of 338 patients who returned to EMS within 72 h (study cases) and 247 patients who visited EMS once (control cases) were included in the analysis. As shown in Table 2, patients who returned to EMS were older than patients who only visited once on average (mean age 49.91 vs. 41.57 years, p < 0.0001). As shown in Table 3, significant differences are evident between the two groups in clinical and operational variables that included triage level (p < 0.0001), stay duration (p < 0.0001), abnormal imaging test results (p = 0.0597), total abnormal blood test results (p < 0.0001), abnormal ECG results (p = 0.0429), new medications prescribed (p = 0.0281), history of admission in the last 30 days (p = 0.0067), shift overlap (p < 0.0001), number of EMS visits in the last 6 months (p < 0.0001), number of comorbidities (p < 0.0001), and diagnosis description upon discharge from the first visit (p < 0.0001). In the study group cases, the proportion of cases assigned “complaint-similar diagnosis” (i.e., discharge diagnosis was identical to the presenting complaint in words or descriptions) and a “differential diagnosis” upon discharge from first EMS visits were 45% and 49%, respectively, compared with 26% and 29% in the control group cases. Notably, the distribution of control cases among the four preset categories of discharge diagnoses was almost equal.

No significant difference is evident in the proportion of patients who returned to EMS and that of the nonreturn patients who sought EMS during morning, evening, or night shifts (p = 0.6925). However, a significant difference is observed for the day of the week attended (p = 0.0337) wherein a higher proportion of patients who only visited once presented on Sundays (22%) compared with patients who returned to EMS (13%), while a higher proportion of patients who returned to EMS had first visits on Tuesdays (17%) and Thursdays (17%) than those who only visited once (11% on Tuesdays and 15% on Thursdays). This comparison concerning the distribution of both groups’ cases across the days of the week is illustrated in Figure 2.

The regression analysis in Table 4 shows that a higher number of abnormal test results (OR 2.811, 95% CI 2.172–3.637), a greater number of comorbidities (OR 1.18, 95% CI 1.004–1.387), abnormal ECG results (OR 0.149 for yes, OR 0.325 for no vs. not requested), shift overlap (OR 0.385, 95% CI 0.204–0.724), and a higher number of historical EMS visits (OR 1.15, 95% CI 1.014–1.304) are independently associated with increased odds of returning to EMS within 72 h. Lower triage acuity levels are correlated with lower odds of returning (OR 0.441, 95% CI 0.252–0.771). The model correctly classified 82.7% of cases, with acceptable calibration and discrimination (Hosmer-Lemeshow p = 0.23, receiver-operating characteristic curve 0.772).

We next conducted correlation analyses to examine the relationships between the return-to-EMS cases’ continuous variables, revealing several statistically significant relationships. Age has a positive correlation with total abnormal tests (r = 0.2265, p < 0.001) and comorbidities (r = 0.6702, p < 0.001). Triage level has a negative correlation with total abnormal tests (r = −0.2747, p < 0.001) and a weak negative correlation with stay duration (r = −0.1099, p = 0.001). The duration of stay has a positive correlation with total abnormal tests (r = 0.2605, p < 0.001) and a negative correlation with triage level (r = −0.1099, p < 0.01). Additionally, the number of EMS visits in the last 6 months has a weak positive correlation with total abnormal tests (r = 0.1429, p < 0.01) and a moderate positive correlation with comorbidities (r = 0.2004, p < 0.001). These correlations are illustrated in Figure 3.

Avoidable EMS returns can pose risks for both patients and the organization involved. Patients may face the risk of their diagnoses being overlooked or of being misdiagnosed during the initial EMS visit, which can be attributed to various factors such as a lack of standardized assessment protocols and/or inadequate discharge instructions. Furthermore, unnecessary or avoidable returns to EMS could put the organization at risk of overutilization of the EMS resources and of other services resources (e.g., workforce, time, equipment, supplies, and medications). Timely identification of the predictors of patients’ return during the first EMS encounter can contribute to minimizing the potential of patients’ return by triggering attentive, extended, alternate, and/or focused care before discharge after the initial EMS encounter. The main goal of our study was to identify the key predictors for the return of EMS cases within 72 h after being discharged from the first EMS visit. This study examined the effects of various patient-, illness-, staff-, and operational-related factors on the risk of unscheduled return visits.

The findings of this study align with previous research, echoing the observations made by Chan et al. [8], Tangkulpanich et al. [9], van der Linden et al. [11], and Lin et al. [14] that demonstrated a heightened risk of patients returning to EMS when initially triaged as critically ill. This consistency highlights the robustness of the association between critical triage and subsequent EMS returns, reinforcing the validity of our results. This finding emphasizes that EMS caregivers should ensure that such cases are only discharged after all the necessary investigations are completed, all appropriate consultations are conducted, and adequate discharge instructions are provided to patients/families in a clear and comprehensible language.

This study also demonstrates that EMS patients over 45 years of age have a higher probability of return visits, which is consistent with the conclusions attained by Chan et al. [8], Tangkulpanich et al. [9], Ahmed et al. [12], and Lin et al. [14], indicating that elderly patients (aged >40 years old) are more prone to return visits. This finding is expected because elderly patients usually have a higher number of comorbidities or chronic illnesses [17]. This predictable finding concurs with that of Ahmed et al. [12] and Nasradeen et al. [13], who determined that sicker patients with more chronic conditions have a higher probability of relapse and deteriorating conditions.

Remarkably, patients who visited EMS on days close to the weekend (on Tuesdays and Thursdays) had a higher probability of return visits compared with those who visited at the beginning of the week (on Sundays). This might be explained by the relative convenience of revisiting EMS on weekends rather than on weekdays. Likewise, the results also positively independently associate the odds of EMS revisits and the number of subacute abnormal test results and the presence of abnormal subacute noncritical ECG results during the first EMS visit. The rationale for this positive correlation is that subacute noncritical findings could indicate the start of imminent deterioration of a condition or an approaching incidence of an acute episode of illness that could take place if disregarded or if not addressed in a timely manner.

This study also aligns with the conclusions of Hiti et al. [10], indicating that patients who undergo at least one handover during an initial EMS visit stay have a higher likelihood of returning. This shared observation emphasizes the significance of effective communication and seamless information transfer among caregivers during the initial EMS encounter. This effective communication can sometimes be challenged by gaps in the information-sharing process that may take place during shift overlaps, when handovers may occasionally be incomplete or quick.

The way the “discharge diagnosis” is written or described upon patient’s discharge from the first visit is demonstrated to be significant. Remarkably, the diagnosis descriptions for the vast majority of the study group cases (around 93%) were either “complaint-similar diagnosis” or “differential diagnosis,” and the remaining 7% had their discharge diagnoses as either “Final Diagnosis” or “Provisional Diagnosis.” This indicates a need to encourage EMS physicians not to discharge patients unless a final definite diagnosis or at least a provisional diagnosis is determined. This can be achieved by adopting standard clinical protocols to be followed by the EMS caregivers for managing the most common and/or the most serious presentations at EMS. Such protocols should include specific guidelines for considering specialty consultations and referral requests, conducting appropriate laboratory investigations, and/or performing the needed diagnostic imaging investigations. Discharging a patient from EMS with an indefinite, uncertain, or unclear diagnosis might be associated with a higher probability of a return visit.

Notably, we do not find a statistically significant correlation between the “key body system affected” or “type/nature of key complaint” and the probability of returning to EMS – two variables that had been identified by numerous studies as predictors for EMS return visits [8‒14]. These studies identified abdominal pain or digestive system disorders [8, 10, 11, 13, 14], genitourinary symptoms [10‒12], and cardiovascular diseases [8, 9, 11] as predictors for return visits. This nonconfirmed correlation can be attributed to the adoption of an extensive list of categories (16 category) for the “key body system affected” variable during data collection and analysis; these categories cover all body systems based on an anatomical classification. However, the selected body systems were further subcategorized into upper and lower variances; this was specifically used with the genitourinary, the gastrointestinal, and the respiratory systems. The development of this list was driven by the need to have more specific categories of the “key body system affected” variance in order to focus on specific body subsystems, particularly for those EMS cases presenting with complaints affecting large body systems or those that might involve more than one body system. For example, the “abdominal pain” complaint could be linked to either the upper or lower gastrointestinal system, the lower urinary system, or the reproductive system in female patients. Similarly, a relatively lengthy list of “type/nature of key complaint” was developed; it was based on EMS physicians’ entries in encounters’ records. This list included 36 categories, and its development was driven by the need to get more specific results in relation to the presenting complaints, as one of the predictors for the EMS revisits.

This study demonstrates that selected factors are associated with higher probability of adult EMS cases' revisits within 72 h. Those factors include exhibiting a higher number of subacute noncritical abnormal blood test results across various body systems, having subacute noncritical abnormal ECG results, discharged from the index visit without being prescribed any medications, having a history of admission in the past 30 days prior to the index visit, being assigned a “complaint-similar” or a “differential” discharge diagnosis description, and attending at the EMS during shifts overlap during the index visit. Furthermore, patients who visited EMS on days close to the weekends may have a higher probability of return visits compared to those who with visited EMS at the beginning of the week.

Our logistic regression identified five predictive factors for adult populations returning to EMS within 72 h with an impressive accuracy level of 82.7%. This statistical approach may facilitate the formulation of a predictive logistic regression formula as a valuable tool that can be integrated into EHRs to enable the proactive identification of cases with a higher likelihood of returning to EMS within 72 h following an initial EMS visit. This may not only enhance the precision of risk assessment but can also provide a practical mechanism for healthcare providers to preemptively address potential unscheduled return-to-EMS scenarios, ultimately contributing to improved patient outcomes and resources optimization.

The nuanced alignment with specific studies provides a deeper understanding of the factors that influence EMS return visits, emphasizing the need for targeted interventions in critical triage scenarios and improved handover procedures to enhance patient outcomes and optimize emergency care services. This study’s conclusions contribute to a more comprehensive understanding and could inspire future research regarding the same, similar, and/or related factors. The findings offer valuable insights for enhancing EMS effectiveness and improving patient outcomes.

One weakness of this study is that it was conducted in a single company-owned hospital that principally provides services to selected beneficiaries (Aramco company employees and their families). Those employees and their families, regardless of whether they are Saudis or expats, have unique socio-economic characteristics that could be atypical to other communities across the KSA cities/towns. Additionally, the hospital involved in this study operates a comprehensive network that includes five EMSs, serving as vital gateways for the company’s community to access various clinical services. This healthcare network cannot be considered as a typical care delivery structure in the KSA because of its unique model with components that are interconnected through an unmatched system. Another limitation is the non-consideration of the “nationality” variable. We chose not to include this variable because the case population shares the same socio-economic level, similar living conditions and lifestyles, comparable educational levels, and a common organizational culture. Therefore, our choice not to consider the nationality variable is not expected to have any confounding effect on the research results.

Another limitation of this study was the utilization of an extensive list of categories for the “key body system affected” and the “type/nature of key complaint” categories, which impaired our ability to examine the correlations between the two variables and the probability of patients returning to EMS. Therefore, we recommend that future research would consider examining study and control groups of larger sizes in order to generate correlation conclusions for these two variables, especially if extensive lists of categories are used for any or both of the two variables.

Despite the acknowledged limitations of this study, management teams at EMS facilities across the KSA should carefully consider the implications of our findings. Specifically, it is sensible for EMS physicians to ensure that EMS patients’ discharge diagnoses are condition- or disease-specific rather than using less precise descriptions such as “complaint-similar” or “differential diagnosis” variances. One suggested approach to standardize this practice among EMS physicians is by providing up-to-date clinical protocols for the most common and the most serious clinical conditions typically encountered in the EMS setting. Such protocols would provide clinicians with guidance throughout the examination and the assessment processes until a definite diagnosis is eventually reached.

The findings and conclusions of this study offer valuable insights that could guide EMS teams in implementing essential operational and practice adjustments. Such adjustments should be designed to strategically minimize the likelihood of adult cases returning to EMS within 72 h. Additionally, the predictive logistic regression formula developed in this study could be integrated into the EHR software, which could serve as an early warning system, presenting timely alerts before a patient’s discharge from the first EMS visit. Such alerts have the potential to prompt reevaluation of the assessments conducted, the care provided, and the diagnosis reached, and hence might encourage a timely review of the discharge decision from EMS. By adopting these recommendations, EMS facilities could integrate a proactive approach to patient care that mitigates the risk of unscheduled return-to-EMS cases and fosters continuous improvement in emergency care delivery.

This investigation reveals distinct factors that are significantly associated with the higher probability of adult EMS cases returning within 72 h. Specifically, patients over 45 years of age, cases triaged at levels II and III, cases exhibiting a higher number of subacute noncritical abnormal blood test results across various body systems, and those with subacute noncritical abnormal ECG results had elevated probabilities of returning to EMS. Furthermore, cases discharged without medication prescriptions, cases with a history of admission in the past 30 days, the ones with discharge diagnoses being “complaint-similar diagnosis” or “differential diagnosis,” patients who visited EMS during shifts overlap, the ones who had a higher average number of EMS visits in the last 6 months, and the ones possessing a greater number of comorbidities all demonstrated significant differences from the control cases, accentuating the increased likelihood of return visits to EMS. Future interventions to reduce EMS revisits among the adult patient population should focus on these predicators and ensure that both appropriate and timely measures are implemented to identify such cases expediently while patients are still in EMS during the first visit, and take such predictors into consideration throughout the episode of care until the patient is finally discharged home.

The authors would like to acknowledge the following professors, academics, and professionals for their guidance and advice throughout the study until its successful accomplishment: Dr. Hayat AlMushcab, Ph.D., Lead. Research Office, JHAH, Dr. Edgar Miller, M.D., Ph.D., Deputy Director, Johns Hopkins Institute for Clinical and Translational Science, Dr. Nae-Yuh Wang, Ph.D., Director, Associate Professor of Medicine, Johns Hopkins University School of Medicine, Ms. Isra’a Ghader, Sr. Associate, Global Knowledge Transfer, Johns Hopkins Medicine International, Ms. May AlMuqarrab, Associate Enterprise Risk Management (ERM) & Patient Safety Professional, Quality and Patient Safety Department, JHAH, Ms. Shahad Alelaiw, Associate ERM & Patient Safety Professional, Quality and Patient Safety Department, JHAH, Dr. Huda Alsayed Ahmed, Senior Enterprise Quality Compliance Professional, Quality and Patient Safety Department, JHAH, and Dr. Ahmed Elrefaey, General Anesthesiologist, Anesthesiology and Critical Care Department, JHAH.

The study followed the guidelines of the Declaration of Helsinki and obtained ethics approval from JHAH’s Institutional Review Board (IRB) committee under Approval No. 23–34. This study was granted an exemption from requiring written informed consent from the participants by JHAH’s IRB committee.

The authors have no conflicts of interest to declare.

This study was not supported by any sponsor or funder.

Alber G. Paules: conceptualization, methodology, formal analysis, validation, supervision, resources, data curation, writing – original draft, writing – review and editing, visualization, and project administration. Anwar B. AlOtaibi and Iyad K. Eid: methodology, validation, supervision, writing – review and editing, and project administration. Ali A. Abandi: methodology, software, validation, investigation, and visualization. Saeed S. Yami: investigation, reviewing, and editing. Alber G. Paules, Anwar B. AlOtaibi, and Iyad K. Eid: experiment design, formal analysis, and manuscript preparation. Emad A. Shabana, Muhammad S. Afzal, and Sobhana Thankaiyan: methodology, resources, validation, and data curation. All authors approved the final manuscript.

The data that support the findings of this study are not publicly available due to information that could compromise the privacy of the population included but are available from Alber G. Paules upon request while adhering to institutional data-sharing policies and the relevant ethical standards.