Challenging Organizational Factors Associated With Admission Delay to Intensive Care Unit—A Novel Quality Indicator

Background Delays in admitting patients to the intensive care unit (ICU) can defer the timely initiation of life-sustaining therapies and invasive monitoring, jeopardizing the success of the treatment. Nevertheless, the availability of research on interventions that reduce or minimize admission delays is limited. Objectives The current study aimed to assess the factors related to delays in admission times of critically ill patients transferred to the ICU. Methods A software was designed to follow-up, compare and measure the defined intervals of the time to admission, implemented at the ICU for 6 months. Measurements included 5 time intervals, referral department, and work shift at admission. Data from 1004 patients admitted to the ICU between July 2017 and January 2020 were analyzed in a retrospective observational study. Results Precisely, 53.9% of total patients were referred from the hospital emergency department, and 44% were admitted during the evening shift. Significant differences were found in time intervals between shifts, showing the morning round had the longer total admission time (median: 67.8 min). Analysis showed that admission time was longer at times of full capacity compared to times of available bed (mean: 56.4 and 40.2 min, respectively; U = 68,722, p < .05). Findings demonstrated a significant shortening of time to admission after implementing a new time monitoring software by the Institutional Quality Control Commission (U  =  5072, p < .001). Conclusions Our study opens doors for potential studies on applying effective initiatives in critical care settings to improve patient care and outcomes. Additionally, it generates new insights regarding how clinicians and nursing teams can jointly develop and promote multidisciplinary interventions in intensive care work environments.


Introduction
Shortening the length-of-stay (LOS) is a priority for all intensive care units (ICUs) as the shortage of ICU beds is a global problem. 1,24][5] ICU admission delays were associated with increased LOS, ICU and in-hospital mortality, 4,5 increased requirements for respiratory support, and longer ventilator care time. 6Patients admitted at the time of bed shortage were found to be more severely ill than those admitted when beds were unoccupied. 7Prolonged waiting time to get to the ICU was associated with higher mortality for surgical patients. 8For mechanically ventilated patients waiting in the emergency department (ED) for ICU, the time effect on mortality emerged after 4 h. 9Moreover, every hour of ICU admission delay may increase the risk of death by 1.5%. 9In an earlier study, 10 a delay in ICU admission was associated with both a greater need for advanced respiratory support (92.3% delay vs. 76.4% no delay) and a longer LOS in the ICU (median 4 days delay vs. 3

days no delay).
There are multiple reasons for ICU admission delays.A recent review 11 explored the organizational factors associated with the incidence of patients' admission delays in critical care.Organizational factors negatively influence approximately 38% of admissions, including teamwork issues, communication breakdowns, lack of shared situational awareness, lack of resources, busy workload (unit/hospital), lack of an available bed, specific requirements such as infection precautions, lack of adequate staff, receiving unit not ready for transfer, and time of transfer (night and weekend transfers).It is important to note that discharge delays may cause a bottleneck effect that prevents admission to the ICU, therefore, it should be referred to as another organizational factor influencing ICU admission delays. 12hile discharge delays have been studied more often, research on interventions that decrease or minimize ICU admission delays is limited. 11A past study reported a successful intervention included changes to improve patient flow from the ED to the ICU through active bed management. 13he patient flow process must be addressed in a multifaceted and interdisciplinary way in order to reduce delays.Lean methodology is considered a continuous improvement model, allowing healthcare organizations to identify and remove wasteful activities in wide clinical settings. 14One of the tools in the lean approach uses analysis of time lags in a defined process to improve patient flow. 15The analysis of time lags allows understanding of the stream of a process, identifying points causing the workflow to become backed up or slower. 16e aimed to assess the factors related to delays in the admission of critically ill patients to the ICU, using a proactive lean approach to analyze the admission process.

Methods
The association between organizational factors in delays of patient admission to the ICU was examined through a retrospective analysis of transfer episodes in the ICU of Barzilai Medical Center in Israel.The study was approved by the Ashkelon Academic College Ethics Committee with a waiver of informed consent, all methods were carried out in accordance with relevant guidelines and regulations (Approval # 25/1-2020).

ICU Description
Barzilai Medical Center ICU is a Medico-Surgical Unit, admitting adult (older than 17 years old) patients from the ED, trauma, operating room, obstetrics/gynecology, ear, nose, and throat, orthopedics, and medical wards.In August 2018, the ICU moved to a new installation growing from 6 to 10 beds.

Data Processing and Analyses
The data analysis was divided into 2 phases: the first phase involved an analysis of admission data for the period between July 1, 2017, and July 30, 2019, in which no monitoring of the stages of the admission process or the times of the admission process were performed.For the analysis of this phase data on times were extracted from the hospital's information system and from hard copies of medical records.The second phase involved an analysis of admission data for the period between August 1, 2019, and January 31, 2020, in which Quality Control Commission (QCC) software was designed and implemented to measure and compare time intervals in the process of admission.The QCC software required ICU nurses to fill in details on every patient admitted to the ICU on the hospital intranet.Details included: the serial number of the patient, date of admission, availability of a free bed, and 5 time points of the admission process.Registered times were: (1) Time "1"-the decision to admit a patient by the ICU physician.(2) Time "2"-a request to transfer a patient out of the ICU was communicated to the admitting ward if no ICU bed was available at Time 1. (3) Time "3"-the time the ICU patient was transferred, freeing the ICU bed, but the bed was yet to be prepared for admission.(4) Time "4"-the ICU bed freed was arranged, and notification about readiness to admit the new patient was communicated to the referring department.(5) Time "5"-the new patient enters the ICU for admission.The time format was HH:MM.
For the purpose of the analysis, 5 time interval measures were defined: (1) first interval: from time 1 to time 2. The time required to decide who is the most appropriate patient for transfer out of the ICU and find an adequate admitting ward.(2) Second interval: from time 2 to time 3.The time required for transfer preparations includes receiving ward permission to transfer.(3) Third interval: from time 3 to time 4. The time required for space preparation for admission.(4) Fourth interval: from time 4 to time 5.The time the referral department gets permission to transfer the patient up to patient admission.(5) Fifth interval: from time 1 to time 4. The time required for the ICU to be ready to admit a new patient.The total time was defined as the entire time from decision to admission (time 1 to time 5).Variables including the referral department and work shift of admission (morning, evening, or night) were also recorded.

Statistical Analysis
Data analysis was carried out using IBM SPSS Statistics 25.0 software.An analysis of descriptive statistics was conducted to explore the percentages of patients referred by ward and shift.Due to the nonnormal distributions, we calculated median and interquartile range (IQR) in addition to mean and standard deviation (SD) measures as they are more robust measures of central tendency and dispersion.Nonparametric tests were applied to explore differences in time intervals.Kruskal-Wallis tests were performed for each time interval to explore differences between shifts and the Mann-Whitney test to compare time intervals between admissions at times of ICU full capacity and admissions at times of no ICU full capacity.Pairwise comparisons between the 3 shifts were applied as a post hoc test following Kruskal-Wallis.Pearson correlations were calculated to examine the association between time intervals.A significance level of .05 was adopted in the analyses.

Results
Precisely, 1004 transfer episodes were registered in the study.In total, 693 patients were admitted before monitoring the process stages and times, and 311 patients were admitted after implementing software to measure and monitor intervals of admission time.A descriptive analysis of admissions to the ICU showed that 53.9% of total patients were referred from the hospital ED, and 41.6% were admitted during the evening shift.The total number of patients referred by ward and shift is shown in Table 1.
As shown in Table 1, there were significant differences among referral departments in the number of referrals (X 2 = 19.56,p = .01).Findings show that the night shift was the less transferburdened among all the departments (20% of admissions).
Table 2 shows the median of time intervals by shift.Kruskal-Wallis tests were performed for each time interval to explore differences between shifts.
As presented in Table 2, the analysis showed that the morning round had the longer total admission time while a shorter time was noted for the night shift.Post hoc test following Kruskal-Wallis showed that statistically significant differences were found among shifts, in the first interval time (time required to decide who is the most appropriate patient for transfer out of the ICU and find an adequate admitting ward) and in the fifth interval time (time required for the ICU to be ready to admit a new patient) showing that morning admissions to the ICU take longer.
To examine the effect of the full capacity of the ICU on admission time, we performed the Mann-Whitney test to compare time intervals between admissions at times of ICU full capacity (N = 269) and admissions at times of no ICU full capacity (N = 735).Analysis showed a significant difference in the fifth time interval, which showed an average of 56.4 min (SD = 86.8)at times of full capacity, compared to an average of 40.2 min (SD = 59.0) at times of available beds (U =68,722, p = .02).
Table 3 shows the Pearson correlation between time intervals.Analysis revealed significant associations between time intervals, mainly positive associations between each interval and the total admission time, and between the second and the fourth time intervals despite not being dependent on each other.
To examine the effect of the new monitoring software on admission time, we performed the Mann-Whitney test to compare time intervals between patients' admissions before monitoring the process stages and times (N = 693) and patients' admissions after implementing the software measuring and monitoring intervals of admission time (N = 311).Figure 1 presents the average times of each interval before versus after intervention (minutes).Analysis showed significant differences in total time to admission, before using the new monitoring software versus after the new monitoring software was implemented (U = 5072, p = .001).In addition, Analysis showed significant differences in the fifth interval time to admission, before using the new monitoring software versus after the new monitoring software was implemented (U = 4750, p = .00).

Discussion
Reducing ICU admission delays requires an interprofessional and multifactorial evaluation approach to the critical care process due to the multiple actors and disciplines involved in critical care. 16Our findings showed that applying a lean approach to analyze the time intervals of admission to ICU and the introduction of a process flow analysis as a novel quality assessment indicator, can effectively reduce the time to admission to ICU.Similar to earlier reports, our analysis found that the ED was the most frequent referral, followed by the combined surgical department and the operating room, then the combined 6 internal medicine departments of the hospital.Both the surgical and medical departments referred most of their patients during the morning shift and less during the night shift.The evening shift was the busiest for patients transferred from the ED and the operating room, being the night shift the less transferburdened from all the departments.Despite the morning round admitting fewer patients and being better staffed than the evening shift, longer admission time was revealed, followed by the night shift and the shorter time noted at the evening shift.It is to note that the morning shift is usually the busiest time at the unit.Although better staffed, our findings point to a possible disproportion between the amount of work expected to be done and the number of staff doing the work.
The evening shift also showed a shorter delay in transferring a patient from ICU to other departments when no ICU beds were available and a shorter time to arrange the bed for new admission.The arrangement of the space for admission requires special cleaning.This activity is done by a team that is not organic to the unit and takes longer to recruit at night.
As expected, a significantly longer time to admission was recorded when no ICU bed was available.As shown, a significant difference was noted up to the fourth lap, as no significant difference was seen from then to the patient admission.Lack of resources and bed availability problems were the most common reason for transfer delays in a study that examined adverse events experienced while transferring critically ill patients from the ED to ICU. 17 Our findings are in line with another study that demonstrated a significant association between the number of ICU beds available and ICU admission within 2 h, 18 with no effect on hospital mortality.
ICU beds and staff availability is an undoubted and undisputed vision for every hospital and society, although only sometimes realistic because of the high cost and the scarcity of   medical and nursing professionals in the intensive care field.
Our findings support a study by Gillman et al 17 in which a busy workload, lack of adequate staff, and receiving units not ready for transfer, were mentioned as organizational factors contributing to ICU admission delays.Finally, our study showed a significant shortening in time to admission after implementing a new time monitoring software, although no change in the ICU workflow stages in the current study has been applied.A possible explanation for our findings may be the well-known Hawthorne effect.As a result of the Hawthorne effect, individuals modify their behavior when they become aware of the fact that they are being watched. 19,20Furthermore, when the medical team actively participates in quality quality, and performance are likely to be improved. 21Process analysis in terms of time intervals measurement is a Lean strategy for reducing or eliminating waste and activities that do not add value to healthcare processes. 22A recent review assessed the effect of Lean on patient flow in ambulatory care and reported improvements regarding shorter LOS and shorter waiting times for both discharged and admitted patients after Lean intervention. 23An earlier study observed that implementing Lean techniques in critically ill processes has positively impacted patient flow, reduced delays in discharge from the ICU to the conventional ward, and increased staff satisfaction within the ICU. 24ne of the main amendable reasons for the delay was identified in our study as the time interval started from the moment a request to transfer a patient out of the ICU was communicated to the admitting ward (at times of the full capacity of the ICU), until the ICU patient was transferred, freeing the ICU bed (but the bed was yet to be prepared for admission).As we learned that the time required for space preparation for admission was also long, and the referral department needs an average of 44.5 min to move the patient to the ICU, another opportunity for intervention was realized as we started to superpose actions that were done in sequence in the past.This data is being acquired now, and we hope to further shorten the ICU admission time.
Several limitations of this exploratory study must be considered in interpreting the results.Manually registering each time can be subjected to manipulation.Though that was periodically supervised, and neither the supervisor nor the staff doing the registration was aware of the intention of registering the data.It is also to note that the initial registers were only used once consistent annotation was achieved.Being a single-site study, our conclusions cannot be directly extrapolated.Nonetheless, our hospital is a secondary trauma-level university institution periodically supervised by the Joint Commission for Quality in Hospital Care Accreditation and Certification.Other similar institutions are then supposed to have to cope with comparable situations.
We did not compare other variables such as patient severity, LOS in the unit, or mortality, but this was beyond the goals of the study, anyhow, shortening time to ICU admission and fastening approaches to improve the level of care are believed to be significant corners to professional care.
Many variables cannot be modified by the ICU staff.The time demanded for the transfer of a stable patient from the ICU to another department depends on the availability of a free bed in the receiving ward.The early hours of the morning shift, before discharges started at the different hospital departments, were puzzling because the admitting ward was also at full capacity.Finally, the ICU team can barely change the time required by the referral department to transfer the patient after the ICU approval.Nevertheless, we believe our study's overall methodological rigor and robust research design should be applicable and generalizable to analyze other patient flow processes.

Conclusions
The current study shows the positive effect of a protracted follow-up of the complex process divided into several stations, where possible preventable situations can be found and modified.As the reasons for delay can present isolated or shared at many steps, it is necessary to analyze every step as a separate lap.This study may lead to process improvements in critical care settings to improve patient care and outcomes.In light of the complexity of the critical care process, future research is required to explain relationships between the organizational and system elements involved in the admission processes.

Figure 1 .
Figure 1.Average time intervals before and after intervention.

Table 1 .
Number of Patients Referred by the Department and Respective Shift (N = 942).
X 2 = 19.56,p < .01. *Data related to the referral department was not reported for 62 episodes preintervention.