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Lack of Utility of a Decision Support System to Mitigate Delays in Admission from the Operating Room to the Postanesthesia Care Unit

Ehrenfeld, Jesse M. MD, MPH*†; Dexter, Franklin MD, PhD; Rothman, Brian S. MD*; Minton, Betty Sue BN, MSN§; Johnson, Diane RN, MSN§; Sandberg, Warren S. MD, PhD; Epstein, Richard H. MD, CPHIMS

doi: 10.1213/ANE.0b013e3182a8b0bd
Economics, Education, and Policy: Research Report

BACKGROUND: When the phase I postanesthesia care unit (PACU) is at capacity, completed cases need to be held in the operating room (OR), causing a “PACU delay.” Statistical methods based on historical data can optimize PACU staffing to achieve the least possible labor cost at a given service level. A decision support process to alert PACU charge nurses that the PACU is at or near maximum census might be effective in lessening the incidence of delays and reducing over-utilized OR time, but only if alerts are timely (i.e., neither too late nor too early to act upon) and the PACU slot can be cleared quickly. We evaluated the maximum potential benefit of such a system, using assumptions deliberately biased toward showing utility.

METHODS: We extracted 3 years of electronic PACU data from a tertiary care medical center. At this hospital, PACU admissions were limited by neither inadequate PACU staffing nor insufficient PACU beds. We developed a model decision support system that simulated alerts to the PACU charge nurse. PACU census levels were reconstructed from the data at a 1-minute level of resolution and used to evaluate if subsequent delays would have been prevented by such alerts. The model assumed there was always a patient ready for discharge and an available hospital bed. The time from each alert until the maximum census was exceeded (“alert lead time”) was determined. Alerts were judged to have utility if the alert lead time fell between various intervals from 15 or 30 minutes to 60, 75, or 90 minutes after triggering. In addition, utility for reducing over-utilized OR time was assessed using the model by determining if 2 patients arrived from 5 to 15 minutes of each other when the PACU census was at 1 patient less than the maximum census.

RESULTS: At most, 23% of alerts arrived 30 to 60 minutes prior to the admission that resulted in the PACU exceeding the specified maximum capacity. When the notification window was extended to 15 to 90 minutes, the maximum utility was <50%. At most, 45% of alerts potentially would have resulted in reassigning the last available PACU slot to 1 OR versus another within 15 minutes of the original assignment.

CONCLUSIONS: Despite multiple biases that favored effectiveness, the maximum potential benefit of a decision support system to mitigate PACU delays on the day on the surgery was below the 70% minimum threshold for utility of automated decision support messages, previously established via meta-analysis. Neither reduction in PACU delays nor reassigning promised PACU slots based on reducing over-utilized OR time were realized sufficiently to warrant further development of the system. Based on these results, the only evidence-based method of reducing PACU delays is to adjust PACU staffing and staff scheduling using computational algorithms to match the historical workload (e.g., as developed in 2001).

From the Departments of *Anesthesiology and Bioinformatics and Surgery, Vanderbilt University, Nashville, Tennessee; Division of Management Consulting, Department of Anesthesia, University of Iowa, Iowa City, Iowa; §Department of Nursing, Vanderbilt University Medical Center; Departments of Anesthesiology, Bioinformatics, and Surgery, Vanderbilt University Medical Center, Nashville, Tennessee; and Department of Anesthesiology, Jefferson Medical College, Philadelphia, Pennsylvania.

Accepted for publication July 26, 2013.

Funding: Departmental resources.

Conflict of Interest: See Disclosures at the end of the article.

Reprints will not be available from the authors.

Address correspondence to Richard H. Epstein, MD, CPHIMS, Department of Anesthesiology, Jefferson Medical College, 111 S 11th St., Suite 6215F Gibbon Bldg., Philadelphia, PA 19107. Address e-mail to richard.epstein@jefferson.edu.

When the phase I postanesthesia care unit (PACU) is at capacity, completed cases need to be held in the operating room (OR) until either a PACU bed becomes available or the patient has recovered sufficiently to be discharged to an alternative location (e.g., a phase II discharge unit). Such “PACU delays” can arise if all physical PACU slots are occupied or there are insufficient nurses to provide safe care, based on the patients’ acuity.

Our group previously published statistical methods that optimize regular workday PACU staffing to achieve the smallest labor cost at a given service level (e.g., 95% of days with no PACU admission delays).1–3 The methodology can also be applied to achieve the highest service level possible without increasing the existing number of staffed hours (i.e., cost).4 For either objective, the approach used is adjustments to the distribution of shifts among start time options and durations that the nurses work.1-4 The computations are performed using historical data for PACU admissions and discharges.1–4 Planning staffing and staff schedules requires computer software, not the back of an envelope, because consideration of hundreds of billions of possible scheduling solutions typically is required.3, 4 A systematic review of other potential strategies to reduce the incidence of PACU delays such as adjusting nurse assignments the day before surgery, changing the sequence of surgical cases, or reducing delays in discharge from the PACU demonstrated that such efforts can be impractical, requiring large changes to organizational processes and lack of availability of sufficient personnel and equipment.4 For example, although, intuitively, case sequencing may seem useful, beneficial reductions in the peak census of the PACU are minimal when ORs have heterogeneous durations of the workday and relatively independent case scheduling, as found epidemiologically to be commonplace.4–6

Another possible approach for reducing PACU delays would be to create a decision support system (DSS) to assist active management of the PACU census on the day of surgery using detection of patient arrival in the PACU from perioperative management systems and automated paging. These processes are similar to those described by Meyer et al.7 in their “INCOMING!” system for expediting the discharge of patients from the PACU to the ward. Such systems could be integrated into electronic PACU bed occupancy displays8 or an alert could be sent to the PACU charge nurse when the PACU is nearly full or at capacity in order to avoid a subsequent PACU delay. Such systems might be especially useful in large PACUs and hospitals with multiple PACUs, where continual manual recalculation of the census and patient flux would be difficult. Such a DSS might also be useful for improving the service level where financial constraints limit staffing to suboptimal levels, or for the infrequent periods when demand is unusually high. However, such a system would provide utility only if the alerts were timely and if resources were available promptly to expedite the exit of PACU patients at the time of the alert. By timely, we mean that the alert needs to be provided sufficiently ahead of the anticipated PACU delay to allow an expedited discharge, but not so far in advance that managers would reasonably hesitate to initiate any special action. Evaluation of whether this can possibly occur was the first of the 2 goals of our article.

Another potential benefit of this DSS would be to help manage contention when 2 ORs are finishing at nearly the same time and are vying for the last available PACU slot. Basing the decision on reducing over-utilized time is an economically rational criterion, because on the day of surgery over-utilized time is expensive compared to the negligible cost of under-utilized time.9 In this scenario, the patient who should be accepted for admission would be the one from the OR that has the larger expected hours of over-utilized time, not necessarily the patient from the room that called first.8 Evaluation of the maximum potential utility of this approach was the second of the 2 goals of our article.

We previously have attempted to implement DSS for these approaches at several facilities, all without success. Our impressions are that the sources of the failures were multifactorial, including: lack of timely documentation of PACU arrivals and discharges, inaccurate representations of PACU assignments, reliance on paper-based documentation, behavioral changes resulting from knowledge by the PACU nurses that their activity was being recorded, clerical as opposed to provider data entry (resulting in inaccurate or delayed data capture), and pre-event or post-event documentation (e.g., due to lack of availability of the electronic charting system during transport). Therefore, we elected to study the maximum potential benefit of such a DSS using simulation, as opposed to direct clinical observation. We reasoned that if utility could not be demonstrated under idealized conditions, actual implementation would fail due to lack of cost-utility.

In order to perform our simulations, we capitalized on several unique characteristics of the PACU environment at Vanderbilt University Medical Center. The hospital has an electronic PACU documentation system that allows reconstruction of the nursing and patient census at every minute of the day. In addition, admissions to the PACU are not constrained either by the number of physical recovery slots (Table 1)10 or by inadequate PACU staffing (Fig. 1). These very unusual attributes provided us with real data from a model system for analysis of hypothetical PACU census management DSS alerts under idealized conditions. The lack of capacity constraint allowed us to evaluate utility in our simulations by reducing staffing (performed by reducing the maximum census) and adjusting the census at which an alert would be triggered. Not only did our use of such an unusual PACU bias our results to maximize the potential utility of DSS alerts, we deliberately biased our analysis assumptions to maximize the potential utility of alerts.

Table 1

Table 1

Since we previously failed at implementation of a PACU census alert system, the hypothesis of our study was that we would not demonstrate sufficient utility for preventing PACU delays to warrant development of a DSS sending alerts when the PACU was at or near capacity.

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METHODS

Study Data and IRB Approval

The Vanderbilt University IRB approved the study without requirement for written patient consent. Deidentified PACU data from regular, non-holiday workdays were extracted from January 1, 2010 to December 31, 2012 from the Vanderbilt perioperative information management system databases. Analyzed fields included the times of patient entry into and discharge from the PACU, and the start and end times that individual PACU nurse(s) were involved in the care of each patient. There were 42,199 admissions evaluated (55.7 ± 0.3 per day) during the 782 workdays in the dataset (mean ± SE). The Vanderbilt PACU studied comprises 2 physically separate units in close proximity, each of which has a charge nurse, and to which patients from either of the 2 main OR suites can be sent to recover following anesthesia. In addition, there is an administrative charge nurse who, among other responsibilities, is responsible for the overall flow into and out of the PACUs.

As shown by Table 1 and Figure 1, admissions to the studied PACU were not limited by inadequate physical PACU bed capacity or by insufficient nurse staffing. Again, these very unusual conditions were necessary to study via simulation the maximum possible benefit of an alert system.

Figure 1

Figure 1

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Description of the Simulated Census Management Alert System

The DSS was based on the following assumptions: (1) at all times there was an available patient who could be moved from the PACU; (2) at all times an appropriate hospital bed was available for such patient(s); (3) at all times, a PACU bed could be made available 30 minutes after the alert by moving such patients and cleaning the slot, and/or procuring more nurses; and (4) the patient to nurse ratio was not less than 2 to 1. If any criterion were not satisfied, the alert transmitted by the DSS would not result in a benefit. Therefore, these assumptions resulted in deliberate overstatement of the best possible performance of the DSS. In the model, the historical arrival PACU arrival and discharge times for all patients were maintained.

Terms used in the model are defined in Table 2. If following an alert the PACU census never exceeded the maximum (i.e., the patient flux out of the unit balanced subsequent admissions), the Alert Lead Time was considered to be infinite. A schematic of the model alert system is presented in Figure 2.

Table 2

Table 2

Figure 2

Figure 2

The PACU census tends to rise progressively from near 0 patients at the start of a day to a peak as the first cases of the day end (i.e., there is a positive slope in the census, since more patients are entering the PACU than are leaving the PACU).11 To avoid correlations among alerts on the day of surgery (e.g., due to an exceptionally busy OR schedule), we studied only the first alert of the day. (For details on the large effect of such auto-correlation, readers are referred to Refs. 1, 3, and 6.) This approach further increased the chance of our finding the alert system useful.

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Assessment of Utility of Census DSS to Prevent a PACU Delay

The PACU census was determined at each of the 1440 1-minute intervals on every day studied. When the specified PACU census was reached, a hypothetical alert was triggered that was assumed to result in the transfer of a PACU patient at the alert lead time. Again, the fact that in practice transfer of most patients out of the PACU would take longer than the minimum notification time implies that our study deliberately overestimates the potential benefit of alerts.

The census maximum of 24 patients (i.e., corresponding to 12 nurses) was the 95th percentile of PACU occupancy during the 782 workdays studied (Fig. 1). As one sensitivity analysis, all calculations were repeated using an occupancy corresponding to a reduction in PACU staffing by 2 nurses (i.e., to 20 patients). As another sensitivity analysis, calculations were repeated using an alert trigger set point from 1 to 4 patients less than the maximum PACU census. The implication of an alert trigger less than the maximum census is that the saturating number of admissions (i.e., the number of admissions required to produce a PACU delay) would be equal to 1 more than the offset. For example, if the trigger were set at 1 less than the maximum census, it would take a net increase in the PACU census of 2 patients during the alert lead time to cause a PACU delay.

Utility of an alert to prevent a PACU delay was assessed according to whether the PACU census subsequently reached a value exceeding the simulated maximum (Fig. 3). If the alert lead time were less than a minimum notification time of 30 minutes, we considered that the alert would have been ineffective (i.e., too late) in preventing the PACU delay. We based this minimum notification time on the expert opinion of our 2 PACU managers (BSM and DJ), who indicated that identifying a patient suitable for expedited discharge, calling for a bed, arranging transport, calling report, moving the patient out of the PACU, and clearing a slot would take at least 30 minutes under ideal circumstances (i.e., usually >30 minutes). If the alert lead time was more than 60 minutes after the alert, we considered the alert to have been ineffective (i.e., too early). A PACU manager is unlikely to credit the DSS alert as preventing such a delay, given the large flux of patients in the PACU (Table 1, Fig. 1) and the fact that patients identified for expedited discharge would be likely to have been discharged soon regardless of being expedited, since they would already have met discharge criteria. Our PACU experts (BSM and DJ) reported that when trying to anticipate problems, they typically do not look attempt to predict events more than an hour into the future. If the alert trigger set point was reached, but the census never exceeded the PACU, we also considered this alert as ineffective (i.e., a false alarm). Thus, we considered an alert potentially effective in avoiding a PACU delay if the alert lead time was between 30 and 60 minutes, inclusive.

Figure 3

Figure 3

We conducted sensitivity analyses by extending the range of utility to wider ranges of the minimum to maximum effective notification time, specifically: 15 to 75 minutes, 30 to 90 minutes, and 15 to 90 minutes. Each of these 3 expanded ranges exceeded the limits identified by our PACU experts, and again represents bias in the direction of showing utility of the DSS alert system.

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Assessment of Utility of DSS Alerts to Reduce Over-utilized OR Time

This assessment was made with an alert triggered at 1 less than the PACU census. Under such conditions, if 2 ORs call for a PACU slot, the room with the largest estimated amount of over-utilized time should get the slot, as this will reduce the expected minutes of over-utilized time.9 Given that OR’s generally call for a PACU slot following the end of surgery, there is an interval during which an assignment could be rescinded if a call was received from another OR with a higher priority for the institution. We considered the maximum interval during which such a decision could be made to be 15 minutes, corresponding to our previous meta-analyses of prolonged extubation.12,13 That is, if 2 patients arrived in the PACU within 15 minutes following an alert, we considered this as a potential opportunity for changing the OR getting the last available PACU slot. We measured such utility, using alerts triggered at 1 patient less than the maximum census, for patients arriving within 5, 10, and 15 minutes of each other. However, even assuming that all ORs had over-utilized time, the maximum beneficial effect of choosing between 2 ORs selected at random would be 50%, since for half the cases, the correct room would have called first and no change would be made. Since the definition of over-utilized time implies that fewer than half of ORs will have no over-utilized time,14–16 this estimate of 50% is deliberately conservative and biased to show utility of the DSS alert system.

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Distribution of Alert Lead Times

The cumulative distribution of alert lead times was calculated for maximum PACU census values between 20 and 24 with the alert trigger set point set at 1 less than the maximum PACU census.

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Statistical Methods

The percent utility of each studied alert trigger set point was calculated as the number of days with an alert showing utility divided by the number days with at least one alert. One-sided 95% conservative upper confidence limits were calculated using the method of Clopper-Pearson and are reported using the phrase: “at most.”17 One-sided upper limits were used as we made nonstatistical assumptions biased to show that the alerts would have utility; thus, the confidence intervals should be limited in a similar fashion.

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RESULTS

At most, 23% of DSS alerts sent when the PACU census was at the maximum or at 1 less than the maximum, respectively, would have potentially allowed the PACU charge nurse to prevent a subsequent PACU delay (Fig. 4A). The remainder of the alerts would have been false alarms (i.e., the PACU subsequently never exceeded the maximum), either too late (<30 minutes after the alert), or too early (>60 minutes after the alert) to be useful (see Table 3, corresponding to Fig. 4A). When the range of the minimum to maximum effective notification time was extended to 15 to 75 minutes, 30 to 90 minutes, and 15 to 90 minutes, the maximum utility among all simulations would have been 55% (Fig. 4, B–D).

Table 3

Table 3

Figure 4

Figure 4

If the alert trigger set point were set at 1 less than the maximum census of 24 patients, at most 45% of alerts would potentially have resulted in reassigning a sole available PACU bed, depending on the range of the minimum to maximum effective notification time to make the change (5 to 15 minutes; Fig. 5). If the alert trigger set point was 1 less than a smaller maximum census of 20 patients (see Methods), potential utility would have occurred for at most 37% of the alerts, similarly depending on the range of effective notification times.

Figure 5

Figure 5

The cumulative distributions of the alert lead times are shown in Figure 6 for maximum PACU census between 20 and 24 and an alert trigger set point at 1 less than the maximum census. At an alert lead time of 30 minutes, between 25% and 45% of alerts would have arrived too late for a patient to be discharged from the PACU to avoid a PACU delay. Between 30% and 65% would have arrived 90 or more minutes prior to the admission causing a PACU delay, too early likely to result in an effort to expedite a PACU discharge.

Figure 6

Figure 6

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DISCUSSION

Performance was insufficient for the simulated PACU census management DSS. At least three-fourths of the alerts, sent when the census reached 1 patient less than the maximum census, would have failed to prevent a subsequent PACU delay. A DSS would also have low utility for reducing over-utilized time on the day of surgery, despite allowing up to a 15-minute interval to reassign a promised PACU slot to another patient. Actual performance would be expected to be even worse, since many of the assumptions on which the model was predicated would not be satisfied.

The suggested minimum threshold calculated for a DSS supplying automated alerts as diagnostic aids used by busy managers was found by Wickens and Dixon18 in their meta-analysis to be 70%. They found that below 70% utility, the combined performance of the human and the automation system was worse than had no automation been used at all.18 The model alert system evaluated in this study meets the inclusion criteria for automation in the meta-analysis: “filtering or focusing attention on information deemed to be of interest” (i.e., the current PACU census) and “forming inferences of the state of the world, by integrating information” (i.e., whether or not a PACU delay is imminent).18 The reason for this 70% threshold is that when the false alert rate from a DSS is high, people tend (reasonably) to ignore recommendations. Wickens and Dixon’s findings18 are directly applicable because it is precisely when the PACU is the busiest that it is the most challenging to have extra nursing supervisors and personnel not focus on direct patient care but instead to coordinate PACU and wards and/or PACU and ORs. The implication for our findings (Figs. 4 and 5, Table 3) is there seems little reason to pursue further this type of approach to reducing the incidence of PACU delays. The problems need to be prevented months ahead when staff scheduling is performed.1–4,6,8

Our study has several limitations. First, as stated repeatedly above, the studied PACU and our simulation assumptions greatly overstate the potential benefit of a PACU census management DSS. This approach was sufficient to reach the conclusion of lack of utility of DSS alerts based on PACU census on the day of surgery. However, the specific reported estimates of utility are biased in the positive direction. Second, because our model was evaluated using retrospective data, it ignores latency considerations studied within anesthesia information management systems (e.g., when nurses chart activity in the PACU relative to its actual occurrence19 and the times for transmission of the recommendations to the PACU managers).20,21 Such issues would further reduce the effectiveness of the DSS, as the intervals over which alerts might be useful are reduced. Third, we did not consider the more complicated scenario of >2 patients presenting simultaneously for admission to the PACU when the unit was near full. However, such instances are rare.

In conclusion, we were unable to show potential benefit of a DSS to mitigate PACU delays on the day on the surgery, despite multiple biases that favored effectiveness. Based on our results, the only evidence-based method of reducing PACU delays is to adjust PACU staffing and staff scheduling to the match the historical workload using computational algorithms.1–4,6,8 Such methods are applied months before the day of surgery.1–4

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RECUSE NOTE

Franklin Dexter is Statistical Editor and the Section Editor of Economics, Education, and Policy for the Journal. The manuscript was handled by Dr. Steven L. Shafer, Editor-in-Chief, and Dr. Dexter was not involved in any way with the editorial process or decision.

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DISCLOSURES

Name: Jesse M. Ehrenfeld, MD, MPH.

Contribution: This author helped design the study and write the manuscript.

Attestation: This author has approved the final manuscript.

Conflicts of Interest: The author declares no conflicts of interest.

Name: Franklin Dexter, MD, PhD.

Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.

Attestation: This author has approved the final manuscript.

Conflicts of Interest: The University of Iowa performs statistical analyses for hospitals using their operating room information systems, including some in this article. Income from the Division’s consulting work is used to fund Division research. Franklin Dexter has tenure and receives no funds personally, including honoraria, other than his salary and allowable expense reimbursements from the University of Iowa. He and his family have no financial holdings in any company related to his work, other than indirectly through mutual funds for retirement.

Name: Brian S. Rothman, MD.

Contribution: This author helped design the study and write the manuscript.

Attestation: This author has approved the final manuscript.

Conflicts of Interest: The author declares no conflicts of interest.

Name: Betty Sue Minton, BN, MSN.

Contribution: This author helped analyze the data and write the manuscript.

Attestation: This author has approved the final manuscript.

Conflicts of Interest: The author declares no conflicts of interest.

Name: Diane Johnson, RN, MSN.

Contribution: This author helped analyze the data and write the manuscript.

Attestation: This author has approved the final manuscript.

Conflicts of Interest: The author declares no conflicts of interest.

Name: Warren S. Sandberg, MD, PhD.

Contribution: This author helped write the manuscript.

Attestation: This author has approved the final manuscript.

Conflicts of Interest: The author declares no conflicts of interest.

Name: Richard H. Epstein, MD, CPHIMS.

Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript. This author is the archival author.

Attestation: This author has approved the final manuscript.

Conflicts of Interest: Richard H. Epstein is President of Medical Data Applications, Ltd., whose CalculatOR™ software includes the PACU staffing analyses mentioned in this article.

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