The ratio of PACU length-of-stay to surgical duration: Practical observations

Exploring management reasoning when discharging high-risk postoperative patients from the post-anaesthesia care unit

INTRODUCTION

Decision-support tools for detecting physiological deterioration are widely used in clinical medicine but have been criticised for fostering a task-oriented culture and reducing the emphasis on clinical reasoning. Little is understood about what influences clinical decisions aided by decision-support tools, including professional standards, policies, and contextual factors. Therefore, we explored management reasoning employed by anaesthesiologists and PACU nurses in the post-anaesthesia care unit during the discharge of high-risk postoperative patients.

METHODS

A qualitative constructivist study, conducting 18 semi-structured with 6 anaesthesiologists and 12 nurses across three Danish teaching hospitals. We analysed data through thematic analysis, utilising Michael Lipsky's theory of "street-level bureaucracy" in combination with David A. Cook's Management Reasoning Framework as a sensitising concept.

RESULTS

Standards are frequently ambiguous, requiring interpretation and prioritisation. This allows for professional discretion by circumventing established policies, reducing task-oriented culture and enhancing the clinical reasoning processes. However, discretion in management reasoning depends on whether the clinician is inclined to uphold or adjust policies to maintain professional standards, influencing discharge decisions.

CONCLUSION

While decision-support tools offer cognitive aid and help standardise patient trajectories, they also limit professional discretion in management reasoning and can potentially compromise care and treatment. This highlights the need for a balanced approach that considers both the benefits and limitations of these tools in clinical decision-making.

A Comparison among Score Systems for Discharging Patients from Recovery Rooms: A Narrative Review

INTRODUCTION

The recovery room (RR) is a hospital area where patients are monitored in the early postoperative period before being transferred to the surgical ward or other specialized units. The utilization of scores in the RR context facilitates the assignment of patients to the appropriate ward and directs necessary monitoring. Some scoring systems allow nurses to select patients who can be discharged directly to their homes.

AIM AND METHODS

The aim of this narrative review was to describe and compare the scoring systems employed to discharge postoperative patients from RR, with a focus on item characteristics.

RESULTS

Nine scoring systems were identified and discussed: the "Aldrete Score System" and its modified version, the "Respiration, Energy, Alertness, Circulation, Temperature Score", the "Post Anesthetic Discharge Scoring System", the "White and Song Score", the "Readiness for Discharge Assessment Tool", the "Anesthesia and Perioperative Medicine Service Checklist", the "Post-Anesthetic Care Tool", the "Post-operative Quality Recovery Scale", and the "Discerning Post Anesthesia Readiness for Transition" instrument.

DISCUSSION AND CONCLUSIONS

To obtain a comprehensive overview, the items included in the scoring systems were compared. Despite the availability of guidelines for patients' discharge readiness from the RR, there is no universally recommended scoring system. Next-generation scores must be improved to ease their use, minimize errors, and increase safety. The main goals of the scores included in this narrative review were to be simple to use, feasible, intuitive, comprehensive, and flexible. However, these goals frequently conflict because patient assessment takes time, and a smart and comprehensive score may not consider some clinical parameters that may be crucial for the discharge decision. Therefore, further research should be conducted on this topic.

Development and Validation of a Clinical Model for Predicting Delay in Postoperative Transfer Out of the Post-Anesthesia Care Unit: A Retrospective Cohort Study

Objective

We aimed to analyze the factors related to delay in transfer of patients in the post-anesthesia care unit (PACU) and to develop and validate a prediction model for understanding these factors to guide precise clinical intervention.

Methods

We collected data from two cohorts of 1153 and 297 patients who underwent surgery and were treated in the PACU at two time points. We examined their clinical features and anesthesia care data using analytical methods such as logistic regression, Random Forest, and eXtreme Gradient Boosting (Xgboost) to screen out variables and establish a prediction model. We then validated and simplified the model and plotted a nomogram. Using LASSO regression, we reduced the dimensionality of the data. We developed multiple models and plotted receiver operating characteristic (ROC) and calibration curves. We then constructed a simplified model by pooling the identified variables, which included hemoglobin (HB), alanine transaminase (ALT), glucose levels, duration of anesthesia, and the minimum bispectral index value (BIS_min).

Results

The model had good prediction performance parameters in the training and validation sets, with an AUC of 0.909 (0.887-0.932) in the training set and 0.939 (0.919-0.959) in the validation set. When we compared model 6 with other models, the net reclassification index (NRI) and the integrated discriminant improvement (IDI) index indicated that it did not differ significantly from the other models. We developed a scoring system, and it showed good prediction performance when verified with the training and validation sets as well as external data. Additionally, both the decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated the potential clinical efficacy of the model in guiding patient interventions.

Conclusion

Predicting transfer delays in the post-anesthesia care unit using predictive models is feasible; however, this merits further exploration.

Postanesthesia Recovery Unit Optimization for Patients With Postictal Agitation Secondary to Electroconvulsive Therapy

OBJECTIVES

The occurrence of postictal agitation (PIA) can rapidly alter and intensify the level of care that electroconvulsive therapy (ECT) patients require during their recovery in the postanesthesia care unit (PACU). This operational analysis was undertaken to determine the impact PIA has on phase 1 PACU resources.

METHODS

This operational analysis was undertaken at the Seattle Division of the US Department of Veterans Affairs Puget Sound Health Care System. From August 2019 to April 2020, we prospectively collected data on the recovery from ECT of 61 unique patients who underwent a total of 334 ECT sessions. Utilization of PACU resources was assessed by determining the PACU length of stay (LOS), onset of PIA, severity of PIA, and duration of agitation in encounters complicated by PIA.

RESULTS

Seventy-nine occurrences of PIA occurred during the 334 ECT encounters. The mean ± SD PACU LOS was longer in encounters complicated by the occurrence of PIA compared with those not complicated by PIA (72 ± 32 and 59 ± 18 minutes respectively; P -value <0.05). Postanesthesia care unit LOS and mean duration of agitation increased as severity of PIA increased.

CONCLUSIONS

The occurrence of PIA can rapidly alter and intensify the level of care that ECT patients may require. Postictal agitation has a significant impact on the phase 1 PACU LOS of patients undergoing ECT. Phase 1 PACU staffing models should factor in the acute and prolonged care needs of patients who develop PIA during the recovery from ECT.

The construction of patient quality management model in oral post anesthesia care unit in China: a grounded theory approach

BACKGROUND

Nowadays, people have paid more and more attention to the quality of physical and mental health recovery after oral surgery anesthesia. As a remarkable feature of patient quality management, it can effectively reduce the risk of postoperative complications and pain in Post Anesthesia Care Unit (PACU). However, the patient management model in oral PACU remains unknown, especially in China. The purpose of this study is to explore the management elements of patient quality management in the oral PACU and to construct the management model.

METHODS

Strauss and Corbin's grounded theory method was used to explore the experiences of three anesthesiologists, six anesthesia nurses and three administrators working in oral PACU. Twelve semi-structured interviews were conducted using face-to-face in a tertiary stomatological hospital from March to June, 2022. The interviews were transcribed and thematically analysed according to QSR NVivo 12.0 qualitative analysis tool.

RESULTS

Three themes and ten subthemes were identified through an active analysis process, including three of the core team members: stomatological anesthesiologists, stomatological anesthesia nurses and administrators, three of the main functions: education and training, patient care and quality control and four of the team operation processes: analysis, plan, do, check.

CONCLUSION

The patient quality management model of the oral PACU is helpful for the professional identity and career development of stomatological anesthesia staff in China, which can accelerate the professional development of oral anesthesia nursing quality. According to the model, the patient's pain and fear will decrease, meanwhile, safety and comfort will increase. It can make contributions to the theoretical research and clinical practice in the future.

Predicting surgical department occupancy and patient length of stay in a paediatric hospital setting using machine learning: a pilot study

Objective

Early and accurate prediction of hospital surgical-unit occupancy is critical for improving scheduling, staffing and resource planning. Previous studies on occupancy prediction have focused primarily on adult healthcare settings, we sought to develop occupancy prediction models specifically tailored to the needs and characteristics of paediatric surgical settings.

Materials and methods

We conducted a single-centre retrospective cohort study at a surgical unit in a tertiary-care paediatric hospital in Boston, Massachusetts, USA. We developed a hierarchical modelling framework for predicting next-day census using multiple types of data—from bottom-up patient-specific orders and procedures to top-down temporal variables and departmental admission statistics.

Results

The model predicted upcoming admissions and discharges with a median error of 17%–21% (2–3 patients per day), and next-day census with a median error of 7% (n=3). The primary factors driving these predictions included day of week and scheduled surgeries, as well as procedure duration, procedure type and days since admission. We found that paediatric surgical procedure duration was highly predictive of postoperative length of stay.

Discussion

Our hierarchical modelling framework provides an overview of the factors driving capacity issues in the paediatric surgical unit, highlighting the importance of both top-down temporal features (eg, day of week) as well as bottom-up electronic health records (EHR)derived features (eg, orders for patient) for predicting next-day census. In the practice, this framework can be implemented stepwise, from top to bottom, making it easier to adopt.

Conclusion

Modelling frameworks combining top-down and bottom-up features can provide accurate predictions of next-day census in a paediatric surgical setting.

Clinical practice and procedures for postoperative care in Sweden: Results from a nationwide survey

A nationwide survey describing Swedish post-anaesthesia care units (PACUs), n = 75 was undertaken. The patients most commonly cared for at PACUs were patients who had undergone laparoscopic surgery, 69.3%, followed by patients who had undergone minor orthopaedic surgery, 68%. At the majority of the PACUs, 86.7%, the staff cared for emergency surgery patients and 48% for day surgery patients. In 31% of the PACUs, a pain relief service was offered through a specific pain service team. During the daytime, the anaesthetist in charge most frequently worked in the operating room 42.7%, and on call in the intensive care unit, 37.3% of the time. In 88% of the PACUs, either all or most registered nurses had a specialist education at an advanced level. The most frequent ratio of registered nurses to patients was 1 to 4, 37.3%. However, Swedish PACUS are also staffed by assistant nurses and the most frequent ratio of registered nurse to assistant nurse was 1:1. Thirty-three (44%) of the PACUs had access to a physiotherapist during the daytime. Almost all PACUs (93.3%) had predetermined discharge criteria but in the majority of PACUs' high-risk patients (68%) were not followed up by an anaesthetist after discharge from the PACU.

Military Surgical Team Performance: The Impact of Familiarity, Team Size, and Nurse Anesthesia Students

PURPOSE

To examine the key factors impacting surgical team performance in a military medical center.

DESIGN

A retrospective, exploratory, cross-sectional design.

METHODS

We reviewed 751 orthopedic surgical cases to determine the association of surgical team familiarity, surgical complexity, team size, and the presence of student registered nurse anesthetists (SRNAs) with the surgical performance measures of total operative time, turnover time, and on-time surgical start.

FINDINGS

We found increases in surgical team familiarity significantly reduced turnover time by 7.84% (1-0.9216 = 0.0784; P = .0260) after controlling for surgical complexity and the presence of an SRNA on the team. Familiarity did not significantly impact total operative time or the odds of a first case on-time start. With a significant interaction of surgical complexity and team size on total operative time, the surgical complexity marginal effect (at the mean of team size) showed that a one-point increase prolonged total operative time by 6.89% (P < .0001), after controlling for team familiarity and an SRNA. The team size marginal effect (at the mean of surgical complexity) showed that adding one member to the surgical team prolonged total operative time by 6.45% (P < .0001), after controlling for team familiarity and an SRNA. Higher surgical complexity not only increased turnover time by 1.46% (P = .0265) while holding surgical complexity and an SRNA presence constant, but also reduced the likelihood of an on-time surgical start by 0.9359 (P = .0060). Larger teams decreased the odds of an on-time start by 0.7750 (P = .0363). We found that SRNAs potentially offer efficiency benefits, as their presence on a surgical team was associated with a 0.82% (1-0.9185 = 0.0815; P = .0007) decrease in total operative time, and a 21.01% (1-0.7899=0.2101; P = .0002) reduction in expected turnover time, after adjusting for confounding variables.

CONCLUSIONS

Surgical efficiency is a modifiable function of surgical teams. Although we suggest additional research, surgical leaders can potentially improve team performance by improving familiarity and forming small and cohesive surgical teams. As OR inefficiencies degrade the financial vitality of healthcare systems, surgical leaders should engage in a multifaceted program to improve efficiency by building familiarity and optimizing team size.