Case-mix adjusted postanaesthesia care unit length of stay and business intelligence dashboards for feedback to anaesthetists

Natural Language Processing (NLP)- and Machine Learning (ML)-Enabled Operating Room Optimization: A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Systematic Review Anchored in Project Planning Theory

The operating room (OR) is a high-stakes, resource-intensive environment where inefficiencies in scheduling, workflow, and resource allocation can significantly impact patient outcomes and healthcare costs. Emerging technologies such as natural language processing (NLP) and machine learning (ML) offer data-driven solutions to optimize surgical workflows, particularly when integrated with structured project planning principles. This systematic review evaluated how NLP and ML techniques, grounded in project management methodologies, can enhance OR management by improving surgical scheduling, workflow efficiency, and resource utilization. A systematic search of PubMed, Scopus, Web of Science, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and Association for Computing Machinery (ACM) Digital Library was conducted between January 1, 2020, and March 15, 2025, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Inclusion criteria focused on studies applying NLP or ML to surgical workflow analysis within a project planning framework. Primary outcomes included improvements in surgical duration prediction, post-anesthesia care unit (PACU) length-of-stay estimation, and OR scheduling efficiency. Nineteen studies met the eligibility criteria, encompassing diverse surgical specialties and geographical settings. Most employed retrospective observational designs using ML models such as ensemble learning, neural networks, and regression-based algorithms. Several studies demonstrated that ML models significantly outperformed traditional scheduling and prediction approaches, while NLP, particularly ClinicalBERT, improved accuracy when analyzing unstructured clinical texts. Risk of bias assessment using the Prediction model Risk Of Bias ASsessment Tool (PROBAST) revealed that five studies were of low risk, eight moderate risk, and six high risk, primarily due to limitations in analysis and external validation. Overall, integrating NLP and ML with project planning principles presents a promising approach to optimizing OR workflows, enhancing efficiency, reducing costs, and improving patient outcomes. However, broader clinical adoption will require cross-institutional validation, improved interpretability, and ethical artificial intelligence (AI) governance.

The Role of Machine Learning in Management of Operating Room: A Systematic Review

Machine learning (ML) is a developing technology that enables the analysis and interpretation of large amounts of data. The purpose of this systematic review was to summarize the available literature on the role of ML in operating room (OR) management. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to search the literature based on pre-defined inclusion and exclusion criteria. A total of 608 studies were found across five different databases (PubMed, EMBASE, Scopus, Web of Science, and IEEE Xplore), of which 21 studies were included in this review after removing duplicates and excluding studies based on the pre-defined inclusion and exclusion criteria. The review highlights how ML has a major impact on surgical case cancellation detection, post-anesthesia unit resource allocation optimization, and surgical case length prediction. Neural networks, XGBoost, and random forests are a few examples of ML algorithms that have shown promise in increasing prediction accuracy and resource efficiency. Nonetheless, issues including privacy concerns and data access remain challenges. The study emphasizes how ML is advancing in surgical medicine and how further innovation is required to fully realize AI's transformative potential for patients, healthcare professionals, and practitioners. Ultimately, integrating AI into OR management holds the potential for improving patient outcomes and healthcare productivity.

Artificial Intelligence in Operating Room Management

This systematic review examines the recent use of artificial intelligence, particularly machine learning, in the management of operating rooms. A total of 22 selected studies from February 2019 to September 2023 are analyzed. The review emphasizes the significant impact of AI on predicting surgical case durations, optimizing post-anesthesia care unit resource allocation, and detecting surgical case cancellations. Machine learning algorithms such as XGBoost, random forest, and neural networks have demonstrated their effectiveness in improving prediction accuracy and resource utilization. However, challenges such as data access and privacy concerns are acknowledged. The review highlights the evolving nature of artificial intelligence in perioperative medicine research and the need for continued innovation to harness artificial intelligence's transformative potential for healthcare administrators, practitioners, and patients. Ultimately, artificial intelligence integration in operative room management promises to enhance healthcare efficiency and patient outcomes.

Dashboard of Short-Term Postoperative Patient Outcomes for Anesthesiologists: Development and Preliminary Evaluation

BACKGROUND

Anesthesiologists require an understanding of their patients' outcomes to evaluate their performance and improve their practice. Traditionally, anesthesiologists had limited information about their surgical outpatients' outcomes due to minimal contact post discharge. Leveraging digital health innovations for analyzing personal and population outcomes may improve perioperative care. BC Children's Hospital's postoperative follow-up registry for outpatient surgeries collects short-term outcomes such as pain, nausea, and vomiting. Yet, these data were previously not available to anesthesiologists.

OBJECTIVE

This quality improvement study aimed to visualize postoperative outcome data to allow anesthesiologists to reflect on their care and compare their performance with their peers.

METHODS

The postoperative follow-up registry contains nurse-reported postoperative outcomes, including opioid and antiemetic administration in the postanesthetic care unit (PACU), and family-reported outcomes, including pain, nausea, and vomiting, within 24 hours post discharge. Dashboards were iteratively co-designed with 5 anesthesiologists, and a department-wide usability survey gathered anesthesiologists' feedback on the dashboards, allowing further design improvements. A final dashboard version has been deployed, with data updated weekly.

RESULTS

The dashboard contains three sections: (1) 24-hour outcomes, (2) PACU outcomes, and (3) a practice profile containing individual anesthesiologist's case mix, grouped by age groups, sex, and surgical service. At the time of evaluation, the dashboard included 24-hour data from 7877 cases collected from September 2020 to February 2023 and PACU data from 8716 cases collected from April 2021 to February 2023. The co-design process and usability evaluation indicated that anesthesiologists preferred simpler designs for data summaries but also required the ability to explore details of specific outcomes and cases if needed. Anesthesiologists considered security and confidentiality to be key features of the design and most deemed the dashboard information useful and potentially beneficial for their practice.

CONCLUSIONS

We designed and deployed a dynamic, personalized dashboard for anesthesiologists to review their outpatients' short-term postoperative outcomes. This dashboard facilitates personal reflection on individual practice in the context of peer and departmental performance and, hence, the opportunity to evaluate iterative practice changes. Further work is required to establish their effect on improving individual and department performance and patient outcomes.

Machine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing

The post-anesthesia care unit (PACU) length of stay is an important perioperative efficiency metric. The aim of this study was to develop machine learning models to predict ambulatory surgery patients at risk for prolonged PACU length of stay - using only pre-operatively identified factors - and then to simulate the effectiveness in reducing the need for after-hours PACU staffing. Several machine learning classifier models were built to predict prolonged PACU length of stay (defined as PACU stay ≥ 3 hours) on a training set. A case resequencing exercise was then performed on the test set, in which historic cases were re-sequenced based on the predicted risk for prolonged PACU length of stay. The frequency of patients remaining in the PACU after-hours (≥ 7:00 pm) were compared between the simulated operating days versus actual operating room days. There were 10,928 ambulatory surgical patients included in the analysis, of which 580 (5.31%) had a PACU length of stay ≥ 3 hours. XGBoost with SMOTE performed the best (AUC = 0.712). The case resequencing exercise utilizing the XGBoost model resulted in an over three-fold improvement in the number of days in which patients would be in the PACU past 7pm as compared with historic performance (41% versus 12%, P<0.0001). Predictive models using preoperative patient characteristics may allow for optimized case sequencing, which may mitigate the effects of prolonged PACU lengths of stay on after-hours staffing utilization.

Artificial Intelligence for Perioperative Medicine: Perioperative Intelligence

The anesthesiologist's role has expanded beyond the operating room, and anesthesiologist-led care teams can deliver coordinated care that spans the entire surgical experience, from preoperative optimization to long-term recovery of surgical patients. This expanded role can help reduce postoperative morbidity and mortality, which are regrettably common, unlike rare intraoperative mortality. Postoperative mortality, if considered a disease category, will be the third leading cause of death just after heart disease and cancer. Rapid advances in technologies like artificial intelligence provide an opportunity to build safe perioperative practices. Artificial intelligence helps by analyzing complex data across disparate systems and producing actionable information. Using artificial intelligence technologies, we can critically examine every aspect of perioperative medicine and devise innovative value-based solutions that can potentially improve patient safety and care delivery, while optimizing cost of care. In this narrative review, we discuss specific applications of artificial intelligence that may help advance all aspects of perioperative medicine, including clinical care, education, quality improvement, and research. We also discuss potential limitations of technology and provide our recommendations for successful adoption.

Identification of Risk Factors Contributing to Prolonged Stay in the Post-anaesthesia Care Unit at a Tertiary Care Hospital in Abu Dhabi, United Arab Emirates

INTRODUCTION

Post-anesthesia care units (PACU) were developed to reduce postoperative morbidity and mortality and the ideal duration of postoperative stay has been proposed as two hours; however, the incidence and risk factors for prolonged stay are variable. The objective of this study was to assess the incidence of prolonged length of stay in the post-anesthesia care unit at Sheikh Khalifa Medical City (SKMC), Abu Dhabi, United Arab Emirates, and identify the risk factors contributing to it.

METHODS AND MATERIALS

This is a retrospective observational study of patients who stayed in the PACU for more than two hours. A total of 2387 patients, both male and female, who underwent surgical procedures between May 2022 to August 2022 at SKMC and were admitted to the PACU after surgery were included in the study and their data were analyzed.

RESULTS

Of the 2387 patients who underwent surgical procedures, 43 (1.8%) had prolonged stays in the PACU. Of these, 20 (47%) were adult cases and 23 (53%) were pediatric cases. The main reasons for the delay in discharge from PACU in our study were the non-availability of ward beds (25.5%), followed by pain management (18.6%).

CONCLUSIONS

We recommend improving the communication between different specialties, restructuring staffing, implementing changes in perioperative management, and changing operating room scheduling to prevent avoidable reasons contributing to a prolonged stay in the PACU.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

The Effect of Dexmedetomidine on Postanesthesia Care Unit Discharge and Recovery: A Systematic Review and Meta-Analysis

BACKGROUND

Current evidence on the effect of dexmedetomidine in early postoperative recovery is limited. We conducted a systematic review to evaluate the effect of dexmedetomidine on the length of stay (LOS) and recovery profile in postanesthesia care unit (PACU) patients.

METHODS

The study protocol is registered on International Prospective Register of Systematic Reviews (PROSPERO; CRD42021240559). No specific funding or support was received. We conducted searches in MEDLINE, Embase, PubMed, and Cochrane Library to March 31, 2021 for peer-reviewed randomized controlled studies comparing adult patients who received intravenous dexmedetomidine and placebo undergoing noncardiac, nonneurosurgical procedures under general anesthesia. All studies reporting statistics relating to the duration of stay in the recovery ward or PACU, the primary outcome, were included. We performed individual random-effect meta-analysis on the primary and secondary outcomes (time to extubation, emergence agitation, cough, pain, postoperative nausea and vomiting, shivering, residual sedation, bradycardia, and hypotension) using Stata version 17.0. Evidence was synthesized as mean difference (MD) and risk ratio (RR) for continuous and dichotomous variables, respectively. The quality of evidence was assessed using the revised Cochrane risk-of-bias tool for randomized trials (RoB 2) tool and Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach.

RESULTS

Thirty-three studies including 2676 patients were eligible for analysis. All studies had low risk or some concerns of overall bias and provided low-to-high certainty evidence for all studied outcomes. Dexmedetomidine was not associated with a significantly increased PACU LOS (MD, 0.69 minute; 95% confidence interval [CI], -1.42 to 2.81 minutes). It was associated with a statistically but not clinically significant prolonged time to extubation (MD, 1 minute; 95% CI, 0.32-1.68 minutes). Dexmedetomidine was associated with significantly reduced incidence of emergence agitation (RR, 0.38; 95% CI, 0.29-0.52), cough (RR, 0.69; 95% CI, 0.61-0.79), pain (RR, 0.50; 95% CI, 0.32-0.80), postoperative nausea and vomiting (RR, 0.54; 95% CI, 0.33-0.86), and shivering (RR, 0.24; 95% CI, 0.12-0.49) in PACU. There was an increased incidence of hypotension (RR, 5.39; 95% CI, 1.12-5.89) but not residual sedation (RR, 1.23; 95% CI, 0.20-7.56) or bradycardia (RR, 5.13; 95% CI, 0.96-27.47) in the dexmedetomidine group.

CONCLUSIONS

The use of dexmedetomidine did not increase the duration of PACU LOS but was associated with reduced emergence agitation, cough, pain, postoperative nausea and vomiting, and shivering in PACU. There was an increased incidence of hypotension but not residual sedation or bradycardia in PACU.