Key Experimental Factors of Machine Learning-Based Identification of Surgery Cancellations

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.

Reducing last-minute cancellations of elective urological surgery-effectiveness of specialist nurse preoperative assessment

Last-minute cancellations in urological surgery are a global issue, resulting in the wastage of resources and delays to patient care. In addition to non-cessation of anticoagulants and inadequately treated medical comorbidities, untreated urinary tract infections are a significant cause of last-minute cancellations. This study aimed to ascertain whether the introduction of a specialist nurse clinic resulted in a reduction of last-minute cancellations of elective urological surgery as part of our elective recovery plan following the Coronavirus disease 2019, the contagious disease caused by severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 pandemic. A specialist urology nurse-led clinic was introduced to review urine culture results preoperatively. Specialist nurses contacted patients with positive urine cultures and their general practitioners by telephone and email to ensure a minimum of 2 days of 'lead-in' antibiotics were given prior to surgery. Patients unfit for surgery were postponed and optimized, and vacant slots were backfilled. A new guideline was created to improve the timing and structure of the generic preassessment. Between 1 January 2021 and 30 June 2021, a mean of 40 cases was booked each month, with average cancellations rates of 9.57/40 (23.92%). After implementing changes on 1 July 2021, cancellations fell to 4/124 (3%) for the month. On re-audit, there was a sustained and statistically significant reduction in cancellation rates: between 1 July 2021 and 31 December 2021 cancellations averaged 4.2/97.5 (4.3%, P < .001). Two to nine (2%-16%) patients were started on antibiotics each month, while another zero to two (0%-2%) were contacted for other reasons. The implementation of a specialist urology nurse-led preassessment clinic resulted in a sustained reduction in cancellations of last-minute elective urological procedures.

Optimizing Operation Room Utilization—A Prediction Model

Background: Operating rooms are the core of hospitals. They are a primary source of revenue and are often seen as one of the bottlenecks in the medical system. Many efforts are made to increase throughput, reduce costs, and maximize incomes, as well as optimize clinical outcomes and patient satisfaction. We trained a predictive model on the length of surgeries to improve the productivity and utility of operative rooms in general hospitals. Methods: We collected clinical and administrative data for the last 10 years from two large general public hospitals in Israel. We trained a machine learning model to give the expected length of surgery using pre-operative data. These data included diagnoses, laboratory tests, risk factors, demographics, procedures, anesthesia type, and the main surgeon’s level of experience. We compared our model to a naïve model that represented current practice. Findings: Our prediction model achieved better performance than the naïve model and explained almost 70% of the variance in surgery durations. Interpretation: A machine learning-based model can be a useful approach for increasing operating room utilization. Among the most important factors were the type of procedures and the main surgeon’s level of experience. The model enables the harmonizing of hospital productivity through wise scheduling and matching suitable teams for a variety of clinical procedures for the benefit of the individual patient and the system as a whole.

Understanding Pediatric Surgery Cancellation: Geospatial Analysis

BACKGROUND

Day-of-surgery cancellation (DoSC) represents a substantial wastage of hospital resources and can cause significant inconvenience to patients and families. Cancellation is reported to impact between 2% and 20% of the 50 million procedures performed annually in American hospitals. Up to 85% of cancellations may be amenable to the modification of patients' and families' behaviors. However, the factors underlying DoSC and the barriers experienced by families are not well understood.

OBJECTIVE

This study aims to conduct a geospatial analysis of patient-specific variables from electronic health records (EHRs) of Cincinnati Children's Hospital Medical Center (CCHMC) and of Texas Children's Hospital (TCH), as well as linked socioeconomic factors measured at the census tract level, to understand potential underlying contributors to disparities in DoSC rates across neighborhoods.

METHODS

The study population included pediatric patients who underwent scheduled surgeries at CCHMC and TCH. A 5-year data set was extracted from the CCHMC EHR, and addresses were geocoded. An equivalent set of data >5.7 years was extracted from the TCH EHR. Case-based data related to patients' health care use were aggregated at the census tract level. Community-level variables were extracted from the American Community Survey as surrogates for patients' socioeconomic and minority status as well as markers of the surrounding context. Leveraging the selected variables, we built spatial models to understand the variation in DoSC rates across census tracts. The findings were compared to those of the nonspatial regression and deep learning models. Model performance was evaluated from the root mean squared error (RMSE) using nested 10-fold cross-validation. Feature importance was evaluated by computing the increment of the RMSE when a single variable was shuffled within the data set.

RESULTS

Data collection yielded sets of 463 census tracts at CCHMC (DoSC rates 1.2%-12.5%) and 1024 census tracts at TCH (DoSC rates 3%-12.2%). For CCHMC, an L2-normalized generalized linear regression model achieved the best performance in predicting all-cause DoSC rate (RMSE 1.299%, 95% CI 1.21%-1.387%); however, its improvement over others was marginal. For TCH, an L2-normalized generalized linear regression model also performed best (RMSE 1.305%, 95% CI 1.257%-1.352%). All-cause DoSC rate at CCHMC was predicted most strongly by previous no show. As for community-level data, the proportion of African American inhabitants per census tract was consistently an important predictor. In the Texas area, the proportion of overcrowded households was salient to DoSC rate.

CONCLUSIONS

Our findings suggest that geospatial analysis offers potential for use in targeting interventions for census tracts at a higher risk of cancellation. Our study also demonstrates the importance of home location, socioeconomic disadvantage, and racial minority status on the DoSC of children's surgery. The success of future efforts to reduce cancellation may benefit from taking social, economic, and cultural issues into account.