Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery

Machine Learning-Based Approach to Predict Last-Minute Cancellation of Pediatric Day Surgeries

The last-minute cancellation of surgeries profoundly affects patients and their families. This research aimed to forecast these cancellations using EMR data and meteorological conditions at the time of the appointment, using a machine learning approach. We retrospectively gathered medical data from 13 440 pediatric patients slated for surgery from 2018 to 2021. Following data preprocessing, we utilized random forests, logistic regression, linear support vector machines, gradient boosting trees, and extreme gradient boosting trees to predict these abrupt cancellations. The efficacy of these models was assessed through performance metrics. The analysis revealed that key factors influencing last-minute cancellations included the impact of the coronavirus disease 2019 pandemic, average wind speed, average rainfall, preanesthetic assessments, and patient age. The extreme gradient boosting algorithm outperformed other models in predicting cancellations, boasting an area under the curve value of 0.923 and an accuracy of 0.841. This algorithm yielded superior sensitivity (0.840), precision (0.837), and F1 score (0.838) relative to the other models. These insights underscore the potential of machine learning, informed by EMRs and meteorological data, in forecasting last-minute surgical cancellations. The extreme gradient boosting algorithm holds promise for clinical deployment to curtail healthcare expenses and avert adverse patient-family experiences.

Elective surgery cancellations in pediatric surgery: rate and reasons

INTRODUCTION

Canceling pediatric elective surgery leads to multiple disturbances regarding the inefficient operating room (OR) management, the financial repercussions, and the psychological impact on the patient and his family. This study aims to identify the reasons for cancellations among the pediatric population in our setting and suggest some convenient solutions.

METHODS

We carried out a prospective and descriptive study over 12 months in the pediatric surgery department of Fattouma Bourguiba University Hospital.

RESULTS

One thousand four hundred twenty-six patients were scheduled for surgery at the pediatric surgery department, of whom 131 (9.2%) were canceled. Medical and anesthesia-related reasons accounted for 62.5% of all cancellations, followed by surgical reasons at 16%, organizational or administrative issues at 11.5%, and patient-related reasons at 10%. The most significant causes were upper respiratory tract infections (URTIs) in 36.6%, abnormal blood test results in 16%, and non-adherence to preoperative fasting in 9.2%.

CONCLUSIONS

The rate of pediatric elective surgery cancellations at Fattouma Bourguiba University Hospital was higher than the accepted average rate (5%). Therefore, to prevent these cancellations as much as possible, efforts should be made to promote children's medical care, operation scheduling, and efficient institution resource utilization.

An In-Depth Analysis of Providers and Services of Cancellation in Anesthesia Reveals a Complex Picture after Systemic Analysis

The variances in operating room (OR) cancellation rates between different service lines and operators within these service lines were assessed by reviewing the electronic medical record (EMR) covering 34,561 cases performed by 199 OR operators in 2018. We assumed that cancellations would differ between different service lines, but the between-operators variance was minimal within the service line. We hypothesized that most variability would be secondary to patient-specific (weekdays, time of year, and national holidays), seasonal and administrative issues. Of 4165 case cancellations, the majority (73.1%) occurred before the patient arrived at the hospital. A total of 60% of all cancellations were within gastroenterology, interventional cardiology, and orthopedics. Cancellation rate variability between surgeons operating within the same service line greatly varied between services from very homogenous to very diverse across providers. The top reasons for cancellation were: date change, canceled by a patient, or "no show". The highest cancellation rates occurred on Mondays and Tuesdays, in January and September, and during weeks associated with national holidays. In summary, cancellation variability must be analyzed at the level of individual specialties, operators, and time variability.

Use of machine learning in pediatric surgical clinical prediction tools: A systematic review

PURPOSE

Clinical prediction tools (CPTs) are decision-making instruments utilizing patient data to predict specific clinical outcomes, risk-stratify patients, or suggest personalized diagnostic or therapeutic options. Recent advancements in artificial intelligence have resulted in a proliferation of CPTs created using machine learning (ML)-yet the clinical applicability of ML-based CPTs and their validation in clinical settings remain unclear. This systematic review aims to compare the validity and clinical efficacy of ML-based to traditional CPTs in pediatric surgery.

METHODS

Nine databases were searched from 2000 until July 9, 2021 to retrieve articles reporting on CPTs and ML for pediatric surgical conditions. PRISMA standards were followed, and screening was performed by two independent reviewers in Rayyan, with a third reviewer resolving conflicts. Risk of bias was assessed using the PROBAST.

RESULTS

Out of 8300 studies, 48 met the inclusion criteria. The most represented surgical specialties were pediatric general (14), neurosurgery (13) and cardiac surgery (12). Prognostic (26) CPTs were the most represented type of surgical pediatric CPTs followed by diagnostic (10), interventional (9), and risk stratifying (2). One study included a CPT for diagnostic, interventional and prognostic purposes. 81% of studies compared their CPT to ML-based CPTs, statistical CPTs, or the unaided clinician, but lacked external validation and/or evidence of clinical implementation.

CONCLUSIONS

While most studies claim significant potential improvements by incorporating ML-based CPTs in pediatric surgical decision-making, both external validation and clinical application remains limited. Further studies must focus on validating existing instruments or developing validated tools, and incorporating them in the clinical workflow.

TYPE OF STUDY

Systematic Review LEVEL OF EVIDENCE: Level III.

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.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

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

This study aimed to provide effective methods for the identification of surgeries with high cancellation risk based on machine learning models and analyze the key factors that affect the identification performance. The data covered the period from January 1, 2013, to December 31, 2014, at West China Hospital in China, which focus on elective urologic surgeries. All surgeries were scheduled one day in advance, and all cancellations were of institutional resource- and capacity-related types. Feature selection strategies, machine learning models, and sampling methods are the most discussed topic in general machine learning researches and have a direct impact on the performance of machine learning models. Hence, they were considered to systematically generate complete schemes in machine learning-based identification of surgery cancellations. The results proved the feasibility and robustness of identifying surgeries with high cancellation risk, with the considerable maximum of area under the curve (AUC) (0.7199) for random forest model with original sampling using backward selection strategy. In addition, one-side Delong test and sum of square error analysis were conducted to measure the effects of feature selection strategy, machine learning model, and sampling method on the identification of surgeries with high cancellation risk, and the selection of machine learning model was identified as the key factors that affect the identification of surgeries with high cancellation risk. This study offers methodology and insights for identifying the key experimental factors for identifying surgery cancellations, and it is helpful to further research on machine learning-based identification of surgeries with high cancellation risk.

Health specialists' views on the needs for developing a digital gaming solution for paediatric day surgery: A qualitative study

AIMS AND OBJECTIVES

To describe the views on the needs of health specialists to consider when developing a digital gaming solution for children and families in a paediatric day surgery.

BACKGROUND

Children's day surgery treatment is often cancelled at the last minute for various reasons, for example due to the lack of information. Digital gaming solutions could help families to be better oriented to the coming treatment. Despite the increasing demands for mHealth systems, there is not enough evidence-based information from the health specialist perspective for developing a digital gaming solution.

DESIGN

A qualitative descriptive study was conducted.

METHODS

Health specialists (N = 15) including 11 nurses, one physiotherapist and four doctors from different areas from one university hospital in Finland were recruited using a snowball sampling method. Semi-structured, face-to-face interviews were conducted in March and April 2019. The data were analysed using inductive conduct analyses. The COREQ checklist was used to report the data collection, analysis and the results.

RESULTS

The data yielded 469 open codes, 21 sub-categories, three upper categories and one main category. The main category the digital gaming solution to support knowledge, care and guidance in children's day surgery included three upper categories: (a) support for preoperative information and guidance, (b) support for intra-operative information and care, and (c) support for postoperative information, care and guidance.

CONCLUSION

Digital gaming solutions could be used to help children and families to be better prepared for upcoming treatments, to support communication in different languages and to improve children's pain management after operations.

RELEVANCE TO CLINICAL PRACTICE

Evidence-based information is important to ensure that future digital solutions answer the real needs of the staff and patients. There is a need for families and children's views to be taken into consideration when developing digital gaming solutions in the hospital context.

Augmented intelligence in pediatric anesthesia and pediatric critical care

PURPOSE OF REVIEW

Acute care technologies, including novel monitoring devices, big data, increased computing capabilities, machine-learning algorithms and automation, are converging. This enables the application of augmented intelligence for improved outcome predictions, clinical decision-making, and offers unprecedented opportunities to improve patient outcomes, reduce costs, and improve clinician workflow. This article briefly explores recent work in the areas of automation, artificial intelligence and outcome prediction models in pediatric anesthesia and pediatric critical care.

RECENT FINDINGS

Recent years have yielded little published research into pediatric physiological closed loop control (a type of automation) beyond studies focused on glycemic control for type 1 diabetes. However, there has been a greater range of research in augmented decision-making, leveraging artificial intelligence and machine-learning techniques, in particular, for pediatric ICU outcome prediction.

SUMMARY

Most studies focusing on artificial intelligence demonstrate good performance on prediction or classification, whether they use traditional statistical tools or novel machine-learning approaches. Yet the challenges of implementation, user acceptance, ethics and regulation cannot be underestimated. Areas in which there is easy access to routinely labeled data and robust outcomes, such as those collected through national networks and quality improvement programs, are likely to be at the forefront of the adoption of these advances.