Reasons for Surgery Cancellation in a General Hospital: A 10-year Study

Cho, Hyun Sun; Lee, Ye Seol; Kim, Ji Man; Kim, Tae Hyun

doi:10.3390/ijerph16010007

Cited by 36 publications

(38 citation statements)

References 14 publications

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“…To sum up, good medical practice in terms of application and knowledge should provide alternative solutions to avoid the cancellation of the surgical process as much as possible (Antaurco & Méndez, 2018;Corona Martínez & Fonseca Hernández, 2010). The cancellation of scheduled surgeries is a negative indicator of the care quality service that affects not only the patient but also his family (Cho, Lee, Lee, Kim, & Kim, 2019;Perroca, Jericó, & Facundin, 2007).…”

Section: Pregnancymentioning

confidence: 99%

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Cancellation of Surgeries by the Patient, Doctor or Institution: An Approach to Legal Ethical Aspects

Anderson¹,

Vargas²,

Fuentes³

et al. 2019

GJHS

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INTRODUCTION: The cancellation of surgery represents a dilemma in establishing relatively adequate cancellation rates according to the factor, because each institution and surgical specialty have different dynamics. Objective: Describe the types of factors present for the cancellation of surgeries in a health institution. Colombia (2017-2018). METHODOLOGY: Descriptive, retrospective, cross-sectional study. We reviewed (3339) records of scheduled surgeries from January to December 2017. In 2018 they were reviewed (1733) between January and June. A total of (5072) records of a Third Level Health Institution of the Department of Cesar / Colombia were reviewed. The Neuronal Multilayer Perceptron Network model and the Gini coefficient were applied to determine the most important factor and therefore the inequality between them. RESULTS: In 2017, there was a surgical cancellation rate of 4% of the total number of scheduled surgeries (3339). For the year 2018, the rate was 3% of the total of scheduled surgeries (1733). The most important factor was due to the patient's adverse conditions. The surgical specialties that had the highest number of cancellations were general surgery followed by orthopedics. CONCLUSION: An evaluation of the factors for the cancellation of programmed surgeries with a high coefficient of inequality is described. In addition, the most important factor was related to the patient. Prospective studies by specialty are proposed for the design of solution and monitoring strategies to avoid surgical cancellations.

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Section: Pregnancymentioning

confidence: 99%

“…Numerous studies have shown that the failures associated with surgeries cancellation are mainly due to the lack of knowledge of the patient's clinical conditions, which are caused by an insufficient preoperative evaluation, that is to say, preventable (Cho et al, 2019;Dobarro et al, 2019;Muñoz et al, 2018).…”

Section: Pregnancymentioning

confidence: 99%

Cancellation of Surgeries by the Patient, Doctor or Institution: An Approach to Legal Ethical Aspects

Anderson¹,

Vargas²,

Fuentes³

et al. 2019

GJHS

View full text Add to dashboard Cite

show abstract

“…Among the works focusing on identifying surgeries with high cancellation risk, a retrospective cohort study [ 29 ] examined the association between patient, surgeon, and system factors and proved that several patient and system factors can be used to identify surgeries with a high likelihood of cancellation. The factors associated with surgery cancellation have been evaluated using chi-squared tests and multivariate logistic regression analyses [ 21 ]. Using multilevel logistic regression, an observational cohort study [ 30 ] identified patient- and hospital-level factors associated with cancellation owing to inadequate bed capacity.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Cui

Gong

et al. 2021

Journal of Healthcare Engineering

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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.

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“…Many studies reported that the incidence of USC was higher than 10%, even more than 40% in Low- and middle-income countries. 21 Literature revealed many reasons for USC, 22 - 24 such as insufficient operating room capacity, lack of hospital beds, failure of equipment, insufficient planning of surgery, absence of staff, patient no-show, and patient’s medical condition. It is essential to analyze the reasons for USC at one’s individual hospital to reduce the cancellation rate.…”

Section: Introductionmentioning

confidence: 99%

Applying Lean Six Sigma to Reduce the Incidence of Unplanned Surgery Cancellation at a Large Comprehensive Tertiary Hospital in China

Zhu

Qian

Zhou

et al. 2020

INQUIRY

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Unplanned surgery cancellation (USC) was an important quality management issue in the course of medical care for surgical patients, which caused inappropriate use of hospital resources and had negative impacts on quality and safety. This study used Lean Six Sigma to reduce the incidence of USC. Following the Lean Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) process, the main factors influencing the USC were identified, such as the time of informing patient admission, the time of submitting operation notice, and the management of test report follow-up. A series of measures were implemented including improving the health education content of virtual bed patients, standardizing the way of communication between the Admission Management Center and the patients, improving the timing of anesthesia evaluation, optimizing the process of operation notice with an information system, and implementing the regulations of virtual bed management. The incidence of USC reduced from 10.21% in Jan. 2016 to 3.8% in Dec. 2016, and the Z-score increased from 1.25 to 1.68, which improved patient safety and demonstrated that Lean Six Sigma was an effective method to solve cross-department issues in hospital.

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Reasons for Surgery Cancellation in a General Hospital: A 10-year Study

Cited by 36 publications

References 14 publications

Cancellation of Surgeries by the Patient, Doctor or Institution: An Approach to Legal Ethical Aspects

Cancellation of Surgeries by the Patient, Doctor or Institution: An Approach to Legal Ethical Aspects

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

Applying Lean Six Sigma to Reduce the Incidence of Unplanned Surgery Cancellation at a Large Comprehensive Tertiary Hospital in China

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