Causal Inference and Analysis of Surgery Cancellation Risks

Abstract

Introduction The provision of services in hospitals is the final level of the health care system chain, which usually provides the patients with advanced medical services, such as surgery. On the other hand, the cancellation of elective surgeries is one of the problems, which reduces the quality of service delivery and decreases hospital's efficiency and patients' satisfaction followed by increases in patients' costs. This study presented an approach based on a fuzzy inference system to better assess these hazards and eliminate the related risks and investigate effective factors in the cancellation of elective surgeries. Materials and Methods The present study conducted a case study in Shahid Arefian Hospital Urmia, Iran, during 2016-2017. Principal factors of surgery cancellations were collected from surgery documents in the hospital. These factors were divided into five classes, including paraclinical, clinical, systematic, surgeon, and patient. The hazards identified in these classes caused surgery cancellation. They were identified using the contribution of an expert team, including operating room supervisors, female and male surgery hospitalization supervisors, as well as two physicians. Results According to the results, the proposed approach was more appropriate for creating discrimination between surgery cancellation hazards, compared to the traditional risk priority number (RPN) method. Surgeon fatigue, high PPT and PT, and airway problems were the first to third important hazards with RPNs equal to 120, 105, and 96, respectively. On the other hand, according to obtained results, not having internal medicine specialist counseling, low thyroid stimulating hormone, and unavailability of beds at intensive care units were three important and priority potential hazards with FRPNs equal to 8, 8, and 6, respectively. Conclusion The proposed approach can better map hospital experts’ opinions to the fuzzy-based risk assessment system since it employs linguistic variables by hospitals’ experts, compared to conventional approaches. Moreover, it can help the hospital managements apply hospital resources to maximise their impacts on improving hospital efficiency.