The Effects of Surgery Cancellation on Patients, Families, and Staff: A Prospective Cross-Sectional Study

Improving Theatre Productivity by Digitising Surgical Equipment Repairs

Introduction A few cancelled surgeries are due to surgical equipment issues representing a significant burden to both patients and National Health Service (NHS) hospitals on waiting lists. Despite this, there remain very few strategies designed to tackle these avoidable cancellations, especially in combination with digitisation. Our aim was to demonstrate improved efficiency through a pilot study in collaboration with Broomfield Hospital (Broomfield, United Kingdom), MediShout Ltd (London, United Kingdom), and B. Braun Medical Ltd (Sheffield, United Kingdom) with the digitalisation of the equipment repair pathway. Methods MediShout digitised two distinct repair pathways: ad-hoc repairs and maintenance equipment services (MES). Pre- and post-digitisation outcome measures were collected including the number of process steps, staff contribution time, non-staff continuation time, turnaround time, cancelled surgeries, planned preventative maintenance compliance, and staff satisfaction. The number of steps, staff contribution time, and non-staff contribution time were calculated using cognitive task analyses and time-motion studies, respectively. Turnaround time and cancellation data were taken from existing hospital data sets and staff satisfaction was measured through two staff surveys. Results Digitising the ad-hoc repair pathway reduced the number of steps by 18 (118 to 100) and saved 74 minutes of total staff time (Broomfield Hospital and B. Braun) per repair, resulting in annual efficiency savings of £21,721.48. Digitising the MES repair pathway reduced the number of steps by 13 (74 to 61) and saved 56 minutes of total staff time per repair, resulting in annual efficiency savings of £3469.44. Turnaround time for the repaired kit decreased by 14 days and 29 days for the digital ad-hoc and digital MES pathways, respectively. Elective operations cancelled due to equipment issues decreased by 44%, from 1.5 operations/month pre-pilot to 0.83 operations/month post-pilot. Planned preventative maintenance compliance across the MES pathway increased by 67% (33% to 100%). Staff satisfaction with the repair pathway improved from 12% to 96%. Conclusion This pilot study showcases the numerous benefits that can be achieved through digitisation and offers an innovative case study to approach avoidable cancellations due to equipment failure.

Predictors of same day cancellation of elective surgery

Same day cancellations of surgery have adverse effects on both patients and health care systems. To date, the majority of research has evaluated reasons for same day cancellation, and relatively little is known about risk factors for cancellation. The aim of this study is to develop and evaluate the accuracy of a model for preoperatively predicting which patients are at risk for experiencing same day cancellation. While accurately predicting which patients are likely to experience same day cancellation remains challenging, predictive models may aid in the early identification of patients at risk for cancellation. Future studies are required to assess whether the use of predictive analytics leads to reduced cancellation rates in practice.

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.

An audit on surgery cancellation in a teaching hospital

Background

Operative procedure cancellations are a dilemma for the healthcare system as well as for the patients. It causes increased workload and cost to our system. For patients, it has major financial, psychological as well as medical consequences. We aim to self-identify the causes of cancellations for efficient operation room management.

Methods

We performed a retrospective chart review in a tertiary academic medical center for the last 66 months of operative records. Subsequently, we performed thematic coding to categorize causes into distinct categories.

Results

Our records showed 5153 cancellations which represent (7.3%) of the total booked procedures. Of these cancellations 91% were ordered before the day of surgery, compared to 9% for same-day cancellations. Cancellations were 58% female patients and 40% male patients. The number one reason for cancellations for both same-day and prior cancellations is the unavailability of the surgical consultant.

Conclusion

Surgical procedure cancellations profile is unique among our settings and has changed over time. Over the last 5 years, the number one reason is unavailability of the surgical consultant. Efforts should be made to identify and correct the underlying reasons to improve patient outcomes in our evolving healthcare system.

Model proposal for calculating waste associated with processing consigned surgical instruments

OBJECTIVE

to evaluate the waste generated from processing surgical instruments consigned in elective orthopedic surgeries and propose a model for calculating waste associated with processing consigned surgical instruments.

METHOD

a quantitative, descriptive-exploratory case study carried out in a large university hospital in two phases: (1) retrospective by consulting administrative records of canceled elective orthopedic surgeries, with provision for the use of consigned materials for identification of the sub-specializations with the greatest demand; and (2) prospective through direct, non-participant observations of processing consigned surgical instruments prepared for the identified surgeries and proposition of a model for calculating waste associated with processing these materials.

RESULTS

hip arthroplasty, spine arthrodesis and knee arthroplasty surgeries were identified as presenting the greatest demand, resulting in 854 boxes of consigned surgical instruments processed and unused. Processing waste was estimated at R$34,340.18 (US$6,359.30).

CONCLUSION

the proposed equation made it possible to calculate the waste related to the production and non-use of boxes of surgical instruments consigned for orthopedic procedures and can equip nurses for planning based on institutional, care and financial data, aiming to make better use of resources through waste identification.

Improving preoperative prediction of surgery duration

BACKGROUND

Operating rooms (ORs) are one of the costliest units in a hospital, therefore the cumulative consequences of any kind of inefficiency in OR management lead to a significant loss of revenue for the hospital, staff dissatisfaction, and patient care disruption. One of the possible solutions to improving OR efficiency is knowing a reliable estimate of the duration of operations. The literature suggests that the current methods used in hospitals, e.g., a surgeon's estimate for the given surgery or taking the average of only five previous records of the same procedure, have room for improvement.

METHODS

We used over 4 years of elective surgery records (n = 52,171) from one of the major metropolitan hospitals in Australia. We developed robust Machine Learning (ML) approaches to provide a more accurate prediction of operation duration, especially in the absence of surgeon's estimation. Individual patient characteristics and historic surgery information attributed to medical records were used to train predictive models. A wide range of algorithms such as Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were tested for predicting operation duration.

RESULTS

The results show that the XGBoost model provided statistically significantly less error than other compared ML models. The XGBoost model also reduced the total absolute error by 6854 min (i.e., about 114 h) compared to the current hospital methods.

CONCLUSION

The results indicate the potential of using ML methods for reaching a more accurate estimation of operation duration compared to current methods used in the hospital. In addition, using a set of realistic features in the ML models that are available at the point of OR scheduling enabled the potential deployment of the proposed approach.