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What can surgery learn from other high-performance disciplines?

ABSTRACT: High-performance disciplines have always been concerned with safety and exceptional performance. They have established a culture of vigilance and accepted that human error is both inevitable and ubiquitous. These disciplines, therefore, have all implemented a 'systems approach' to error by focusing on predicting, preventing, rescuing and reporting errors that occur so that they can constantly adapt and improve. Given the complexity of surgery, and the error-prone environment within which it takes place, extracting positive behaviours from other high-performance disciplines will serve to improve performance and enhance patient safety. Surgery is being practiced in an ever-changing environment. Currently, there is less available operative experience for surgical trainees; multi-morbidity in patients is growing and rapidly evolving technology means that more high-tech equipment is being used in procedures. This article evaluates the effectiveness of current surgical protocol in reducing errors and possible modifications that can be made to fit the new environment that surgery is now being practiced in. It will then describe how three different high-performance disciplines: aviation, professional sport and Formula 1, have developed in their approaches to safety and excellence, which will serve as the basis for a discussion about what more can be learnt from these disciplines so that the surgical profession can continue to excel in the face of change.

SUBMITTER: O'Logbon J

PROVIDER: S-EPMC7305423 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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What can surgery learn from other high-performance disciplines?

O'Logbon Jessica J

Annals of medicine and surgery (2012) 20200424

High-performance disciplines have always been concerned with safety and exceptional performance. They have established a culture of vigilance and accepted that human error is both inevitable and ubiquitous. These disciplines, therefore, have all implemented a 'systems approach' to error by focusing on predicting, preventing, rescuing and reporting errors that occur so that they can constantly adapt and improve. Given the complexity of surgery, and the error-prone environment within which it take ...[more]

PMID: 32577226

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Project description:Neuroscience models commonly have a high number of degrees of freedom and only specific regions within the parameter space are able to produce dynamics of interest. This makes the development of tools and strategies to efficiently find these regions of high importance to advance brain research. Exploring the high dimensional parameter space using numerical simulations has been a frequently used technique in the last years in many areas of computational neuroscience. Today, high performance computing (HPC) can provide a powerful infrastructure to speed up explorations and increase our general understanding of the behavior of the model in reasonable times. Learning to learn (L2L) is a well-known concept in machine learning (ML) and a specific method for acquiring constraints to improve learning performance. This concept can be decomposed into a two loop optimization process where the target of optimization can consist of any program such as an artificial neural network, a spiking network, a single cell model, or a whole brain simulation. In this work, we present L2L as an easy to use and flexible framework to perform parameter and hyper-parameter space exploration of neuroscience models on HPC infrastructure. Learning to learn is an implementation of the L2L concept written in Python. This open-source software allows several instances of an optimization target to be executed with different parameters in an embarrassingly parallel fashion on HPC. L2L provides a set of built-in optimizer algorithms, which make adaptive and efficient exploration of parameter spaces possible. Different from other optimization toolboxes, L2L provides maximum flexibility for the way the optimization target can be executed. In this paper, we show a variety of examples of neuroscience models being optimized within the L2L framework to execute different types of tasks. The tasks used to illustrate the concept go from reproducing empirical data to learning how to solve a problem in a dynamic environment. We particularly focus on simulations with models ranging from the single cell to the whole brain and using a variety of simulation engines like NEST, Arbor, TVB, OpenAIGym, and NetLogo.

Project description:BackgroundLittle is known about treatment expectations of patients with spinal metastases undergoing radiotherapy and/or surgery. Assuming that patients with spinal metastases share characteristics with patients who had spinal surgery for non-cancer related conditions and with advanced cancer patients, we performed a systematic review to summarize the literature on patient expectations regarding treatment outcomes of spinal surgery and advanced cancer care.MethodsA comprehensive search was performed in MEDLINE, EMBASE and PsycINFO for studies between 2000 and sep-2019. Studies including adult patients (> 18 years), undergoing spinal surgery or receiving advanced cancer care, investigating patients' pre-treatment expectations regarding treatment outcomes were included. Two independent reviewers screened titles, abstracts and full-texts, extracted data and assessed methodological quality.ResultsThe search identified 7343 articles, of which 92 were selected for full-text review. For this review, 31 articles were included. Patients undergoing spinal surgery had overly optimistic expectations regarding pain and symptom relief, they underestimated the probability of functional disability, and overestimated the probability of (complete) recovery and return to work. Studies highlighted that patients feel not adequately prepared for surgery in terms of post-treatment expectations. Similarly, advanced cancer patients receiving palliative treatment often had overly optimistic expectations regarding their survival probability and cure rates.ConclusionsPatients tend to have overly optimistic expectations regarding pain and symptom relief, recovery and prognosis following spinal surgery or advanced cancer care. Pretreatment consultation about the expected pain and symptom relief, recovery and prognosis may improve understanding of prognosis, and promote and manage expectations, which, in turn, may lead to better perceived outcomes.Trial registrationPROSPERO registration number: CRD42020145151 .

Project description:BACKGROUND:As the geriatric population grows, the need for hospitals performing high quality emergency general surgery (EGS) on older patients will increase. Identifying clusters of high-performing geriatric emergency general surgery hospitals would substantiate the need for in-depth analyses of hospital-specific structures and practices that benefit older EGS patients. The objectives of this study were therefore to identify clusters of hospitals based on mortality performance for geriatric patients undergoing common EGS operations and to determine if hospital performance was similar for all operation types. METHODS:Hospitals in the California State Inpatient Database were included if they performed a range of eight common EGS operations in patients 65 years or older, with a minimum requirement of three of each operation performed over 2 years. Multivariable beta regression models were created to define hospital-level risk-adjusted mortality. Centroid cluster analysis was used to identify groups of hospitals based on mortality and to determine if mortality-performance differed by operation. RESULTS:One hundred seven hospitals were included, performing a total of 24,279 operations in older patients. Hospitals separated into three distinct clusters: high, average, and low performers. The high-performing hospitals had survival rates 1 to 2 standard deviations better than the low-performers (p < 0.001). For each cluster, high performance in any one EGS operation consistently translated into high performance across all EGS operations. CONCLUSION:Hospitals conducting EGS operations in the geriatric patient population cluster into three distinct groups based on their survival performance. High-performing hospitals significantly outperform the average and low performers across every operation. The high-performers achieve reliable, high-quality results regardless of operation type. Further qualitative research is needed to investigate the perioperative drivers of hospital performance in the geriatric EGS population. LEVEL OF EVIDENCE:Study Type Prognostic, level III.

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What can surgery learn from other high-performance disciplines?

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What can surgery learn from other high-performance disciplines?

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