A comprehensive evaluation of work-related performance factors is a prerequisite to developing integrated and long-term solutions to workplace performance improvement. This paper describes a work-factor classification system that categorizes the entire domain of workplace factors impacting performance. A questionnaire-based instrument was developed to implement this classification system in industry. Fifty jobs were evaluated in 4 different service and manufacturing companies using the proposed questionnaire-based instrument. The reliability coefficients obtained from the analyzed jobs were considered good (0.589 to 0.862). In general, the physical work factors resulted in higher reliability coefficients (0.847 to 0.862) than non-physical work factors (0.589 to 0.768).
Background Patients’ choices of providers when undergoing elective surgeries significantly impact both perioperative outcomes and costs. There exist a variety of approaches that are available to patients for evaluating between different hospital choices. Objective This paper aims to compare differences in outcomes and costs between hospitals ranked using popular internet-based consumer ratings, quality stars, reputation rankings, average volumes, average outcomes, and precision machine learning–based rankings for hospital settings performing hip replacements in a large metropolitan area. Methods Retrospective data from 4192 hip replacement surgeries among Medicare beneficiaries in 2018 in a the Chicago metropolitan area were analyzed for variations in outcomes (90-day postprocedure hospitalizations and emergency department visits) and costs (90-day total cost of care) between hospitals ranked through multiple approaches: internet-based consumer ratings, quality stars, reputation rankings, average yearly surgical volume, average outcome rates, and machine learning–based rankings. The average rates of outcomes and costs were compared between the patients who underwent surgery at a hospital using each ranking approach in unadjusted and propensity-based adjusted comparisons. Results Only a minority of patients (1159/4192, 27.6% to 2078/4192, 49.6%) were found to be matched to higher-ranked hospitals for each of the different approaches. Of the approaches considered, hip replacements at hospitals that were more highly ranked by consumer ratings, quality stars, and machine learning were all consistently associated with improvements in outcomes and costs in both adjusted and unadjusted analyses. The improvement was greatest across all metrics and analyses for machine learning–based rankings. Conclusions There may be a substantive opportunity to increase the number of patients matched to appropriate hospitals across a broad variety of ranking approaches. Elective hip replacement surgeries performed at hospitals where patients were matched based on patient-specific machine learning were associated with better outcomes and lower total costs of care.
This research paper proposes a brain tumor detection system using neural networks. The authors use a dataset of Brain MRI Images for Brain Tumor Detection obtained from Kaggle.com and compare the performance of two models of Convolutional Neural Network (CNN). The first model is a simple CNN, and the second model is a model of hybrid deep learning Long-Short-Term Memory in Convolutional Neural Networks (CNN-LSTM). The experiments show that the CNN-LSTM model outperforms the simple CNN model in terms of accuracy, sensitivity, and specificity. The proposed system achieves high accuracy and can be used for accurate and efficient brain tumor detection. Cancer detection is a crucial task in the field of the health imaging. Traditional methods of detecting brain tumors are time-consuming and require a lot of expertise. With the advent of deep learning and neural networks, the detection of brain tumors has become more efficient and accurate. The use of neural networks for brain tumor detection has the potential to revolutionize the field of medical imaging and improve patient outcomes.
BACKGROUND Patients’ choices of providers when undergoing elective surgeries significantly impact both perioperative outcomes and costs. There exist a variety of approaches that are available to patients for evaluating between different hospital choices. OBJECTIVE This paper aims to compare differences in outcomes and costs between hospitals ranked using popular internet-based consumer ratings, quality stars, reputation rankings, average volumes, average outcomes, and precision machine learning–based rankings for hospital settings performing hip replacements in a large metropolitan area. METHODS Retrospective data from 4192 hip replacement surgeries among Medicare beneficiaries in 2018 in a the Chicago metropolitan area were analyzed for variations in outcomes (90-day postprocedure hospitalizations and emergency department visits) and costs (90-day total cost of care) between hospitals ranked through multiple approaches: internet-based consumer ratings, quality stars, reputation rankings, average yearly surgical volume, average outcome rates, and machine learning–based rankings. The average rates of outcomes and costs were compared between the patients who underwent surgery at a hospital using each ranking approach in unadjusted and propensity-based adjusted comparisons. RESULTS Only a minority of patients (1159/4192, 27.6% to 2078/4192, 49.6%) were found to be matched to higher-ranked hospitals for each of the different approaches. Of the approaches considered, hip replacements at hospitals that were more highly ranked by consumer ratings, quality stars, and machine learning were all consistently associated with improvements in outcomes and costs in both adjusted and unadjusted analyses. The improvement was greatest across all metrics and analyses for machine learning–based rankings. CONCLUSIONS There may be a substantive opportunity to increase the number of patients matched to appropriate hospitals across a broad variety of ranking approaches. Elective hip replacement surgeries performed at hospitals where patients were matched based on patient-specific machine learning were associated with better outcomes and lower total costs of care.
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