Artificial intelligence predictive analytics performs fairly well in solving complex operational problems – outpatient MRI appointment no-shows, especially – using a modest amount of data and basic feature engineering, and can help cut down on such no-shows, according to findings published in the American Journal of Roentgenology. What's convenient and beneficial about the data is that in many cases it's readily retrievable from frontline IT systems that are commonly used in hospital radiology departments. It can also be readily incorporated into routine workflows, which the authors said can improve the quality and efficiency of healthcare delivery. Learn on-demand, earn credit, find products and solutions. To train and validate this model, the team of researchers extracted records of 32,957 outpatient MRI appointments scheduled between January 2016 and December 2018 from their institution's radiology information system, while acquiring a further holdout test set of 1,080 records from January 2019.

Artificial intelligence predictive analytics performs fairly well in solving complex operational problems -- outpatient MRI appointment no-shows, especially -- using a modest amount of data and basic feature engineering, and can help cut down on such no-shows, according to findings published in the American Journal of Roentgenology. What's convenient and beneficial about the data is that in many cases it's readily retrievable from frontline IT systems that are commonly used in hospital radiology departments. It can also be readily incorporated into routine workflows, which the authors said can improve the quality and efficiency of healthcare delivery. Learn on-demand, earn credit, find products and solutions. To train and validate this model, the team of researchers extracted records of 32,957 outpatient MRI appointments scheduled between January 2016 and December 2018 from their institution's radiology information system, while acquiring a further holdout test set of 1,080 records from January 2019.

Weekly outpatient MRI appointment no-show rates for 1 year before (19.3%) and 6 months after (15.9%) implementation of intervention measures in March 2019, as guided by XGBoost prediction model. September 10, 2020 -- According to ARRS' American Journal of Roentgenology (AJR), artificial intelligence (AI) predictive analytics performed moderately well in solving complex multifactorial operational problems -- outpatient MRI appointment no-shows, especially -- using a modest amount of data and basic feature engineering. "Such data may be readily retrievable from frontline information technology systems commonly used in most hospital radiology departments, and they can be readily incorporated into routine workflow practice to improve the efficiency and quality of health care delivery," wrote lead author Le Roy Chong of Singapore's Changi General Hospital. To train and validate their model, Chong and colleagues extracted records of 32,957 outpatient MRI appointments scheduled between January 2016 and December 2018 from their institution's radiology information system, while acquiring a further holdout test set of 1,080 records from January 2019. Overall, the no-show rate was 17.4%.

According to ARRS' American Journal of Roentgenology (AJR), artificial intelligence (AI) predictive analytics performed moderately well in solving complex multifactorial operational problems--outpatient MRI appointment no-shows, especially--using a modest amount of data and basic feature engineering. "Such data may be readily retrievable from frontline information technology systems commonly used in most hospital radiology departments, and they can be readily incorporated into routine workflow practice to improve the efficiency and quality of health care delivery," wrote lead author Le Roy Chong of Singapore's Changi General Hospital. To train and validate their model, Chong and colleagues extracted records of 32,957 outpatient MRI appointments scheduled between January 2016 and December 2018 from their institution's radiology information system, while acquiring a further holdout test set of 1,080 records from January 2019. Overall, the no-show rate was 17.4%. After evaluating various machine learning predictive models developed with widely used open-source software tools, Chong and team deployed a decision tree-based ensemble algorithm that uses a gradient boosting framework: XGBoost, version 0.80 [Tianqi Chen].