Automatic Forecasting of Radiology Examination Volume Trends for Optimal Resource Planning and Allocation.

The aim of the study was to evaluate the performance of the Prophet forecasting procedure, part of the Facebook open-source Artificial Intelligence portfolio, for forecasting variations in radiological examination volumes. Daily CT and MRI examination volumes from our institution were extracted from the radiology information system (RIS) database. Data from January 1, 2015, to December 31, 2019, was used for training the Prophet algorithm, and data from January 2020 was used for validation. Algorithm performance was then evaluated prospectively in February and August 2020. Total error and mean error per day were evaluated, and computational time was logged using different Markov chain Monte Carlo (MCMC) samples. Data from 610,570 examinations were used for training; the majority were CTs (82.3%). During retrospective testing, prediction error was reduced from 19 to < 1 per day in CT (total 589 to 17) and from 5 to < 1 per day (total 144 to 27) in MRI by fine-tuning the Prophet procedure. Prospective prediction error in February was 11 per day in CT (9934 predicted, 9667 actual) and 1 per day in MRI (2484 predicted, 2457 actual) and was significantly better than manual weekly predictions (p = 0.001). Inference with MCMC added no substantial improvements while vastly increasing computational time. Prophet accurately models weekly, seasonal, and overall trends paving the way for optimal resource allocation for radiology exam acquisition and interpretation.

Creating a Synthetic Clinical Trial: Comparative Effectiveness Analyses Using an Electronic Medical Record.

PURPOSE: Electronic medical records (EMRs) are a vast resource of potentially mineable data that can be used to complement and extend clinical trials. Extracting and analyzing EMR data are impeded by technical complexities associated with large, multiformat databases. We sought to develop and validate a framework that would overcome the difficulties associated with EMR data and create a simple, portable, and expandable system to better use this resource. MATERIALS AND METHODS: An Internet-accessible program was developed in Python that applied user-defined criteria to identify and extract patient data from Memorial Sloan Kettering databases. A Worker Application composed of individual modules was developed to identify each patient's functional status, smoking status, and treatment classification. The validity of this approach was tested by identifying, extracting, and analyzing data from a patient cohort that paralleled a practice-changing, prospective, randomized phase III clinical trial performed at a different institution. We called this a synthetic clinical trial. RESULTS: Our synthetic clinical trial identified and extracted data on a cohort of 281 patients with lung cancer who matched inclusion criteria and received their first treatment between October 2003 and July 2010. The data extraction modules were precise and accurate, with F-measures greater than 0.98. Results were similar in directionality and magnitude to the chosen comparator clinical trial. CONCLUSION: Our framework offers an accurate and user-friendly interface for identifying and extracting EMR data that can be used to create synthetic clinical trials. Additional studies are needed to validate this approach in other patient cohorts, replicate our findings, and leverage this methodology to improve patient care and accelerate drug development.

Generation of particulate matter during handling of needle and syringe packaging.

PURPOSE: Generation of airborne particulate matter during the handling and opening of various syringe and needle packages in a laminar-airflow workbench (LAFW) and in a biological safety cabinet (BSC) was measured to compare the effects on air cleanliness conditions (International Organization for Standardization [ISO] class 5 within the LAFW or BSC and ISO class 7 in buffer areas). METHODS: Twenty-five to 50 packages of each of 12 needle or syringe products were opened. Probes were configured to count airborne particles during the separation of strip packages and the opening of packages by peeling back the top web or pushing the device through the packaging (for soft packages) or by twisting apart hard packages. RESULTS: The numbers of particles were not significantly different between the LAFW and BSC. The separation of strip packages generated visible particles and raised airborne particle counts. Peeling open plastic film packages and opening hard plastic packages generated fewer airborne particulates than did pushing devices through the packaging. For all methods of package opening, average counts downstream from the direct compounding area exceeded ISO class 5 conditions. Counts in the LAFW buffer area did not exceed ISO class 7. CONCLUSION: All methods of separating and opening the packaging of needles and syringes generated particles. The peel-and-present technique generated the lowest particulate volume. The LAFW and BSC were equally effective in maintaining low particle counts.