Abstract
Radiology turnaround time is an important quality measure that can impact hospital workflow and patient outcomes. We aimed to develop a machine learning model to predict delayed turnaround time during non-business hours and identify factors that contribute to this delay. This retrospective study consisted of 15,117 CT cases from May 2018 to May 2019 during non-business hours at two hospital campuses after applying exclusion criteria. Of these 15,177 cases, 7,532 were inpatient cases and 7,585 were emergency cases. Order time, scan time, first communication by radiologist, free-text indications, and other clinical metadata were extracted. A combined XGBoost classifier and Random Forest natural language processing model was trained with 85% of the data and tested with 15% of the data. The model predicted two measures of delay: when the exam was ordered to first communication (total time) and when the scan was completed to first communication (interpretation time). The model was analyzed with the area under the curve (AUC) of receiver operating characteristic (ROC) and feature importance. Source code: https://bit.ly/2UrLiVJ RESULTS: The algorithm reached an AUC of 0.85, with a 95% confidence interval [0.83, 0.87], when predicting delays greater than 245 minutes for "total time" and 0.71, with a 95% confidence interval [0.68, 0.73], when predicting delays greater than 57 minutes for "interpretation time". At our institution, CT scan description (e.g. "CTA chest pulmonary embolism protocol"), time of day, and year in training were more predictive features compared to body part, inpatient status, and hospital campus for both interpretation and total time delay. This algorithm can be applied clinically when a physician is ordering the scan to reasonably predict delayed turnaround time. Such a model can be leveraged to identify factors associated with delays and emphasize areas for improvement to patient outcomes.
Highlights
The time taken from clinician ordering to radiology scan interpretation time is an important quality measure that can impact hospital workflow and patient outcomes
We aimed to develop and test a machine learning model aided by Natural language processing (NLP) components that could predict delay in interpretation of on-call radiology cases, followed by identification of the factors that contributed most to the prediction
The dataset was composed of 15,117 Computed Tomography (CT) reports (Table 1). 7,532 cases were defined as inpatient cases, while 7,585 were emergency cases. 14,169 cases were conducted at Hospital Campus 1, and 948 cases were conducted at Hospital Campus 2
Summary
The time taken from clinician ordering to radiology scan interpretation time is an important quality measure that can impact hospital workflow and patient outcomes. Delays in performing radiology scans or interpreting them can prolong the time until a proper treatment. Predicting whether a delay can occur after a radiological exam request can allow the ordering clinician to better prepare their immediate treatment plan. Machine learning can integrate various clinical features and clinician communication to result in a comprehensive prediction baseline, as seen in previous predictive studies [5]. While NLP has been recognized as a useful outlet, it is still a naive space that has yet to be fully tapped into, for integrating free-form clinical text into predictive studies [8]
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