e13560 Background: A common cause of preventable harm is the failure to detect and appropriately respond to clinical deterioration. Timely intervention is needed, particularly in cancer patients, to mitigate the effects of adverse events, disease progression, and medical error. This problem requires effective clinical surveillance, early recognition, timely notification of the appropriate clinician, and effective intervention. Methods: Applying a user-centered systems engineering design approach, we designed and implemented a surveillance-and-response system to improve the detection and response to clinical deterioration in cancer outpatients. The surveillance system predicts 7-day risk of UTEs, defined as clinically meaningful changes in the patient’s treatment course or cancer care pathway (e.g., any unplanned/unexpected: clinic or ER visit, hospital admission, or major treatment change and/or delays, and/or death). Data inputs consist of: 1) patient activity and health data collected by a Fitbit monitor; 2) geolocation data to measure activity outside the home (i.e., locations preselected at study onset); 3) clinical data from the hospital’s electronic health record; and 4) patient-reported outcomes measures (i.e., PROMs; the NCCN Distress Thermometer, the Comprehensive OpeN-Ended Survey or CONES, Global Health Score, items from the Consumer Assessment of Healthcare Providers and Systems (CAHPS)). Herein, we measured the effectiveness of Fitbit data alone to UTEs in a pilot sample of patients. Dimension reduction of Fitbit variables was first carried out by using Pearson correlation analysis to eliminate redundant variables. As UTEs are rare events, they were oversampled using the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset. A random forest classification model was trained to predict 7-day UTE risk. Model accuracy was determined by calculating the mean of Stratified 5-Fold Cross-Validation with 10 repeats. Results: Fitbit data was collected over a 6-8-week period from 14 head and neck cancer patients receiving surgical resection, outpatient chemotherapy, and/or radiotherapy. We identified six UTEs in 5 patients. A random forest classification model was developed from 10 variables derived from 7 Fitbit measures. The following variables were averaged or summed daily: average heart rate (HR), resting HR, below 50% or zone 1 of maximum HR, zone 2 and zone 3 HR combined (i.e., 70-100% of max HR), total daily calories, steps, and sleep in minutes. We achieved a model accuracy of 94% (ROC AUC: 0.984, Precision-Recall AUC: 0.985). Conclusions: Activity and health data collected by a commercial activity monitor demonstrated effectiveness in predicting patient UTEs when an oversampling procedure was used to adjust for class imbalance (i.e., low UTE rate). Future studies are recommended to verify and validate this result in a larger patient sample.
Data Normalization and Standardization: Impacting Classification Model Accuracy
