P23: Development of an Error Prevention System for Patient-Reported Outcome Measures: A Pilot Study
Poster Presenter
Kathryn Evans
Associate Clinical Scientist
WCG Analgesic Solutions United States
Objectives
The objective of this study was to develop an error prevention system for patient-reported outcome measures collected in a hand-held electronic diary.
Method
Following a literature search and examining previous clinical study data, one type of error was selected as the focus of this pilot study. A technology plan for programming electronic handheld devices to detect this error and present a retraining message was implemented.
Results
Self-reported pain score inversion—patients entering their numerical score for “worst pain in the past 24 hours” lower than their “average pain in the past 24 hours” score—was selected for automated detection on the ePRO device. This data entry error was selected because it can only be explained by misunderstanding of the scales, regardless the patient’s actual pain intensity. The “training tip” presented was an abbreviated version of training material the patient viewed at the beginning of the trial. This tip only appeared for patients who inverted their pain scores on a daily diary entry. The error prevention system was successfully implemented on electronic handheld devices that are currently in use for a randomized, double-blind, placebo-controlled clinical trial of a novel medication for painful diabetic neuropathy.
Conclusion
Inaccuracies and variability in patient reported outcomes can lead to trial failure. It is important to be able to detect these inaccuracies and correct them over the course of a clinical trial in order to protect study endpoints. Patient reported symptom severity scores are subjective, the true meaning of which is known only to the patient; however, errors and inaccuracies in reporting can still be detected and resolved before entering the erroneous data into the study data set.
For this initial exploration, we configured the error prevention system to detect perhaps the most clear-cut of these errors: pain severity score inversion. There is no subjective state compatible with this pattern of response—average daily pain cannot be more severe than worst daily pain. The ability of this system to detect and correct data errors in real time will reduce the probability of trial failure due to inaccurate reporting. The usability of this system, and its impact on accuracy of pain reporting, will be reported when the trial is complete.
Authors: Kathryn Evans, Andrea Marraffino, Nathaniel Katz
