The current system of identifying adverse events from customer complaints is labor intensive and time consuming. FDA guidelines require health care providers and manufacturers to submit voluntary reports of adverse events associated with products within the 15 days of compliant. However, the challenge companies face is to swift through the massive volumes of the complaints and require highly trained professionals to review, validate and
identify the adverse event to prepare report in timely fashion (<15 days of
complaint) for FDA submittal.
Goal
A fortune 100 big pharma/MD&D client approached Intuceo® team to automate and improve
the process efficiency leveraging AI/ML. Applying AI effectively to determine whether a complaint is
potentially an adverse event or not requires not only base level prediction capability but also ability
of the AI model to rationalize its prediction. A Yes/No decision of AI model may help in reducing the
volume of complaints, but it doesn’t eliminate the pains takingly time-consuming efforts from experts
in manually annotating the explanation to why that Event is Adverse Event or not. While most AI
cognitive models provide base level Prediction capabilities, they seldom come with “Explainable AI
intelligence”. Intuceo® team of experts along with patented Intuceo® auto ml tools for pattern
recognition and predictive models, partnered with the client and implemented a AI/ML solution that
accomplished both the goals, “Accurately Identify potential Adverse Event” along with rationale on
WHY with the explainable AI feature.
- Any adverse event is reportable to FDA if the event: Is fatal, is life-threatening, is permanently or significantly disabling, Requires or prolongs hospitalization, Causes a congenital anomaly
- These adverse events must be reported to FDA as soon as possible but no later than within 15 calendar days following the initial receipt of the information
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Having an AI/ML driven solution with both AE detection along and effective explanation resulted in high through put to process more complains in given time window, significant reduction
in expert professional’s time and number, resulting in the timely FDA submittal.
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Results
Having an AI/ML driven solution with both AE detection along and effective explanation resulted in high through put to process more complains in given time window, significant reduction
in expert professional’s time and number, resulting in the timely FDA submittal.
