Enabling Automated Adverse Event Identification in Clinical Trials
Enabling Automated Adverse Event Identification in Clinical Trials
Sai Anurag Modalavalasa
Sai Anurag Modalavalasa
While configuring our platform to enable data capture for a multi-year oncology trial, a clinician pointed out that toxicity and adverse event (AE) reporting becomes particularly difficult in multi-site studies with large patient volumes.
That conversation led us to examine how AE reporting is actually performed in practice.
How AEs are identified and reported in practice
In most clinical trials, AEs are not directly “observed as AEs” at the point of care. They are reconstructed from longitudinal health record data. The inputs typically include:
Patient-reported symptoms during visits or follow-ups
Abnormal laboratory values
Changes in vital signs
Findings documented in clinical notes and discharge summaries [1,2,3]
From these source records, the study team performs a structured sequence of steps:
Determine whether a reported symptom or abnormal value qualifies as an AE
Map the event to a standard terminology framework (e.g., CTCAE)[4,5]
Assign severity grading based on clinical criteria
Establish onset and resolution dates
Assess whether the event is related to the investigational treatment
Document any intervention taken to manage the event
Convert all of the above into structured case report entries for submission
Each AE is therefore not a single data point, but a structured object reconstructed from multiple clinical judgments applied to source records.
Complexity of this process leads to under-reporting and delays
A single patient in an interventional study may generate multiple symptoms, laboratory abnormalities, overlapping events with different onset and resolution dates. Each of these needs to be tracked separately, with consistent grading and attribution. As trial size and duration increase, the number of such reconstructions required increases significantly.
This operational challenge directly contributes to under-reporting and delayed reporting of AEs. Retrospective institutional audits have identified multiple measurable quality gaps in AE reporting, including delayed SAE submission, onset-to-report lag, missed safety assessments and incomplete documentation of safety deviations [6, 7, 8, 9, 10, 11, 12].
AEs constitute the principal measure of patient safety in real-world evaluations of interventions [13,14]. Regulatory frameworks require that SAEs be reported within defined timelines to ethics committees and authorities[15,16]. Under-reporting and delayed reporting directly impact patient safety in a trial. Furthermore, poor AE reporting quality impacts investigators by limiting the reliability and generalizability of trial outcomes.
How Foster addresses this challenge
We focused on a simple question: What would it take to automatically abstract and report AEs directly from the source notes used by study teams?
We built a system that allows clinicians and coordinators to submit clinical information in their preferred format (dictated notes, ambient conversations, laboratory reports and diagnostic PDFs, scanned handwritten documents). Our system then performs data abstraction to determine:
AE identification
CTCAE-based grading
event timelines (onset and resolution)
treatment attribution
intervention details
It then generates structured AE reports in real-time that can be reviewed and validated by study teams.
Our goal is to reduce underreporting and delays in AE reporting and enable investigators to run high-quality clinical trials at scale. If you are facing challenges in AE identification and reporting in clinical trials, please reach out to us.
While configuring our platform to enable data capture for a multi-year oncology trial, a clinician pointed out that toxicity and adverse event (AE) reporting becomes particularly difficult in multi-site studies with large patient volumes.
That conversation led us to examine how AE reporting is actually performed in practice.
How AEs are identified and reported in practice
In most clinical trials, AEs are not directly “observed as AEs” at the point of care. They are reconstructed from longitudinal health record data. The inputs typically include:
Patient-reported symptoms during visits or follow-ups
Abnormal laboratory values
Changes in vital signs
Findings documented in clinical notes and discharge summaries [1,2,3]
From these source records, the study team performs a structured sequence of steps:
Determine whether a reported symptom or abnormal value qualifies as an AE
Map the event to a standard terminology framework (e.g., CTCAE)[4,5]
Assign severity grading based on clinical criteria
Establish onset and resolution dates
Assess whether the event is related to the investigational treatment
Document any intervention taken to manage the event
Convert all of the above into structured case report entries for submission
Each AE is therefore not a single data point, but a structured object reconstructed from multiple clinical judgments applied to source records.
Complexity of this process leads to under-reporting and delays
A single patient in an interventional study may generate multiple symptoms, laboratory abnormalities, overlapping events with different onset and resolution dates. Each of these needs to be tracked separately, with consistent grading and attribution. As trial size and duration increase, the number of such reconstructions required increases significantly.
This operational challenge directly contributes to under-reporting and delayed reporting of AEs. Retrospective institutional audits have identified multiple measurable quality gaps in AE reporting, including delayed SAE submission, onset-to-report lag, missed safety assessments and incomplete documentation of safety deviations [6, 7, 8, 9, 10, 11, 12].
AEs constitute the principal measure of patient safety in real-world evaluations of interventions [13,14]. Regulatory frameworks require that SAEs be reported within defined timelines to ethics committees and authorities[15,16]. Under-reporting and delayed reporting directly impact patient safety in a trial. Furthermore, poor AE reporting quality impacts investigators by limiting the reliability and generalizability of trial outcomes.
How Foster addresses this challenge
We focused on a simple question: What would it take to automatically abstract and report AEs directly from the source notes used by study teams?
We built a system that allows clinicians and coordinators to submit clinical information in their preferred format (dictated notes, ambient conversations, laboratory reports and diagnostic PDFs, scanned handwritten documents). Our system then performs data abstraction to determine:
AE identification
CTCAE-based grading
event timelines (onset and resolution)
treatment attribution
intervention details
It then generates structured AE reports in real-time that can be reviewed and validated by study teams.
Our goal is to reduce underreporting and delays in AE reporting and enable investigators to run high-quality clinical trials at scale. If you are facing challenges in AE identification and reporting in clinical trials, please reach out to us.
