Can Chatbots Be Trusted to Capture Adverse Event Reports?

In the evolving landscape of clinical research and pharmacovigilance, digital tools are transforming how we engage patients and collect safety data. Among these, AI-driven chatbots are gaining attention as scalable, always-on interfaces capable of capturing adverse event (AE) reports. But when the stakes involve patient safety, regulatory compliance, and public trust—can chatbots really be trusted with such critical data?

The Promise of Chatbots in AE Reporting

Traditional adverse event reporting methods—phone hotlines, emails, and paper forms—often present barriers to timely and complete data capture. Chatbots offer a promising alternative:

• 24/7 Availability: Patients and caregivers can report AEs in real-time, at their convenience.

• Multilingual Support: Chatbots can support multiple languages, improving accessibility and inclusivity.

• Engaging UX: Conversational interfaces often feel more intuitive, especially for younger, digitally native populations.

• Real-Time Data Collection: Structured and timestamped AE data can be captured and routed for immediate review.

These benefits are particularly relevant in decentralized clinical trials (DCTs), post-market surveillance, and real-world evidence (RWE) generation efforts.

The Trust Gap: What’s at Stake?

Despite the advantages, several challenges temper enthusiasm:

1. Data Accuracy & Completeness

Can a chatbot interpret nuanced patient language? For example, a participant might say: "I felt weird after taking the drug"—is that fatigue, dizziness, or something more serious?

Chatbots must be trained on domain-specific language models and configured to ask intelligent follow-up questions. Without this, the risk of underreporting or misclassifying events grows significantly.

2. Regulatory Compliance

Health authorities like the FDA and EMA expect adverse event reporting to adhere to strict guidelines. This includes timelines, documentation, and escalation procedures. Chatbots must:

• Log all interactions

• Flag potential AEs automatically

• Escalate unstructured or ambiguous reports for human review

Failing to meet these standards can lead to non-compliance and jeopardize trial integrity or product approvals.

3. Privacy & Consent

AE data is inherently sensitive. Trust hinges on whether patients understand how their data will be used and whether the chatbot complies with HIPAA, GDPR, and 21 CFR Part 11. Data encryption, informed consent workflows, and audit trails are essential components of any compliant chatbot solution.

4. Bias & Accessibility

AI models powering chatbots may unintentionally exhibit bias—misinterpreting inputs from certain dialects, cultural backgrounds, or individuals with cognitive impairments. Rigorous validation and inclusive training datasets are critical to prevent systematic data gaps.

Where Chatbots Shine—and Where They Don’t (Yet)

Strengths	Limitations
Rapid initial screening of AEs	Struggles with ambiguous, complex narratives
Scalable patient engagement	May miss subtle clinical context
Reduced burden on site staff	Requires robust backend review pipeline
Helpful for follow-ups and reminders	Not a replacement for trained human oversight

The key takeaway: Chatbots can support AE reporting workflows—but not own them outright.

Best Practices for Deployment

For organizations considering chatbots in pharmacovigilance or clinical trials, here are key recommendations:

1. Co-develop with Pharmacovigilance ExpertsDesign decision trees and conversation flows in collaboration with clinicians and safety officers.

2. Integrate with Safety SystemsEnsure chatbot-collected data flows into validated safety databases or EDC systems with appropriate tagging and escalation.

3. Use Hybrid ModelsCombine chatbot frontends with human-in-the-loop review for flagged responses and follow-ups.

4. Audit, Validate, ImproveContinuously monitor chatbot performance using test scripts and feedback loops to improve detection accuracy.

5. Ensure Clear Patient CommunicationInform users upfront that they are interacting with a bot, and clarify how and when human support is available.

Touchcore’s Perspective

At Touchcore Systems, we support life sciences and medtech companies with digital health platforms that emphasize compliance, reliability, and patient-centricity. In projects involving chatbots for AE monitoring or clinical engagement, our approach includes:

• Custom NLP pipelines trained on medical terminology

• Modular consent and audit components

• Seamless integration with existing safety platforms

• Regulatory-grade testing aligned with GxP and Part 11 validation standards

We believe that chatbots can play a powerful role in modern AE reporting workflows, but only as part of a broader digital safety ecosystem that includes robust human oversight and compliance governance.

Final Thoughts

Can chatbots be trusted to capture adverse event reports?

Yes—with the right safeguards. When thoughtfully designed and implemented, chatbots can enhance patient engagement and streamline safety workflows. But blind trust is never the answer. These tools must earn their place through rigorous validation, transparent design, and ongoing oversight.

To explore how Touchcore can support your digital safety and reporting systems, get in touch with us at partner@touchcoresystems.com