The Do’s and Don’ts of Using AI in the Rx Navigation Space

The pharmacy and benefits landscape has become too complex for manual processes alone. Between high-velocity drug launches, opaque pricing models, shifting formularies, and unstructured regulatory data, teams navigating pharmacy decisions need stronger tools. Artificial intelligence, when used responsibly, can dramatically improve the speed, accuracy, and consistency of Rx Navigation workflows. But like any powerful technology, it comes with guardrails.

Below are the key do’s and don’ts for applying AI safely and effectively in the Rx Navigation domain, along with high-impact use cases we see emerging in the field.

DO: Use AI to Identify Anomalies in Claims Data

Claims data drives pricing, repricing, forecasting, and clinical decision support. But claims feeds often contain anomalies: coding inconsistencies, mismatched NDCs, outdated prices, or payer-specific quirks, that can propagate downstream errors.

How AI helps

Machine learning models can detect patterns that deviate from normal activity across millions of claims. Examples include:

• Inaccurate ingredient cost outliers

• Dispense or days-supply inconsistencies

• NDCs that don’t match labeler-product-package codes

• Unusual pricing shifts unaligned with market trends

• Claims that conflict with formulary rules or benefit design

AI can flag these issues early, allowing pharmacy teams to intervene before errors affect pricing or member guidance.

DO: Leverage AI to Mine Unstructured FDA Drug-Label Data

FDA drug labels (Structured Product Labeling, or SPL) contain critical safety, dosing, and clinical information, but much of it remains unstructured and difficult to extract programmatically.

AI makes this workable by:

• Parsing long, text-heavy label documents

• Extracting sections like indications, contraindications, warnings, and dosage forms

• Normalizing terminology for consistent downstream use

• Enabling richer search across label data sets

• Identifying newly added black-box warnings or label updates

This significantly reduces the manual effort of keeping drug information accurate and up-to-date within Rx Navigation systems. You can use this free information to augment information you already have from places like MediSpan or FDB.

DO: Use AI to Aggregate Data Across Disparate Sources

Rx Navigation depends on reconciling multiple, often conflicting sources:

• Claims data

• FDA label data

• Pricing feeds (AWP, WAC, NADAC, etc.)

• Payer formularies

• Clinical guidelines

• State-level regulatory requirements

• Manufacturer announcements

AI can align and merge this information, detect inconsistencies, and surface a unified, coherent view for pharmacists, analysts, and plan sponsors. This directly improves accuracy in pricing, benefit determinations, and consumer guidance.

DON’T: Rely on AI Without Guardrails to Generate Clinical or Medical Advice

AI systems, especially large language models, can hallucinate facts, misinterpret clinical context, or present outdated safety information. In the Rx space, this is dangerous.

Key risks:

• Incorrect dosing suggestions

• Fabricated clinical rationales

• Misinterpretation of drug interactions

• Outdated safety or boxed-warning content

• Overgeneralized assumptions about member-specific care

Guardrails you must enforce:

• Require AI to cite the source for any clinical statement

• Restrict generation of medical advice to vetted, structured data only

• Always include expert human oversight

• Log all AI-generated outputs for auditability

• Maintain strict version control of drug information and pricing sources

In short: AI should augment human decision-makers, not replace them.

DON’T: Assume AI Outputs Are Absolute Truth

Even when AI is trained on authoritative data, it can:

• Misinterpret ambiguous text

• Overfit to rare patterns

• Infer causal relationships where none exist

Always validate outputs using a combination of:

• Human review

• Using multiple LLMs and building consensus

• Rule-based systems

• Third-party data reconciliation

• Continuous monitoring for drift

Conclusion

AI is transforming Rx Navigation by enabling deeper insight, faster processing, and more complete data than ever before. But responsible use is not optional… especially when impacts flow directly to patient safety, affordability, and clinical outcomes.

Use AI to detect anomalies, mine unstructured data, and unify information.

Do not rely on AI for clinical conclusions without strict safeguards.

With the right balance of innovation and oversight, AI can help make pharmacy navigation simpler, safer, and more transparent for everyone involved.