Building AI agents for healthcare and life sciences

In healthcare and life sciences, AI is proving its worth where it matters most: real-world patient outcomes. 

AI agents are delivering measurable efficiency gains: 16,000 hours of annual research time saved at Pfizer and clinical documentation study delivered in hours, not weeks at Novo Nordisk.

For technical leaders evaluating their agentic investments, these production implementations demonstrate concrete ROI. The question isn't whether to build agents—it's how to architect systems that meet healthcare's unique regulatory requirements and clinical safety standards while delivering real results.

From analysis to action: the agent advantage

Agents represent a fundamental shift from AI that requires constant human input to systems that reason through problems, plan multi-step approaches, and execute tasks.

This shift is especially impactful in the healthcare world, where data often lives in fragmented systems that don't talk to each other, making it harder to see the complete picture of a patient's health. Access to these systems let alone the expertise to understand one if not all of the data types is prohibitive at worst and time consuming at best. Agentic systems can understand clinical context, ingest information from multiple unrelated sources, process multiple kinds of data (images, text, audio, etc.), and apply all of these capabilities to taking meaningful actions within healthcare workflows.

What does this look like in practice? Instead of a clinician manually pulling data from five different systems, reviewing it, and then updating a care plan, an agent can monitor patient vitals across those systems, recognize concerning patterns, draft updated care recommendations based on current guidelines, and route them to the right clinician for approval. The agent handles the coordination and analysis; the clinician makes the final decision.

This shift from traditional AI to AI agents is particularly significant for healthcare because it tackles the process completion problem. Healthcare workflows don't just need information, they need actions taken across multiple systems to actually complete care delivery. Agents can bridge those gaps.

Production implementations delivering measurable results

AI-powered healthcare agents are already delivering real-world results in areas such as hands-on medical care, healthcare organization administrative loads, and researching new drug treatments.

Research acceleration and drug development

Pfizer reduced annual research time by 16,000 hours using Claude to handle literature review, data synthesis, and documentation, freeing researchers to focus on scientific problem-solving rather than tedious workflow management. For organizations facing pressure to accelerate drug development timelines while managing costs, this productivity gain demonstrates measurable ROI. 

Administrative workflow automation

Novo Nordisk automated clinical study report generation using Claude, reducing documentation production from 10+ weeks to 10 minutes. The pharmaceutical company built NovoScribe, an AI-powered platform using Claude Code and MongoDB Atlas, to handle regulatory documents that previously required entire departments. Clinical study reports—comprehensive trial summaries running up to 300 pages—now generate automatically while maintaining regulatory compliance standards. Staff writers who previously averaged 2.3 reports annually can now produce documentation in minutes rather than months.

Healthcare-specific considerations

Despite the obvious benefits, healthcare agent implementations face unique technical challenges: data fragmentation across incompatible systems, strict regulatory requirements, and direct impact on patient safety. These constraints require different architecture decisions than typical enterprise deployments.

The combination of complex medical contexts, regulatory requirements, and direct impact on patient outcomes creates an implementation environment where thoroughness trumps speed. In this section we will address three distinct challenges and how to address them to get the most from your agentic solutions.

Data fragmentation and interoperability

Data fragmentation across healthcare ecosystems creates significant integration challenges. Clinical departments operate specialized systems—radiology maintains image repositories, labs store results in separate databases, pharmacy records live elsewhere entirely. Legacy medical devices compound these problems, many developed decades before modern API integration patterns existed.

Integration complexity often shows up in:

• EHR vendor incompatibilities (Epic, Cerner, AllScripts)
• Departmental data silos requiring cross-system orchestration
• Legacy medical device integration challenges
• Real-time synchronization needs for time-sensitive clinical decisions

As a result, successful integration requires three key decisions: 

1. Connectivity approach:

• Direct integration with existing systems
• Custom connectors (APIs or MCP)
• Middleware to bridge communication gaps

2. Data formatting:

• Standardized ingestion processes for varied data types
• Format conversion between incompatible systems
• Handling unstructured clinical text vs. structured data

3. Synchronization requirements:

• Real-time access for time-sensitive decisions
• Latency tolerance based on clinical urgency
• Batch processing for non-urgent workflows"

Regulatory and compliance requirements

Regulatory compliance has to be part of every organization’s agent architecture from day one, not as an afterthought. Almost every country has baseline requirements around data privacy and comprehensive audit trails that fundamentally shape your system's architecture. If you don't build with those requirements in mind, you will be forced to rebuild sooner rather than later, possibly after facing steep fines.

Some regulatory considerations that need to be part of your agent architecture include:

• HIPAA compliance for AI data processing workflows
• Evidence-based validation of patient outcome improvements
• Documentation requirements for AI decision audit trails

Generic enterprise AI solutions don't meet healthcare regulatory requirements. HIPAA's recent cybersecurity guidelines require comprehensive observability and accountability for AI systems processing PHI, while the EU AI Act classifies most healthcare AI solutions as high-risk systems. Your healthcare organization needs robust risk management systems, clear data governance ensuring bias-free training data, detailed technical documentation, and human oversight mechanisms that go beyond checkbox compliance.

Maintaining human authority and clinical oversight

Agent autonomy delivers efficiency gains, but clinical decisions require human authority. Implementation architecture must enforce this boundary through:

• Transparent reasoning that clinicians can understand and validate
• Clear escalation pathways for complex or ambiguous cases
• Override capabilities allowing clinicians to reject AI recommendations
• Fail-safe defaults that prioritize patient safety over operational efficiency

The goal is creating AI agents that make excellent clinicians even better, not replacing the human expertise that patients depend on for compassionate, nuanced care. When implemented thoughtfully, these systems enhance diagnostic accuracy by spotting patterns across massive datasets, apply evidence-based guidelines consistently during hectic clinical situations, and alert doctors to subtle signs of patient deterioration before they become emergencies. Throughout these applications, clinical authority remains intact while administrative burden decreases, allowing more focus on direct patient care.

Implementation strategy

Production implementations at Pfizer and Novo Nordisk demonstrate specific efficiency gains: 16,000 hours of annual research time saved, automated processing of thousands of daily referrals, and 40-50% improvements in quality control workflows. How do you build and deploy agents that deliver similar results while meeting regulatory requirements and clinical safety standards?

Where to start

The best agent initiatives begin by targeting the things that everyone already agrees need fixing. Clear metrics make all the difference here, because they show whether the solution actually works and help build momentum for wider adoption.

Pick your battles

Initial implementations should target high-visibility or widely felt problems with clear metrics. Documentation efficiency stands out as the most promising starting point—organizations see measurable results quickly, clinical staff feel the immediate impact, and voice-based documentation delivers especially rapid wins that build organizational confidence for broader deployments.

The opportunity extends beyond simple transcription. AI agents can condense lengthy patient encounters into concise summaries, extract relevant data points from unstructured clinical notes, and convert information between incompatible system formats.

Patient engagement and communication are another promising area for initial implementation, especially for routine administrative tasks and basic patient inquiries. Letting an AI agent take over tedious or repetitive work not only provides immediate operational benefits; it also helps build overall trust and confidence that agents can be a boon to other areas of your organization.

Be thoughtful and prioritize compliance

Diagnostic support delivers significant value but requires thoughtful planning due to regulatory requirements and workflow integration complexity. Start with lower-risk applications that keep humans in the loop:

• Abnormal lab value flagging: Systems highlight concerning results for clinician review
• Drug interaction checking: Automated recommendations that pharmacists validate
• Clinical guideline reminders: Evidence-based suggestions during care planning

These applications provide immediate value, integrate into existing workflows without major disruption, and build team experience with agent systems before tackling higher-stakes use cases.

Building momentum

Success with initial implementations opens the door to enterprise-wide capabilities. The key is moving from point solutions to shared infrastructure that serves multiple departments.

Core capabilities

Build shared infrastructure that serves multiple departments rather than isolated point solutions. This approach reduces redundant development costs, accelerates time-to-value for new use cases, and ensures systems evolve alongside advancing technology capabilities.

For example, a single natural language processing engine that understands clinical terminology can power documentation automation, patient communication systems, and clinical note analysis across your organization. Rather than three separate NLP investments—one for documentation, one for patient communications, one for clinical decision support—you can build institutional knowledge in one area and deploy it across multiple use cases. The same principle applies to unified data integration platforms: build once, deploy across departments, and maintain centralized expertise as technology advances.

Building trust through transparency

Agents interact with both your workforce and your patients, and each group will respond differently to AI-assisted processes. Trust-building matters as much as technical deployment.

For patients, transparency matters. Make it clear when they're interacting with an AI agent versus a human, explain what the agent can and cannot do, and provide straightforward pathways to human specialists when needed. This clarity builds confidence and encourages broader adoption of AI-assisted services.

Internal adoption follows similar principles. Your organization already has change management processes for new clinical systems. Apply them here. Staff need to understand how agents work, when to trust their recommendations, and how to escalate concerns. 

Present agents as partners that handle repetitive coordination work, allowing clinical teams to focus on what they do best: complex medical decisions and patient interaction.

Tackling the bigger challenges

At this stage, your organization has built core capabilities, demonstrated measurable wins, and developed teams with hands-on agent experience. The lessons learned from early implementations provide the foundation for more complex deployments.

The observability and human-in-the-loop mechanisms you built for simpler use cases become even more critical as complexity increases. More than ever, your implementation needs:

• Comprehensive audit trails that track every agent decision and data source used, enabling clinical review and regulatory compliance
• Real-time monitoring systems that detect when agents encounter edge cases or uncertain scenarios requiring human judgment
• Escalation protocols that automatically route complex cases to appropriate clinical experts based on clear criteria
• Performance metrics that measure not just technical accuracy but clinical outcomes and workflow integration success

Driving AI transformation at scale

For technical leaders evaluating agent investments, production implementations at Pfizer, Novo Nordisk, and other organizations demonstrate measurable efficiency gains across research, administrative workflows, quality control, and regulatory documentation.: 

• Success requires thoughtful implementation that balances technological capability with the unique challenges to the healthcare industry. This approach delivers quick wins that build confidence while establishing the technical foundation needed for evolving into more sophisticated agent initiatives. 
• Evaluate whether your workflows have sufficient complexity to warrant agent architecture. As a general rule of thumb, some organizations achieve better ROI with simpler automation solutions that address their specific problems more efficiently than full agent implementations.
• The path forward demands partnership between technology and clinical teams to ensure AI agents enhance, rather than replace, human judgment. Healthcare leaders who embrace this collaborative approach, prioritize patient safety through robust testing and escalation pathways, and build modular systems that evolve to keep pace advancing AI capabilities will lead the way in creating strong, sustainable, and incredibly valuable systems for their organizations.

Production implementations prove the gains are achievable: measurable efficiency improvements across research, administrative workflows, and quality control. Your path forward is architecting systems that capture these benefits while maintaining the rigorous regulatory and clinical safety standards healthcare demands.