Human-Centered AI: Reimagining Document Management in Healthcare
In the high-stakes world of pharmaceutical manufacturing, document inefficiencies aren't just inconvenient—they're a barrier to innovation and compliance, and they create frustration for the people who need to access critical information. When a global healthcare leader faced mounting challenges with thousands of critical batch records, we developed a solution that balanced cutting-edge AI with human-centered design, transforming not just systems but the daily experience of the people who use them.
- 90% reduction in manual document processing time, freeing staff for higher-value activities
- Documents now searchable by key parameters across 8 product families, democratising access to information
- Improved manufacturing quality controls through better data access for frontline teams
- Enhanced compliance reporting with faster information retrieval, reducing staff stress during audits
- New analytical capabilities unlocked by structured data extraction, empowering better decision-making
Services
Sectors
The Challenge
A multinational pharmaceutical company was struggling with more than 50,000 batch record documents spread across eight product families. The documents—a mix of structured forms, handwritten notes, and scanned paperwork—contained critical manufacturing information but were virtually impossible to search, analyse, or leverage effectively.
Through in-depth interviews and observation sessions with staff, we uncovered the human impact of these technical challenges.
Quality assurance specialists were spending up to 70% of their time manually searching for information rather than applying their expertise. Production managers described feeling constant anxiety about missing critical details during reviews. Compliance officers worked late into evenings before audits, manually compiling reports from disparate sources.
Our research revealed that manual document handling was particularly problematic for:
- Batch records with handwritten annotations that required specialised knowledge to interpret
- Quality assurance documentation that needed to be cross-referenced across multiple sources
- Production verification processes where delays impacted manufacturing schedules
- Compliance reporting requirements that created periodic stress and overtime
Our human-centered AI solution transforms pharmaceutical documentation through a four-stage workflow: capturing diverse document types, classifying them using advanced algorithms, verifying extraction with human expertise, and integrating structured data across enterprise systems. This seamless process eliminates manual handling while preserving institutional knowledge.
Our Human-Centred Approach
We developed a tailored solution through a human-centred, iterative process that put people at the heart of our technological innovation:
1. Ethnographic research and co-design
We began by shadowing document handlers, quality assurance teams, and compliance officers to understand their daily challenges. Through collaborative workshops, we mapped pain points and co-created potential solutions with the actual end-users, ensuring the technology would serve human needs.
2. Journey mapping
We created detailed user journey maps for different document workflows, identifying friction points where intelligent automation could have the most meaningful impact on daily work practices and decision-making.
3. Proof of concept design
We translated user insights into a working prototype, co-designed with key stakeholders. This participatory approach allowed us to demonstrate the technical feasibility while ensuring the solution addressed real human needs before committing to full-scale development.
4. Custom AI architecture with human oversight
Our technology solution was designed with humans in the loop at critical decision points:
- Document classification algorithms trained on user-categorised examples
- Computer vision systems to locate and interpret handwritten text with human verification
- OCR optimisation for extracting printed information with feedback mechanisms
- A document structure understanding engine that learned from human corrections
5. Iterative testing with end-users
We conducted regular testing sessions with document handlers, creating feedback loops that steadily improved both the technical accuracy and the user experience. These sessions revealed unexpected use cases and edge cases that shaped subsequent development.
6. Integration and implementation with change management
The solution was deployed using Google Cloud technologies with careful attention to the human aspects of technology adoption. We developed custom training materials, conducted hands-on workshops, and established a feedback mechanism to support users through the transition.
Outcomes and Impact
Our human-centered intelligent document management solution is delivering transformative results that improved both systems and people's working lives:
- Centralised access to critical manufacturing information, creating a more equitable information ecosystem
- Successfully extracted handwritten data with >95% accuracy while maintaining context and meaning
- Created searchable indexes across millions of data points, democratizing access to institutional knowledge
- Reduced quality investigation times by 75%, transforming how teams collaborate during critical inquiries
- Built a platform that learns and improves through feedback, placing human expertise at the center of AI development
Beyond the technical metrics, we observed significant improvements in staff satisfaction and collaboration. Quality assurance teams reported feeling more confident in their decisions, while production teams appreciated the reduced administrative burden.
The solution now processes over 25,000 pages monthly, with a continuous feedback mechanism that allows users to flag issues and suggest improvements, creating a learning system that grows more valuable over time.
