Building a Healthcare AI Platform?
Transforming Healthcare AI with 95% Response Accuracy and 3 Integrated Platform Capabilities
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Case Study Summary
Executive summary
A digital health company was building a patient-advocacy platform for people whose medical records are scattered across providers, tests, and clinical notes. Its AI helps patients understand their records, identify possible gaps in their care, and prepare questions for their doctors. However, its existing AI workflows needed greater structure, reliable patient-data integration, and controlled updates to ensure consistent and traceable behavior.
WhizzBridge strengthened the platform through three core engineering capabilities: structured AI workflows, integrated patient medical records, and centralized AI administration. The team introduced benchmark testing and regression evaluations, integrated structured patient histories through a FHIR-based health-data service, and established modular AWS infrastructure. The implementation targeted 95% response accuracy, 40% faster AI processing, and 100% traceability of prompt configurations, transforming the platform into a more controlled and manageable healthcare AI solution.
95%
Response accuracy
Grounded in patient records
40%
Faster AI processing
Async cloud workflows
100%
Prompt traceability
Tested before release
Challenges
- Grounding AI in Patient Records: The AI needed to generate responses based on each patient's available medical information rather than behave like a generic chatbot. Its outputs required traceability, auditability, and safety controls to support patient education and conversations with healthcare professionals.
- Controlling AI Behavior and Updates: Existing AI logic operated through multi-step workflows. Changes to prompts could affect what patients were told, creating a need for controlled configuration, benchmarking, and regression testing instead of ad hoc experimentation.
- Integrating Fragmented Health Data: Patient histories were spread across providers, tests, and clinical notes. The platform needed a reliable way to retrieve structured medical records and make them available to its AI workflows.
- Supporting Scalable Cloud Infrastructure: APIs, storage, queues, and background jobs had to work together as patient numbers, medical records, and AI interactions grew, without compromising data integrity or the ability to explain AI behavior.
The digital health platform needed to manage sensitive patient information while ensuring its AI workflows remained accurate, traceable, and reliable.
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WhizzBridge’s Solution
WhizzBridge treated the AI as software with defined inputs, stages, and checks, while establishing a reliable patient-data foundation for its workflows.
- Structured AI Pipeline: Evolved the existing multi-step AI workflows into a configuration-driven pipeline with explicit stages and controlled data flows, using stored patient information rather than relying solely on free-form prompts.
- Testable AI Configuration: Separated prompt behavior and pipeline logic from application code, enabling the team to evaluate changes against benchmark datasets and regression tests before release.
- Structured Patient Data Integration: Designed and built an integration with an external health-data service to retrieve structured, long-term patient records, including secure authentication, retries, rate-limit handling, and error recovery.
- Scalable Cloud Architecture: Separated patient-facing services, AI processing, health-data storage, background jobs, and infrastructure so individual components could evolve independently.
- Centralized Administration: Provided an admin interface for managing patients and AI prompt settings, while connecting the existing patient web app to backend services and secure sign-in for phone and desktop access.
Results We Achieved
WhizzBridge transformed the platform's existing AI workflows into a structured, testable system, supported by integrated patient records and modular cloud infrastructure.
- 95% AI Response Accuracy: Established a benchmark-driven AI evaluation process to improve response consistency and ensure outputs remain grounded in individual patient records.
- 40% Faster AI Processing: Optimized AI workflows and asynchronous cloud processing to improve the efficiency of patient-data retrieval and AI operations.
- 100% Prompt Configuration Traceability: Introduced centralized configuration management, allowing AI prompt changes to be tracked, evaluated, and tested before release.
- 3 Integrated Platform Capabilities: Connected structured AI workflows, FHIR-based patient-data integration, and centralized administration into a unified healthcare AI platform.
The platform now provides AI-powered patient advocacy grounded in individual medical histories, helping patients understand their records and prepare for conversations with their care teams without diagnosing, prescribing, or making clinical decisions.
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