Regulatory scope
HIPAA Privacy and Security Rules
AI Patient Information System That Cut Record Retrieval Time by Over 60%
A global medical technology company held patient medical and medication records in a CRM that had become the bottleneck in its own workflow. Retrieval was multi-step, slow and dependent on staff knowing exactly where to look - and the delay landed on patient care. Seasia built Smart-PIS: a conversational retrieval layer over governed clinical data, with Microsoft Copilot handling extraction, structured clinical presentation and automated follow-up actions.
Overview
INDUSTRY
Healthcare IT / Medical Technology
Geography
United States
A global medical technology company held patient medical records and medication information inside a CRM built around the business rather than around the clinical question being asked of it. Retrieving a single patient's picture meant knowing which module held which field and moving through several screens to assemble it. Smart-PIS removes the navigation entirely.
Healthcare professionals ask for what they need in their own words; an AI-powered chatbot interprets the request and automates extraction of the relevant records from the CRM, returning them structured and in clinical context rather than as raw query output. The same interaction drives follow-up actions such as patient notifications, medication reminders, and appointment scheduling.
Overview
A global medical technology company held patient medical records and medication information inside a CRM built around the business rather than around the clinical question being asked of it. Retrieving a single patient's picture meant knowing which module held which field and moving through several screens to assemble it. Smart-PIS removes the navigation entirely.
Healthcare professionals ask for what they need in their own words; an AI-powered chatbot interprets the request and automates extraction of the relevant records from the CRM, returning them structured and in clinical context rather than as raw query output. The same interaction drives follow-up actions such as patient notifications, medication reminders, and appointment scheduling.
INDUSTRY
Healthcare IT / Medical Technology
Geography
United States
Project Scope & Business Challenges
The client needed to connect fragmented clinical, operational, and revenue workflows without replacing the EHR. The key challenge was eliminating manual reconciliation and care-management processes that could not scale with patient growth. Key challenges included:
Retrieval latency inside a complex CRM.
- Clinical and support staff needed several navigational steps to assemble a single patient's medication and record picture. Automating complex querying, processing and retrieval demanded high accuracy - in this setting a retrieval error becomes a medication error.
Historical data migration without inconsistency or downtime.
- Years of patient records had to move into the new retrieval layer with referential integrity intact, against a system that had to stay available around the clock for urgent clinical access.
Trust and adoption of AI output.
- Staff accustomed to manual retrieval will not act on an answer they cannot trace. Every AI-surfaced record needed provenance, and the transition had to work without extensive retraining.
Solution Offered by Seasia
Seasia built an AI-assisted retrieval layer that combines natural-language search, Copilot-driven data extraction, grounded responses, and contextual clinical presentation. The solution was designed to reduce navigation effort while keeping every response traceable to source records.
Conversational retrieval interface
Natural-language intent parsing with entity extraction for patient identifiers, medication names, encounter types and date ranges, so staff query in plain language instead of navigating CRM hierarchies.
Copilot-driven data extraction
Microsoft Copilot automates structured extraction from the CRM, pulling relevant records against the parsed query with high accuracy and minimal retrieval time.
Grounded response generation
Responses are grounded in retrieved CRM records rather than generated freely, so every answer traces to a source record.
AI chatbot for guided access
The chatbot answers frequent questions, surfaces records and guides users through system features, lowering the training burden that blocked adoption.
Structured clinical presentation
Retrieved data is organized and rendered contextually – medication lists, encounter history, record summaries – formatted for clinical readability rather than raw query output.
Automated follow-up actions
Search results trigger downstream workflow: patient notifications, medication reminders, and follow-up appointment scheduling.
Phased data migration
Historical records migrated in controlled phases with row-level reconciliation and rollback points, removing the inconsistency and downtime risk identified at kickoff.
Role-based access and PHI minimization
Access scoped by role, with prompt-level PHI minimization, so identifiable data is not passed to the model beyond what the query requires.
Full query audit trail
Every query, retrieval and response logged with user, timestamp and source records for compliance review and clinical audit.
Analytics and reporting
Power BI dashboards surface usage patterns, retrieval performance and query trends.
High-availability architecture
Built on Azure for always-on access, so urgent clinical queries do not depend on business hours.
Our Tech Stack
The platform was structured using a scalable, secure, and integration-ready technology stack suitable for fintech, retail, and payment workflows.
AI and NLP
Microsoft CopilotCopilot NLP capabilities
AI-powered conversational chatbot
Cloud
Microsoft Azure for data storage and AI processingAnalytics
Power BI
Standards & Integrations
CRM integration
SNOMED CT
RxNorm and NDC for medication identity
AI and NLP
Microsoft CopilotCopilot NLP capabilities
AI-powered conversational chatbot
Cloud
Microsoft Azure for data storage and AI processingAnalytics
Power BI
Standards & Integrations
CRM integration
SNOMED CT
RxNorm and NDC for medication identity
Compliance & Security
The platform was structured with a scalable, secure, and integration-ready technology stack suitable for fintech, retail, and payment workflows.
Safeguards
Encryption at rest and in transit, role-based access control, multi-factor authentication, immutable audit logs
Data handling
PHI minimization in model prompts; de-identified data in development and test
AI governance
Human review of AI output, grounded retrieval, NIST AI RMF-aligned controls
Features & Outcomes Delivered
The final solution brought together intelligent patient record search, guided chatbot access, automated CRM extraction, reminders, follow-up scheduling, secure role-based access, and phased data migration into one streamlined clinical support workflow.
AI-Powered Clinical Retrieval & Assistance
- Natural-language patient record search
- AI chatbot for record access and system guidance
- Copilot-automated data extraction from CRM
- Contextual clinical information presentation
Patient Engagement & Workflow Automation
- Automated patient notifications and medication reminders
- Automated follow-up appointment scheduling
Data Governance, Security & Migration
- Phased historical data migration with reconciliation
- Role-based access control across clinical and administrative users
- Full query and access audit trail
Analytics, Reliability & Platform Operations
- Power BI operational analytics
- Always-on high-availability architecture
Results
Over 60% reduction in patient information retrieval time, enabling quicker response for patient care
24/7 availability ensuring critical information access for medical emergencies at any hour
Streamlined workflows and reduced manual data-entry errors, improving patient safety
Staff time redirected from administrative retrieval to patient interaction and care
Historical records migrated with no interruption to live clinical access
Planning Clinical AI Deployment?
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