Smart on FHIR: AI-Powered Patient Symptom Analyzer

The Smart on FHIR: AI-Powered Patient Symptom Analyzer by Mindbowser enables care teams to capture, analyze, and act on patient-reported symptoms instantly. Built using FHIR standards and deployed on AWS services such as Amazon HealthLake and Amazon Comprehend Medical, it transforms unstructured symptom data—from chat, voice, SMS, and wearables—into structured, SNOMED/LOINC-coded clinical inputs that integrate seamlessly with leading EHRs (Epic, Cerner, Athenahealth).

Core Value and Key Advantages 1. Real-Time Symptom Capture & Structuring: AI chatbots and voice bots collect patient data anytime, anywhere and convert it into structured FHIR data mapped to clinical codes using AWS AI/ML services. 2. Automated Escalation and Triage: When symptoms indicate high risk, care teams are alerted immediately, enabling timely clinical intervention. 3. Symptom Trend Visualization: Dashboards display symptom progression, risk levels, and patient interaction status—enabling proactive decision-making. 4. Proven Operational Impact: Delivered improvements such as a 45% increase in early detection of high-risk symptoms, 40% higher triage accuracy, and a 50% reduction in manual symptom assessment effort. 5. Enterprise-Grade Security & Governance: HIPAA-ready design, leveraging AWS compliance frameworks and scalable from pilot to enterprise deployments. 6. Accelerated Launch with AWS AI Accelerators: Includes reusable components and pre-built frameworks enabling deployment up to 30–40% faster. 7. Full IP Ownership & Trusted Delivery: Offered with perpetual licensing and backed by Mindbowser’s global delivery footprint—50+ healthcare solutions across more than 10 countries.

Key AWS-Enabled Advantages

1. Real-time symptom capture using Amazon Lex, Amazon Transcribe Medical, and Amazon Comprehend Medical, stored securely on Amazon S3.
2. Automated escalation workflows powered by Amazon EventBridge, AWS Lambda, and real-time alerts via Amazon SNS.
3. Visual analytics with Amazon QuickSight and storage via Amazon RDS/Redshift for symptom trends and triage outcomes.
4. Machine learning-based early risk detection through Amazon SageMaker.
5. HIPAA-ready deployments using AWS IAM, KMS, CloudTrail, and Config for security and compliance.
6. Faster deployments with CloudFormation and DevOps pipelines on AWS CodePipeline/CodeBuild.

Use Cases

1. Efficient symptom triage during patient intake, virtual visits, or mobile interactions
2. Early detection of worsening symptoms, enabling timely care escalation
3. Reduction in administrative burden via automated data structuring
4. Enhanced engagement through integrations across chat, voice, SMS, and wearables