Advanced Healthcare Data Analytics Solutions
We Simplify Health Data #LikeAPro

We do not use generative AI for blood data interpretation, as rule-based logic offers greater transparency, reproducibility, and auditability, which are essential for clinical decision support and regulatory alignment. The platform uses an expert system / rule-based AI engine to drive the health analytics.

Yes. The engine integrates laboratory results with patient demographics, vital parameters, known comorbidities, and family history, wherever available. Typically, the answers to these questions come to us from inputs to our Personal Health Questionnaire (PHQ). These inputs help generate context-aware Premium Smart Reports, enabling more individualised and clinically relevant interpretation.

Our Smart Health Reports have undergone validation by our internal medical domain teams and have also been independently reviewed by over 150 clinicians across diagnostic laboratories and hospitals worldwide (10+ countries). The interpretive logic is aligned with established international guidelines, including ADA guidelines for diabetes, ACC/AHA guidelines for hypertension and lipid management, and WHO criteria for BMI classification. For analytes that are not governed by a specific international guideline, interpretation is based on the validated reference intervals provided by the reporting laboratory.

HV is an AI-powered Personal Health Analytics and Clinical Decision Support platform that transforms laboratory data into easy-to-understand, medically validated health insights for individuals and healthcare providers.

The platform is not as a diagnostic device, rather a personalized health analytics and decision-support insights based on laboratory data, radiology, physicals and personal health data. The treating physician remains the sole authority for diagnosis, clinical interpretation, and management decisions. The platform is designed to support clinicians through holistic, rule-based analysis and to help patients better understand their health parameters. The report is intended to augment, but never replace, clinical judgement.

• Approach:Relies on explicitly programmed rules, expert knowledge, and logical reasoning.
• Input: Human experts define facts, rules, ontologies, and relationships.
• Learning: Minimal or no learning from raw data; focuses on reasoning over predefined knowledge.
• Examples: Expert systems, medical diagnosis systems using knowledge bases, semantic reasoning engines.
• Advantages:
  
  • Transparent and explainable.
  • Works well when domain knowledge is strong but data is scarce.
• Limitations:
  
  • Hard to scale; requires constant manual updates.
    
  • Less adaptable to new patterns not covered in the rules.