The Rise of Personal Health Graphs

From scattered files to connected stories

For decades, a patient’s health history has been trapped in PDFs, paper files, emails, and disconnected lab portals. Each hospital and lab sees only a slice of the picture. Doctors see snapshots. Patients remember only what they can. Important signals are lost in the noise.

A personal health graph changes this. Instead of storing isolated files, it captures relationships. It connects what happened, when it happened, and how it relates to everything else in your body.

What exactly is a personal health graph

A personal health graph is a structured map of your entire medical life. It combines:

• Lab results like glucose, thyroid, liver and kidney panels
• Imaging reports from X ray, CT, MRI, ultrasound and more
• Prescriptions, dosages, and start or stop dates
• Diagnoses and doctor notes across specialties
• Vitals like blood pressure, heart rate, weight, and oxygen
• Wearable data and symptom logs where available

The New England Journal of Medicine has highlighted how longitudinal records and patient timelines can reveal disease patterns far earlier than isolated events. You can see one example in their work on learning health systems here: NEJM learning health system perspective.

Why the world is moving toward health graphs

Most of healthcare today is not held back by a lack of treatment. It is held back by a lack of visibility. Doctors rarely receive complete histories. Patients rarely see trends across years. Families cannot easily connect flares, lab changes, and medication switches.

Harvard Medical School has written about the patient data problem and how fragmented information harms continuity of care and decision making. Their analysis makes a simple point. Without connected data, even the smartest system will miss context. Harvard overview of patient data fragmentation.

A health graph solves this by combining all data types into one timeline. It turns episodes of care into an ongoing story of your health.

Why India needs this more than anyone

India carries a heavy burden of chronic conditions. Diabetes, thyroid disorders, PCOS, hypertension, and chronic kidney disease are rising across age groups. Studies in journals such as The Lancet show that India’s non communicable disease load is among the highest in the world, and that early detection depends on long term tracking rather than one time tests.

You can see an example of this framing here: Lancet analysis of chronic disease in India.

At the same time, India is rolling out ABHA and the national digital health framework. This is a rare opportunity to tie identity, records, and intelligence together in one patient centered infrastructure.

How Aether builds your personal health graph

Aether converts any medical file into structured health data. PDFs, images, lab reports, prescriptions, and imaging summaries are all parsed and mapped to standard medical concepts. Each data point is linked to time, source, and clinical meaning.

The World Health Organization has published guidance on building integrated, patient centered health information systems. Aether’s approach follows those principles by focusing on interoperability, longitudinal records, and patient control. You can explore the WHO digital health recommendations here: WHO guidance on digital health systems.

In practice, this means your Aether account becomes a living health graph. The more records you add, the clearer the picture becomes.

Sources and further reading

• New England Journal of Medicine on learning health systems
• Harvard Medical School on the patient data problem
• The Lancet on India’s chronic disease burden
• WHO digital health system guidelines

Information only. Not medical advice.

Next steps

• Create your free Aether account and upload your recent lab reports.
• Add at least one year of reports to start building a meaningful health graph.
• Share a read only timeline with your clinician at your next visit.