Transform health data into answers that improve care

AI can help to answer complex questions that lead to cost and time savings – if you get it right.
population health data analytics

Delivers predictors from bedside to boardroom

population health data analytics

Growing library of pre-built, tested algorithms

healthcare data analytics

Start seeing clinical and utilization predictions for your patients quickly

healthcare data analytics

Models built on 100M+ patients

Employing AI is no longer a luxury, it’s a necessity

We are living in an era of data overload and burned-out clinicians. In healthcare, AI can equip patients and providers to understand health risks and improve outcomes at lower cost.

We build AI into our population health solutions. We make sense of massive amounts of data so healthcare organizations and providers can decide what is best for their patients, individuals can take control of their own health, and researchers can conduct more rigorous real-world evidence studies.

Who we help

data at the point of care

ACOs, payers, and value-based care networks

Deploy a broad suite of predictors to improve patient outcomes, reduce excess cost, and inform contracting decisions.

beneficiary claims data api

Providers

Transparent predictive insights delivered at the point of care or during care planning identify patients who will benefit from additional attention and why.

aco success

Individuals

Health Picture, compatible with the new Patient Access API rule, enables beneficiaries to connect to participating payers to see their curated health history and a  personalized risk profile.
aco solution

Researchers

HDAI scientists collaborate with interested investigators and industry clients on a broad range of real world evidence studies.

The HDAI difference

Bring individuals into focus

Old way

One size fits all

What is Michelle’s risk adjustment
factor (RAF)?

2.1

RAF score

1.00 is average

Single score

Based on generic calculator

beneficiary claims data api

Sample patient, Michelle F | age 73

Parkinson’s, hypertension,
tobacco user, no recent hospitalizations
or recent ER visits

HDAI

Personalized Population Health

How does Michelle compare to other women her age?

5.1x risk

of hypothyroidism
onset

2x risk

of dementia onset

1.5x risk

of mortality in next 90 days

2.5x risk

of hospitalization due to falls and infection

AND MORE:

Including predictions of adverse events, chronic condition onset, total cost, high-value procedures, specialty utilization and more.

Over 100 predictions/individual

The HDAI difference

Bring individuals into focus

Sample patient, Michelle F | age 73

Parkinson’s, hypertension, tobacco user, no recent hospitalizations or ER visits.
performance improvement in healthcare
See how

Old way

One size fits all

What is Michelle’s risk adjustment
factor (RAF)?

2.1

RAF score

1.00 is average

Single score

Based on generic calculator

HDAI

Personalized Population Health

How does Michelle compare to other women her age?

5.1x risk

of hypothyroidism
onset

2x risk

of dementia onset

1.5x risk

Mortality in next 90 days

2.5x risk

of hospitalization due to falls and infection

AND MORE:

Including predictions of adverse events, chronic condition onset, total cost, high-value procedures, specialty utilization, and more.

Over 100 predictions/individual

Most AI companies take from providers and healthcare organizations—their data, their attention, their money. We are trying to give them something they sorely need: not just insight but time to look patients in the eye and have the right conversation with them.

Nassib Chamoun

Founder, President, & CEO

HDAI Partners​