Transform patient care with predictive analytics
You can improve the likelihood of good outcomes – with personalized insights that bring attention to what patients need today and tomorrow
Predictors at the patient level by provider, by group, and for your full population
Growing library of 100+ pre-built, tested, stable, and calibrated predictive analytics
Start seeing clinical and utilization predictions for your ACO patients quickly
Robust analytics that are built on billions of health events from 100M+ individuals
Employing AI is no longer a luxury, it’s a necessity
Care planning today is limited by the information that is available in a patient’s health record and the time that a clinician can take to review it.
Our analytics tools can scan an individual’s complete health history, run the person’s data against sophisticated predictive models and offer up individualized risk profiles to maximize the probability of a good outcome for your patient.
This will allow highly trained medical professionals at ACOs, physician offices, and inpatient and post-discharge settings to spend less time sifting through gigabytes of incomplete data and more time using clinical judgement that is supported, not replaced by analytics.
Who we help
ACOs, payers, and value-based care networks
Deploy a broad suite of data analytics to improve patient outcomes, reduce excess cost, and inform contracting decisions for your ACO.
Providers
Transparent predictive analytics delivered at the point of care or during care planning identify patients who will benefit from additional attention and why.
Researchers
HDAI data 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)?
RAF score
Single score
Based on generic calculator
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
2.5x risk
AND MORE:
Including predictions of adverse events, chronic condition onset, total cost, high-value procedures, specialty utilization and more.
The HDAI difference
Sample patient, Michelle F | age 73
See how Old way
One size fits all
What is Michelle’s risk adjustment
factor (RAF)?
RAF score
1.00 is average
Single score
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
2.5x risk
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
Partners
Certifications
