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

population health data analytics

Predictors at the patient level by provider, by group, and for your full population

population health data analytics

Growing library of 100+ pre-built, tested, stable, and calibrated predictive analytics

healthcare data analytics

Start seeing clinical and utilization predictions for your ACO patients quickly

healthcare data analytics

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 currently allows thousands of highly trained medical professionals at ACOs, physician offices, and inpatient and post-discharge settings to spend less time, every day, sifting through gigabytes of incomplete data and more time using clinical judgement that is supported, not replaced by analytics.

Who we help

Health Data Analytics for ACO

Value-based Care Organizations

Improve patient outcomes and efficient allocation of resources by prospectively identifying high-risk patients and high-value network participants

Health Data Analytics for ACO

Hospitals and Health Systems

Identify and address excess mortality, length of stay, and adverse events with proactive patient targeting and network optimization driven by predictive analytics

Independent Physician Groups

Address and prevent future adverse events by focusing on the patients where you can have the greatest impact

Medicare Advantage and Commercial Payers

Insights that identify specific populations at risk and preventive strategies at the patient level for reduced utilization and improved outcomes

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

Health Data Analytics

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.

The HDAI difference

Bring individuals into focus

Sample patient, Michelle F | age 73

Parkinson’s, hypertension, tobacco user, no recent hospitalizations or ER visits.
Health Data Analytics
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

Partners

Certifications