How it works

Let’s start with a truth. There is no shortage of analytic tools in healthcare today, and new products are constantly emerging. Unfortunately, most platforms miss key details and fail to identify the patients that are the most impactable. Worse still, they are all too easy to ignore. That’s where we come in.

This is Sara.

Sara is in pretty good health. She exercises when she can, watches what she eats, and tells her doctor that she is taking her daily pills. For a woman of her age, she seems to be low risk and low maintenance. But is she?

The problem.

Her doctors may not realize it at first, but Sara is at high risk for medication non-adherence and very high risk of readmission. Here’s the good news: By understanding who she is, and asking the right questions, and providing the proper support, Sara’s care team can keep her healthy, active and at home.

Predict & Prevent

  • First Admissions

    Which patients are at highest risk of a preventable emergency admission

  • Readmissions

    Which are likely to not follow discharge instructions or keep follow up appointments

  • Medication Noncompliance

    Who is at high risk of not taking their prescribed meds due to cost, complex drug regimens, or lack of family or caregiver support

  • Post-Op Complications

    Which patients require closer monitoring when returning home after medical procedures

  • Inappropriate Treatment Setting

    Know who can be discharged to in-patient/SNF, and who can safely be sent home

How it works.

Truth is, you can’t get a full picture of a patient’s health and risk of complications just by studying his or her medical records. And big data often ignore social determinants of health and other telling details. To truly predict and prevent adverse events and optimize treatment settings, a platform needs to dig deeper to understand nuances and subtleties. We are that platform.

Lifestyle & Behavioral Data

Lifestyle & Behavioral Data

4,000+ Person-specific data points

4,000+ Person-specific data points

Ability to Pay

Ability to Pay

Motor Vehicle Data

Motor Vehicle Data

Medical History

Medical History

Retail & Consumption Habits

Retail & Consumption Habits

Smokers

Smokers

Our Solution.

Our pioneering approach is twice as accurate as other models at predicting and preventing readmissions, non-adherence, and adverse events. And our ability to predict which patients are most impactable is unique in the industry. While other firms rely on claims, ZIP code or street-level data, Forecast Health draws on 4,000+ person-specific data points for more accurate predictions. Does the patient own a car or live near public transportation? Is he a smoker? Does she have high credit card or student loan debt and is unable to afford her medications (though she doesn’t tell her physician)? Answers to questions like these can tell us a lot about how to provide the best care for each patient. Using our advanced data analytics engine, organizations can also use person-level social determinants of health to improve existing and planned in-house clinical and business information systems.

The Solution

Sara does not own a car, and lives several blocks from the nearest bus stop. This increases the likelihood that she will not make it to her follow-up appointment or the pharmacy. She is also a widow, lives alone and has been juggling a few bills — indicators that she is less likely to stay on her prescribed regimen. We put this information at her care manager’s fingertips, so he can help arrange transportation assistance, send her home with a two month supply of her medications, and arrange other caregiver support. The result: Sara will be healthier, independent, and out of the hospital.

Twice as right

Our predictions are twice as accurate as other widely used models.

Provider-friendly

We provide patient-specific information embedded in the hospital EMR.

Patient centric

Patient-level information allows for personalized care plans.

10 %

%

Reduction in readmissions for a typical hospital that switches from one of the commonly used predictive model to Forecast Health.

If a patient's risk arises from cost, the ACO can provide discount meds; if it comes from limited caregiver support, they can order a visiting nurse. Forecast Health provides the patient-specific insights organizations need to provide higher quality care.

Dr. Brian P. Goldstein Dr. Brian P. GoldsteinChief Health System Officer at University of Washington Medicine, Member of Forecast Health's Board of Directors

INTERESTED IN LEARNING MORE? GET A CUSTOM POPULATION HEALTH REPORT.

A successful predictive analytics program starts with an understanding of the opportunities in your population. Receive a custom analysis of your ACO claims data, including insights on leakage and provider-specific care coordination opportunities, available only from Forecast Health

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