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FIND THE FH PATIENTS IN YOUR PRACTICE
The Family Heart Foundation has created a machine-learning FIND FH algorithm that analyzes medical and insurance records to identify probable FH patients with profiles consistent with FH. Over 350 variables, including diagnosis codes, procedure codes, prescription codes, lab values, gender and age, were evaluated on over 221 million Americans (children and adults) with or at risk for cardiovascular disease. Through a partnership with a national claims vendor, the Family Heart Foundation can identify patients with probable FH in a HIPAA compliant manner, ensuring that clinicians are alerted to probable FH patients in their practice while patient identities remain de-identified to the foundation.
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Find the FH patients in your practice

The Family Heart Foundation has created a machine-learning FIND FH® algorithm that analyzes medical and insurance records to identify probable FH patients with profiles consistent with FH. Over 350 variables, including diagnosis codes, procedure codes, prescription codes, lab values, gender and age, were evaluated on over 221 million Americans (children and adults) with or at risk for cardiovascular disease. Through a partnership with a national claims vendor, the Family Heart Foundation can identify patients with probable FH in a HIPAA compliant manner, ensuring that clinicians are alerted to probable FH patients in their practice while patient identities remain de-identified to the foundation.
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Why FIND FH?

Familial hypercholesterolemia or FH is a common life-threatening genetic disorder that leads to early and aggressive heart disease. Recent studies suggest 1 in 250 people have FH, yet over 70% have not been diagnosed.

How FIND FH works?

FIND FH® utilizes machine learning technology to analyze insurance and medical record data to create algorithms that identify medical records that contain features consistent with a person having FH for further screening.

The FIND Collaborative Learning Network

Family Heart Foundation established a Collaborative Learning Network (CLN), FIND CLN to bring academic and community health systems together to learn and share best practices from implementing the FIND Model to identify and treat Familial Hypercholesterolemia. Collaborative Learning Networks differ from hierarchical and typical matrix organizational structures in that it is more flexible and adaptable allowing groups to distribute decision-making to solve problems important to them which is a concept called, “Co-production”.
The FIND CLN is an action-oriented network organizational structure which typically comprises of the following,

  1. Participants (patients, clinicians, researchers, community organizers, patients etc.) aligned around a common goal
  2. Standards, processes, policies and infrastructure to enable multi-actor collaboration
  3. A common where information, knowledge, resources and know-how are created and shared to achieve that common goal

Global Aim:

To save generations of families from heart disease through timely identification and improved care of familial hypercholesterolemia (FH) and elevated Lipoprotein(a)

Family Heart Foundation CLN Infrastructure

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Current FIND CLN Participants are as follows:

  • Dartmouth-Hitchcock Medical Center
  • Emory Healthcare
  • OhioHealth
  • University of Pennsylvania Health System
  • UT Southwestern Medical Center
  • Individuals living with FH

 

CLN—Service Area Hyperlipidemia Populations

FIND

Resources for your practice

Apply the power of advanced data analytics to your practice: identify medical records with key features of FH.
Read our publication in Lancet Digital Health to learn more about FIND FH

The CASCADE FH® Registry is the only national registry for patients with FH in the U.S.
Order free patient education resources for your practice.

Clinical Partners

  • Dartmouth-Hitchcock Medical Center
  • Emory Healthcare
  • Geisinger Health System
  • OhioHealth
  • University of Pennsylvania Health System
  • UT Southwestern Medical Center
 
SPONSORS
 
Amgen Find FH Sponsor