Cardiogram, a start-up that uses optical sensors from smart watches to detect diabetes and atrial fibrillation, has signed its first reimbursement agreement with Oscar Health insurance.
The new partnership is built on the back of clinical validation results for the company’s DeepHeart deep learning algorithm. In a 2018 study conducted in collaboration with UC San Francisco, the company was able to detect diabetes in patients with an accuracy of 85%.
Oscar Health members who own an Apple, Garmin or Android smart watch can take advantage of this service using the Cardiogram health monitoring application.
If the app highlights a potential problem with atrial fibrillation or diabetes, patients can have a free blood sample for diabetes measurement taken at a local diagnostic center or perform an ECG at home using a kit. If the test is positive, Cardiogram will put the patient in touch with a primary care physician for a follow-up appointment or recommend a physician for appropriate treatment.
This represents the first reimbursement agreement between Oscar Health and a digital healthcare provider. When Cardiogram diagnoses diabetes or atrial fibrillation to a patient, it charges Oscar Health for the cost of the services.
The service is offered at no cost to Oscar Health members and claims from Cardiogram will not be subject to a member’s deductible.
Cardiogram plans to sign other agreements with insurance companies within the end of the year, as well as to extend the number of devices supported.
The agreement is particularly interesting for several reasons:
- See a start-up developing an app operating as a provider of digital health services
- Foresees the remuneration of the prevention service provided on the basis of the achieved results (outcome based)
- Offers an additional service free of charge to insured persons
- Put the risk and burden of prevention on the digital service provider.
This is an example that should also be carefully evaluated by public health, in order to sign agreements with private companies to experiment with new models of prevention.