Artificial intelligence startup nabs $12M+ to monitor medication adherence via smartphone
|AiCure's Artificial Intelligence visually confirms medication adherence--Courtesy of AiCure|
AiCure has raised a $12.25 million Series A led by New Leaf Venture Partners to back its novel medication adherence platform. The company's HIPAA-compliant tech uses notifications and monitoring via a smartphone camera to ensure timely medication usage.
The tech has been backed by the National Institutes of Health with grants totaling about $7 million to get it this far. The NIH grants were slated specifically for companies that would "significantly impact drug research and therapy," the company noted.
The system uses facial recognition and motion-sensing software to observe patient behavior via smartphone. The patient is prompted to take medication and then does so while holding the smartphone so the camera can verify that action.
"AiCure is the only effective and scalable platform that we have seen that solves one of the most significant problems in efficiency and quality of healthcare today," says Vijay Lathi, managing director of New Leaf, who joined the AiCure board as part of the financing.
Added AiCure CEO Adam Hanina, "Accurately understanding patient behavior through artificial intelligence defines a new category of medication adherence monitoring."
Last year, medication monitoring startup competitor Proteus received FDA clearance specifically for medication adherence and submitted to the FDA a drug that utilizes the tech jointly with biopharma Otsuka.
The company recently said that it had the first hospital adopt its technology--which must be ingested alongside the relevant drug to offer medication monitoring. Lake Tahoe, CA-based nonprofit rural health system Barton Health will implement Proteus Discover for its patients with uncontrolled and comorbid hypertension.
Proteus Discover works via co-encapsulated products created through specialty pharmacy services with a Proteus ingestible sensor that's about the size of a grain of sand. It activates when it reaches the stomach and a skin-worn patch records the time of ingestion and individualized data such as heart rate, activity and rest. This data is sent to the patient via mobile app where it is available to share with their healthcare provider.
The AiCure medication adherence approach requires a bit more responsibility on the part of the patient, but it seems easier and cheaper for healthcare organizations to deploy.
The startup's tech relies upon the automation of directly observed therapy. It essentially replaces a human monitor who might watch a patient consume medication in order to confirm adherence. The system directly visualizes patient behavior and doesn't require any human review.
Patients are first reminded via smartphone of an upcoming dose, then the patient holds the smartphone camera up to view the consumption of that medication. The tech has been validated against drug levels in blood samples. Its feasibility has been studied in various patient populations including elderly stroke patients to clinical trial patients in schizophrenia and HIV prevention trials.
Additional investors in the Series A include Pritzker Group Venture Capital and Tribeca Venture Partners, as well as Biomatics Capital, which was established by Boris Nikolic, the former chief adviser for science and technology to Bill Gates.
- here is the AiCure announcement
Special Report: Big data dominates as deep learning, AI seal the deal
Validic app enables remote medical device data capture via smartphone camera
Stealth machine learning startup raises $10M to develop a heart disease detection algorithm
Startup gets $7M Series A, GE partnership to apply machine learning to medical imaging
Deep learning radiology startup Enlitic wins Aussie partner, $10M Series B
Artificial intelligence company Berg selected to help analyze genomic data of 100,000 U.K. citizens
U.K. to fund camera-based patient monitoring prototype development after initial study
Smartphone-based diagnostic device could deliver quick, at-home lab results