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Cenorin is a Kent, WA-based company in the Healthcare, Pharmaceuticals, and Biotech sector.
Forus Health is a medical technology start up founded in 2010 by a group of engineers with the goal to eradicate avoidable blindness. Over the years Forus Health has designed and developed affordable technology solutions to increase access and affordability of eye care. Our first product 3nethra classic is a compact and portable non-mydriatic fundus camera used for capturing digital images of the posterior (retina) and anterior (cornea) segments of the eye. The device is used for identifying common eye problems such as diabetic retinopathy, glaucoma, ARMD, cataract etc. The device is also integrated to a cloud based telemedicine platform which enables remote diagnosis. There are 40 million people who are blind globally, and 15 million live in India. The high incidence of blindness in India is due to a combination of several issues, but mostly due to limited access to eye care facilities, high cost of treatment/surgery and lack of awareness. The objective of Forus Health is to use technology to increase access, bring down the cost of diagnosis(affordability) and raise awareness (the three As). The key to our solution is not just the technology, but the way in which we have designed the deployment of the product.
Active Ice Inc is a Norman, OK-based company in the Healthcare, Pharmaceuticals, and Biotech sector.
HedgePath Pharmaceuticals, Inc. is a clinical stage biopharmaceutical company that discovers, develops and plans to commercialize innovative therapeutics for patients with cancer. The Company may also explore acquiring or licensing innovative therapeutics addressing unmet needs and orphan indications beyond cancer. We are focused on the development of therapies for more than one type of cancer, with initial emphasis in the U.S. market, based upon repurposing the currently marketed antifungal drug itraconazole for new indications in oncology.
Carta empowers hospitals to personalize the delivery of care to the individual needs of each patient. Our insight here is that personalizing care is not only good from an outcome/clinical perspective (where most people focus in the context of personalization) but is also the best way to optimize operations. Currently, hospitals have to over provision their resources because they are set up to serve the generic patient; planning ahead for exactly the resources needed– no more, no less– is the best way to gain efficiency. The approach we`re advocating and enabling is to: 1) Find past patients similar to the current one being treated 2) Quantify what exactly happened to them during their journey through the hospital (this is where our model comes in) 3) Use machine learning to project what the particular patient in question will need, and what the patient can expect their experience to be in the hospital We`re applying this approach now to two use cases– supplies projection and bed usage projection– and we have a bunch of other use cases we`re planning on addressing in the future.