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Physicians choice medical billing is a Dania, FL-based company in the Healthcare, Pharmaceuticals, and Biotech sector.
With more than 26 billion medical claims and 7 million more added every day, we have the industry`s strongest and fastest growing data framework. Add the simple, at-a-glance visuals, and the personalized expertise from our data scientists and you have a full spectrum of tools that help validate strategies and optimize workflows that benefit providers, payers, and patients across your entire system.
Heyday is a people-first skincare company, bringing together human touch, expertise, and data to personalize and empower people to take care of their skin. We opened our first retail location in 2015 – taking the facial and indulgence out of the spa, elevating the estheticians as the experts they are, and removing the overwhelm out of building an at-home skincare routine. Today, we have 9 locations in New York, Los Angeles, and Philadelphia. We`ve performed 225,000 facials, collected data on skincare conditions, preferences, and purchases, and become an employer of choice for a world-class team of estheticians we call Heyday Skin Therapists. Our next chapter is to take these ingredients and create unparalleled personalization and guidance across content, communities, recommendations, and services in both our physical stores and our e-commerce experience. `Heyday` means a period of your life when you`re feeling at your best. Above all, we exist to help people achieve their `heyday` – to show up in the world as their best selves, every day. We start with the skin you`re in.
SecureHorizons is a Kansas City, MO-based company in the Healthcare, Pharmaceuticals, and Biotech sector.
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.