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Founded by Dr. Samuel Brown, Brown Fertility is a fully comprehensive fertility clinic offering patients exceptional results through personalized care and affordable treatment plans. With over 15 years experience and the management of more than 5,000 IVF cycles, Dr. Samuel Brown consistently achieves success rates above the national average. From basic evaluation and testing to the most advanced procedures and treatments available, Brown Fertility is dedicated to Conceiving Miracles™.
Relievant Medsystems, Inc., a privately held medical device company pioneering the INTRACEPT therapy of nerve ablation within vertebral bodies for the treatment of chronic low back pain (CLBP). The INTRACEPT Intraosseous Nerve Ablation System is a patent-protected, minimally invasive, implant-free therapy that utilizes radiofrequency energy delivered into the vertebral body of the spine to ablate the Basivertebral Nerve (BVN). This nerve, characterized in peer-reviewed literature reporting studies of pain signal transmission and in preclinical and clinical evaluations, is a significant contributor in the transduction of pain arising from degeneration of the vertebral bodies associated with CLBP. The INTRACEPT Intraosseous Nerve Ablation System leverages proprietary instruments and methods to disable this nerve.
Marcon Group Inc is a San Rafael, CA-based company in the Healthcare, Pharmaceuticals, and Biotech sector.
Uprise Health is a company that offers digitally enabled employee assistance programs, mental health services, chronic condition management, and managed behavioral health solutions.
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.