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Vesta Healthcare is an industry-leading technology and clinical services organization, dedicated to connecting caregiver insights to the rest of the care team.
Angel Attic Community Inc is one of the leading companies in Healthcare, Pharmaceuticals, and Biotech industry. Angel Attic Community Inc is based in Middleton, WI. You can find more information on Angel Attic Community Inc at www.atticangel.org
Igentify® is a digital health company. We help clinicians, laboratories, and patients access, interpret, and use genetic information and education more effectively. Igentify developed the world`s first Digital Genetic Assistant, a software-based decision support platform. Our platform is capable of providing a fully scalable end-to-end genetic counseling solution in both clinical as well as non-clinical (direct-to-consumer) genetic testing set up. The platform is built for various genotyping technologies like microarray and next-generation sequencing (NGS). It can be customized to a broad spectrum of genetic and genomic tests offered by the test providers. Founded in Oct. 2016, Igentify`s team of globally renowned geneticists, highly experienced software developers, creative animators, and UX/UI designers, is redefining the protocols of traditional medicine. In Jan. 2019, Deloitte selected Igentify as one of the Israeli startups at the forefront of the Digital R&D Revolution.
HealthSpace Integrated Solutions is a Glen Allen, VA-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.