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Health Professionals and Allied Employees - AFT/AFL-CIO is a Emerson, NJ-based company in the Healthcare, Pharmaceuticals, and Biotech sector.
Carbon Health is a modern, tech-enabled healthcare company transforming the primary care, urgent care, and virtual care experience. With a team of expert physicians, engineers, and designers, Carbon Health pushes the boundaries of medicine to deliver personal, accessible and high quality care. We aim to give patients connection to their medical records, quick access for immediate and ongoing healthcare needs, and a network of healthcare experts. We want patients to feel as though they have an advocate - like a doctor in their family. With a growing network of trusted clinics, and an end-to-end platform that supports ongoing care management and virtual appointments, Carbon Health ensures world-class care is affordable and always in reach.
Nomad Health is the first digital marketplace for healthcare jobs, efficiently connecting quality clinicians with rewarding career opportunities and taking the busywork out of finding clinical work. We are a well-funded Series C startup backed by First Round Capital, RRE Ventures, .406 Ventures, Polaris Partners, Icon Ventures, and Kevin Ryan (founder of MongoDB, Zola, Gilt, and DoubleClick). The U.S. healthcare system is experiencing a staffing crisis. Employers spend $20 billion per year recruiting clinicians to provide care for patients around the country. Nomad replaces antiquated staffing agencies with modern technology to efficiently source, qualify, and hire medical talent on demand. Clinicians find better jobs with higher pay. Employers fill roles faster with higher quality care. Nomad is a fast growing team of technologists, creators, and industry experts passionate about modernizing healthcare staffing so doctors and nurses can get back to the work they do best: caring for others.
Briarwood Manor is a Lockport, NY-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.