Organizations that use healthcare data struggle with the challenge of extracting actionable insights and value from the massive and growing amount of data being generated. The sheer volume of structured and unstructured data offers substantial opportunities to develop breakthrough clinical and commercial solutions yet the data is not “answer-ready”. After years of working with healthcare data, our team of data scientists, engineers, statisticians, medical advisors, and domain experts, dedicated ourselves to finding solutions to efficiently collect, organize, standardize, improve, analyze, and transform data to enable those serving patients to confidently and consistently achieve the best outcomes.
Our name is adopted from the ancient Greek Antikythera mechanism, a 2,000-year-old mechanism described as the first analog computer - the oldest known example of such a device used to predict astronomical positions and eclipses. Despite its advanced age, its sophisticated technology bridges antiquity and modernity.
The team at Kythera Labs has deep expertise in health care and has been solving problems for decades using proven data science concepts and unique technologies. Our team understands that trust is at the heart of innovation and it is something we work at every day.
Datavant and Kythera recently hosted a webinar that covered several topics related to patient privacy and identity resolution, ranging from the important difference between identifying and knowing to covering real-world applications using patient-level information, such as patient engagement campaigns, patient hub operations, and rebates auditing.
Data is an asset which businesses and industries can not ignore. If used properly, data provides its users a competitive advantage, however, without a sound approach, data may fall short of its full potential. To help ensure success, organizations should start with the critical first step of identifying what questions need to be answered and what problems need to be solved, then quickly mobilize to identify what data is needed to answer those questions. Data needed for strategic purposes almost always comes from different sources, in different formats, and in varying quality that can complicate utilization, especially for machine learning applications. As data and analytics are evolving, so to are data technology platforms - and keeping up is critical.