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In the past few years, the healthcare industry has undergone an ample amount of changes. These changes are more in the ways how the healthcare industry stores data. Moving backward in our distant past, remind us of the old paper-based method to keep health records. It doesn't exist anymore in our present. Instead, we have new data-keeping methods, i.e., online digital records where storing & sharing information is easy.
Leveraging the power of machine learning in healthcare to improve outcomes has primarily rested in the hands of data scientists--until now. Healthcare.ai--open source predictive analytics software--is on a mission to democratize machine learning--to make it accessible to everyone in healthcare (not just data scientists) with the right technical skillset and tools (e.g., BI developers, SQL developers, data architects, and project managers). Machine learning and artificial intelligence (AI) are transforming healthcare. Health systems are increasingly using predictive analytics to better prioritize at-risk patients and optimize care decisions. Healthcare.ai makes it easy to create predictive models on your healthcare data--and is unlike any other machine learning tool in the industry.
Healthcare is drowning in data. An article by CIO.com outlines How CIOs Can Prepare for Healthcare'Data Tsunami.' Digitization of healthcare information, EHR systems, precision and personalized medicine, health information exchange, consumer health, Internet of Medical Things (IoMT), and other major trends affecting healthcare are accelerating this data growth rate. Artificial Intelligence (AI) and Machine Learning (ML) are powerful tools that empower healthcare organizations to process the tsunami of healthcare data in near real time, maximize the value of this data, and delivering actionable insights near real-time insights that in turn enable healthcare to maximize the Quadruple Aim Objectives of improving patient outcomes, reducing healthcare costs, and improving the experiences of both patients and healthcare professionals. Increasingly healthcare organizations run AI / ML workloads in the cloud to reduce costs, and improve security, agility, and scalability. Clinician burnout is a major concern across healthcare providers with 42 percent of physicians indicating burnout in a recent research study by Medscape, and their job is the major lifestyle factor, with too many bureaucratic tasks (for example charting, paperwork) cited as the major task factor.