In Eric Topol’s recent book, “Deep Medicine: How Artificial Intelligence Can Make Healthcare Human again,” he puts forth the idea that using technology to enhance humanity is the ultimate objective. In healthcare, it’s having all the data about a person assimilated and analyzed—liberating doctors from keyboards so they can look patients in the eye.
With a goal of helping healthcare organizations—and the people they care for—make more informed decisions from mountains of data and messages, EarlySign initially built a unique framework for created highly accurate machine learning models. Analyzing data from tens of millions of historical medical records with billions of data points to train our algorithms on patterns of disease signals, EarlySign’s computer scientists were able to anticipate a significant number of scenarios in which specific patients were liable to get specific diseases.
With validated studies that this technology had potential for early detection of high burden disease, EarlySign provided a platform from which providers could determine which patients to engage and intervene much earlier, with the goal of preventing downstream complications.
Taking advantage of technology
Relying on machine learning based “mechanization” to handle the tedious review of a charts and other clinical data, EarlySign engineers devised an ability to adaptively identify complex trends, patterns and interrelationships over time, taking various health conditions and lifestyle data into consideration, performing fine grained sophisticated analysis and inference that may even be impossible to do manually done by humans. Having applied a new machine-based approach to create a new model for identifying patients at risk of disease, EarlySign proceeded to build their platform which allowed for rapid cycle development and deployment of disease specific and custom models that allow computers to automate and speed the delivery of insights that help clinicians provide a new level of care and personalization with the benefit of early detection.
The result: Healthcare organizations can use these insights as part of their efforts to provide earlier diagnoses and personalized preventive engagement strategies to address different illnesses, diseases, and health conditions.
EarlySign’s machine learning solutions are being used increasingly in the analysis of massive bodies of data in new healthcare environments… from laboratories and hospitals to payers and life sciences companies. At every step, these tools are replacing crude rules and methods with more accurate and sophisticated methods leading to better yield—hence the creation of new opportunities for personalized care and outreach. Since we provide a core technology that helps our clients identify patients at higher risk, the chain of healthcare delivery becomes more focused and efficient.