Israel-based Medial EarlySign and Geisinger Health System have partnered to apply advanced artificial intelligence and machine learning algorithms to Medicare claims data to predict and improve patient outcomes. An EarlySign-Geisinger proposal has been selected as one of 25 participants to advance to Stage 1 of a technology challenge from the Centers for Medicare and Medicaid Services to accelerate the development of AI and machine learning solutions for healthcare. "Approximately 4.3 million hospital readmissions occur each year in the U.S., costing more than $60 billion, with preventable adverse patient events creating additional clinical and financial burdens for both patients and healthcare systems," says David Vawdrey, Geisinger's chief data informatics officer. "Together with our partner EarlySign, we have forged a dynamic team that is rapidly developing novel solutions to achieve the Quadruple Aim of improving the patient experience of care, improving the health of populations, reducing cost and improving clinical care provider satisfaction," adds Vawdrey. The AI vendor and Danville, Penn.-based regional healthcare provider intend to develop models that predict unplanned hospital and skilled nursing facility admissions within 30 days of discharge and adverse events such as respiratory failure, postoperative pulmonary embolism or deep vein thrombosis, as well as postoperative sepsis before they occur.

Medial EarlySign (earlysign.com), a leader in machine-learning based solutions to aid in early detection and prevention of high-burden diseases, today announced the results of new research with Mayo Clinic assessing the effectiveness of machine learning for predicting cardiac patients' future risk trajectories following hospital discharge. The peer-reviewed retrospective data study, Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention, published in JACC: Cardiovascular Interventions, evaluated the ability of machine learning models to assess risk for patients who underwent percutaneous coronary intervention (PCI) inside the hospital and following their discharge. The analyzed algorithm was developed by Medial EarlySign data scientists to identify patients at highest risk of complications and hospital readmission after undergoing PCI, one of the most frequently performed procedures in U.S. hospitals. "Contemporary risk models have traditionally had little success in identifying patients' post-PCI risks for complications, in-patient mortality, and hospital readmission. This study shows that machine learning tools may enable cardiology care teams to identify patients who may be on high-risk trajectories," said Rajiv Gulati, MD, Ph.D., Interventional Cardiologist at Mayo Clinic.

Artificial intelligence has made some great developments toward speeding up cancer diagnosis so far in 2017. Last month it was announced that AI from Sophia Genetics was helping to accelerate patient diagnosis across Latin America. Earlier this year researchers at Stanford University developed a deep learning algorithm that can analyse skin cancer as accurately as a human doctor. Now, Israel-based company, Medical EarlySign has announced the ability of its AI tool to identify the top 1% at highest risk of undiagnosed colorectal cancer (CRC). The machine learning developer announced the first-year results of its implementation with Maccabi Healthcare Services (MHS), for ColonFlag, a tool developed in collaboration with MHS to identify individuals with a high probability of having CRC.