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Geisinger-AI vendor aim to reduce adverse events, avoid readmissions

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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 Machine Learning Algorithm Predicts Risk for Prediabetics Becoming Diabetic Within 1 year - insideBIGDATA

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Medial EarlySign, a developer of machine learning tools for data-driven medicine, announced the results of its clinical data study on identifying and stratifying prediabetic patients at high risk for progressing to diabetes within one year.


Artificial Intelligence Tool to Predict Patients at Risk for Lower GI Disorders -

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LGI Flag, a machine learning-based solution developed by health care technology pioneer Medial EarlySign, will be implemented this month in SLUCare patient-care offices. The clinical risk identification tool will use ordinary medical data to help flag patients at greater risk of harboring lower GI disorders associated with chronic occult bleeding such as colorectal cancer, precancerous adenomas, polyps, irritable bowel disease, ulcers, and diverticulitis. The system flags patients using ordinary data, collected over the course of routine care, and sophisticated machine learning techniques, enabling health care providers to focus attention on patients who are most likely to benefit from further evaluation and possible intervention, review their charts, and determine next steps. "The ability to identify high-risk patients sooner and intervene with them earlier enables us to help improve care and long-term survival rates," said William Manard, MD, SLUCare Family and Community Medicine physician and Chief Medical Informatics Officer. "Earlier detection and treatments for lower GI disorders can also lead to improved and more manageable outcomes. This is an ideal way to use technology to deliver a higher quality of care for our patients in the greater St. Louis community."


New Study Shows EarlySign's Machine Learning Algorithm Can Predict Which Cardiac Patients are at High-Risk Following Discharge

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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.


This Israeli startup aims to catch disease early with data not diagnostic tests - MedCity News

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For many diseases, early diagnosis and intervention remain key to good outcomes. That has been the rationale behind screening tests for numerous diseases including colorectal cancer. But like many startups that are looking for clues in data, Medial EarlySign -- based in Hod HaSharon, Israel -- is leveraging artificial intelligence to mine EHR data to detect colorectal cancer risk much earlier. Pushing diagnosis further upstream could have many benefits and not just for patients in terms of better outcomes. If the data reveals risk it can push people to take screening tests, that they otherwise don't take.