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THCB Spotlights: Jeremy Orr, CEO of Medial EarlySign - The Health Care Blog

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Today on THCB Spotlights, Matthew speaks with Jeremy Orr, CEO of Medial EarlySign. Medial EarlySign does complex algorithmic detection of serious diseases, working on early detection of cancer and the progression of chronic disease such as diabetes. Tune in to hear more about this AI/ML company that has been working on their algorithms since before many had even heard about machine learning, what they've been doing with Kaiser Permanente and Geisinger, and where they are going next. Filmed at the HLTH Conference in Las Vegas, October 2019.


Medial EarlySign Presenting at Lab Institute 2019

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Medial EarlySign's CMO, Dr. Jeremy Orr, will be speaking at a session entitled "Analyte to Algorithm to Action to Impact" at G2 Intelligence's 37th Annual Lab Institute. Machine learning models are beginning to play a critical role in care transformation efforts, especially as they relate to risk adjustment, population health, and alternative payment models. Dr. Orr will review the development and validation of several powerful models to predict high burden disease at earlier, more treatable stages. He will further review the challenge of getting these predictors into clinical and laboratory workflow, how ML results are messaged to both patients and clinicians, and how clinical outcomes related to these predictors are measured. Dr. Orr will close with a look ahead to how ML models and approaches will improve care delivery for laboratorians and other clinicians, and how ML empowers the transition to value based care.


8 AI Diagnostics and Imaging Startups for Digital Health

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Investing in emerging technologies can be extremely risky. It can also be extremely rewarding – and not just for your bank account. Technologies like artificial intelligence have the potential to change the world in many different ways. One of the industries where AI is already making real advances is healthcare, such as the ability to design and validate drug candidates to treat disease in less than two months. That has attracted the attention of plenty of deep-pocketed investors into AI healthcare startups, which have made more deals than any other AI industry since 2014, according to research firm CB Insights, with more than 80 AI diagnostics and medical imaging companies leading the way across 150 deals and counting.


Using AI to predict the future of cardiovascular diseases SciTech Europa

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Medial EarlySign are a leader in machine-learning based solutions in the early detection and prevention of high burden diseases using AI technology to detect the early warning signals and health risks associated with major diseases. The peer-reviewed retrospective data study, Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention, published in the Journal of the American College of Cardiology: "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 analysed 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.


Machine Learning Algorithm Can Predict Which Cardiac Patients Are High-Risk Post Discharge -

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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. The analysis was based on electronic health records (EHR), demographics, and social data collected from a cohort of 11,709 unique Mayo Clinic patients who underwent 14,349 PCIs during 14,024 hospital admissions. The patients' mean age was 66.9, most were male (71.5%), 45.9% were obese, and 59.8% had a history of heart attacks. The study highlights the potential of AI solutions in supporting cardiology care teams in identifying and treating these high-risk patients.


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.


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."


AI-Based Tool Identifies Patients at High Risk of Having Colorectal Cancer -

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Medial EarlySign, a leader in machine-learning (ML) based solutions to improve disease management, has shown successful results for the live, physician-supported implementation of its solution for aiding in earlier colorectal cancer detection, in new research published by the Journal of Oncology CCI. According to a recent study, the solution examined medical records of 79,000 adult Maccabi members who had not been compliant with colorectal cancer (CRC). From these, 688 men and women were identified as higher risk for CRC and recommended for further evaluation. Doctors of flagged individuals were notified and asked to follow up with their patients. Of these, 254 had colonoscopies performed by Maccabi physicians, and 19 colorectal cancers were found, as well as 22 with advanced adenomas (high-risk precancerous lesions).