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 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.

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.

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.

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.

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."

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).

A machine learning technology developed to spot dangerous medical conditions before they worsen has shown its value in a trial. Based in Kfar Malal, Israel, Medial EarlySign is one of dozens of companies now developing Artificial Intelligence algorithms which can scan large volumes of medical records to pick out warning signs buried in patient data. Medial's latest algorithm looks to identify diabetes patients who are at highest risk of having renal dysfunction within the next 12 months. Medial EarlySign's machine learning-based model analysed dozens of factors contained in Electronic Health Records (EHRs), including laboratory test results, demographics, medication, diagnostic codes and others, to predict who might be at high risk for having renal dysfunction within the period. The company chose to isolate less than 5% of the 400,000 diabetic population selected from its database of 15 million patients, and the algorithm was able to identify 45% of patients who would progress to significant kidney damage within a year, prior to becoming symptomatic.

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, Medial 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.

Artificial intelligence start-up Medial EarlySign in a new study has shown how the combination of AI and EHR data can facilitate early detection and treatment of kidney problems and can help slow down – or even prevent – progression to end-stage renal disease. Medial EarlySign's machine learning-based model analyzed dozens of factors residing in electronic health records, including laboratory test results, demographics, medications, diagnostic codes and others, to predict who might be at high risk for having renal dysfunction within one year. By isolating less than 5 percent of the 400,000 diabetic population selected among the company's database of 15 million patients, the algorithm was able to identify 45 percent of patients who would progress to significant kidney damage within a year, prior to becoming symptomatic, the start-up reported. This represents 25 percent more patients than would have been identified by commonly used clinical tools and judgment, the company contended. "Immense efforts are invested in developing treatment protocols to reduce the number of patients who will develop renal dysfunction due to diabetes," said Ran Goshen, MD, Medial EarlySign's chief medical officer.