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Ori Geva
Co-Founder and CEO at Medial EarlySign
Ori Geva is a seasoned professional in the digital healthcare industry with a strong background in developing innovative health solutions.
Co-founder of Medial EarlySign, where he leads a skilled team in creating and selling unique digital health products that utilize machine learning for personalized healthcare services.
Previously co-founded Medial Research, focusing on applying machine learning to medical records for potential commercial use.
Brings extensive experience from roles at Pontis, a leader in personalized real-time marketing, where he served in various high-level positions driving product innovation and customer engagement.
Also a co-founder of Frevo Software, dedicated to developing financial systems for asset management.
Academically, Ori holds a Bachelor of Science in Mathematics (with a minor in Computer Science) from Bar Ilan University, graduating Cum Laude, and has a background in military intelligence.
Known for integrating diverse work disciplines to tackle complex challenges and ensuring end user satisfaction.
Formerly associated with various esteemed organizations including 8400 - The Health Network, WHnetworks inc, ESC Medical Systems, and Information Objects, among others.
Highlights
Pleased to share that EarlySign’s Chief Clinical Research and Compliance Officer, Eran Choman, has been selected to present an educational workshop at the upcoming #euromedlab in Rome.
EUROMEDLAB is the biggest European congress in the field of Clinical Chemistry and Laboratory Medicine with more than 5,000 delegates and 4,000 visitors from over 100 countries.
The presentation is titled “ColonFlag: How to prioritize and care for patients at high risk for developing colon cancer.” The findings in this presentation have been validated in multiple research publications and will cover a variety of topics ranging from the current state and obstacles to screening to a discussion of AI models based on real-world data, use cases, and will conclude with clinical and economic impact.
This presentation demonstrates how clinical laboratories can differentiate themselves and generate new revenue sources. Eran will also review how clinical labs can play a larger role in value based care strategies and population health initiatives by offering clinical insights derived by #AI models based on their clinical data.
#ClinicalInsights #machinelearning #valuebasedcare
Optimizing Care Management to Prioritize Care with Clinical Machine Learning
The unpredictable nature of healthcare costs coupled with the financial anxiety it can bring has created significant obstacles for those seeking both preventative and ongoing care. With the pandemic, many people delayed care due to risk of infection or inability to get an appointment. A recent New York Times article cited a Gallup study which indicated that “…nearly four of 10 Americans said they had put off care in 2022 because of cost, the highest number since Gallup started asking people about delaying care more than 20 years ago[i].” Further, it's no secret that physicians and other healthcare professionals are burdened like never before, with many seeking to leave the profession. Facing the spread of misleading health information[ii] coupled with administrative burdens, physicians simply do not have time to comply with ever increasing regulatory requirements as well as treatment guidelines. A recent study published in the Journal of General Internal Medicine indicated that if “…a physician followed all of the guidelines for preventive, chronic, and acute care, that would require nearly 27 hours per day.” Guidelines have become a constant frustration for physicians, having to spend precious time with patients on conversations that are unlikely to change habits, thus crowding out discussions that may be directly related to personalized care and treatment.[iii] While increasing access to care, patient encounters can be optimized by both impact on health and cost effectiveness. To streamline clinical workflow and to prioritize the most relevant patient visits, a new class of AI-enabled technologies are helping to identify and enrich subpopulations who could benefit from earlier treatment, and to identify and prioritize patients when interventions stand more chance of impacting the serious complications from the onset of disease. The goal is to use available solutions that take an unneeded administrative burden away from the physician and allow them to optimize the time spent with patients and enhance the cherished doctor/patient relationship. At EarlySign, we have developed models that use clinical machine learning to help identify those who may be at highest risk for complications of serious disease. By giving care management teams the ability to access data from validated models to prioritize patients for focused and efficient outreach, they create powerful opportunities for early intervention strategies and more effective prioritization of clinical resources. Hence, by giving clinicians and health systems predictive insights, care teams across the continuum can now “work at the top of their license.”
[i] Megan Brenan, Gallup Research, January 17, 2023 [ii] Dhruv Khullar, MD, MP, JAMA Network, September 23, 2022 [iii] Justin Porter, MD, et al., NIH National Library of Medicine, January, 2023
