Medial EarlySign AlgoMarker
For early identification, prioritization and intervention for patients in danger of complications from serious diseases, EarlySign's AlgoMarkers achieve clinically validated results by deriving insights from routine medical information. Working with U.S. and international healthcare organizations, EarlySign solutions are clinically proven to work in improving the health of individuals and populations.
Ori Geva, Co-founder and CEO
Medial EarlySign
- Respond (Decrease transmission & spread), such as: Optimal preventive interventions & uptake maximization, Cutting through “infodemic” & enabling better response, Data-driven learnings for increased efficacy of interventions
EarlySign’s AI-based solutions solve the problem of creating insights from “hidden” factors residing in electronic health records, including routine laboratory test results, demographics, medications, and diagnostic codes, to accurately predict those at highest risk of having or developing specific life-threatening conditions. These insights create powerful opportunities for early intervention strategies and more effective prioritization of resources for people who need it most.
One example is EarlySign’s ability to effectively code diabetes patients, early detection of patients progressing to Type 2 diabetes, as well as progression to other complications. Estimates are that approximately $2.43 million could be saved for every 100,000 adult lives in an integrated health system.
In 2020, EarlySign launched a AI-powered algorithm with Maccabi Healthcare Services to identify individuals estimated to be at the highest risk of severe COVID-19 complications. Flagged patients are sent for immediate testing, allowing for medical procedures to begin as quickly as possible. As of April 2020, the algorithm had identified the top 2% of highest-risk patients (approximately 40,000 people).
EarlySign’s AI-based software models help identify, stratify, and prioritize people facing the highest risk for severe complications of disease including colon and lung cancer, diabetes, chronic kidney disease, cardiovascular conditions, plus flu and COVID19.
Each of our solutions has been created with direct input from our healthcare clients. EarlySign partners with these clients to deliver personalized machine learning-based clinical solutions to aid in early identification and prevention of high-burden disease. Our clients and development partners include:
- Healthcare and Provider Organizations
- Laboratory
- Payers
- Life Sciences
- Clinical Research
EarlySign maintains an "Innovation Partner Group" comprised of recognized leaders across the global healthcare community; recruited from academic, commercial, and national health systems; and will represent disciplines including clinical practice, innovation, population health, quality, and other positions. Characterized by the free and open exchange of ideas, the environment fosters cooperation and willingness to face challenges. We strive to create and recommend positive solutions that will benefit patients, their families, and the professionals that care for them.
- Improved outcomes related to early detection of high burden disease
- Earlier, targeted interventions can lead to lower cost of care
- Point-of-care, patient–level predictive information
- Personalized, patient specific insights
- Solutions Validated in Research and Clinical Practice
Based on ongoing feedback, our solutions are optimized to accelerate efforts to identify those patients on high-risk trajectories in need of early intervention, allowing clients to engage them earlier, and focus on keeping them healthier longer.
- Growth: An initiative, venture, or organisation with an established product, service, or business/policy model rolled out in one or, ideally, several contexts or communities, which is poised for further growth
- Artificial Intelligence / Machine Learning
- Big Data
Working together with health system partners in the US and Israel, EarlySign's new AI COVID-19 AlgoMarker has been shown to identify patients estimated to be at increased risk of suffering severe complications if infected with coronavirus. With data currently growing and updated daily to refine the model, EarlySign made a decision to make its algorithm available to health systems at no cost for the software license and related services.
Immediate action was critical to combat this pandemic, and EarlySign's goal was to provide health systems a fast, reliable, and no-cost approach to identify and connect with patients at highest risk. People must see their medical conditions addressed earlier while health systems can benefit from more efficient allocation of essential clinical resources.
Additionally, working with larger data sets and claims data, EarlySign is working with Geisinger Health System to develop a novel way to identify patients a highest risk for adverse readmission. The development effort, as part of the CMS AI Challenge (US), is still underway.
We put the Doctor-Patient relationship at the center of what we do.
By helping healthcare systems identify patients at high-risk for either already having or being on developing life-altering medical conditions, sometimes even before they appear symptomatic, physicians benefit from using data they already have—and squeeze every bit of value out of that data to deliver insights that inform and support decision making. For patients, the nature of risk detection for a variety of diseases is changing dramatically, as more sophisticated markers continue to emerge and healthcare systems “cross the chasm” from being early adopters and innovators to mainstream use. EarlySign remains in the forefront of this embryonic movement, delivering increasingly sophisticated markers to help healthcare providers improve the live of their patients and help prevent unneeded illness and suffering.
As noted, EarlySign clients were able to:
- Identify individuals estimated to be at the highest risk of severe COVID-19 complications. The algorithm had identified the top 2% of highest-risk patients (approximately 40,000 people)
- In over two years of implementation with Israel’s Maccabi Healthcare Services, the LGI AlgoMarker solution has found more than 50 cancers and pre-cancerous conditions, while creating significant cost savings through more effective interventions and prioritizing resources.
From a global perspective, our intent is to engage patients and caregivers—along with clinical and healthcare leaders—to seek input as to what would constitute an optimal care experience. We will also review how effective utilization of data can optimize predictive analytics to improve care quality and beneficially impact the overall cost of the provision of care. We will also identify and review important developments and trends and how these may be impacted growing consumerism, social determinants of care, access to care, and more.
By targeting chronic diseases like diabetes, various cancers, as well as infectious disease like COVID19 and influenza which negatively impact 10's of millions of people worldwide, EarlySign will rely on its global market presence with technology and provider partners around the globe. Current engagements and commercial market targets are the US, EU, UK, plus China, Japan, and Singapore.
One of the primary challenges and costs in healthcare today arises from the late diagnosis of chronic diseases and resulting complications. Hospitals and health systems chose to work with EarlySign with the goal of preempting and preventing disease on a more individualized level, rather than merely reacting to symptoms. Hence, solution metrics are derived from predictive models that can aid in the earlier detection of deterioration trajectories. Through clinical risk stratification, clients benefit from having more accurate and personalized models based on differential risk and more targeted subpopulations with higher positive predictive value, providing the ability to effectively divide patients into risk-related groups—thereby optimizing the ability to deliver the most cost-effective care delivery coupled with intervention options to improve outcomes.
For example, EarlySign’s LGI AlgoMarker identifies individuals at highest risk of harboring a variety of lower GI disorders associated with chronic occult bleeding, ranging from diverticulitis to colorectal cancer. In over two years of implementation with Israel’s Maccabi Healthcare Services, the solution has found more than 50 cancers and pre-cancerous conditions, while creating significant cost savings through more effective interventions and prioritizing resources.
We will continue partnering with out clients to monitor, measure, and improve results.
- Israel
- United Kingdom
- United States
- China
- Germany
- Japan
- Portugal
- Singapore
- Switzerland
- United Kingdom
- United States
Like any emerging company with a disruptive technology, barriers for EarlySign are high but not insurmountable:
- Innovative technologies with limited ROI data are a tough sell in the current market with strong focus on cutting costs
- As we emerge from the pandemic, many healthcare organizations are struggling to regain their balance, caring for impacted front-line workers, as well as lost revenue--and new solutions are not yet a priority
The opportunity for EarlySign is that as the healthcare community begins to emerge from the disruption caused by the coronavirus pandemic, the market is demanding solutions that help get patients back to care as quickly and seamlessly as possible. There is real danger in millions of people having missed routine screenings plus ongoing care and treatment for chronic disease that can lead to unnecessary illness and even death.
The ability to leverage and deploy ML/AI to identify and prioritize patients for care is critical. EarlySign is working with its partners and is poised to play a role in addressing the backlog of care through identifying and those who will benefit from prioritization in care delivery.
Additionally, clinical trials on new treatments has stalled. EarlySign can help CRO's better recruit and manage trials.
- For-profit, including B-Corp or similar models
EarlySign staff members have membership and support many professional and charitable organizations:
- HIMSS
- American Hospital Association
- American College of Healthcare Executives
- Beryl Patient Experience Institute
- 8400 The Health Network
The employment of AI and ML across the healthcare sector has become increasingly relevant since the onset of the COVID-19 pandemic. To ensure it is effectively implemented, it is crucial that emerging companies like EarlySign have the opportunity to work closely with healthcare organizations that can function as a true partner in order to maximize results. We will be transparent about the technology, and clearly communicate the methodology underlying their results so that the entire healthcare community may benefit.
We understand that AI efforts should initially be narrow in focus and success will increase over time. We aim to prioritize healthcare resources and assist healthcare providers to improve care delivery and the experience of care for patients and families. Towards that objective, we will to target carefully and show value and deploy solutions that are relevant--and with achievable goals: Reduce cost; increase quality; elevate value to the individual patient, and reduce human suffering. We seek to provide great value from the implementation of AI to help health care organizations identify and treat the population that is most at need. As healthcare leaders increasingly integrate these solutions the future of patient health will continue to look brighter.
- Bill and Melinda Gates Foundation
- GSK
- McKinsey and Company
- BD
- InfoSys
- Johns Hopkins
- Swiss Re
- Clinton Health Access Initiative
EarlySign seeks partners with a shared commitment to both global health and the health of individuals. Clearly, we are a small organization, but our impact can be large. These organizations could help by identifying and introducing EarlySign's solution into healthcare organizations across the continuum that would benefit from our predictive analytics solution.
Vice President Marketing