Sorin Stircu

Sorin Stircu, Regional MedTech Strategist at E*HealthLine, is on a mission to help MedTech/Healthcare companies build and grow profitable businesses with the help of A.I./M.L.-based Digital Ecosystems. He is trained in strategic & tactical planning and project management for the MedTech/Healthcare industry including product differentiation, positioning, life cycle management, and global market development. Sorin promotes innovative programs that capitalize on new analytic technologies for Digital Health to include: Artificial Intelligence, Machine Learning, R&D, Smart Electronic Health Records, Population Health Management, ePrescribing, Point of Care Marketing, Patient and Physician Engagement and Clinical Decision Support. He helped companies build new digital strategies and switch from a B2C/B2B business model to a partner-partner business model with a huge return of investment in a highly competitive market.

Emerging technologies, innovative software and medical devices are revolutionising the healthcare industry. Decentralising clinical trial data is helping to unlock the full potential these tools by enabling people in different places to work securely on data without compromising patient privacy . One of the areas that I find most exciting is the use of predictive modelling and artificial intelligence in federated machine learning – a way to learn from data without removing it from the location where it is stored. This takes us beyond the current established concept of federated databases. By combining several new technologies, we can not only have distributed data – but also distributed 'data fictionalisation' (i.e. the learning from data). This enables owner control of the data during learning. In practice, this disruptive approach ensures that the study is built around the patient, rather than a centralized trial site. It unleashes new ways to use data, transforming how scientists conduct R&D in the discovery and management of various diseases. This new patient-centred approach means data can be collected anywhere – at a hospital or research centre, or from the patient's home using telemedicine tools. This produces data that is far more representative of a patient's real-world activities during their participation in a clinical study. Building clinical trials around patients in their homes and in the community through remote visits and monitoring, enhances recruitment and increases convenience for participants. The implementation of this decentralised research approach is well accepted by patients, offering measurable benefits. It means fewer site visits, making trial participation less daunting. Today's remote advanced technologies have provided many opportunities for healthcare organizations to enhance the overall care experience, improve the health of populations and reduce per capita healthcare costs, globally. As a result, we have seen readmissions among congestive heart failure (CHF) patients in...
Deep learning is a form of machine learning with the potential to extract meaningful information from the mountains of data generated by healthcare companies. One of the major obstacles to embracing deep learning in the world of health arises from challenges around data sharing. As we all know, while there is no shortage of patient data, it may be dispersed across several sites. For example, data on a single patient could be found in their doctor's office, on hospital systems, in a clinical trial database, a patient registry, company files and held by an insurance company. Given privacy and data protection issues, working with all this data can be very challenging. However, it is not impossible. This is a problem that I believe can be solved through what is known as federated distributed learning procedure. That's quite a mouthful but let me explain. Federated distributed learning is defined as learning from data, without removing the data from the location where it is stored. The beauty of this, in my opinion, is that it offers a chance to unlock the potential of the data without moving it or jeopardising data protection. Collaborative data mining It also opens to door to great collaboration. Data collaborative innovation is when a group of actors from different data domains work together toward common goals. Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Our approach to advancing federated deep learning has been fueled by growth in the number of sources of data, including increased access to published research, electronic health records, the various "omics" fields and even social media. I am building federating deep learning models and algorithms that analyze molecular and medical imaging libraries, as well as patient profiles...
By unleashing the power of machine learning, we can better understand behaviour, empower patients to make smarter decisions – and save billions of euros . Unhealthy lifestyles are driving an explosion in chronic conditions, including obesity, diabetes and cardiovascular disease. By choosing to smoke, having an inconsistency in maintaining a healthy diet and opting out of exercising, we place ourselves at risk of ill-health. At the same time, some patients are neglecting to take their medicines as prescribed or are misusing antibiotics – with devastating consequences. Around twenty–one percent (21%) of US healthcare costs are attributable to the influence of human behaviour. For example, poor medication adherence alone costs the US more than $100 billion annually. Harvard and the World Economic Forum have estimated that non-communicable diseases result in economic losses for developing economies equivalent to four to five percent (4-5%) of their GDP per annum. A patient-centric approach to behaviour change promises not only to improve clinical outcomes, but to address the rising demand for health services. Better education and awareness can help individuals to make smarter choices. There are a range of interventions available, but the challenge is providing the right patient with the right behaviour change intervention at the right time. If We Can Predict, We Can Prevent Now we have new tools at our disposal, informed by research from psychology and behavioural economics, and powered by technological advances. As someone with a keen interest in behaviour change and the predictive power of analytics, I believe machine learning can help to make our health systems more sustainable. Artificial Intelligence (AI) allows us to evaluate how an individual makes lifestyle decisions and tailor behaviour change programmes to suit their needs. When considering an example of poor medication adherence, if we are aware of who is at risk and...
According to the latest estimates of the WHO, 422 million people suffer from diabetes worldwide, and the number is growing steadily. As someone who is passionate about using eHealth to solve the biggest challenges in modern healthcare, diabetes stands out as one of the defining problems of our era. Managing diabetes well is essential to the wellbeing of millions of people, to the sustainability of our health systems, and to the long-term durability of our economies. The scale of the problem is immense but technology can help us rise to the challenge. Cognitive Artificial Intelligence (AI), facilitated by analytical predictive-diagnostics and revolutionary medical devices are transforming the way healthcare is delivered and managed throughout the world. Or, in other words, today’s computers can use patient data from multiple sources, including genomic sequencing and sensors, to diagnose disease, inform treatment decisions, and predict outcomes. It is my objective to bring the AI revolution to diabetes. When it comes to diabetes care, Machine Learning and Artificial Intelligence can collect information from various devices to create personalized programmes that support medication adherence and blood glucose management. At my company, we have developed a Digital Connected Health Platform™ that works with all diabetes devices. Our goal is to facilitate the analysis of data so that we can help patients stay healthy, avoiding the severe complications that can accompany advanced or uncontrolled diabetes. The insights provided by systems such as ours allow physicians to consistently intervene with patients on a real-time basis, paving the way for a more dynamic kind of disease management. It enables the use of wearables, sensors, devices and home health monitoring systems to transmit data from a patient to their care providers. The system also delivers reminders to patients, prompting them to check their blood glucose levels, take their medication or...