BIG DATA in HEALTH CARE
By Dr Gidi Stein, Physician, Researcher and serial entrepreneur
Big Data. Machine Learning. AI. All popular buzzwords focused on the same issue: Technology today allows us to access a tremendous amount of information that, if analyzed correctly, can provide us with insights and direction that will change the world.
These technologies have already impacted our lives in areas such as credit card fraud protection, autonomous driving and cybersecurity. Healthcare, however, has been far behind in adopting AI and incorporating it into the clinical process. Recently, the healthcare industry has begun to catch up and there are at least three areas of health where AI may have considerable impact:
Enabling physicians to have more face time with their patients
Today, physicians are faced with mounting dumps of clinical data per patient that they need to browse through just to understand the true clinical picture and to identify what may be hidden. Moreover, physicians’ time is wasted on repetitive tedious administrative tasks and workflow inefficiencies, which occupy the precious time physicians need to have with their patients. AI may play a significant role in facilitating high quality personalized decision support and risk assessment at the point of care, coupled with automation of workflows and administrative tasks.
Imaging diagnostics done better, faster and with less variability in quality
The exponential growth in the volume of complex imaging studies (due to availability and low cost of technology), along with a way-too-slow pace of growth in well-trained imaging specialists, leads to increased bottle-necks mainly in the areas of radiology, pathology, dermatology and ophthalmology. In developing countries, the problem is more severe, as the imaging technologies are available, but skilled imaging specialists are very hard to find. AI can play a significant role in solving these bottle-neck challenges by implementing large scale automation of the imaging diagnostic process and prioritizing for review imaging with high probability of clinical findings.
Reducing organizational efficiency for better outcomes
Organizational inefficiencies in processes and resource allocation results in increased morbidity, mortality and preventable wasteful cost. Identifying patients at risk of deterioration in the different wards that may need ICU or increased care, large-scale prioritization of tasks, diagnostics and physician attention are just a few examples of the challenges faced daily. AI can address these challenges on a large scale, driving healthcare organizations to be more efficient leading to better patient outcomes.
Consumer and caregiver empowerment through Big Data
Patients and caregivers are looking for solutions that will help them take control and better manage their health and the health of their loved ones, identify risks and help them make better healthcare-related decisions. AI, powered by large-scale clinical records and sensor data, can play a significant role in providing personalized decision support and health management for the consumer.
We are only at the beginning, and innovation in these areas is already growing and spreading worldwide. There are many unknowns, business models are sometimes shaky, technologies are mostly not robust and the interplay between the providers and technology is still to be debated. However, one thing is clear: the revolution is here, and AI will take center stage in the way healthcare will be delivered in the upcoming years.
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