The health care sector is in need of a revolution, Manoharan believes. “Healthcare systems and providers are under huge pressure, now more than ever with the COVID-19 pandemic,” she says, to which she adds, “We are dealing with global staff shortages, aging populations, and lifestyle-related chronic diseases.”
At the same time, digital transformation is driving exponential growth of health data. With digitization and connectivity becoming ubiquitous, we’re more capable than ever to collect information about individual and population health. But putting that data to use is a huge challenge.
“I remember clinicians telling me, ‘Just because we have a lot of data, don’t overload me with that, I need relevant and precise information, at the point of decision making,’” says Manoharan.
Artificial intelligence provides unprecedented opportunities to put all this data to good use and help physicians, clinicians, health care workers, and patients to make more informed decisions.
“AI enables our devices, systems, software, and services to be context-aware, precise, personalized, predictive, and pro-active,” Manoharan says. “By turning data into actionable insights for precision health, we will enable precise and personalized care across the health continuum.”
But in addition to improving the precision of health care, AI can make the entire experience of medical care more human. With the help of AI, doctors will spend less time poring over data and medical records and will have more time to spend with patients.
“AI can help free clinicians from more mundane tasks, so that they are able to focus on what they do best and engage with patients in a more precise and personalized way, with the potential to increase value over time,” Manoharan says.
While a lot of the discussion surrounding AI is about software replacing humans, in health care, AI must be considered as an augmenting factor.
“A lot of the decisions that clinicians make on a daily basis are incredibly complex and require more than an AI- or data-driven approach alone. It’s the augmented intelligence and support for decision making at the right time that helps to make a difference to effective patient management,” Manoharan says, adding that taking a human-centered approach to AI development is important. “I strongly believe that clinicians and AI have unique strengths that complement and augment each other, not replace each other.”
AI systems must also be supported by tools that can integrate them into different IT and data systems. “To get meaningful insights from data with AI, interoperability is really key,” Manoharan says.
The interoperability and integration challenge is one of the key factors that separates academic research from practical applications of AI. Research usually revolves around developing AI models that work on carefully curated sets of health data. In real life, however, data is messy, fragmented, and hard to access. In many cases, the lack of a proper data infrastructure is the main barrier in the way of applying AI to existing applications.
“Today’s healthcare data is often difficult to exchange, analyze, and interpret. Many point solutions with AI already exist today but the healthcare supplier environment is highly fragmented. Healthcare providers are in need of integrated offerings combining the best available HealthTech innovations from different suppliers into one seamless and complete patient-centric and disease-focused solution,” Manoharan says.
Solving this problem will need a concerted effort between tech vendors, hospitals, and health care organizations. “We need to connect data from equipment from multiple vendors, linking to hospital IT systems, and we need data standards that allow you to make sense of data in a uniform way, using one data language; what we call semantic interoperability,” Manoharan, adding, “Connected data lakes are essential here.”
Data lakes are large repositories that do not impose schematic restrictions on the data stored in them. Data can be stored in raw formats such as text files, images, and videos, as well as well-structured spreadsheets. The data can then be mined and queried with data science and machine learning tools.