As healthcare businesses transform for a post-COVID-19 era, they are embracing digital technologies as essential for outmaneuvering the uncertainty faced by businesses and as building blocks for driving more innovation. Maturing digital technologies such as social, mobile, analytics and cloud (SMAC); emerging technologies such as distributed ledger, artificial intelligence, extended reality and quantum computing (DARQ); and scientific advancements (e.g., CRISPR, materials science) are helping to make innovative breakthroughs a reality.
These technologies are also proving essential in supporting COVID-19 triage efforts. For example, hospitals in China are using artificial intelligence (AI) to scan lungs, which is reducing the burden on healthcare providers and enabling earlier intervention. Hospitals in the United States are also using AI to intercept individuals with COVID-19 symptoms from visiting patients in the hospital.
Because AI and machine learning (ML) definitions can often be confused, it may be best to start by defining our terms.
AI can be defined as a collection of different technologies that can be brought together to enable machines to act with what appears to be human-like levels of intelligence. AI provides the ability for technology to sense, comprehend, act and learn in a way that mimics human intelligence.
ML can be viewed as a subset of AI that provides software, machines and robots the ability to learn without static program instructions.
ML is currently being used across the health industry to generate personalized product recommendations to consumers, identify the root cause of quality problems and fix them, detect healthcare claims fraud, and discover and recommend treatment options to physicians. ML-enabled processes rely on software, systems, robots or other machines which use ML algorithms.
For the healthcare industry, AI and ML represent a set of inter-related technologies that allow machines to perform and help with both administrative and clinical healthcare functions. Unlike legacy technologies that are algorithm-based tools that complement a human, health-focused AI and ML today can truly augment human activity.
The full potential of AI is moving beyond mere automation of simple tasks into a powerful tool enabling collaboration between humans and machines. AI is presenting an opportunity to revolutionize healthcare jobs for the better.
Recent research indicates that in order to maximize the potential of AI and to be digital leaders, healthcare organizations must re-imagine and re-invent their processes and create self-adapting, self-optimizing “living processes” that use ML algorithms and real-time data to continuously improve.
In fact, there’s consensus among healthcare organizations hat ML-enabled processes help achieve previously hidden or unobtainable value, and that these processes are finding solutions to previously unsolved business problems.
Despite these key findings, additional research surprisingly finds that only 39% of healthcare organizations report that they have inclusive design or human-centric design principles in place to support human-machine collaboration. Machines themselves will become agents of process change, unlocking new roles and new ways for humans and machines to work together.
In order to tap into the unique strengths of AI, healthcare businesses will need to rely on their people’s talent and ability to steward, direct, and refine the technology. Advances in natural language processing and computer vision can help machines and people collaborate and understand one another and their surroundings more effectively. It will be vital to prioritize “explainability” to help organizations ensure that people understand AI.
Powerful AI capabilities are already delivering profound results across other industries such as retail and automotive. Healthcare organizations now have an opportunity to integrate the new skills needed to enable fluid interactions between human and machines and adapt to the workforce models needed to support these new forms of collaboration.
By embracing the growing adoption of AI, healthcare organizations will soon see the potential benefits and value of AI — such as organizational and workflow improvements that can unleash improvements in cost, quality and access. Growth in the AI health market is expected to reach $6.6 billion by 2021 — that’s a compound annual growth rate of 40%. In just the next couple of years, the health AI market will grow more than 10 times.
AI generally, and ML specifically, gives us technology that can finally perform specialized nonroutine tasks as it learns for itself without explicit human programing shifting nonclinical judgment tasks away from healthcare enterprise workers.
What will be key to the success of healthcare organizations leveraging AI and ML across every process, piece of data and worker? When AI and ML are effectively added to the operational picture, we will see healthcare systems where machines will take on simple, repetitive tasks so that humans can collaborate on a larger scale and work at a higher cognitive level. AI and ML can foster a powerful combination of strategy, technology and the future of work that will improve both labor productivity and patient care.
Brian Kalis is a managing director of digital health and innovation for Accenture’s health business.
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