Artificial Intelligence in Healthcare: Promising Future, But Barriers Remain | Market Insights™
December 12, 2018
The future looks promising for AI-based automation…but barriers that restrict its universal acceptance remain
The future looks promising for AI-based automation…but barriers that restrict its universal acceptance remain
While the explosion of data, cognitive overload, endless documentation, and user burnout in the healthcare industry are driving demand for AI, it has implications far beyond technology: the majority of AI decisions impact business processes, customer experience, and cost – key concerns for chief executives
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Healthcare organizations are pouring billions into embedded AI across the value chain, driving an estimated quadrupling of AI investments in the next three years, according to Everest Group. The firm predicts that healthcare AI investments will grow from US$1.5 billion in 2017 to exceed US$6 billion by 2020, representing a compound annual growth rate of 34 percent.
While AI is a relatively new area in the healthcare space and its adoption is in the nascent stage, digitalization of healthcare is accelerating healthcare enterprises’ interest in AI. AI has the potential to transform healthcare processes and dramatically reduce costs and improve efficiencies.
For example, healthcare payers are leveraging AI for product development, policy servicing, network management and claims management. Examples include:
Currently, the area where payers are adopting AI to the greatest extent is in care management.
Likewise, the highest adoption of AI by healthcare providers is for care and case management. Providers also are employing AI tools to:
These findings and more are discussed in Everest Group’s recently published report, “Dr. Robot Will See You Now: Unpacking the State of Artificial Intelligence in Healthcare – 2019.” The firm has analyzed the market from the vantage point of 27 leading healthcare enterprises and closely examined the distinctive attributes of the leaders, who are far ahead of the other industry participants in terms of AI capability maturity. The report identifies best practices, illustrates the impact generated, and offers proposed a roadmap for market stakeholders.
***Download a complimentary abstract of this report here. ***
“While healthcare enterprises are still in the nascent stages of AI adoption, the scale of opportunity in AI demands C-level vision,” said Abhishek Singh, vice president of Information Technology Services at Everest Group. “AI presents unique opportunities for healthcare enterprises – allowing them to improve customer experience, achieve operational efficiency, enhance employee productivity, cut costs, accelerate speed-to-market, and develop more personalized products. In the case of the leading healthcare organizations, their CEOs and CIOs are acknowledging the transformative power of AI, rapidly building appropriate AI strategies, and building a robust, overarching business plan to harness its benefits.”
Additional key findings:
Experts and enterprises around the world have talked a lot about the disturbing concept of AI being used to build and test AI systems, and challenge decisions made by those systems. I wrote a blog on this topic a while back.
Disquieting as it is, our AI research makes it clear that AI for AI with increasingly minimal human intervention has moved from concept to reality.
Here are four key reasons this is the case.
Before AI emerged, organizations focused on production support to optimize the environment after the software was released. But those days are going to be over soon, if they aren’t already. The reality is that today’s increasingly dynamic software and Agile/DevOps-oriented environments require tremendous automation and feedback loops from the trenches. Developers and operations teams simply cannot capture and analyze the enormous volume of needed insights. They must leverage AI intelligence to do so, and to enable an ongoing interaction channel with the operating environment.
Unlike traditional software with defined boundary conditions, AI systems have very different edge scenarios. And AI systems need to negate/test all edge scenarios to make sense of their environment. But, as there can be millions of permutations and combinations, it’s extremely difficult to manually assure or use traditional automation to test AI systems for data biases and outcomes. Uncomfortable as it may be, AI-layered systems must be used to test AI systems.
The L0-L5 autonomous vehicle framework proposed by SAE International is becoming an inspiration for technology developers. Not surprisingly, they want to leverage AI to build intelligent applications that can have autonomous environments and release. Some are even pushing AI to build the software itself. While this is still in its infancy, our research suggests that developers’ productivity will improve by 40 percent if AI systems are meaningfully leveraged to build software.
Although enterprises used to take pride in building boundary walls to protect their IP and using best of breed tools, open source changed all that. Most enterprises realize that their developers cannot build an AI system on their own, and now rely on open source repositories. And our research shows that 20-30 percent of an AI system can be developed by leveraging already available code. However, scanning these repositories and zeroing in on the needed pieces of code aren’t tasks for the faint hearted given their massive size. Indeed, even the smartest developers need help from an AI intelligent system.
There’s little question that using AI systems to build, test, and fight AI systems is disconcerting. That’s one of the key reasons that enterprises that have already adopted AI systems haven’t yet adopted AI to build, test, and secure them. But it’s an inevitability that’s already knocking at their doors. And they will quickly realize that reliance on a “human in the loop” model, though well intentioned, has severe limitations not only around the cost of governance, but also around the sheer intelligence, bandwidth, and foresight required by humans to govern AI systems.
Rather than debating its merit or becoming overwhelmed with the associated risks, enterprises need to build a governing framework for this new reality. They must work closely with technology vendors, cloud providers, and AI companies to ensure their business does not suffer in this new, albeit uncomfortable, environment.
Has your enterprise started leveraging AI to build, test, or fight AI systems? If so, please share your experiences with me at [email protected].
Artificial Intelligence (AI) has been the stuff of science fiction for decades and more recently has become a rampant buzzword in business media headlines. But CIOs need to know if there are realities amid the hype. Is AI actually delivering value and not just Proofs of Concept? In other words, are the business bona fides showing up yet?
Wednesday, September 12, 2018 | 11 a.m. EST | Hosted by GAVS Technologies with featured speaker, Ashwin Venkatesan, Practice Director, Everest Group
AIOps is poised to become the next big thing in IT management. By maintaining the fidelity of data and generating insights, it has ability to influence business decisions. In the era of digital transformation, the adoption of AIOps is imperative for businesses with dynamic and complex IT environments.
Join GAVS for a webinar on “Artificial Intelligence-led Alert Correlation – Enabling the Journey towards Zero Incidents” featuring expert guest speaker Ashwin Venkatesan.
Ashwin Venkatesan, Practice Director, Everest Group
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