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artificial intelligence

Three Digital Healthcare Takeaways from HIMSS 2019 | Blog

By | Blog, Healthcare & Life Sciences

I experienced three pleasant surprises at last week’s Healthcare Information and Management System Society (HIMSS) conference. They were all about a perfect storm that is building to correct all that has been wrong in the digital healthcare space all these years.

Healthcare Companies are Exploring Cures for Their #DigitalHeadache

Payers and providers alike are growing increasingly disillusioned with the outcomes of their digital programs. In fact, 78 percent of the healthcare leaders we surveyed in late 2018 indicated some sort of failure with their digital initiatives, whether big or small. The good news here is that most forward-thinking leaders are going back to the drawing board to redefine their digital strategy. Anthem, Intermountain Healthcare, and New York Presbyterian are great examples of organizations that have taken up the cudgels to fix digital healthcare where it fails – organization and culture.

There’s Increased Focus on “Enabling” the Patient Experience

To make the “patient experience” successful, enterprise leaders are taking a step back and focusing their attention on creating experiences for their workforce, clinicians, and partners (e.g., physician group, CMS, government agencies.) Don’t get me wrong, patients still need to be at the center of our universe. However, the personas that enable and deliver experience for patients need a fix first.

Innovation is Coming from Unexpected Sources

It was heartening to see the likes of Amazon, Google, Microsoft, and Salesforce steal the march from the big boys in the healthcare tech space – i.e., Cerner and EPIC – in asserting themselves as the technology visionaries in healthcare. Their focus on healthcare microservices is a relief for healthcare executives trying to navigate the “all or nothing” approach of the EMRs.

There is one player that seems keen on reinventing itself: Optum. Through a nimble product and services strategy, Optum is touching upon on all the hot buttons – MLR, analytics, PBM, and claims. Optum is the specialist vendor to watch out for when it comes to healthcare.

Last, but not least, what really took the cake were the innovative and exciting POCs related to clinical AI and visualization that Israel and Ireland – yes, the countries – showcased in their booths. These were some of the most fully baked solutions that I have seen in my 10 years attending HIMSS.

Hence, it’s with good reason that I left fairly impressed with the developing ecosystem knocking on the doors of healthcare organizations that are hungry for outcomes.

I will sign off by sharing an illustration from our recent study that analyzed the investments 27 of the leading healthcare payers and providers have made in artificial intelligence (AI), a key marker in the world of digital healthcare. This study objectively analyzed these investments from the perspective of ROI achieved.

Assessing 27 healthcare players (payers and providers) on their Artificial Intelligence investments

As you can see, there is a wide variance even within such a small sample set of healthcare organizations. FOMO (Fear Of Missing Out) pushed a lot of organizations to invest in the flashy new toy called AI. However, not all of them embarked on their investment journey by first enabling the core components of capability.

The difference between the best and the rest in healthcare is simply this: the ones to get the best ROI – those on the top right – are taking their journey through step functions that enable not only technology but also an organizational culture of innovation.

Please contact me at [email protected] if you’d like to hear more about my take-aways from the HIMMS conference or our study, named “Dr. Robot Will See You Now: Unpacking the State of Artificial Intelligence in Healthcare – 2019.”

 

Investments in Healthcare AI Will Quadruple by 2020, According to Everest Group | Press Release

By | Press Releases

New research predicts US$6 billion investment will drive innovations in patient identity verification, opioid abuse detection and individually tailored healthcare.

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:

  • Use of fingerprints, eye texture, voice, hand patterns and facial recognition to reduce the time taken for customer verification
  • Leveraging of machine learning with integrated claims data and analytics to detect opioid use patterns that suggest misuse
  • AI-powered wearable devices and mobile applications to help customers with personalized advice
  • Chatbots and virtual assistants to predict the right answer to standard customer inquiries and assist customers in navigating through the insurance plan selection process.

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:

  • collaborate more effectively with patients
  • reduce the time required for aggregating, storing, and analyzing patients’ data
  • streamline workflows
  • monitor patients remotely
  • detect diseases faster and more accurately
  • come up with better treatments.

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:

  1. Nearly two-thirds of spending on AI in healthcare is driven by North America. The North American market is also expected to be the fastest growing during the next five years, driven by regulatory mandates for use of electronic health records, increasing focus on precision medicine and a strong presence of service providers engaged in developing AI solutions for healthcare.
  2. Around 75 percent of all AI initiatives in healthcare are still driven by large enterprises as most mid- and small-sized firms are taking a wait-and-see approach.
  3. With a boom in enterprise AI, talent scarcity has become one of the biggest leadership challenges in implementing and evolving AI capabilities.
  4. Application of machine learning (ML) for structured data and natural language processing (NLP) for unstructured information have become mainstream in the healthcare industry.
  5. Cognitive technologies are expected to play an important part in health plans’ technology strategies going forward. Also, providers are looking to increasingly leverage deep learning to explore more complex, non-linear patterns in data, such as that found in research papers, doctors’ notes, textbooks, clinical reports, health histories, X-rays and CT and MRI scans.

Using AI to Build, Test, and Fight AI: It’s Disturbing BUT Essential | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog

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.

Software is Becoming Non-deterministic and Intelligent

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.

Testing AI Biases and Outcomes is not Easy

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 Autonomous Vehicle Framework is Being Mirrored in Technology 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.

The Open Source Ecosystem is Becoming Indispensable

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 without the Hype: The Real Role of AI in Business Today | In the News

By | In The News

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?

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Also available online at Applications Europe Magazine