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AI

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.”

 

Stop Trivializing AI: It is not just Automation | Blog

By | Automation/RPA/AI, Blog

AI is certainly being used to attempt to solve many of the world’s big problems, such as health treatment, societal security, and the water shortage crisis. But Everest Group research suggests that 53 percent of enterprises do not – or are not able to – differentiate between AI and intelligent automation and what they can do to help them compete and grow. This trivialization of AI is both eye opening and frustrating.

While it’s true that automation of back-office services is one strong case for AI adoption, there are many more that can deliver considerable value to enterprises. Examples we’ve researched and written about in the past year include intelligent architecture, front-to-back office transformation, talent strategies, and AI in SDLC.

It’s been said that “audacious goals create progress.”  So, how should enterprises think more creatively and aspirationally in their leverage of artificial intelligence to extract real value? There are three ingredients to success.

Think beyond Efficiency

Enterprises are experimenting with AI-driven IT infrastructure, applications, and business services to enhance the operational efficiency of their internal operations. We have extensively written about how AI-led automation can drive 10-20 percent more savings over traditional models. But enterprises have far more to gain by experimenting with AI to fundamentally transform the entire landscape, including product design customer experience, employee engagement, and stakeholder management.

Think beyond CX

Most enterprises are confusing putting bots in their contact center with AI adoption. We discussed in an earlier post that enterprises need to get over  their CX fixation and drive an ecosystem experience with AI at the core. Our research suggests that while 63 percent of enterprises rank CX improvement as one of their top three expectations of artificial intelligence, only 43 percent put newer business model among their top three. We believe there are two factors behind this discouraging lack of aspiration: market hype-driven reality checks (which are largely untrue), and enterprises’ inability to truly grasp the power of AI.

Think beyond Bots

While seemingly paradoxical, humans must be central to any AI adoption strategy. However, most enterprises believe bot adoption is core to their AI journey. Even within the “botsphere,” they narrow it down to Robotic Process Automation (RPA), which is just one small part of the broader ecosystem. At the same time, our research shows that 65 percent of enterprises believe that AI will not materially impact their employment numbers, and that bodes well for their realization of the importance of human involvement.

And, what do enterprises need to do?

Be Patient

Our research suggests that 84 percent of enterprises believe AI initiatives have a long gestation period, which undoubtedly leads to the business losing interest. However, given the nature of these technologies, enterprises need to become more patient in their ROI expectation from such initiatives. Though agility to drive quick business impact is welcome, a short-sighted approach may straight jacket initiatives to the lowest hanging fruits, where immediate ROI outweighs longer term business transformation.

Have Dedicated AI Teams

Enterprises need AI champions within each working unit, in appropriate size alignment. These champions should be tech savvy people who understand where the AI market is going, and are able to contextualize the impact to their business. This team needs to have evangelization experts in who can talk the language of technology as well as business.

Hold Technology Partners Accountable

Our research suggests that ~80 percent of enterprises believe their service partners lack the capabilities to truly leverage artificial intelligence for transformation. Most of the companies complained about the disconnect between the rapid development of AI technologies and the slowness of their service partners to adopt. Indeed, most of these partners sit on the fence waiting for the technologies to mature and become enterprise-grade. And by then, it is too late to help their clients gain first-mover advantage.

As AI technologies span their wings across different facets of our lives, enterprises will have to become more aspirational and demanding. They need to ask their service partners tough questions around AI initiatives. These questions need to go far beyond leveraging AI for automating mundane human tasks, and should focus on fundamentally transforming the business and even creating newer business models.

Let’s create audacious goals for artificial intelligence in enterprises.

What has been your experience adopting AI beyond mundane automation? Please share with me at [email protected].

AI for Experience: From Customers to Stakeholders | Sherpas in Blue Shirts

By | Automation/RPA/AI, Blog, Customer Experience

Everest Group’s digital services research indicates that 89 percent of enterprises consider customer experience (CX) to be their prime digital adoption driver. But we believe the digital experience needs to address all stakeholders an enterprise touches, not just its customers. We touched on this topic in our Digital Services – Annual Report 2018, which focuses on digital operating models.

Indeed, SAP’s recent acquisition of Qualtrics and LinkedIn’s acquisition of Glint indicates the growing importance of managing not only CX, but also the digital experience of employees, partners, and the society at large.

AI Will Usher in the New Era of the Digital Experience Economy

Given the deluge of data from all these stakeholders and the number of parameters that must be addressed to deliver a superior experience, AI will have to be the core engine powering this digital experience economy. It will allow enterprises to build engaging ecosystems that evolve, learn, implement continuous feedback, and make real time decisions.

 

AI’s Potential in Transforming CX is Vast

Today, most enterprises narrowly view the role of AI in CX as implementing chatbots for customer query resolution or building ML algorithms on top of existing applications to enable a basic level of intelligence. However, AI can be leveraged to deliver very powerful experiences including: predictive analytics to pre-empt behaviors; virtual agents that can respond to emotions; advanced conversational systems to drive human-like interactions with machines; and even to deliver completely new experiences by using AI in conjunction with other technologies such as AR/VR, IoT, etc.

Digital natives are already demonstrating these capabilities. Netflix delivers hyper personalization by providing seemingly as many versions as its number of users. Amazon Go retail stores use AI, computer vision, and cameras to deliver a checkout free experience. And the start-up ecosystem is rampant with examples of cutting-edge innovations. For instance, HyperSurfaces is designing next-gen user experiences by using AI to transform any object to user interfaces.

But focusing just on the customer experience is missing the point, and the opportunity.

 AI in the Employee Experience

AI can, and should, play a central role in reimagining the employee journey to promote engagement, productivity, and safety. For example, software company Workday analyzes 60 data points to predict attrition risk. Humanyze enables enterprises to ascertain if a particular office layout supports teamwork. If meticulously designed and tested, AI algorithms can assist in employee hiring and performance management. With video analytics and advanced algorithms, AI systems can ensure worker safety; combined with automation, they can free up humans to work on more strategic tasks.

AI in the Supplier and Partner Experience

Enterprises also need to include suppliers and other partners in their experience management strategy. Using predictive analytics to automate inventory replenishment, gauge supplier performance, and build channels for two-way feedback are just a few examples. AI will play a key role in designing systems that not only pre-empt behaviors/performance but also ensure automated course correction.

AI in the Society Experience

Last but not least, enterprises cannot consider themselves islands in the environment in which they operate. They must realize that experience is as much about reality as about perception. Someone who has never engaged with an enterprise may have an “experience” perception about that organization. Some organizations’ use of AI is clearly for “social good.” Think smart cities, health monitoring, and disaster management systems. But even organizations that don’t have products or services that are “good” for society must view the general public as an important stakeholder. For example, employees at Google vetoed the company’s decision to engage with the Pentagon for use of ML algorithms for military applications. Similarly, employees at Microsoft raised concerns over the company’s involvement with Immigration and Customs Enforcement in the U.S.  AI can be leveraged to predict any such moves by pre-empting the impact that a company’s initiatives might have on society at large.

Moving from Customer to Stakeholder Experience

As organizations make the transition to an AI-enabled stakeholder experience, they must bear in mind that a piecemeal approach will not work. This futuristic vision will have to be supported by an enterprise-wide commitment, rigorous and meticulous preparation of data, ongoing monitoring of algorithms, and significant investment. They will have to cover a lot of ground in reimagining the application and infrastructure architecture to make this vision a distinctive reality.

What has been your experience leveraging AI for different stakeholders’ experiences? Please share with us at [email protected] and [email protected].

 

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.