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Clinical Laboratories and Pathology Groups

Hosted by Robert Michel
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Wall Street Journal Reports IBM May Sell Watson Health Due to Data Challenges and Unprofitability

Might this be a sign that AI platforms like Watson still cannot diagnose the wide range of patients’ conditions as accurately as a board-certified clinical pathologist?

Computer technology evolves so quickly, products often become obsolete before fulfilling their expected potential. Such, apparently, is the case with Watson, the genius artificial intelligence (AI) brainchild of International Business Machines Corp. (IBM) which was going to revolutionize how healthcare providers diagnose disease. In some areas of healthcare, such as analyzing MRIs and X-rays, AI has been a boon. But from a business perspective, Watson has failed to turn a profit for IBM, so it has to go.

In February, The Wall Street Journal (WSJ) reported that IBM is looking to sell its Watson Health unit because it is not profitable, despite bringing in $1 billion annually in revenue. The sale of Watson Health, the article states, would be aligned with IBM’s goal of streamlining the company and focusing its energies on cloud computing and other AI functions. Because one goal of the Watson project was to give physicians a tool to help them diagnose patients more accurately and faster, the problems that prevented Watson from achieving that goal should be of interest to pathologists and clinical laboratory managers, who daily are on the front lines of helping doctors diagnose the most challenging cases.

In a follow-up article, titled, “Potential IBM Watson Health Sale Puts Focus on Data Challenges,” the WSJ wrote, “… some experts found that it can be difficult to apply AI to treating complex medical conditions. Having access to data that represents patient populations broadly has been a challenge, experts told the Journal, and gaps in knowledge about complex diseases may not be fully captured in clinical databases.”

“I believe that we’re many years away from AI products that really positively impact clinical care for many patients,” Bob Kocher, Partner at Venrock, a venture-capital firm that invests in healthcare IT and related services, told the WSJ.

IBM Watson was promoted as a major resource to help improve medical care and support doctors in making more accurate diagnoses. However, in “IBM’s Retreat from Watson Highlights Broader AI Struggles in Health,” the WSJ reported that “IBM spent several billion dollars on acquisitions to build up Watson [Health] … a unit whose marquee product was supposed to help doctors diagnose and cure cancer … A decade later, reality has fallen short of that promise.”

In 2018, Dark Daily covered the beginnings of Watson’s struggles in “IBM’s Watson Not Living Up to Hype, Wall Street Journal and Other Media Report; ‘Dr. Watson’ Has Yet to Show It Can Improve Patient Outcomes or Accurately Diagnose Cancer,” and again in 2019 in “Artificial Intelligence Systems, Like IBM’s Watson, Continue to Underperform When Compared to Oncologists and Anatomic Pathologists.”

 previous Jeopardy champions Ken Jennings and Brad Rutter with Watson on the show in 2011
IBM initially created Watson (above center) to be an AI tool capable of a wide variety of applications, starting with answering questions. In January 2011, Watson made headlines when it defeated previous Jeopardy champions Ken Jennings (left) and Brad Rutter (right) on the popular television game show. After its triumph, IBM announced it would transition Watson for use in medical applications and promoted it as a major resource to help improve medical care and support healthcare professionals in making more precise diagnoses. The company called its new division IBM Watson Health and stated that massive data sets would be the key to accomplish its healthcare mission. That same year, The Dark Report had an IBM executive do a presentation about Watson Health at the Executive War College on Laboratory and Pathology Management. (Photo copyright: CBS News.)

Watson’s Successes and Failures in Healthcare

During the years following Watson’s Jeopardy win, Watson Health made some positive advances in the fields of healthcare data analytics, performance measurements, clinical trial recruitment, and healthcare information technology (HIT). 

However, Watson Health also experienced some high-profile failures as well. One such failure involved a collaboration with MD Anderson Cancer Center, established in 2013, to help the health systems’ oncologists develop new tools to benefit cancer patients. MD Anderson ended the relationship in 2018 after spending more than $60 million on the project, citing “multiple examples of unsafe and incorrect treatment recommendations,” made by the Watson supercomputer, Healthcare IT News reported.

Watson Health later readjusted the development and sales of its AI drug discovery tools and altered its marketing strategy amid reports of disappointing sales and skepticism surrounding machine learning for medical applications.

Underestimating the Challenge of AI in Healthcare

Since its inception, Watson Health has achieved substantial growth, mainly through a series of acquisitions. Those targeted acquisitions include:

  • Merge Healthcare, a healthcare imaging software company that was purchased for $1 billion in 2015,
  • Phytel, a health management software company that was purchased for an undisclosed amount in 2015,
  • Explorys, a healthcare analytics company that was purchased for an undisclosed amount in 2015, and
  • Truven Health Analytics, a provider of cloud-based healthcare data, analytics, and insights that was purchased for $2.6 billion in 2016.

“IBM’s Watson Health business came together as a result of several acquisitions,” said Paddy Padmanabhan, founder and CEO of Damo Consulting, a firm that provides digital transformation strategy and advisory services for healthcare organizations. “The decision to sell the business may also have to do with the performance of those units on top of the core Watson platform’s struggles,” he told Healthcare IT News.

It should be noted that these acquisitions involved companies that already had a product in the market which was generating revenue. So, the proposed sale of Watson Health includes not just the original Watson AI product, but the other businesses that IBM put into its Watson Health business division.

Padmanabhan noted that there are many challenges for AI in healthcare and that “historical data is at best a limited guide to the future when diagnosing and treating complex conditions.” He pointed to the failure with MD Anderson (in the use of Watson Health as a resource or tool for diagnosing cancer) was a setback for both IBM and the use of AI in healthcare. However, Padmanabhan is optimistic regarding the future use of AI in healthcare. 

“To use an oft-quoted analogy, AI’s performance in healthcare right now is more akin to that of the hedgehog than the fox. The hedgehog can solve for one problem at a time, especially when the problem follows familiar patterns discerned in narrow datasets,” he told Healthcare IT News. “The success stories in healthcare have been in specific areas such as sepsis and readmissions. Watson’s efforts to apply AI in areas such as cancer care may have underestimated the nuances of the challenge.”

Other experts agree that IBM was overly ambitious and overreached with Watson Health and ended up over-promising and under-delivering.

“IBM’s initial approach misfired due to how the solution AI was trained and developed,” Dan Olds, Principal Analyst with Gabriel Consulting Group, told EnterpriseAI. “It didn’t conform well to how doctors work in the real world and didn’t learn from its experiences with real doctors. It was primarily learning from synthetic cases, not real-life cases.” 

Was Watson Already Obsolete?

Another issue with Watson was that IBM’s marketing campaign may have exceeded the product’s design capabilities. When Watson was developed, it was built with AI and information technologies (IT) that were already outdated and behind the newest generation of those technologies, noted Tech Republic.

“There were genuine AI innovation triggers at Watson Health in natural language processing and generation, knowledge extraction and management, and similarity analytics,” Jeff Cribbs, Research Vice President at Gartner Research, told Tech Republic. “The hype got ahead of the engineering, as the hype cycle says it almost always will, and some of those struggles became apparent.”

Can Artificial Intelligence Fulfill its Potential in Healthcare?

The fact that IBM is contemplating the sale of Watson Health is another illustration of how difficult it can be to navigate the healthcare industry in the US. It is probable that someday AI could make healthcare diagnostics more accurate and reduce overall costs, however, data challenges still exist and more research and exploration will be needed for AI to fulfill its potential.

“Today’s AI systems are great in beating you at chess or Jeopardy,” Kumar Srinivas, Chief Technology Officer, Health Plans, at NTT DATA Services told Forbes. “But there are major challenges when addressing practical clinical issues that need a bit of explanation as to ‘why.’ Doctors aren’t going to defer to AI-decisions or respond clinically to a list of potential cancer cases if it’s generated from a black box.”

And perhaps that is the biggest challenge of all. For doctors to entrust their patients’ lives to a supercomputer, it better be as good as the hype. But can AI in healthcare ever accomplish that feat?

“AI can work incredibly well when it’s applied to specific use cases,” gastroenterologist Nirav R. Shah, MD, Chief Medical Officer at Sharecare, told Forbes. “With regards to cancer, we’re talking about a constellation of thousands of diseases, even if the focus is on one type of cancer. What we call ‘breast cancer,’ for example, can be caused by many different underlying genetic mutations and shouldn’t really be lumped together under one heading. AI can work well when there is uniformity and large data sets around a simple correlation or association. By having many data points around a single question, neural networks can ‘learn.’ With cancer, we’re breaking several of these principles.”

So, in deciding to divest itself of Watson Health, IBM may simply be as prescient now as it was when it first embraced the concept of AI in healthcare. The tech giant may foresee that AI will likely never replace the human mind of a trained healthcare diagnostician.

If this proves true—at least for several more years—then board-certified clinical pathologists can continue to justifiably refer to themselves as “the doctor’s doctor” because of their skills in diagnosing difficult-to-diagnose patients, and because of their knowledge of which clinical laboratory tests to order and how to interpret those test results.

—JP Schlingman

Related Information:

IBM Explores Sale of IBM Watson Health

IBM’s Retreat from Watson Highlights Broader AI Struggles in Health

Potential IBM Watson Health Sale Puts Focus on Data Challenges

IBM Sale of Watson Health Could Enable Renewed Focus on Cloud Growth

IBM Reportedly Looking to Sell its Unprofitable Watson Health Business

IBM Watson: Why Is Healthcare AI So Tough?

Hoping to Become Heavyweights in Healthcare Big Data, IBM Watson Health Teams Up with Siemens Radiology and In Vitro Diagnostics Businesses

IBM Watson Health to Acquire Truven Health Analytics and Its Millions of Patient Records for $2.6 Billion

Artificial Intelligence Systems, Like IBM’s Watson, Continue to Underperform When Compared to Oncologists and Anatomic Pathologists

IBM’s Watson Not Living Up to Hype, Wall Street Journal and Other Media Report; ‘Dr. Watson’ Has Yet to Show It Can Improve Patient Outcomes or Accurately Diagnose Cancer

Damo Consulting Survey Predicts Future Health Network Spending Will Primarily be on Improving EHRs; Could be Positive Development for Medical Laboratories

Survey shows healthcare providers plan to wait for AI and digital health technologies to mature before making major investments in them

Clinical laboratories must develop strategies for connecting to their client doctors’ electronic health record (EHR) systems. Thus, a new survey that predicts most healthcare networks will continue to focus health information technology (HIT) spending on improving their EHRs—rather than investing in artificial intelligence (AI) and digital healthcare—provides valuable insights for medical laboratory managers and stakeholders tasked with implementing and maintaining interfaces to these systems.

According to Damo Consulting’s 2019 Healthcare IT Demand Survey, when it comes to spending money on information technology (IT), healthcare executives believe AI and digital healthcare technologies—though promising—need more development.

Damo’s report notes that 71% of healthcare providers surveyed expect their IT budgets to grow by 20% in 2019. However, much of that growth will be allocated to improving EHR functionality, Healthcare Purchasing News reported in its analysis of Damo survey data.

As healthcare executives plan upgrades to their EHRs, hospital-based medical laboratories will need to take steps to ensure interoperability, while avoiding disruption to lab workflow during transition.

The survey also noted that some providers that are considering investing in AI and digital health technology are struggling to understand the market, the news release states.

“Digital and AI are emerging as critical areas for technology spend among healthcare enterprises in 2019. However, healthcare executives are realistic about their technology needs versus their need to improve care delivery. They find the currently available digital health solutions in the market are not very mature,” explained Paddy Padmanabhan (above), Chief Executive Officer of Damo Consulting, in a news release. (Photo copyright: The Authors Guild.)

Providers More Positive Than Vendors on IT Spend

Damo Consulting is a Chicago-area based healthcare and digital advisory firm. In November 2018, Damo surveyed 64 healthcare executives (40 technology and service leaders, and 24 healthcare enterprise executives).  Interestingly, healthcare providers were more positive than the technology developers on IT spending plans, reported, which detailed the following survey findings:

  • 79% of healthcare executives anticipate high growth in IT spending in 2019, but only 60% of tech company representatives believe that is so.
  • 75% of healthcare executives and 80% of vendor representatives say change in healthcare IT makes buying decisions harder.
  • 71% of healthcare executives and 55% of vendors say federal government policies help IT spending.
  • 50% of healthcare executives associate immaturity with digital solution offerings.
  • 42% of healthcare providers say they lack resources to launch digital.  

“While information technology vendors are aggressively marketing ‘digital’ and ‘AI,’ healthcare executives note that the currently available solutions in these areas are not very mature. These executives are confused by the buzz around ‘AI’ and ‘digital,’ the changing landscape of who is playing what role, and the blurred lines of capabilities and competition,” noted Padmanabhan in the survey report.

The survey also notes that “Health systems are firmly committed to their EHR vendors. Despite the many shortcomings, EHR systems appear to be the primary choice for digital initiatives among health systems at this stage.”

Some Healthcare Providers Starting to Use AI

Even as EHRs receive the lion’s share of healthcare IT spends, some providers are devoting significant resources to AI-related projects and processes.

For example, clinical pathologists may be intrigued by work being conducted at Cleveland Clinic’s Center for Clinical Artificial Intelligence (CCAI), launched in March. The CCAI is using AI and machine learning in pathology, genetics, and cancer research, with the ultimate goal of improving patient outcomes, reported Becker’s Hospital Review.

“We’re not in it because AI is cool, but because we believe it can advance medical research and collaboration between medicine and industry—with a focus on the patient,” Aziz Nazha, MD, Clinical Hematology and Oncology Specialist and Director of the CCAI, stated in an article posted by the American Medical Association (AMA).

AI Predictions Lower Readmissions and Improve Outcomes

Cleveland Clinic’s CCAI reportedly has gathered data from 1.6 million patients, which it uses to predict length-of-stays and reduce inappropriate readmissions. “But a prediction itself is insufficient,” Nazha told the AMA. “If we can intervene, we can change the prognosis and make things better.”

The CCAI’s ultimate goal is to use predictive models to “develop a new generation of physician-data scientists and medical researchers.” Toward that end, Nazha notes how his team used AI to develop genomic biomarkers that identify whether a certain chemotherapy drug—azacitidine (aka, azacytidine and marketed as Vidaza)—will work for specific patients. This is a key goal of precision medicine

CCAI also created an AI prediction model that outperforms existing prognosis scoring systems for patients with Myelodysplastic syndromes (MDS), a form of cancer in bone marrow.

Partners HealthCare (founded by Brigham and Women’s Hospital and Massachusetts General Hospital) recently announced formation of the Center for Clinical Data Science to make AI and machine learning a standard tool for researchers and clinicians, according to a news release.

Meanwhile, at Johns Hopkins Hospital, AI applications track availability of beds and more. The Judy Reitz Capacity Command Center, built in collaboration with GE Healthcare Partners, is a 5,200 square feet center outfitted with AI apps and staff to transfer patients and help smooth coordination of services, according to a news release.

Forbes described the Reitz command center as a “cognitive hospital” and reports that it has essentially enabled Johns Hopkins to expand its capacity by 16 beds without undergoing bricks-and-mortar-style construction.

In short, medical laboratory leaders may want to interact with IT colleagues to ensure uninterrupted workflows as EHR functionality evolves. Furthermore, AI developments suggest opportunities for clinical laboratories to leverage patient data and assist in improving the diagnostic accuracy of providers in ways that improve patient care.

—Donna Marie Pocius

Related Information:

2019 Healthcare IT Demand Survey

Digital and AI are Top Priorities in 2019 as EHR Investments Continue to Dominate

Healthcare IT Spending Priorities Include Big Data Analytics, AI

Healthcare IT Demand Survey: Digital and AI are Top Priorities in 2019 as EHR Systems Continue to Dominate IT Spend

Cleveland Clinic Launches Clinical AI Center: 4 Things to Know

Cleveland Clinic Ready to Push AI Concepts to Clinical Practice

Cleveland Clinic Creating Center for AI in Healthcare

Partners HealthCare Embraces Democratization of AI to Accelerate Innovation in Medicine

Johns Hopkins Hospital Launches Capacity Command Center to Enhance Hospital Operations

The Hospital Will See You Now