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Healthcare Analytics: A pragmatic approach

Taming the challenges in healthcare with artificial intelligence.

Rajib GhoshBy Rajib Ghosh

At last there is some respite for those of us in the healthcare analytics and technology business. The neverending uncertainty and word storms emanating from the nation’s capital is now silent. Perhaps temporarily but what a relief. We now can make strategies and plan for execution for the next six months to a year. In the meantime, if you would like to know about how the Affordable Care Act touched many lives for the better read this article in the Huffington Post. A Senate healthcare bill is now dead, and the hope of fixing what is not working in healthcare in a pragmatic bipartisan way is now rising. That’s the good news.

Now is a good time to focus on the future of healthcare analytics. Among my friends and colleagues in the industry, I have observed that the discussions in this area have somewhat moved from “big data analytics” to “artificial intelligence (AI).” Analytics bolsters performance of AI. Machine learning makes AI become smarter. In my last couple of columns, I have described the visible euphoria among the innovator and investor communities, which has led a large number of startups to work on AI-based solutions for healthcare. So far, success has been limited, yet I love the enthusiasm; that’s what makes America great. When hundreds of great minds start working on a problem everyone wins. But before I go any further let’s do a quick recap of the state of the industry.

A recently published investment report from Silicon Valley Bank showed that tech-focused investment firms are aggressively investing in healthcare companies that are developing artificial intelligence and machine learning technologies for biopharma and diagnosis tools (see Figure 1).

Figure 1: Investment in healthcare companies that are developing AI and machine learning technologies for biopharma and diagnosis tools. Source: Silicon Valley Bank

Another report from the healthcare startup accelerator Rock Health shows that during the first half of the year 2017 investment in digital health technology companies remained strong with nine deals worth $100 million. None of those are AI companies. In February 2017, CB Insights reported that there are 106 AI startups in healthcare backed by various small, medium and large venture firms. Not all will make it at the end, but it is interesting to see how startups are now trying to offer “actions” rather than just platforms for “big data” analytics.

IBM Watson: Poster Child of AI in Healthcare

The million-dollar question is: How to create value using AI in healthcare? IBM created its massive “Watson” expert system a few years back. The hope was very high that this “supercomputer,” with almost bottomless knowledge and fast learning ability, would make doctors “irrelevant.” That, however, has not happened . . . yet. The first few larger pilots failed to produce results. The cost was high, just like the “big data” projects that usually work for large, deep-pocketed health systems in the country. Costly deployments are meant to solve the most complex problems in healthcare such as developing precision medicine for cancer patients. That is a giant leap but not so pragmatic in the short term. Later on, IBM took Watson to the cloud and started offering its services in the “utility” business model for smaller-scale use cases and for smaller organizations.

Figure 2: Investment in digital health technology companies remained strong in first half of 2017. Source: Rock Health

So what is wrong in this picture about Watson, the so-called poster child for application of AI in healthcare? Simply put, to make Watson do its job, it needs certain types and volumes of data as the training data set. Humans, essentially doctors, would have to find time out of their already crammed schedule to build the knowledge base for the AI and connect the dots for Watson before the AI can perform on its own as an assistant. That’s proved to be a tall order for various reasons as evident from MD Anderson Cancer’s decision to walk away from the Watson project earlier this year. Recently, the CEO of Social Capital venture fund referred to Watson as a “joke” in a CNBC interview. IBM later did a rebuttal; the technology is real, but clearly it is over hyped. PR and corporate marketing departments often get ahead of themselves, and there lies the problem. The AI industry needs focus, steady wins without undesired exuberance or marketing hype. The technology needs to be robust, and it needs to solve real-world problems consistently at an affordable cost. Innovators need to understand the real problems, preferably firsthand. AI needs to create real business opportunities or unleash new tangible and quantifiable efficiencies.

Pragmatic Use of AI in Healthcare: Short Term and Long Term

In the long term, precision medicine for treating disease conditions is a very apt use case for AI. Watson, for example, can identify six different kinds of cancer. As the genomic data for a large population becomes more readily available, precise detection of gene mutation and corresponding medication (or gene therapy) can be identified by AI in near real time. NIH has defined that as a “cancer moonshot” program with the objective to advance research for cancer therapies by 2020. In the short term, however, there are other use cases where first generation AI can bring value to the healthcare. A few of them are listed below:

  • Automated image analysis. Radiology is perhaps one of the most suitable fields for the application of AI. Image analysis is an established area for machine learning. IBM Watson is already being used for this use case by one imaging vendor. This area will see significant growth in the coming years.
  • Intelligent patient triage and first level of primary care. Luckily, the ACA has survived for now. But owing to ACA, the demand for primary care is increasing. There is a shortage of primary care physicians. While other transformational care delivery models using nurses instead of physicians are deployed in a limited way, an opportunity exists to deliver basic primary care via intelligent agents (AI) at retail clinic settings.
  • Intelligent agents in mental healthcare. In a recent paper published by researchers from Stanford School of Medicine and Woebot Labs demonstrated how fully automated agents (i.e., AI) produces efficacy in the treatment of anxiety and depression among young adults (18-28). Given the nationwide shortage of behavioral health providers, this use case for AI could have major impact.

Overall, AI in healthcare still has a long way to go. Unlike consumer marketing or the advertising industry, healthcare is a difficult domain to penetrate. In healthcare, appropriate and adequate “signals” are harder to get to train AI quickly and continuously. I have no doubt that given the rate of exponential technology growth, a decade from now AI will become an integral part of the healthcare delivery. But for the time being I would like to share a dose of pragmatism with my colleagues who are getting inundated with the euphoria and PR campaigns coming from all directions about AI taking over healthcare soon.

Rajib Ghosh ( is an independent consultant and business advisor with 20 years of technology experience in various industry verticals where he had senior-level management roles in software engineering, program management, product management and business and strategy development. Ghosh spent a decade in the U.S. healthcare industry as part of a global ecosystem of medical device manufacturers, medical software companies and telehealth and telemedicine solution providers. He’s held senior positions at Hill-Rom, Solta Medical and Bosch Healthcare. His recent work interest includes public health and the field of IT-enabled sustainable healthcare delivery in the United States as well as emerging nations.

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