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Healthcare Analytics: Storms forming over healthcare technology and politics

U.S. policy makers and technology developers should work together, side-by-side, to make healthcare better and affordable for all.

Rajib GhoshBy Rajib Ghosh

Summer is over, and we are gearing up for the second part of the year with some trepidation. Uncertainties about the healthcare industry continues unabated as new court cases are getting introduced to undermine the framework of the Affordable Care Act (ACA). According to filed court cases in Texas and 19 other states, the Justice Department wants to waive the obligation of health insurance companies to cover pre-existing conditions of their members. Meanwhile, the Trump administration has allowed health insurance providers to sell cheaper, “skinnier” insurance plans with reduced coverage than ACA plans. What is the point of an insurance plan if it does nothing when one needs it the most?

The net effect of these actions will be syphoning the healthy population from the ACA marketplace, thus creating havoc for the rest of the people who have to stay. If as a country we do not recognize healthcare as a fundamental right, then the least we should do is abide by the fundamental principle of the health insurance business – create a large, mixed pool of members where risks could be appropriately balanced. Unfortunately, it seems we’re heading in the opposite direction. Are we going to go back to widespread personal bankruptcies due to healthcare-related costs? Time will tell.

Telemedicine to Rise with CMS Patronage

CMS has announced support for reimbursing healthcare providers when they see patients via telemedicine. Source: ThinkStock

CMS has announced support for reimbursing healthcare providers when they see patients via telemedicine.
Source: ThinkStock

On the bright side, the Center for Medicare and Medicaid (CMS) has announced support for reimbursing healthcare providers when they see patients via telemedicine. Having spent many years in that segment of the industry, I would say this is a bold but much-needed step forward. After years of beating around the bush regarding telemedicine, CMS has finally taken this step as healthcare moves out of the four walls of clinics and hospitals.

The demand for healthcare is outstripping supply in many areas, especially in rural areas and in areas with low-income, vulnerable populations. Affordable technology has to play a bigger role in making healthcare equitable for all. Startups playing in this field are expected to see an uptick in their businesses. Potentially, this would also create paid services for remote monitoring of chronic patients via digital health solutions, thus ushering in volume and velocity attributes to the healthcare big data space and subsequently to big data analytics.

Dr. Watson in Jeopardy

Artificial Intelligence (AI) in healthcare, on the other hand, took a jolt recently when news surfaced that IBM Watson provided unsafe and incorrect recommendations for cancer treatment. Internal IBM documents provided strong criticism of the Watson for Oncology system, stating that serious questions could be brought up about the “process for building content and the underlying technology.” Sound familiar? The 2015 debacle of Theranos comes to my mind.

I have raised this issue before in this column. The initial knowledge for AI to perform in terms of diagnoses and treatment was created by humans based on hypothetical patient scenarios and the existing literature at that point in time, but diseases shift, mutate or evolve. How do you adjust the learnings of a system built in that manner?

Google recently announced its foray into healthcare AI with deep-learning algorithms for heart attack prediction. While the deep-learning model Google is using is quite good, it is still far from being perfect or better than the existing risk-based prediction model used by doctors. One of the major shortcomings of AI today is that it can’t explain why a certain decision is being taken, which impacts the trust of patients and doctors alike.

AI Needs Clinical Trials for Clinical Adoption

These examples of two industry behemoths, both still struggling to crack the AI nut within healthcare in a trustworthy way, exhibit a not-so-encouraging pattern. Neither of these companies have shown an appetite to put their AI through a real-life, double-blinded randomized clinical trial process. Their drive for time-to-market is based on their experience with technology development life cycle, but that’s not how medicine is developed.

The only way the clinical community would trust a clinical solution is when it sees evidence of efficacy in multiple trial settings. Treating disease is not the same as identifying someone’s face with 90 percent accuracy on an online image. The sooner AI developers understand that and stop trying to circumvent the need for clinical trials, the better it would be for everyone. Otherwise, AI will struggle to find a permanent home in clinical settings.

Healthcare Analytics can get a Boost from Blockchain

Blockchain is making the rounds in the healthcare industry this year. In my previous column I talked about implementation of a healthcare data exchange solution using blockchain. I am convinced that blockchain will find more and rapid adoption in the developing world than in the developed countries. We are already seeing that in blockchain applications in land registration and supply chain management in China and India. But is blockchain important for data analytics?

Data quality is a big concern among many analytics organizations. A 2016 KPMG report based on surveys across the globe found that only 38 percent of the executives in large organizations trust their own data. Analytics is useless when trust associated with the accuracy of data is so low. Recently a couple of startups have shown that for the retail industry, consumers empowered by blockchain-based data acquisition platforms could prove to be quite effective in improving data quality. Blockchain doesn’t enable new analytic techniques, but it improves trust in the data quality and accuracy. Is that applicable to healthcare analytics too?

Clinical trials for pharma companies could benefit from data sharing by blockchain-enabled trial participants, provided they are ready to pay for that data. Drug efficacy analysis will be much better as quality and volume of underlying data increase. The insurance industry heavily relies on accurate consumer data for future product offerings. Once again blockchain might be able to help by delivering a trusted framework that empowers consumers to share their data-driven motivation. My bet is such enabling of consumers will happen faster in developing countries than in developed nations.

When it comes to healthcare infrastructure and quality, developed countries always maintained a strong lead over developing nations. New technological innovations will close that gap in the near future, similar to what cell phones did for the telecom industry a couple of decades ago. Instead of sabotaging its healthcare industry, U.S. policy-makers and technology developers should work together, side-by-side, to make healthcare better and affordable for all.

Rajib Ghosh ( is the founder and CEO of Health Roads, LLC, a consulting company for enabling digital transformation in healthcare organizations. He has 25 years of technology experience in various industry verticals where he had management roles in software engineering, data analytics, program management, product management, business operations and strategy development. Ghosh spent a decade and half in the U.S. healthcare industry as part of a global ecosystem of medical device manufacturers, medical software vendors, telemedicine and telehealth solution providers. He’s held senior positions at Hill-Rom, Solta Medical and Bosch Healthcare. His recent work includes leading data-driven digital transformation in the public health space, including county-level healthcare agencies and organizations focused on underserved populations.

Editor’s note

The opinions expressed in this column are those of the author and do not necessarily reflect the opinions of Analytics magazine or INFORMS.

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