Markets We Serve: Navigating Change in Healthcare — First in a four-part series

by | Jun 21, 2018

For several of us in the healthcare world, the national Triple Aims of reducing cost while improving care quality/outcomes and increasing access to care continue to be the guiding light in terms of investment and value we deliver to our customers. As we analyze information and business systems enabling delivery of healthcare for large payers like CMS and payers/providers like the VA and the MHS, we are looking at innovations that will help us achieve these aims.

A growing aging population, combined with increasing chronic and non-chronic disease rates both within the US and globally, implies that the demand for individualized therapy will gradually accelerate in the years to come. ‘P4 medicine’, originally coined by Dr. Leroy Hood, MD, PhD, to characterize a personalized, predictive, preventative and participatory method of treatment seems to be gaining traction. For those of you searching for where “precision medicine” belongs in this discussion, look no further than Dr. Euan Ashley’s blog.

As the life science industry tackles advances in thought like ‘P4 medicine’ in the face of a growing disease burden, the importance of Real World Data aggregated from a plethora of sources beyond the Electronic Health Records (EHRs) and claims data sources to include wearables, patient self-reported data, surveys, and social media assumes an increasingly vital role. The healthcare industry’s ability to aggregate, securely process, and harmonize massive quantities of both structured and unstructured data is rapidly evolving with the adoption of the Cloud, Gigabit Ethernet, advanced data analytics tools, and, most importantly, the triad of Artificial Intelligence (AI), Machine Learning, and Natural Language Processing technologies.

Tech titans like Google and Microsoft have switched their strategies from “Mobile First” to “AI First.” At HIMSS 2018, Eric Schmidt very eloquently painted a future vision for a doctor’s assistant, “Liz,” a machine that would seamlessly observe, record, and translate the doctor-patient engagement in its entirety and even play a pseudo-intelligent and suggestive role from time-to-time. Studies indicate offloading the entire administrative burden from the physician to a machine can free up ~50-66% of the physician’s time, time that can be spent with patients instead of paperwork. Contrast this futuristic scenario (although Dr. Schmidt promised to come on stage at HIMSS with Liz in tow, if invited, within 10 years) with the notion of a machine recording the most private of conversations between a physician and his/her patient in the Cloud, a great recipe for some fierce information privacy debates in the coming years.

Despite large investments by both government and industry over the past decade to establish and harden the backbone of health information technology and health data exchange, significant challenges still remain. The digitization of health records is nowhere near complete, even within the US, and interoperability is still a major obstacle being tackled by some of the smartest minds (see Da Vinci Project). Some of the nation’s largest providers and payers are in the throes of modernizing their EHR/EMR systems: the Military Health System’s GENESIS program, which is already underway, and the Department of Veterans Affairs more recent decision to follow suit.

The healthcare quality landscape has also evolved substantially over the past few years. In April 2015, Congress passed the Medicare Access and CHIP Reauthorization Act (MACRA), which repealed the Sustainable Growth Rate formula and its Medicare Physician Fee Schedule cuts and replaced it with the Quality Payment Program (QPP), a new model that focuses on quality and cost measurement, reporting, and payment adjustments. In addition, the IMPACT Act of 2014, combined with growing use of EHRs, has provided the means and the need for new quality measures and re-evaluation of existing ones to ensure alignment with new CMS priorities, such as the Meaningful Measures and Patients Over Paperwork initiatives. The notion of reducing provider burden is somewhat central to both these initiatives, and CMS is leading the charge on this with numerous compliance initiatives.

On the healthcare cost aspect, the reduction of Fraud, Waste and Abuse (FWA) remains a challenge. As in every “arms race,” the bad actors continue to rely on identifying and exploiting the loopholes in large systems like Medicare and those of the Veterans Affairs (VA). The recent announcement by CMS and the VA to join hands in combating FWA is a great step in the right direction. Industry’s contribution in creating a “Healthcare Kill Chain” driven by data-driven intelligence is necessary to complement the government’s efforts in tackling this costly menace.   

While SpaceX simultaneously landing two boosters back on Earth after launching a Tesla Roadster into solar orbit on the world’s largest rocket, and Lidar systems interfacing with automotive-grade Linux at the heart of self-driving cars may appear to make more front page news, the healthcare industry’s quantum leaps to come may be more exciting in many ways as AI in the Cloud meets Pharmacogenomics. If we agree that innovation will always outpace the necessary regulations to safely and securely translate benefits to patients, then the ability to navigate change is imperative for government, industry and the public. Healthcare firms possessing hybrid capabilities spanning technology, healthcare cost and quality, and life science domain expertise will be the best positioned organizations to help customers navigate such change in an Agile world.  

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