The MIT Sloan Management Review dropped the shoe on the state of big data analytics with a report this month declaring that while big data is now a mainstream idea, it is far from being a mainstream practice. Healthcare was singled out as a laggard among various sectors that were studied.
Major challenges related to implementing big data analytics in healthcare have been evident for the past couple of years. Some of the top issues are:
-- Inflated expectations: health systems have been sold the idea that big data is the silver bullet for population health management. However, ROI from analytics has been hard to quantify, which has led to leadership taking a cautious approach to investments
-- Data management challenges and interoperability among systems: perhaps no other topic has been beaten to death more than this, with technology vendors taking an enormous amount of heat for data blocking, among other things
-- Lack of co-ordination among stakeholders: while the role of chief data officer has become more prominent, data silos continue to exist, leading to data bottlenecks that impact the ability to harness data for improved insights.
-- Operationalization of analytics: Most analytics solutions are “offline”, meaning they operate in a stand-alone fashion, and analytics is not integrated into day to day clinical workflows
Despite these challenges, it is widely acknowledged that healthcare is a tremendous opportunity area for big data analytics. However, innovation has been slow as a result of margin pressures and conflicting priorities in a sector undergoing a massive transition.
Partnerships for innovation in value-based care
Last year, the U.S. Department of Health & Human Services set a goal of aligning 90 percent of all Medicare fee-for-service payments to quality and value by 2018. Similar initiatives implemented by state governments, health systems, ACOs and health plans, have put providers under pressure on care quality guidelines and value-based reimbursement programs.
Providers have limited options today for obtaining actionable insights through the implementation of big data technologies. I had explored emerging partnership models that can enable providers in an earlier column here, specifically the coming together of Quest Diagnostics, a leader in diagnostic testing, and analytics platform provider Inovalon. At the recently concluded HIMSS, I had the opportunity to visit with Quest CIO Lidia Fonseca and Inovalon CEO Keith Dunleavy, MD, on their new platform in production for the past six months. The conversation provided insights into how delivering big data analytic solutions real time at the point of care can make a difference to costs and quality of care.
Data DiagnosticsTM, the analytics platform set up with this partnership , blends Quest’s 20 Billion lab test records with Inovalon’s access to claim records on 130 million patients. The combined dataset covers roughly one in three Americans. Providers and affiliated physicians gain access to analytical reports through a single user interface from within their EMR systems.
Over the years, Quest has developed interfaces that connect to nearly 600 EMR systems installed in provider organizations. In the absence of true interoperability between provider clinical systems, this is as close as it can get to it. Combined with Inovalon’s complementary datasets on a cloud, analytical reports – over a hundred of these so far – generate real-time insights with a single click. A pay-per-click model minimizes costs by allowing physicians to choose only the reports they need, with the practice or health system paying the cost with the expectation that better care will help them attain financial incentives under value-based payment models. This, and minimal set-up costs, increases the platform’s ease of use and commercial viability.
The operationalization of analytics reports by integrating them into Quest’s Care360TM connectivity platform – used by 420,000 physicians across the country - addresses one of the significant gaps today in big data analytics implementations where standalone analytics platforms are unable to deliver real-time insights at the point of care. Physicians order analytics reports with the same process they use now to order lab tests from Quest, which is significant from a user experience standpoint, given all the frustrations with the usability of current EMR systems.
The analytics reports identify specific actions the physician may take to improve the patient’s care according to quality, risk, utilization and other metrics and criteria which determine value-based reimbursement models. Proprietary predictive modeling algorithms provide incremental value to more sophisticated users looking at proactive interventions with their patients.
The unlocking of value
The Quest-Inovalon partnership is important because it combines two massive and complementary datasets to create unique value for the provider community as well as payers and health systems transitioning from fee-for-service to value-based payment models. However, having a mountain of data is one thing, and delivering real-time insights from the data at the point of care is a whole another thing. The Data Diagnostics platform does this effectively by providing real-time visibility to patient history and risk eligibility, especially for affiliated physicians to whom large health systems are “downstreaming” the risks. The platform also delivers incremental value by helping to identify opportunities to reduce and eliminate waste arising from unnecessary or duplicative tests and procedures. In that sense, it contributes to the improvement of overall efficiencies in the healthcare system. The partnership is also a template for data monetization models for other health systems, especially those with broad national reach.
Though the proprietary datasets provide extensive coverage of the patient population across the nation, physicians whose patients are not covered in the combined database will not immediately benefit from historical medical reports. Also, with the emerging sources of data from wearables and the Internet of Things (IoT), the platform’s ability to ingest data from new sources and provide incremental value will be critical to the platform’s growth and success.
Much has been written in the past year about “disruptive” silicon valley startups that can solve healthcare’s complex problems through innovation that the incumbent players were thought to be incapable of. However, many of these startups have their own problems today, with a couple of high-profile healthcare “unicorns” in trouble with regulators for questionable practices. It may be that best practices are emerging from the very same incumbent industry leaders that, instead of being disrupted, are setting the stage for the next round of innovation in healthcare.
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