As Value Based Care Models Gain Traction, Pressure to Adopt New Technology Grows

January 28, 2019

By making faster, more data-driven decisions to drive better patient outcomes, healthcare organizations are increasingly able to do the right thing without sacrificing their bottom line.

A recent article on Healthcare Finance delves into a new Deloitte study surrounding use of new technologies to manage populations in a value-based care environment. The report analyzes four years worth of data collected from 4,500 hospitals (both those with value- and quality-based financial incentives and those without) and attempts to answer the question “WHAT kinds of technology are health systems investing in, and how strongly do value- and quality-based financial incentives influence these decisions?” So what did they find?

Two key results emerged from the research: 1) value/quality-incentivized hospitals were MUCH more likely to adopt technologies to help them coordinate health and care for their populations, but 2) there was no significant difference between incentivized and non-incentivized hopsitals when it came to the adoption of technologies for improving operations (i.e. reducing cost and waste). This makes sense, as operational technologies like electronic health records have little practical impact on quality or proactive delivery of care. The most effective tools for succeeding in value-based contracts are data-driven and facilitate communication and coordination across settings of care. Only those types of tools enable providers to spot which patients are at risk and marshal the resources needed to mitigate that risk in a timely way.

The report says:  “Investment in population health management solutions can pay off in many ways. In 2009, Adventist HealthCare partnered with Conifer Health Solutions to overhaul its population health management approach. Conifer’s solution helped Adventist identify high-risk patients, connect them with primary care physicians, and assign a care management nurse to oversee outreach and care plans. After a year of implementation, the health of nearly half of the targeted patients improved and they moved out of the high-risk category.

So why is the adoption of population health technologies moving so slowly? The article author writes: “Today, many hospitals appear to be in a holding pattern, waiting until the tide shifts to begin the journey toward new payment models. Waiting to invest, however, could put organizations at risk of falling behind on the adoption curve, Deloitte cautioned.” This is the ongoing, decades-long slog of the value-based care model—few organizations are willing to invest in long-term, data-driven workflows unless stable incentives are there, but these incentives come and go with each new political administration.

The author encourages hospital executives to start investing in new technologies anyway – ones that go beyond the more common adoption of technologically-driven strategies for organizing and analyzing clinical data and information. These technologies will “continue to be critical” but healthcare systems need to look to “patient and provider engagement technologies, such as virtual care, and core operational and financial applications.” Taking this kind of approach early, to avoid playing “catch-up” later becomes increasingly important as value-based payment model adoption continues to gain traction, both in the public (government) as well as the private (commercial insurance) sectors.

Through all of this, there is an increasingly important role that machine learning and artificial intelligence can play when it comes to value-based care. By using a machine to process enormous amounts of patient data—even those data coming from disparate sources—healthcare providers now have a practical and cost-effective set of tools to succeed more quickly, and to a greater extent, in value-based payment contracts. By making faster, more data-driven decisions to drive better patient outcomes, healthcare organizations are increasingly able to do the right thing without sacrificing their bottom line.

Delta Brain

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