What it will take for big data to achieve its potential in healthcare
Over two days in Boston last week, leaders and decision-makers at healthcare organizations of all shapes and sizes convened to compare notes about the ways they're leveraging ever-growing troves of clinical, financial and operational data.
At the HIMSS Big Data and Healthcare Analytics Forum, industry experts shared strategies and best practices for managing initiatives of increasing complexity – from data governance to population health management, artificial intelligence to precision medicine.
Here are a few things we learned at the event.
The future is sooner than we think
The event kicked off with a keynote from Michael Rogers, who calls himself a "practical futurist," and sees major near-term changes on the horizon, where the mantra in healthcare will be "low-cost delivery, quantified outcomes, maximized quality." (Listen to our podcast Q&A with him here.)
In Boston, Rogers looked ahead to the next decade or so, where he sees "massive vertical consolidation" (think: purchasing monthly health memberships, with unlimited virtual consultations for subscribers, from CVS or Amazon instead of buying insurance). He sees an omni-online world of virtualization and sensors and "secure digital identities," he said.
"We cannot underestimate how connected we will be," Rogers explained. "There will be seamless handoffs between home, car, work. At some point in mid-2020s, we have to decide when we tell kids what offline means. They’ll be more worried about losing internet access than electricity."
For all of technology's sky-high potential, it's critical to start with the basics
Jason Burke, chief analytics officer at UNC Health Care & School of Medicine, explained how his health system managed to be just one of two to ascend to the top rung of HIMSS Analytics' Adoption Model for Analytics Maturity.
It began with a basic ground-level commitment to data governance, he said.
"We certainly think it's no longer optional," said Burke. "It never really was, and it's definitely not now. We're pursuing what I call a multi-pronged strategy around data governance – we’ve hired data governance professionals into a team, called Data Governance, under an Executive Director of Data Governance." Smart health systems, he implied, should be making similar changes to their own org charts.
Data is essential for effective care coordination and pop health
In Boston, Harry Saag, MD, medical director for network integration and ambulatory quality at NYU Langone Health, and Simon Jones, MD, professor in the Department of Population Health at NYU School of Medicine, described how they're leveraging data to broaden the reach of their phone-based care interventions and boost usage of their patient portal.
Among the innovations: "We created heatmaps of the New York area and where our usership is, where folks are using various features of the portal," said Saag. That focus on patients' so-called "ZNA," the ZIP Codes in which they live, NYU Langone has made big gains in targeting its outreach and engaging patients electronically.
But there's no limit to where analytics can improve efficiency and quality
A longtime healthcare IT pioneer, Partners HealthCare is continuing to use data in new and interesting ways, now that's it's a major Epic shop.
In Boston, three clinicians and a data scientist from Partners showed how they're leveraging analytics to spread information-enabled nursing practices, monitor and troubleshoot decision support tools and enable peak performance from their heavily-customized electronic health record.
Partners has found success by capitalizing "building blocks of data-driven culture," one of them said, and has learned along the way that effective data is centralized and organized, embedded in workflow from the start, easy to analyze and interpret, tactical and relevant.
Healthcare consumerism is here, and its unique data demands new approaches
But to be truly valuable, data needs to be an essential part of the entire healthcare ecosystem, not just the inpatient acute-care setting, said Lynda Chin, MD, executive director for real-world education detection and intervention at The University of Texas System.
In a Boston keynote, Chin discussed ways to help ensure both clinicians and patients are empowered with the data they need to proactively manage care.
One way she's helping advance that case is with a pilot program she co-developed in Texas that created an Amazon-like platform to host various digital health products, while "also enabling interoperability across those technologies and integration with traditional care delivery services."
The big idea of the project is that "care delivery is not equivalent to a retail transaction," researchers said. "There is no single device, app, or piece of data in isolation that will deliver benefits to patients."
Or, as Chin explains: In the age of consumerism, pop health and value-based care, "it's very clear to me we're going to need a different way to get the data together."
Effective chronic disease management depends on "data that isn't just limited to the electronic health record," she said. "And a lot of that data resides outside the traditional healthcare system."
The future is bright – but data democratization is key
That idea of information liberation was the major theme of a unique presentation delivered by Boston University Academy High School sophomore Justin Aronson, who designed and built a website, fueled by public data from NIH's ClinVar archive, to help labs doing genetic testing to check on their own variant classifications.
Data democratization like that practiced by NIH is essential to the ongoing maturation of artificial intelligence and machine learning, said Aronson, and more organizations, public and private, need to follow suit in opening up their troves of digital information.
"The U.S. is doing a lot to make its healthcare data accessible to the public," said Aronson. "Many organizations within the U.S. government and at HHS have started to open their healthcare data up to the public. However, this is not to say that the US could not do more to make data available," said Aronson.
"Even those who have access to data will be limited in how they can use other data sets in conjunction with their own to produce much more useful and accurate algorithms," he added. "Only by democratizing data can we broadly provide the people who can truly use data for good with the information that they need."