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Data and Analytics Will Create a More Transparent, Fairer Health Care System

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Read more about author Venky Ananth.

Consider a picture of data-enabled health care. Health records, insurance claims, and telehealth appointments occur from your phone. Wellness apps track everything from stride length to arrhythmia. Digital glucose monitors keep tabs on blood sugar to help manage diabetes. And post-procedure monitoring checks in on your adherence to prescription drugs.

Surely this is today’s picture, now that the COVID-19 pandemic has birthed a digital health care revolution, right? Think again. That was the landscape of data-driven health care in 2019, months before the pandemic took hold. COVID-19 undoubtedly hit the gas for some of these trends, but we’ve been on the data-enabled highway for quite some time.

From consolidation among providers to new imperatives for transparency and value-based approaches, the health care industry is grappling with a far-reaching transition that will impact patients and providers alike. Enterprises that successfully navigate these changes will combine a holistic approach to their data and leverage partnerships to help interpret it – and the results can create a more transparent, fairer health care system for all.

Data and Analytics Will Enable Value-Based Care, Transparency

The data explosion is real, with an estimated 15-fold increase in health care data between 2013 and 2020. The need to manage all of that extends beyond the individual patient. Insurance companies are transitioning from benefit administrators to care coordinators amid the shift from fee-for-service to value-based care. As patients pay providers to solve problems rather than provide defined episodic care, the onus is on providers to produce health outcomes within their hospital ecosystems. That means insurers must coordinate benefits entire journeys of care, no longer specific procedures. Data is critical to both, especially as patients float between primary doctors, specialists, surgeons, and pharmacists.

And that’s just for a single patient. Think of the data across entire populations! The regulatory push for price transparency is driving providers to show costs up front so patients can choose between them. Pricing models and aggregated information are vital to this. Beyond that are claims data for pools of individuals, not to mention reams of socioeconomic and demographic data. Insurance providers are sitting on mountains of it, with new imperatives to develop management systems around it. In the past, insurers simply saw a provider claim and reimbursed based on coverage. Now they need to look at pricing and determine how to drive value-based care – again, to solve a problem, not just administer a procedure.

Of course, interpreting all that data is a monumental task. Artificial intelligence, predictive data modeling, and decentralized interpretation are all tools at our disposal, but only if we apply a coherent strategy throughout. Providers need to stitch together historical data for individual patients so doctors can intervene at the right time. And migrating the data onto the cloud is one thing; designing scalable models that ingest and use the data, with analytics and eventually predictive capabilities and AI, is entirely another.

The Transition Is Creating New Players, or the Other Way Around

The transition to digital enablement has brought new players into traditional spaces – or the other way around, depending on how you think of it. Companies like Oscar Health, Teladoc, and Babylon Health are leveraging telehealth to disrupt traditional models, while Amazon’s Pill Pack is doing the same for pharmacies. 

And Amazon is joining other digital leaders, like Apple and Alphabet, to venture into health care partnerships with consumer data and a deep understanding of consumer behavior. Alphabet’s algorithm, for example, can diagnose diabetic retinopathy in images as well as a board-certified ophthalmologist. When the FDA launched a precertification program for digital health software in 2017, seven out of nine companies selected were tech companies – including Apple – not traditional health care companies. The disruption isn’t limited to patient care: Organizations like Benevolent AI and Aria Pharmaceuticals are transforming drug discovery with in-silico chemistry and molecular modeling for CAD-enabled drugs.

Legacy players aren’t sitting still. Some have merged forces (think CVS and Aetna) to streamline telemedicine and insurance. Others have brought in patient management software, otherwise known as medical practice management (MPM) systems, to handle patient-specific data so they can focus on the care journey.

Enabling Fairer Patient Outcomes

All this competition – with startups, digital leaders, and legacy incumbents playing in the same field – is fueling a more patient-centric future, with data the glue that holds it all together. For all the malice heaped at America’s health care system (rated by a median 48% of Americans in a 2021 Pew survey as below average, with nearly one in five saying it’s the worst among developed nations), data holds the potential to drive more fairness in health outcomes.

Take social, economic, behavioral, and environmental factors, for example. These social determinants govern more than 70% of health outcomes, and understanding them is vital to disease prevention. Magellan Health analyzed data in 2016 to launch an initiative aimed at preventing suicide among Medicaid users in the greater Phoenix area. By leveraging behavioral health screenings and referrals to mental-health services, and addressing determinants like housing and food insecurity, Magellan was able to cut suicides by two-thirds within the first 90 days.

Two years later, the FDA approved Ibrance, a cancer drug developed through a collaboration between Pfizer and Roche, thanks to real-world data gathered from electronic health records – not just clinical trials. Data is critical to all this, and it’s not just looking in the rearview mirror. Going forward, health care companies can use artificial intelligence and machine learning to do everything from predicting vaccine demand to researching ways to better diagnose heart disease. Much of that can be done with strictly anonymized data across patient populations – an increasingly vital imperative to conducting medical research amid legitimate concerns over patient privacy.

Providers Just Want to Help

Amid this rush toward data enablement, it’s important to consider the widespread concern around data protection. Another Pew survey, published in 2020, found over half of U.S. adults indicated they had recently opted against using a product or service over concerns around personal information. But here again, we should observe that most health care practitioners want to use data expressly to improve patient outcomes, as opposed to making another buck.

For some context, data analytics underpin four key pillars of the health care industry: experience enhancement, risk mitigation, business transformation, and revenue and profit maximization. Providers rank them in that same order of importance. Asked by the Infosys Knowledge Institute in a 2018 report to name the scenarios where data analytics would be extremely relevant if the possibilities were endless, 33% of nearly 200 key stakeholders across the health care and life-sciences industries named experience enhancement – in other words, improving outcomes for patients and other stakeholders. By the same metric, just 19% named revenue and profit maximization.

How We Get There

At its most basic level, today’s user data enables us to capture the moments in which an event occurs, logging various markers and contextualizing the event against other data, related or not, to build predictive analytics around future outcomes. Robust processing can take all those strands of data – from in-hospital vitals to on-the-go biometrics to the broader ecosystem of social determinants – and contextualize them with predictive analytics. And the cloud, which IDC predicted would surpass traditional datacenters for overall storage by 2021, can speed this up considerably.

To get there, health care organizations must first identify the stakeholders – care providers, patients, benefit managers, payers, and more – across the ecosystem. Organizations need to envision the journey each stakeholder takes, define the data standards, and establish boundaries around interaction. They must communicate the interaction methods and become comfortable dealing with exceptions rather than rules. Then, enterprises must find technology partners to provide the infrastructure to share data and stay connected. Within this connected ecosystem, they need to categorize data pools around access, longevity, and learnability. Finally, they’ll need to measure and improve the value of these capabilities to create predictive analytics that help improve health outcomes and prevent negative incidents.

Effective stakeholders in the health care space will do this, riding the industry’s digital transformation with an appetite to leverage patient data into better outcomes – for their businesses and their patients. With the right approach, data and analytics can enable a fairer, more transparent U.S. health care system. Getting there will take holistic strategy, selective partnerships, and openness toward new ways of doing things.

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