Why True Healthcare Data Intelligence Depends on an AI-Powered Cloud

By Jordan Bazinsky, Executive Vice President Payment Integrity and Retail, Cotiviti
Twitter: @Cotiviti
Twitter: @JordanBazinsky

One of the biggest frustrations of the COVID-19 pandemic has been unraveling why we still lack the data needed to successfully contain the coronavirus outbreak.

Amid testing shortages, especially in the early months of the pandemic, we saw advancements in our ability to predict where spikes in COVID-19 cases could occur and where hidden outbreaks may already exist based on trends in flu-like illnesses. But even in Western countries, public health officials struggled to perform contact tracing, in part due to a dearth of data.

Now, with more than 100 million Americans fully vaccinated and 2.55 million doses administered daily, scattered vaccination records could make it difficult for individuals to verify their vaccination status, especially if they lose the paper cards that serve as proof of vaccination. Even before the pandemic, some states struggled to collect immunization data from disparate sources. And while the rise of digital passports may sound like a step in the right direction, lack of a single digital standard could undermine their utility.

But what if patient data was streamed into an AI-powered cloud? It’s a move that not only would ensure that healthcare data is centralized, but also that it is accurate, easily retrievable and readily available for analysis.

The Future of Healthcare Data Access

While the COVID-19 pandemic underscored the need for centralized healthcare data, there were signs that the industry struggled to access the right data at the right time long before the pandemic.

In 2018, nearly one-third of consumers reported experiencing a gap in information exchange with their physician. This included the need to provide their medical history because their chart could not be located, bring a test result to an appointment, or even retake a test because the results could not be found.

Meanwhile, a 2020 Black Book report found that fragmented and siloed data—increasingly common as consumers shift care away from hospitals toward lower-cost settings of care—leads to higher healthcare costs. This is true even as record sharing among providers has increased, the survey found, and it is largely due to redundant healthcare tests and procedures.

Then the pandemic arrived. Suddenly, 22% of consumers faced challenges obtaining their healthcare data electronically or were unable to access it, a separate Black Book survey revealed. Among healthcare leaders surveyed, 93% said problems with patient data exchange prevented clinicians from having a complete view of patients’ medical history upon COVID-19 admission. This put patient care at risk, especially when patients had comorbid conditions that raised their risk of developing complications from the disease.

Today, as healthcare providers and consumers navigate a next normal of “anywhere care,” the number of sources of data has dramatically increased, from telehealth to retail sites, legacy systems, remote monitoring devices and health and wellness apps. Consumers expect that this data—including data from wearables and apps, if they choose to share it—will be accessible by healthcare providers and available for analysis.

How can healthcare organizations bring together data assets that are seemingly very different to gain greater insight into consumers’ care needs, their health risks and opportunities to close care gaps and improve health? Over the past year, cloud computing technology emerged as a vital resource for healthcare. During the height of the pandemic, cloud applications facilitated real-time monitoring in high-risk settings and near-real-time data aggregation. They also enhanced collaboration across multiple settings of care. For example, at Mayo Clinic, use of a Cloud Healthcare API facilitated real-time access to healthcare data at the point of care. Meanwhile, at Blessing Health System in Quincy, Ill., use of cloud technology captures data for patients with mild or low-risk symptoms of COVID-19 from remote patient monitoring devices. When AI software detects deterioration in the patient’s condition, the cloud-based system notifies a care team member, who schedules a telehealth visit.

When cloud solutions are paired with AI-driven analytics, healthcare organizations become much better equipped to combine data from disparate systems—including data around social determinants of health—for a single patient view. In doing so, organizations strengthen their ability to make evidence-based decisions that enhance quality of care and advance population health. They also gain greater insight into ways to reduce administrative inefficiency, a key contributor to healthcare costs.

A Modern Solution for Actionable Insight

The pandemic is accelerating adoption of cloud computing in healthcare, with the market projected to grow from $28.1 billion in 2020 to $64.7 billion in 2025. In recent years, we’ve seen large systems make the move to a cloud-based platform, from Beth Israel Deaconess Medical Center in Boston to Atrium Health in Charlotte, N.C., to Kaiser Permanente, which will leverage the cloud to provide more expansive decision support analytics for clinicians.

But opportunities to take advantage of cloud computing’s potential are not limited to large systems. Smaller organizations can make the move to the cloud by leveraging the infrastructure and applications of cloud providers through a platform-as-a-service model. The less sophisticated the healthcare provider, the more sophisticated the cloud computing infrastructure should be. For example, organizations that do not have deep analytics expertise in-house should seek cloud solutions with data analytics support. This helps speed analysis of clinical information, empowering clinicians to make the right decisions at the right time based on the member’s needs.

The right cloud computing platform also should empower healthcare organizations to make sense of disparate data types—from social determinants of health data to medical records, pharmacy and laboratory information—to gain a longitudinal view of the patient. This helps eliminate the challenges associated with fragmented and siloed data, providing a more detailed understanding of the opportunities and challenges patients face and the action steps needed to improve outcomes and reduce risk.

More and more, healthcare organizations also are finding value in developing partnerships that facilitate access to a data lake to supercharge their ability to analyze both structured and unstructured data. For example, at Providence St. Joseph Health, which operates hospitals in eight states, use of a data lake to integrate the supply chain data with the EHR enables physicians to select lower-cost medications with similar efficacy, significantly reducing care costs.

By evaluating opportunities to centralize healthcare data with the help of cloud solutions, healthcare organizations can bolster their access to actionable insight in a world of anywhere care. It’s an approach that distinguishes healthcare organizations from their competitors while providing greater value for all.