In the race to unearth enterprise insights, the modern health system is like a prospector whose land contains precious metals deep beneath the surface. Until the organization extracts and refines those resources, their value is all but theoretical. In much the same way, only after harmonizing its data can a health system run analytics to inform stronger decision making and realize its full potential.
In a survey commissioned by InterSystems, Sage Growth Partners found that most health system executives prioritize analytics as a fundamental step toward their broader goals. But they don’t have the tools to get there — at least not yet.
Just 16% of integrated delivery networks rate their clinical data quality as excellent, 55% consider their supply chain data poor or average, and 87% say their claims data is poor, average, or good. All told, only 20% of organizations fully trust their data. Yet providers recognize the urgent need for healthy data to power analytics, as evidenced by the 80% who say creating and sharing high-quality data is a top priority for the next year.
These data challenges have real consequences. Poor, untimely decisions and the inability to identify gaps in care translate to severe financial impacts for the enterprise and less desirable outcomes for patients. But while the precious metals remain underground, health systems have the opportunity to start digging today.
Barriers to Healthcare Insights
Now 12 years after the HITECH Act accelerated the move to electronic data, healthcare has yet to address bedrock issues such as the lack of a centralized database, challenges integrating multiple data sources, low-quality information, and the failure to create standardized reports. Sage’s findings revealed a harsh truth: Health systems cannot use analytics to generate actionable insights until they overcome these obstacles.
More than half of surveyed executives acknowledge that poor data impedes enterprise decision making and their ability to identify gaps in care. What’s more, 51% point to data integration and interoperability as the most significant barriers to leveraging analytics for the good of the organization.
On the ground, the disconnect has meant that health systems are strapped with huge data latency and duplication challenges, despite massive investments in data warehouses. Although many organizations designed dashboards and predictive or prescriptive models, most of these tools either fail to reach production or scale past the walls of a single department due to workflow integration issues. Clinical, claims, and other data, meanwhile, remain siloed.
Health systems simply haven’t built the infrastructure to produce accurate, real-time, high-quality data.
Healthy Data and Analytics: Healthcare’s Future
COVID-19 forced C-suites to make big decisions more often and more quickly, from managing overworked staff to allocating resources among sick and dying patients. Even tracking health outcomes morphed into a tall task. The whiplash of the pandemic led the industry to an inflection point: 85% of executives told Sage that real-time, harmonized data is vital for leaders to make informed operational decisions.
To make the right moves at the right time, health systems need the most reliable information. That requires strong data technology from start to finish, encompassing pipeline capabilities, aggregation, normalization, standardization, a robust data model, and consistent access.
If any element of that equation is missing, health system decision making will continue to lag. But success can transform the enterprise.
Imagine a group of executives — each trusting their data — receiving timely, standardized reports about their health system. Knowing the underlying data is healthy, they would all be confident in the veracity of the insights and ready to draw conclusions. One InterSystems customer, for example, can see in real time and retrospectively how many patients are within a given department, empowering informed staffing decisions and lowering costs with the click of a button.
Clinical departments stand to gain similar benefits. Interoperability enables them to see previously hidden correlations, improving patient care and outcomes. At InterSystems, we saw how a precise understanding of data enabled a health system client to set effective data governance protocols, which steered clinicians to take quick, knowledgeable action when it mattered most.
And at a time when artificial intelligence and machine learning models promise to optimize patient care, it’s all the more important that clinicians trust the data driving those insights. Otherwise, these advances will struggle to deliver anything beyond hype.
Bridge the Healthcare Insights Gap
Most health systems recognize that it’s time to harmonize their data in pursuit of analytics-driven insights. Organizations that don’t act quickly can bet that their competitors will. When everyone is sitting on precious metals, the only reasonable option is to invest in the technologies that are proven to sift soil and rock from the gold.
Fred S Azar, PhD, MBA is responsible for Analytics/AI&ML Business Development at InterSystems.
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