In healthcare, where the stakes are incredibly high, the speed at which we can share, process, and act on data significantly influences patient outcomes and brings success in value-based care arrangements. A recent survey found that only 47% of organizations are using data aged less than 24 hours for working care gaps, and just 39% of organizations use similarly recent data for patient stratification and building cohorts. Stale data can mean inadequate care and missed opportunities to manage medications or chronic conditions. It also means higher operating costs, as support staff use inaccurate chase lists for patient engagement and routine preventative care like vaccines and screenings. Patient outcomes, operational efficiency, and financial health are all functions of data velocity.
Data isn’t a byproduct of operations—it’s a critical asset that can make the difference between scraping by and stellar performance. Below, I’ll discuss why data velocity should be top of mind for every healthcare organization, and how you can tailor the flow of data to meet your needs.
How high-speed data powers high-impact healthcare
- Clinical performance. The patients of greatest need – typically those with multiple chronic conditions and medications – have frequently changing health statuses and dozens or hundreds of healthcare interactions each year. Many of these interactions occur outside of the primary care provider’s EHR. Providing meaningful care requires action on near real-time data. If providers and the expanded care team operate on days-old (or, in some cases, months-old) data, they’re operating blindly – reducing efficiency, missing quality and compliance opportunities, and eroding patient trust.
- Financial health and operations. As healthcare costs rise and the environment grows increasingly competitive, timely insights are essential for meeting ambitious goals. Fresh data supports performance monitoring, which makes strategies stronger and workflows more reliable and effective. For example, real-time data enables organizations to track weekly trends, calibrate staff for risk adjustment, or to act on timely care transition opportunities. Similarly, relying on stale data to measure fiscal performance could lead to financial peril if you find out too late that you’re off track on an important goal. But with real-time insights, it’s possible to course correct quickly and stay on track.
- AI and Machine Learning. 61% of organizations refresh their data at least daily for business intelligence analytics, but when building and running artificial intelligence models, that number drops to 32%. A program can’t reach meaningful conclusions with outdated data. New, dexterous large language models can turn a firehose of information into something meaningful, which allows healthcare organizations to generate actionable and accurate insights from massive amounts of data.
The risks of stale data
- Missed opportunities. Fresh data allows healthcare organizations to intervene when it matters, but lags in data recency mean those windows to deliver preventive or responsive care close.
- Duplicated efforts. Limited resources could lead to a wild goose chase if data lags. Chasing false positives, like a care gap that’s already been closed, costs healthcare organizations in staffing and resources, from multiple phone calls to ineffective in-person outreach.
- Undermined effectiveness and trust. Providers’ trust also hangs in the balance. If care teams conducting patient outreach, like follow-up calls to close care gaps, use outdated data, there’s a greater risk of frustration and mistrust of an organization’s systems when they’re attempting to treat patients. Today’s care teams must trust and rely on data to deliver effective care. If they don’t (or can’t) trust the data they’re given, they may not use it or the systems that deliver it.
- Eroded patient experience. Confidence is key in driving patient loyalty and clinical adherence. A complete patient profile ensures comprehensive knowledge, allowing healthcare providers to access critical information promptly and log health results, which gives patients the best experience possible. When multiple providers have access to the same patient records, it streamlines communication and delivers on the promise of efficient, coordinated care.
Calibrate your data velocity to your goals
Where data freshness is paramount when a quick reaction matters most, there are also instances where static or highly selective data can help eliminate unnecessary noise. Data analysts, for example, can afford to wait out “frothy,” less organized data, opting instead for cleaner retrospective-based insights. Claims, for example, can trickle in slowly, so a picture will be more complete with data that’s one month old than it is this week.
One way to control the data accelerator is through a data analytics platform. The ability to create custom reports means organizations aren’t stuck with what comes out of the box and can dial data recency up or down based on their specific needs. A data analytics platform also offers lineage and transparency, meaning organizations can trace and contextualize data-driven insights, looking backward at how and when they emerged and from what source.
High-quality, efficient healthcare requires data that’s delivered rapidly to the teams and providers who need it most. High-velocity data reduces wasted time and shortens the window from insight to action, improving performance from BI spreadsheets to patient bedsheets. It’s time to merge into the data fast lane.
About Nick Stepro
Nick Stepro is the Chief Product and Technology Officer at Arcadia, where he leads the design of the next wave of advanced healthcare analytics applications. He’s currently focused on evolving the architecture of the company’s next-generation healthcare data platform to simplify the continuous acquisition and orchestration of petabytes of data and millions of patient records across the modern healthcare enterprise.
