According to the Office of the National Coordinator, 96% of hospitals own EHR (electronic health record) technology.
While their widespread adoption has mitigated many technical challenges associated with data capture and storage, EHRs were never designed as natural repositories for real-time and unstructured data, or foundations for advanced analytics.
EHRs store relatively static or binary data—history, demographics, observations and treatments—rather than moment-to-moment changes in a patient’s condition. As Riddle notes in a 2015 study, “EHRs are great repositories of data and can accomplish some low-level data aggregation… these systems were never designed to address the level of data analytics needed to advance care delivery from reactive to proactive.”
The engineering of EHRs makes it prohibitive to store or integrate unstructured data, including video and conversations among the clinical team. Consequently, much of that valuable information is lost or left unused. However, those data elements, combined with static, EHR-stored information can, uncover surgical variations, risks to patient safety, and inefficiencies in care team workflow and communication.
However, the central role EHRs play in day-to-day clinical operations and workflow requires the seamless integration of peripheral capabilities, such as machine learning and advanced analytics, to:
● Capture and aggregate real-time information from myriad sources;
● Integrate it with static and retrospective data stored in the EHR to add even richer and more holistic detail to a patient’s condition;
● Apply advanced analytics to uncover actionable insights; and
● Share that information with the entire care team.
In short, the analytics with the most data inputs are often the best analytics.
This need can be applied to just about any unit within a hospital, but none so more than the surgical suite.
Connecting the Dots
Investments in advanced analytics in the surgical suite are driven by organizations that are migrating toward value-based care models seek to improve care quality, including improving care quality and outcomes, reducing clinical variation and reducing healthcare costs.
Actionable information is key to minimizing patient risk and surgical variation. Surgical teams must have access to the full patient picture that pulls from all of a hospital’s data sources, including admissions/discharge/transfer, laboratories, radiology, surgery, pharmacy, vital signs and medical records, to make informed decisions and performance improvements. Data must also be presented in a way that is meaningful to a particular patient’s care.
One of the objectives of advanced analytics is to seek interrelationships among seemingly unrelated measurements and sources of data to determine whether these interrelationships can reveal insights that would not normally be visible by observing a single parameter or multiple parameters individually.
Supercharge the EHR
With advanced analytics, surgical teams gain greater visibility into each surgical episode, and can identify and understand the root causes of outcome variability. In addition, the ability to easily interface with the data and share it among team members empower hospitals to automate workflow processes, improve training protocols and observe surgical process in a real-world context.
A decade ago, healthcare’s major data challenge was capture and storage. Today, it’s analysis. Specifically, it’s using statistical algorithms and machine learning techniques to draw actionable insights from massive datasets, including unstructured data, to facilitate real-time clinical decision making. Advances in these areas have allowed hospitals to supercharge their EHR investments and use those systems as foundations for advanced analytics and for painting a more holistic portrait of a patient’s condition.
Paul Summerside, MD, is chief medical officer of caresyntax, a surgical data, analytics and automation company.
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