How to Use Clinical Decision Support Systems to Improve Patient Outcomes

Clinical decision support systems have come a long way from their early implementations in the 1970s. As stand-alone systems that were expensive to build and difficult to use — and given the legal and ethical resistance at the time to using computers to practice medicine — CDSSs tended to be restricted to “academic pursuits,” as a Nature article put it.

Decades later, these systems integrate with other clinical applications, run in browsers or on mobile devices, and support clinical teams in a variety of care settings, from the intensive care unit to primary care.

Two trends have pushed the industry to adopt clinical decision support, says Kevin Phillips, vice president of product management for Philips Capsule. One is near-universal adoption of electronic medical records and computerized physician order entry systems. The other is the push in the 21st Century Cures Act and the Interoperability and Patient Access Final Rule to make it easier to extract data from clinical systems

“The deployment of technology has raised awareness of the value of data in healthcare,” Phillips says. “Getting data from the EMR system as well as FDA-approved medical devices to develop decision support rules has never been easier.”

How Do Clinical Decision Support Systems Work?
At its core, clinical decision support is about helping clinicians and the patients they care for, says Saif Khairat, an associate professor of health informatics and health services research at the University of North Carolina at Chapel Hill. The most common uses for decision support are medical diagnosis, care alerts and reminders, medication management and chronic disease management.

“It’s a health IT solution that provides clinicians or patients with person-specific information and intelligence to offer recommendations in a timely fashion that improve care outcomes and reduce medical or medication errors,” he says.

Broadly speaking, clinical decision support systems are classified as either knowledge-based or non-knowledge-based.

Knowledge-based systems rely on a series of rules, written as if-then statements, to look at the patient data (the system’s input) and generate a recommended action (the output). These comprise the majority of clinical decision support systems — and the transparency of rules helps to drive adoption, Phillips says: “Clinical teams understand the data that’s going in and the rules that are generating the alerts.”

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