Understanding the Basics of Clinical Decision Support Systems

With an increasing amount of data accessible and an increasing obligation on the part of healthcare professionals to offer value-based treatment, clinical decision support systems are fast emerging as indispensable tools.

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In the current regulatory and reimbursement landscape, providers’ top priorities include minimizing clinical variation and redundant testing, guaranteeing patient safety, and preventing complications that could lead to costly hospital readmissions. Tapping into the untapped potential of big data is crucial to accomplishing these objectives.

Clinical decision support (CDS) systems are intended to assist in sorting through massive volumes of digital data in order to recommend therapies for the future, notify healthcare professionals about information that may be accessible but which they may not have seen, or identify possible issues, such harmful pharmaceutical combinations.

Chief Medical Officer of Atrius Health Dr. Joe Kimura stated, “It’s unreasonable to expect the average clinician to integrate all of it into their decision-making effectively and reliably. The amount of information we need to understand is getting so untenable.”

“We need to make sure that technology is helping us if we truly want to ensure that every human being receives excellent care.”

Creating intuitive, user-friendly, and efficient protocols for alarms, alerts, and decision-making pathways remains a significant challenge for many organizations, despite the fact that CDS tools are frequently integrated into electronic health records (EHRs) to expedite workflows and leverage pre-existing data sets.

Poorly implemented clinical decision support technologies that overload users with unnecessary information or annoying workflow freezes that need additional clicks to avoid can lead to alert fatigue and clinical burnout.

Industry lists of health IT dangers and patient safety frequently include alert and alarm fatigue. Experts such as the ECRI Institute caution that users may quickly lose their sensitivity to crucial signals if they are constantly bombarded with low-priority popups, noises, or texts.

According to a 2016 research that was published in JAMA Internal Medicine, EHR users handle notifications for more than an hour every day on average. Every day, primary care physicians get an average of 76.9 notifications.

Even if many of those warnings originated from test results, pharmacies, or other doctors, not all of them were produced by clinical decision support systems (CDS)—in fact, adding CDS alerts to an inbox already bursting at the seams might be counterproductive.

How can healthcare organizations avoid the frequent problems of ineffective workflow processes and poorly disseminated notifications while yet producing important and effective clinical decision support technologies that enhance the quality of care?

Clinical Decision Support: What Is It?

Any instrument that gives filtered or situation-specific information to physicians, administrative personnel, patients, caregivers, or other members of the care team is referred to as clinical decision support.

The goals of CDS are to increase the quality of care, prevent mistakes or unfavorable outcomes, and boost the productivity of the care team.

According to HealthIT.gov, clinical decision support (CDS) can involve a wide range of tools for decision-making, including “contextually relevant reference information, clinical guidelines, focused patient data reports and summaries, focused order sets, clinical guidelines, computerized alerts and reminders to care providers and patients, among other tools.”

Although digital clinical guidelines and diagnostic support frameworks are not necessary, the contemporary definition of CDS frequently emphasizes health IT modules, apps, or analytics that make use of an organization’s big data assets.

“HIT functionality that builds upon the foundation of an EHR to provide persons involved in care processes with general and person-specific information, intelligently filtered and organized, at appropriate times to enhance health and health care” is the definition of CDS for providers taking part in Stage 2 of the EHR Incentive Programs.

To promote the creation of new CDS tools that may aid in population health management, precision medicine, value-based care, patient safety, and operational efficiency, the Office of the National Coordinator chose not to further define the term.

According to CMS eHealth University, “CDS is not only for doctors or nurses; it is also for support staff, patients, and other caregivers.”


The fundamental ideas of CDS have countless applications in patient care, ranging from early infection detection to providing insights into highly customized cancer treatments.

The following are a few encouraging use examples from the provider community:

An Alabama hospital that used a computerized surveillance method saw a 53 percent reduction in sepsis death rates. Real-time analytics reminded healthcare professionals of the best ways to treat patients with the fatal illness and informed them of any new sepsis diagnosis or deteriorating vital signs.

A CDS tool is now being used by Mayo Clinic to assist nurses in doing thorough and accurate phone screens of individuals who are looking for appointments or advice. In order to make sure that triage nurses do not overlook crucial information on the patient’s health, automated decision software leads them through a series of standardized questions based on current treatment guidelines.

It was discovered by Harding University and Unity Health-White County Medical Center that integrating genetic testing data with a CDS system might cut ED visits by 42% and hospital readmissions by 52%. Testing for interactions between drugs and genes has been done on high-risk patients, and the results have contributed to anticipated cost savings of about $4300 per capita.

Developed by Yale and the Mayo Clinic, a clinical decision support program helps patients who present with head injuries by assessing the severity of the damage and providing information based on industry recommendations. In addition to possibly decreasing the frequency of pointless CT scans—a costly test that isn’t always necessary—the program informs patients about chosen therapies.

Clinical decision support technologies designed to cut down on superfluous lab usage at a Department of Veterans Affairs facility in Indiana helped reduce overall test volume by 11.18 percent annually, saving over $150,000 in costs without sacrificing the quality of care.