Clinical decision support system

A clinical decision support system (CDSS or CDS system) is a form of healthcare technology. The purpose of a CDS system is to assist medical practitioners when they make patient care decisions. The system can help in several ways, including collecting information into one accessible place, assisting in ordering routine tests, analyzing patient information, and integrating seamlessly with electronic health record (EHR) systems. CDS systems can help medical practitioners be more efficient and accurate, control costs, curb waste in the medical system, and reduce patient inconvenience.

Overview

CDS systems originated in the 1950s with special databases built to provide physicians with up-to-date treatment information. In the 1960s, hospitals began using the earliest forms of computerized patient information systems that maintained basic records, including patient admission files and lab results. Some facilities used more low-tech CDS systems in which librarians looked up information and physically attached recent articles and studies to the patients’ files.

As computer technology increased in scope and decreased in size, more healthcare systems turned to this technology to assist physicians. Programs were developed to help physicians quickly access patient records, find relevant expert information, prescribe medications, and handle record keeping and billing. By the 1990s, many physicians were using personal digital assistants (PDAs) to assist with care.

The early 2000s saw continued advances in computerized technology and increased research in medical fields. At the same time, there was increased focus on decreasing healthcare costs and reducing the amount of errors practitioners made. This led to the development of new CDS systems that could assist physicians with diagnosis and treatment processes while reducing waste, costs, and errors. In the 2010s and 2020s, systems began incorporating predictive analytics, allowing medical professionals to forecast patient deterioration, hospital readmissions, and other health outcomes. CDS systems also utilize personalized medicine approaches, integrating genomic data to tailor treatments to individual patients. All of this worked to improve patient care and reduce inconvenience from mistaken diagnoses or treatments or duplicate testing.

One key function of the earliest CDS systems was to alert healthcare practitioners of potential problems. A physician might receive a notice that a patient recently received the same blood tests they were about to order, or a pharmacist might receive a warning that the prescription they were about to fill caused a dangerous interaction with a drug the patient received from another pharmacy earlier. These alerts reduced costs and improved patient safety. However, alert fatigue—where excessive or irrelevant alerts cause clinicians to become desensitized, causing them to potentially ignore critical warnings and negatively affect patient safety—remained a challenge. Efforts have focused on improving the intelligence and interface of CDS alerts to enhance user experience and effectiveness by avoiding alert fatigue.

Modern CDS systems help practitioners narrow potential diagnoses based on symptoms and patient history, predict future health conditions, automatically update patient records, and streamline routine tests and procedures. Some systems are basic and designed for limited functions, such as providing information about prescription drugs and identifying which medications are covered by a patient’s insurance. Other systems are complicated, with multiple programs that can integrate patient records, diagnostics, testing guidelines, and artificial intelligence-driven analytics.

By the mid-2020s, many CDS systems included natural language processing features, which could extract information from clinical notes, cloud-based platforms providing real-time updates, and integration with smartphones, tablets, and wearable technology for continuous patient monitoring. Artificial intelligence and machine learning became essential in advanced CDS systems in the 2020s, assisting in medical imaging analysis, early detection of conditions like sepsis, and improving diagnostic accuracy. However, with these advancements, concerns about data privacy, cybersecurity, compliance with government regulations, and bias increased.

The Office of the National Coordinator for Health Information Technology (ONC), through its 2023 Health Data, Technology, and Interoperability (HTI-1) Final Rule, transitioned the former CDS certification criterion to the more comprehensive Decision Support Intervention (DSI) framework by January 2025. The DSI criterion is required for systems seeking to meet the EHR definition and to qualify as a Certified Electronic Health Record Technology (CEHRT) under the Centers for Medicare & Medicaid Services (CMS) programs. These changes have significantly modernized CDS by introducing greater transparency and risk management, particularly for artificial intelligence– and machine learning-based tools.

Bibliography

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“Clinical Decision Support Software Frequently Asked Questions (FAQs).” U.S. Food and Drug Administration, 20 Dec. 2024, www.fda.gov/medical-devices/software-medical-device-samd/clinical-decision-support-software-frequently-asked-questions-faqs. Accessed 2 Apr. 2026.

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“HTI-1 Decision Support Interventions (DSI) Fact Sheet: Health Data, Technology, and Interoperability—Certification Program Updates, Algorithm Transparency, and Information Sharing Final Rule.” Office of the National Coordinator for Health Information Technology, Dec. 2023, www.healthit.gov/wp-content/uploads/2023/12/HTI-1_DSI_fact-sheet_508.pdf. Accessed 2 Apr. 2026.

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Sutton, Reed T., et al. “An Overview of Clinical Decision Support Systems: Benefits, Risks, and Strategies for Success.” NPJ Digital Medicine, vol. 3, no. 17, 2020, doi:10.1038/s41746-020-0221-y. Accessed 2 Apr. 2026.