Abstract
Advances in the field of predictive modeling using artificial intelligence and machine learning have the potential to improve clinical care and outcomes, but only if the results of these models are presented appropriately to clinicians at the time they make decisions for individual patients. Clinical decision support (CDS) systems could be used to accomplish this. Modern CDS systems are computer-based tools designed to improve clinician decision making for individual patients. However, not all CDS systems are effective. Four principles that have been shown in other medical fields to be important for successful CDS system implementation are (1) integration into clinician workflow, (2) user-centered interface design, (3) evaluation of CDS systems and rules, and (4) standards-based development so the tools can be deployed across health systems.
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