Optimizing Clinical Decision Support System Functionality by Leveraging Specific Human-Computer Interaction Elements: Insights From a Systematic Review.

Abstract

Background: Clinical decision support systems (CDSSs) play a pivotal role in health care by enhancing clinical decision-making processes. These systems represent a significant advancement in medical information systems. However, optimizing their effectiveness requires accounting for various human-computer interaction (HCI) elements that influence their functionality and user acceptance.

Objective: This study aimed to identify and categorize key HCI elements that impact CDSS performance to enhance system usability, adaptability, and decision-making accuracy.

Methods: We conducted a systematic literature review, identifying 923 studies from the databases PubMed, Scopus, and Web of Science. Papers were screened and selected based on predefined inclusion criteria. A rigorous quality assessment process was applied to ensure the relevance and reliability of the included studies. Ultimately, of the 923 papers identified, 43 (4.7%) that specifically addressed HCI elements applicable to CDSS environments were included in the final analysis. Data extraction and synthesis were performed to answer the research questions regarding HCI elements.

Results: A total of 12 distinct HCI elements were identified, each with the potential to influence CDSS functionality. These elements align with the International Organization for Standardization (ISO) 9241-11 framework, which defines usability in terms of effectiveness, efficiency, and satisfaction. "User satisfaction," "flexibility," and "individuality" enhance satisfaction by improving system adaptability and user acceptance. "Visibility," "explainability," and "user control" strengthen effectiveness by supporting decision-making and error prevention. "Ease of use" improves efficiency by streamlining interactions and reducing cognitive load. Some elements influence effectiveness and efficiency, such as "data entry," which ensures structured inputs for decision accuracy while optimizing workflows. Likewise, "alerts" provide timely information for effective decision-making and, simultaneously, are designed to avoid overwhelming users and maintain system efficiency. "Simplification" and "mental effort" also optimize workflows and reduce complexity. Furthermore, "interface" impacts effectiveness and efficiency by supporting accurate decision-making and streamlining user interaction. This categorization, aligned with ISO 9241-11, underscores the context and task dependency of usability, highlighting that HCI elements must be adapted to different user needs and environments for effective clinical decision-making.

Conclusions: This study addresses a critical gap in CDSS research by offering a comprehensive framework of HCI elements tailored to the CDSS environment. Incorporating these elements into system design can improve user satisfaction, reduce data errors, and enhance the accuracy of medical decisions. The findings lay the groundwork for future research, offering practical guidelines for developing more reliable and efficient CDSS systems in medical informatics fields.