Clinical decision support systems (CDSSs) are becoming more often incorporated into healthcare environments to increase patient outcomes, minimize medical mistakes, and boost clinical efficiency via evidence-based suggestions for clinicians throughout patient care (Butzner & Cuffee, 2021). Yet, implementing and improving these systems continues to be complicated. This presentation will analyze several Clinical Decision Support Systems (CDSS) technologies to assess their use in promoting health equality for all patients in our healthcare organization.
The primary goal of CDSS is to provide prompt information to physicians, patients, and others to guide healthcare choices. CDSS tools consist of order sets tailored for specific ailments or patient types, suggestions, patient-specific information databases, preventative care reminders, and alarms for hazardous circumstances (Hartasanchez et al., 2022). CDSS can save expenses, enhance productivity, and minimize patient disruption. CDSS may simultaneously handle many areas, such as notifying physicians of potential duplicate tests a patient is scheduled to have (Lyles et al., 2021).
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CDSS is a system designed to improve health-related decision-making by delivering tailored information to physicians, workers, patients, or others to promote healthcare (Lyles et al., 2021). On the other hand, to provide genuine support for guideline-driven and equity-driven care, the creators of the algorithms and the CDSS systems must work toward eliminating prejudice (Butzner & Cuffee, 2021). By adopting this method, CDSS may effectively fulfill the commitment to promoting both guideline-based and equity-based care that enhances results and the general health of all individuals.
As the nurse informaticist, I am responsible for leading efforts to select appropriate Clinical Decision Support Systems for our organization to advance health equity. A CDSS may enhance health service quality, safety, efficiency, and effectiveness by using insights from healthcare analytics (Musen et al., 2021). The American Medical Informatics Association defines informatics as the comprehensive knowledge and advancement of organizing, analyzing, managing, and using the information in healthcare (Rodriguez et al., 2020). Informatics can enhance efficiency in healthcare operations and can improve patient outcomes.
CDSS stakeholders include end-users such as physicians, nurses, laboratory technologists, pharmacists, and patients. Also, sales and marketing teams and CDSS product development and maintenance teams, including system administrators, system developers, system architects, project managers, and system maintenance staff, are involved (Hartasanchez et al., 2022). The project manager, system designer, and developer collaborate closely as a team under the guidance of the CDSS architect. The project manager monitors the project’s timeline, efficient resource use, and budget. The system analyst/designer collects CDSS requirements and creates a conceptual model of the CDSS. The developers are accountable for implementing and testing the CDSS. The CDSS is primarily used by physicians, nurses, laboratory technologists, pharmacists, and patients. Each individual has their own set of expectations about CDSS, some of which are fulfilled while others remain unsatisfied.
An instance of a CDSS application is Epic Systems’ tool, which provides evidence-based suggestions and alerts to healthcare organizations throughout patient care. This enhances the precision and swiftness of decision-making, eventually delivering more effective services to patients. A study by Musen et al. (2021) shown that this software has had a beneficial impact on patient care by enhancing diversity, equality, and inclusion initiatives in healthcare delivery. The software assists healthcare institutions in providing thorough and equitable care by offering tools to identify and address health disparities. Subica & Brown (2020) state that Epic is globally acknowledged as the most frequently used Electronic Health Record (EHR). Organisations may use the system’s many features, including pooled data, module integration, and the development of decision assistance tools.
The application of information and communication technologies in the healthcare industry is predicated on the creation of digital technologies, databases, and other applications that aim to improve healt
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