Clinical Decision Support System


Information systems are a rapidly developing technology being integrated into the healthcare sector, which seeks to modernize the operational and clinical capacities of medical facilities and its staff. A technology known as the clinical decision support (CDS) system can combine research, protocols, informatics, and patient data into databases. Its function is critical in guiding and supporting medical professionals during the process of patient evaluation and treatment.

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An informed decision-making process drastically improves patient outcomes by limiting human error as well as improving both the speed and effectiveness of treatment options. The CDS system was chosen since the nursing staff is faced with many decisions when working with and treating patients. Nurses must follow complex protocols and are at the forefront of hospital operations in inpatient care.

I believe that the system has high potential in the healthcare field; however, it requires further development within the realm of artificial intelligence and should be carefully monitored to avoid complete dependence of human judgment on the technology. The CDS system is a necessary integration into the hospital information system network since it is a critical supplementary tool for the improvement of quality in patient outcomes.

System Design and Real-World Effectiveness

CDS systems are closely intertwined with electronic health records (EHRs), using patient-specific data, personal information, and medical history. The system is designed to provide a series of recommended actions based on the available patient data as well as any additional information input by the staff based on examination and situational evidence (Byrne et al., 2014). Expenses are reduced by improving the efficiency and quality of treatment.

From a medical perspective, the system can prevent errors, provide warnings, and ensure adherence to clinical guidelines. In terms of hospital operations, the system can reduce the length of stay, manage diagnostic testing, and aid in the implementation of preventive care (Musen, Middleton, & Greenes, 2014).

In a real-world setting, the system is effective on a basic level of providing alerts and recommendations based on input patient data. However, it lacks the integrated interaction in the healthcare process for which it is being designed. There are some challenges, such as interoperability issues with software and IT infrastructure in each individual healthcare organization. Furthermore, there are difficulties adopting CDS into clinical workflows as well as ensuring its validity due to rapidly changing protocols and available medical information.

Data Types and Research Support

The use of the CDS information system from a technical standpoint consists of using data mining in order to analyze patient history or records and compare them to relevant medical information available in the system’s databases. Patient data may include demographic information, diagnoses, medications, diagnostic test results, and allergies. The system actively evaluates its knowledge base to present a variety of clinical interventions, including symptoms, treatment protocols, care plans, event-driven alerts, alerts (such as drug interactions), documentation, data reports, and references (Byrne et al., 2014).

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Although the CDS system is based on providing research data to medical professionals, it has been actively used for various investigations revolving around healthcare practices. In recent years, a number of research studies have been conducted to analyze the effects of CDS integration on changes in hospital practices and its impacts on patient care. Driven by nationwide initiatives, many organizations are adopting evidence-based medicine, which CDS can provide. Furthermore, the system is used to analyze the efficiency of clinical workflow and how its methodology can have clinical and economic effects on healthcare.


Nurses are the largest group of employees in the healthcare sector, directly working with patients, and are the primary users of the CDS information systems. Many of the information technologies have been adopted by executives in an attempt to increase productivity and eliminate various burdens that make nursing a high-stress profession. Medical professionals, including nurses, participate in the design of CDS systems, especially in many patient-related aspects such as patient adherence to disease management.

Nurse leaders, considering experience and feedback from colleagues, participate in councils and initiatives which seek to enhance care coordination introducing information technologies. The design process seeks to accommodate input from interdisciplinary teams attempting to improve patient care (Nelson & Staggers, 2014).

System Enhancement

CDS systems are currently faced with various implementation challenges and lack the full range of capabilities, which it could potentially achieve. The most significant required enhancement, based on conducted studies and feedback, is an improvement of system usability. At first, that requires introducing relevant guidelines and training for health practitioners using the system. Furthermore, the system design should be interactive and intuitive, based on a familiar operating system. Usability can also apply to the physical device on which the CDS system is installed, availability of options for information input, and its effectiveness over time (Thum et al., 2014).


Overall, the CDS system is a viable introduction to medical organizations, which significantly assists with clinical workflow efficiency and healthcare costs. Using this type of information technology, nurses can improve the quality of patient care through a variety of factors. Nurse effectiveness improves as the system can eliminate redundant processes, rapidly provide information, and reduce human error. Patients experience a higher quality of care through evidence-based practice, which aids with disease management, therefore reducing repeated hospitalizations and length of stay, which, in turn, drives down out of pocket costs.


Byrne, C., Sherry, D., Mercincavage, L., Johnston, D., Pan, E., & Schiff, G. (2014). Key lessons in clinical decision support implementation. Web.

Musen, M.A., Middleton, B., & Greenes, R.A. (2014). Clinical decision-support systems. In E. Shortlife & J. Cimino (Eds.), Biomedical informatics (pp. 643-674). London, United Kingdom: Springer.

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Nelson, R., & Staggers, N. (2014). Health informatics: An interprofessional approach. Amsterdam, Netherlands: Elsevier Health Sciences.

Thum, F., Kim, M., Genes, N., Rivera, L., Beato, R., Soriano, J., … Hwang, U. (2014). Usability improvement of a clinical decision support system. In A. Marcus (ed.) International conference of design, user experience, and usability (pp. 125-131). Heraklion, Greece: Springer.

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