Clinical Decision Support Systems

Benefits of CDSSs Drawbacks of CDSSs
The implementation of CDSSs reduces the potential risks of medication errors.
The determination of accurate medication doses is frequently challenging and particularly critical in emergency situations. Although calculations traditionally involve complex and accurately recalled formulas, mathematical errors may still occur and lead to harmful health outcomes. CDSSs provide nurses and physicians with simple and quick access to complete drug monographs with the patient’s weight, age, and disease and drug-specific dosing calculations (Castaneda, et al., 2015). Easily accessible and accurate medication information may substantively reduce the risks of medication errors.
Alert fatigue may occur.
CDSSs may generate an excessive number of recommendations and alerts. As a result, health care providers start to ignore them regardless of their importance. This practice may result in negative outcomes for patients’ health.
The use of CDSSs reduces misdiagnoses.
Diagnoses errors comprise a substantial part of all medical errors. There are several major causes of misdiagnoses that include uncommon disease processes, cognitive errors, provider bias, and atypical presentations. CDSSs may drastically reduce misdiagnoses as they contain various support tools that may be used to ensure the diagnosis’s accuracy when it is not obvious or unclear.
The integration of CDSSs may be connected with integration issues and challenges in data collection.
CDSSs should be integrated with existing information systems and all data should be presented to the full extent. However, all data cannot be collected and that is why the CDSSs’ guidance cannot be completely accurate.
CDSSs provide care teams with reliable and consistent information.
The time-sensitive detection of the most relevant and appropriate evidence-based knowledge may be a challenging task. Internet search engines and Medline frequently cannot provide accurate answers as they return article abstracts or multiple results with various reliability and relevance. CDSSs provide clinicians and nurse practitioners with trusted sources for correct clinical decision-making.
CDSSs are characterized by substantial costs of design and adoption.
For providers, the implementation of CDSSs traditionally entails significant investments. In addition, the integration with the existing infrastructure and customization may increase the adoption’s total cost.
The implementation of CDSSs is cost-effective.
As CDSSs help clinicians to determine the diagnosis, calculations, and the correct doses of medicines, make appropriate relevant, and order relevant tests, they may substantially eliminate unnecessary expenditures related to misdiagnosis.

It goes without saying that CDSSs may be regarded as highly essential supportive tools in nursing practice. According to Zikos and DeLellis (2018), “proper use of clinical information is especially important in an effort to make sound clinical decisions and provide quality health services.” Despite potential alert fatigue and the prevailing significance of evidence, CDSSs are beneficial for Advanced Nurse Practitioners, especially at the beginning of their practice (Nibbelink, et al., 2018). For instance, the patient with heart failure who had already received treatment in another hospital will get high-quality health care delivery due to the use of the CDSS. First, the system will incorporate all essential information concerning the patient that includes his medical history and previous medication history. The patient’s data will subsequently expand with completed test results. Due to the recommendations and alerts of the CDSS, the patient will not be subdued to unnecessary or duplicate testing. In addition, the information concerning the patient’s previous medication that was prescribed in another hospital will substantially reduce the risk of a medical error.


Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A. Goy, A., & Suh, K. S. (2015). Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. Journal of Clinical Bioinformatics, 5(4). Web.

Nibbelink, C. W., Young, J.R., Carrington, J. M., & Brewer, B. B. (2018). . Critical Care Nursing Clinics of North America, 30(2), 237-246.

Zikos, D., & DeLellis, N. (2018). BMC Medical Research Methodology, 18(137).

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