Sample Nursing Essays on Clinical Decision Support Systems

Potential Benefits and Drawbacks to Clinical Decision Support Systems (CDSSs)

A clinical decision support system (CDSS) is an application used by health providers to analyze data to make informed decisions and improve patient care. CDSS is a variation of the decision support system which business leaders mostly use to support organizational management. Examples of CDSS included laboratory information systems (LISs) and pharmacy information systems (PISs). LISs are used to help improve the activities of a medical laboratory and prevent medical errors. PISs are used in health settings to reduce medication errors, increase patient safety, report drug usage, and track costs of drugs (Chi & Kurilo, 2017). CDSS enables integrated workflows, provides assistance during care delivery, and offers care plan recommendations (Casimir, 2015). When using CDSSs, data mining has to be first conducted to examine a patient’s medical history in conjunction to the available clinical research. This analysis can help health providers to predict potential events when a patient is engaged in treatment such as drug interactions. CDSSs have various benefits and drawbacks.

Benefits Drawbacks
Improves efficiency in healthcare delivery. CDSSs improve efficiency in care delivery through faster processing of orders, reducing test duplications, and decreasing adverse events. Information overload. Since CDSSs analyze all aspects of a problem, they might result in information overload whereby a health provider might be in a dilemma of what information to consider or disregard. Not each bite of information presented by the CDSSs might be necessary for clinical decision making.
Improves quality of care. CDSSs improve the quality of care by increasing the application of clinical pathways and guidelines. CDSS also facilitate the use of up-to-date clinical evidence, and improve clinical documentation (Shiffman, 2016). Associated with high costs. CDSSs are associated with high maintenance, support, and training costs. Once the system has been incorporated into a health setting, the firm has to train its personnel on how to use it.
Reduces medication errors. CDSSs reduce medication errors by giving physicians and nurses easy and quick access to drug-specific dosing calculators that allow them to give the right amount of dosage to patients. Prevents decision-makers from honing their skills further. Although CDSSs are often integrated into health settings to make everyday decisions faster, the system leads to excessive dependence, thereby preventing decision-makers from honing their skills further.


Patient Scenario

As an advanced practice nurse (APN), I would use a clinical decision support system on a patient with obstructive sleep apnea (OSA) who experiences frequent episodes or partial upper airway blockage when sleeping. Such episodes of upper airway blockage often result in sleep fragmentation, increased fluctuations in intrathoracic pressure, and hypercarbia (Hanna & Izzo, 2020). In the long-term, such episodes often lead to death. As an APN, I would use a CDSS to establish the patient’s medical history. This approach would help me to identify the factors that contribute to the patient’s condition. I will then use the CDSS to identify how previous health providers had attended to the patient’s situation. In case I am not satisfied with the treatment procedures other health providers used in managing the patient situation, I would use the CDSS to identify the best treatment procedure for OSA. In my perspective, the best treatment for OSA might be continuous positive airway pressure (CPAP). I would then read the available information regarding the benefits and disadvantages of CPAP, then present it to the patient to allow him or her to make informed decisions related to that treatment procedure. This approach ensures the patient’s compliance.




Casimir, P. (2015). Role of clinical decision support systems in improving clinical practice. MOJ Clinical & Medical Case Reports, 2(6).

Chi, A., & Kurilo, M. B. (2017). Measurement and improvement across immunization information systems: Paving pathways for pharmacy data exchange. Research in Social and Administrative Pharmacy, 13(4).

Hanna, J., & Izzo, A. (2020). Surgical Treatment Options for Obstructive Sleep Apnea. Updates in Sleep Neurology and Obstructive Sleep Apnea [Working Title].

Shiffman, R. N. (2016). Best Practices for Implementation of Clinical Decision Support. Health Informatics Clinical Decision Support Systems, 99–109.