Electronic Clinical Decision Support Tool for Nurses

Innovative technologies are an essential component of effective healthcare services. While the importance of healthcare experts’ knowledge is not to be underestimated, technological advances also allow for a wide range of opportunities to address health issues and provide the needed services. The devices that assist nurses in making clinical decisions should be valued particularly high due to the opportunities that they provide in risk management and quality improvement.

By deploying devices that help to monitor changes in patients’ health status and locate immediate threats to their health, one will create a framework for maintaining patients’ health at the required level and increasing chances for recovery.

The application of the Electronic Clinical Decision Support Tool (ECDST) bears especially high significance since it allows DNPs to integrate the information from the Electronic Health Records (EHR) database, as well as the latest changes in the clinical setting, in order to produce a solution for a particular health concern. Therefore, the standards for the provided health services have to be met respectively.

At present, a rigid framework for eliciting requirements for quality management and the delivery of the required outcomes exists. The proposed tool for ensuring that all instructions are met and that the needs of patients are understood properly suggests completing five crucial phases (Castaneda et al., 2015). These incorporate the stages of eliciting requirements, analyzing them, identifying the essential standards of care, managing the specified principles by applying them to the environment of clinical care, and offering careful monitoring and support for patients to increase chances for recovery (“Clinical decision support,” n.d.). The described framework should be integrated into the clinical setting by DNPs in order to manage patient information effectively and make clinical decisions that contribute to a rise in recovery rates.

Applying the identified framework to the context of the clinical care, a DNP will have to revisit the current principles of managing patients’ needs and responding to changes in their well-being. This can be accomplished by studying the existing guidelines, identifying their strengths and weaknesses, as well as locating gaps, and distilling the requirements that will correspond it the set quality standards of care (Liu, Lu, Ma, Chen, & Qin, 2016).

The design of the techniques that will help to introduce the proposed standards into the nursing setting and encourage nurses to use them is to be regarded as the next stage of introducing ECDST into the target environment. Afterward, the strategies for supervising the completion of the stated nursing techniques in the clinical setting will have to be included (Mrayat, Norwawi, & Basir, 2013), the described steps should be seen as the guide for a DNP to improve the current clinical setting and make the performance of the nursing staff more effective. The resulting increase in quality and a chance to reduce the length of the hospital stay will show the importance of revisiting the existing standards for clinical decisions.

The issue of data management is the primary reason for reconsidering the existing framework and integrating the latest technological innovations into the process of decision-making in the clinical context. Because of the need to incorporate the analysis of several factors into the analysis prior to locating the measures for addressing patients’ needs, an ECDST is needed to boost nurses’ performance and reduce the probability of a mistake. Medical errors have an especially negative effect on the chances for patients’ recovery in the clinical environment, which is why the integration of the proposed steps for addressing the issue on a DNP level is required.

In order to elicit critical requirements for improving the quality of clinical care, a DNP should consider combining direct and indirect approaches. While the former can be used to determine the available tools based on the method of interactions, the latter offers a taxonomy based on the type of data (Iqbal & Suaib, 2014). As a result, a DNP will infer both what types of information should be incorporated into the decision-making process and how the available information should be processed in order to produce a valid decision and improve a patient’s condition. The suggested technique will increase the probability of making a decision that will lead to the best possible patient outcomes and eliminate the threat of an error.

By applying the ECDST to the clinical setting, a DNP will infer crucial information about the factors that cause changes in patients’ well-being to occur. As a result, a DNP will be capable of designing a health management framework that will lead to an improvement and a faster recovery of patients (Dayan et al., 2017). By integrating technological innovations into the clinical setting, one will receive an opportunity to monitor alterations in patients’ health status, thus responding faster to new threats and obtaining the data that informs the choice of appropriate health management tools.

For a DNP, the application of the ECDST tool implies contributing to the creation of better health policies and clinical guidelines for nurses. As a result, the instances of mismanaging patients’ needs and increasing the length of their stay in the hospital with the following rise in the probability of a nosocomial infection will be reduced to a minimum.

References

Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A.,… Suh, K. S. (2015). Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. Journal of Clinical Bioinformatics, 5(1), 4.

Clinical decision support. (n.d.). Web.

Dayan, P. S., Ballard, D. W., Tham, E., Hoffman, J. M., Swietlik, M., Deakyne, S. J.,… Mark, D. G. (2017). Use of traumatic brain injury prediction rules with clinical decision support. Pediatrics, 139(4), 1-10.

Iqbal, T., & Suaib, M. (2014). Requirement elicitation technique: A review paper. International Journal of Computer and Mathematical Sciences, 3(9), 1-24.

Liu, X., Lu, R., Ma, J., Chen, L., & Qin, B. (2016). Privacy-preserving patient-centric clinical decision support system on naive Bayesian classification. IEEE Journal of Biomedical and Health Informatics, 20(2), 655-668.

Mrayat, O. I., Norwawi, N. M., & Basir, N. (2013). Requirements elicitation techniques: Comparative study. International Journal of Recent Development in Engineering and Technology, 1(3), 1-10.