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NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics

Tool Kit for Bioinformatics

Since the emergence of the COVID-19 virus, many have been concerned about the security of their health, particularly those who visited hospitals during the COVID-19 outbreak and were worried about contracting the virus in the hospital environment (Wu et al., 2020). Early measures, such as quick identification and treatment of the COVID-19 infection, may increase people’s feeling of security. This aim is achievable with the use of Health Information Technology, including Clinical Decision Support System (CDSS) and Best Practice Advisory (BPA) alerts (Wu et al., 2020). Thus, this paper will provide a tool kit for implementing CDSS and BPA alerts.

NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics

Evidence-Based Policy

The burden of this pandemic has increased healthcare workers’ workload and significantly inflated healthcare costs. Thus, patients, care providers, and even health systems would face major issues owing to the shortage of medical professionals and medical equipment if the spread of this illness is not treated and controlled as soon as feasible. Moulaei (2022) argues that to treat and prevent the spread of illness effectively, and healthcare providers need to pay close attention to the first signs of COVID-19 infection. They argue that optimizing the use of the CDS system would help physicians make more informed decisions about patients’ diagnoses, treatments, and follow-ups more swiftly. As a result, physicians can make a correct diagnosis more rapidly and limit the outbreak. In the form of computerized alerts, reminders, patient reports, and clinical trial tools, the CDS system offers guidance, knowledge, and information to patients and healthcare professionals (Moulaei, 2022).

Technology in the medical field, namely health information technology, has simplified the delivery of high-quality, timely treatment. The Affordable Care Act requires healthcare providers to adopt and fully use health information technology to increase quality, improve patient outcomes, and reduce healthcare costs. According to the author Fry (2021), a learning health system prepared to traverse the increasingly complicated healthcare environment requires a fully developed electronic health record (EHR) with clinical decision support (CDS). At the point of care, clinicians may benefit from a wide range of integrated clinical decision-support technologies inside electronic health records (EHRs), which improve clinical decision-making by providing relevant information. Clinicians may utilize the EHR’s integrated CDS tool, the Best Practice Advisory (BPA) alert, to improve patient outcomes and operational efficiencies (Fry, 2021).


Implementing policies into practice is necessary to reach the intended goal; having policies in place is not enough. Gaining support from the key stakeholders is essential for successfully putting the plans into action. Establishing and communicating guiding concepts, norms, and policies to the whole healthcare workforce is essential (Akhloufi et al., 2022). The healthcare institution has to have weekly meetings with physicians, nurses, hospital administrators, nurse informaticists, and information technology specialists to develop an efficient CDS system and BPA alerts. Each week, the team will get together to discuss how they can improve the technology by adding new features that make them more user-friendly and reduce the number of mistakes that may be made during its use. Additionally, the weekly meetings will also provide training on the efficient use of technology (Akhloufi et al., 2022).  

Planning for the implementation may begin after meetings and training sessions have been held, with the development team discussing the project’s aims and objectives. When the strategy is ready, the development team will work with a system vendor to figure out how to best integrate technology to meet the goals (Akhloufi et al., 2022). Vendors of these systems will roll out a beta version, or minimum viable product, so that healthcare organizations may test it out and offer feedback. As a result of this feedback, vendors will better understand the demands of healthcare teams and may make necessary adjustments to the system. A CDS tailored to the requirements of both patients and healthcare professionals is essential for improved health outcomes (Akhloufi et al., 2022).

Practical Recommendations

Stakeholders Education 

Implementing the technology successfully requires buy-in from every legitimate person. After the healthcare organization has determined what it hopes to accomplish with the help of cutting-edge technology, it is time to teach its staff to utilize that technology to its full potential. With the support of information technology teams, healthcare organizations may hold weekly training sessions, seminars, and webinars to educate and teach professionals about the effective use of technology, as well as to listen to staff issues and provide answers (Lukowski et al., 2020).

Several studies have highlighted the advantages of classroom-based team training interventions and simulation. Both simulation-based and traditional classroom training may be used to assess professionals’ technical competence and fill training gaps in the effective use of technology in healthcare (Bienstock & Heuer, 2022).

Monitor Data to Evaluate Outcomes

Once the CDS system and Best practice advisory (BPA) alert have been implemented successfully, it will be important to assess their impact on patient outcomes for COVID-19. The CDS system has the potential to enhance health outcomes since it aids in the rapid and correct detection of disease, hence reducing its spread, and also provides guidance, knowledge, and information in the form of alerts to patients and clinicians. Health outcomes will improve, healthcare expenses will decrease, and patients will feel safer and more secure. This may result in cost reductions for healthcare organizations (Karthikeyan et al., 2021).

Saegerman et al. (2021) showed that using the CDS system facilitated the rapid identification of patients with COVID-19. The authors claim that the widespread coronavirus infectious disease-19 (COVID-19) has created massive destruction, severe acute respiratory failures, and an increase in the number of people visiting emergency departments (EDs) at a time when diagnostic labs are understaffed. Helping triage patients and providing resources to those most in need, developing clinical decision support systems for real-time clinical diagnosis of COVID-19 has emerged as an essential tool in this pandemic (Saegerman et al., 2021). 

NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics

A Specific Example of Bioinformatics

By employing a clinical decision support tool to guide them through diagnostic evaluations, clinicians may significantly reduce the time it takes to examine patients presenting with COVID-19 symptoms (Gavrilov et al., 2021). To stop the further spread of the virus in a healthcare facility or emergency department, it is essential to quarantine patients who appear with signs of COVID-19. On the other hand, needless isolation may hold up treatment, take up beds that other patients might use, and squander personal protective equipment. After the doctor has responded to several questions about the patient’s risk factors, symptoms, and imaging data, the CDS system leads practitioners through a standardized COVID-19 diagnostic workup of the patient based on the most recent recommendations (Gavrilov et al., 2021).

Integration of CDS systems with Best Practice Advisory Alerts has various advantages. Studies have shown that adopting a CDS system improves the safety of patients and medical staff. It is a quick and reliable method for detecting viruses and reduces the likelihood of false negative findings that might endanger the lives of other patients and healthcare workers. Time saved during diagnosis and in quarantine are two significant advantages of the CDS system (Gavrilov et al., 2021).


Before the implementation of the CDS system 

After the implementation of the CDS system 

Time to make an accurate diagnosis of COVID-19

1-2 days

5-6 hours

Healthcare costs 



Unidentified patients in quarantine

10-20 patients 

5 patients

False Negative Results 

7-8 false negative results

3-4 false negative results 


The administration and management of COVID-19 were among the topics that this study sought to investigate to determine the feasibility of using CDS systems. Since the CDS system swiftly diagnoses COVID patients, it aids healthcare professionals in limiting its spread. This reduces the risk of irreversible complications and even many deaths while lowering the costs of unnecessary treatments and diagnostic procedures, reducing the time required for diagnostic procedures, and improving clinical performance and patient-related outcomes.


Akhloufi, H., van der Sijs, H., Melles, D. C., van der Hoeven, C. P., Vogel, M., Mouton, J. W., & Verbon, A. (2022). The development and implementation of a guideline-based clinical decision support system to improve empirical antibiotic prescribing. BMC Medical Informatics and Decision Making, 22(1). https://doi.org/10.1186/s12911-022-01860-3

Bienstock, J., & Heuer, A. (2022). A review on the evolution of simulation-based training to help build a safer future. Medicine, 101(25), e29503. https://doi.org/10.1097/MD.0000000000029503

Fry, C. (2021). Development and evaluation of best practice alerts: Methods to optimize care quality and clinician communication. AACN Advanced Critical Care, 32(4), 468–472. https://doi.org/10.4037/aacnacc2021252

Gavrilov, D., Kuznetsova, T., Gusev, A., Korsakov, N., & Novitskiy, R. (2021). Application of a clinical decision support system to assess the severity of the new coronavirus infection COVID-19. European Heart Journal, 42(Supplement_1). https://doi.org/10.1093/eurheartj/ehab724.3054

Karthikeyan, A., Garg, A., Vinod, P. K., & Priyakumar, U. D. (2021). Machine learning-based Clinical Decision Support System for early COVID-19 mortality prediction. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.626697

NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics

Lukowski, F., Baum, M., & Mohr, S. (2020). Technology, tasks and training – Evidence on the provision of employer-provided training in times of technological change in Germany. Studies in Continuing Education, 1–22. https://doi.org/10.1080/0158037x.2020.1759525

Moulaei, K. (2022). Diagnosing, managing, and controlling COVID-19 using Clinical Decision Support systems: A study to introduce CDSS applications. Journal of Biomedical Physics and Engineering, 12(02). https://doi.org/10.31661/jbpe.v0i0.2105-1336

Saegerman, C., Gilbert, A., Donneau, A.-F., Gangolf, M., Diep, A. N., Meex, C., Bontems, S., Hayette, M.-P., D’Orio, V., & Ghuysen, A. (2021). Clinical decision support tool for diagnosis of COVID-19 in hospitals. PLOS ONE, 16(3), e0247773. https://doi.org/10.1371/journal.pone.0247773

Wu, G., Yang, P., Xie, Y., Woodruff, H. C., Rao, X., Guiot, J., Frix, A.-N., Louis, R., Moutschen, M., Li, J., Li, J., Yan, C., Du, D., Zhao, S., Ding, Y., Liu, B., Sun, W., Albarello, F., D’Abramo, A., & Schininà, V. (2020). Development of a clinical decision support system for severity risk prediction and triage of COVID-19 patients at hospital admission: an international multicentre study. European Respiratory Journal, 56(2). https://doi.org/10.1183/13993003.01104-2020

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