NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics
NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics
Student Name
Capella University
NURS-FPX 6414 Advancing Health Care Through Data Mining
Prof. Name
Date
Tool Kit for Bioinformatics
Since the emergence of the COVID-19 virus, concerns have arisen regarding health security, especially among those who visited hospitals during the outbreak and were worried about contracting the virus in the hospital environment (Wu et al., 2020). Early measures such as rapid identification and treatment of COVID-19 infections can enhance people’s sense of security. This objective can be achieved through the use of Health Information Technology, including Clinical Decision Support Systems (CDSS) and Best Practice Advisory (BPA) alerts (Wu et al., 2020). Thus, this paper provides a toolkit for implementing CDSS and BPA alerts.
Evidence-Based Policy
The burden of the COVID-19 pandemic has increased the workload for healthcare workers and significantly inflated healthcare costs. Consequently, patients, care providers, and health systems could face significant challenges due to the shortage of medical professionals and equipment if the spread of the illness is not effectively controlled (Moulaei, 2022). To effectively treat and prevent the spread of the illness, healthcare providers must closely monitor the early signs of COVID-19 infection. Optimizing the use of CDSS can assist physicians in making more informed decisions about patient diagnoses, treatments, and follow-ups, leading to quicker and more accurate diagnoses and outbreak containment (Moulaei, 2022).
NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics
In the medical field, particularly health information technology, the delivery of high-quality, timely treatment has been simplified. The Affordable Care Act mandates healthcare providers to adopt and fully utilize health information technology to improve quality, patient outcomes, and reduce healthcare costs (Fry, 2021). A fully developed electronic health record (EHR) with clinical decision support (CDS) is essential for a learning health system capable of navigating the complex healthcare landscape. Integrated clinical decision support technologies within electronic health records, such as Best Practice Advisory (BPA) alerts, enhance clinical decision-making by providing pertinent information to clinicians (Fry, 2021).
Guidelines
Effective policy implementation requires the support of key stakeholders. Communicating guiding principles, norms, and policies to the entire healthcare workforce is essential (Akhloufi et al., 2022). Healthcare institutions should conduct regular meetings involving physicians, nurses, hospital administrators, nurse informaticists, and information technology specialists to develop an efficient CDSS and BPA alert system. These meetings aim to improve the technology’s user-friendliness and minimize errors during its use. Additionally, they offer training on efficient technology usage (Akhloufi et al., 2022).
Following meetings and training sessions, planning for implementation may commence, with the development team defining project goals. The development team collaborates with system vendors to integrate technology effectively to achieve these goals (Akhloufi et al., 2022). Vendors may introduce a beta version or minimum viable product for healthcare organizations to test and provide feedback, leading to system adjustments tailored to the needs of patients and healthcare professionals (Akhloufi et al., 2022).
Practical Recommendations
Stakeholders Education
Successful technology implementation requires the buy-in of all relevant stakeholders. Healthcare organizations can educate their staff on maximizing technology’s potential by conducting weekly training sessions, seminars, and webinars, while also addressing staff concerns (Lukowski et al., 2020). Studies have shown the benefits of classroom-based team training interventions and simulation for assessing technical competence and addressing training gaps in healthcare technology use (Bienstock & Heuer, 2022).
Monitor Data to Evaluate Outcomes
After successfully implementing the CDSS and Best Practice Advisory (BPA) alert systems, it is crucial to evaluate their impact on COVID-19 patient outcomes. The CDSS system’s potential to enhance health outcomes through rapid and accurate disease detection can reduce its spread, lower healthcare costs, and increase patient safety (Karthikeyan et al., 2021).
Saegerman et al. (2021) demonstrated that the CDSS system facilitated rapid identification of COVID-19 patients, aiding triage efforts in understaffed diagnostic labs during the pandemic. This clinical decision support tool plays a crucial role in managing the pandemic (Saegerman et al., 2021).
A Specific Example of Bioinformatics
Clinicians can significantly reduce the time required to evaluate patients with COVID-19 symptoms by using a clinical decision support tool for diagnostic assessments (Gavrilov et al., 2021). Effective quarantine of patients with COVID-19 symptoms is essential to prevent further virus spread in healthcare facilities. The CDSS system guides practitioners through a standardized COVID-19 diagnostic workup based on the latest recommendations, streamlining the process (Gavrilov et al., 2021).
The integration of CDSS systems with Best Practice Advisory (BPA) alerts offers several advantages, including improved patient and staff safety, rapid virus detection, and time-saving benefits (Gavrilov et al., 2021).
NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics
Process
Before the implementation of the CDSS system | After the implementation of the CDSS system | |
| Time to make an accurate diagnosis of COVID-19 | 1-2 days | 5-6 hours |
| Healthcare costs | $9500 | $2000 |
| Unidentified patients in quarantine | 10-20 patients | 5 patients |
| False Negative Results | 7-8 false negative results | 3-4 false negative results |
NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics
Conclusion
This study examined the feasibility of using CDSS systems in the administration and management of COVID-19. The CDSS system’s ability to swiftly diagnose COVID-19 patients aids healthcare professionals in containing its spread, reducing complications, lowering unnecessary treatment costs, shortening diagnostic procedures, and improving clinical performance and patient outcomes.
References
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
