Learning Objectives:
Evaluate evidence-based practice models and theories for clinical decision support (CDS) and quality improvement applications
Evaluate peer-reviewed research for evidence-based applications of clinical decision support (CDS) and quality improvement
Recommend clinical decision support (CDS) solutions
Justify clinical decision support (CDS) recommendations using research in evidence-based practice
Recommend solutions to clinical decision support (CDS) limitations
submit your Literature Review Matrix and submit your presentation about applying evidence-based practice to make a quality improvement to a CDS system.
For each article you find, fill in the columns of the Literature Review Matrix.
Review the resources.
Consider the models and theories used in your selected articles.
Reflect on which of the evidence-based practices you would recommend for quality improvement in your workplace.
Part 1: Literature Review Matrix
Submit your completed Literature Review Matrix that contains the four research articles you researched and reviewed.
Part 2
In APA format summarize the following:
Based on your research, address the following in your presentation:
Synthesize your findings from your four articles, focusing on applicable models and/or theories relevant to CDS, quality improvement in your workplace, and on applicable evidence-based practice in nursing.
Recommend CDS or information to consider in clinical decision making and explain your rationale for the recommendation. Be specific.
Justify your recommendation. Be specific and provide examples.
Recommend how you would address possible limitations or challenges, including:
Explain how you would avoid alert fatigue.
Explain under what conditions you would allow an override to an alert.
Explain how you would monitor compliance.
Identify factors that might contribute to continuous overrides.
Justify conditions under which an override may be necessary.
Provide references in APA style at the end of your presentation
Clinical Decision Support
Student’s Name
Institutional Affiliations
Clinical Decision Support
Computerized clinical decision support systems (CDSS) are among the technological reforms that are taking place in the contemporary healthcare environment. According to Sutton et al. (2020), in order to reap maximum benefits from CDSS, it is highly imperative that today’s healthcare organizations and professionals understand the benefits, risks, and success strategies for the technology. Various authors have explored the benefits of CDSS in improving healthcare delivery and health outcomes for patients. This assignment will summarize findings from various articles. It will also highlight key recommendations for healthcare organizations and healthcare professionals in their efforts to integrate CDSS.
The four reviewed articles have explored the use of CDSS in healthcare organizations for the improvement of patient care and healthcare quality for patients with type 2 diabetes mellitus. Alford et al. (2018) utilized the principles and concepts of Rogers’ Diffusion of Innovations Theory to assess how primary care settings can optimize the use of a clinical decision support system (CDSS) by ensuring meaningful use and enhanced quality of care for older adults with type 2 diabetes mellitus. They found that the successful adoption of CDSS highly depends on the consideration of the needs of the end-user and understanding of how the tool can be put into meaningful use. In another study, Holbrrok et al. (2009) examined the effectiveness of electronic decision support in improving the management of diabetes mellitus in community-based primary care. Findings from this study indicate that a shared electronic decision support system improves healthcare processes, enhances the quality of care, and increases satisfaction among patients with type 2 diabetes. Similar insights presented by James (2021) support the positive association between the use of the DSMES Algorithm and protocol training and improved e-referrals, enhanced diabetes education, and improved self-care management among medically underserved patients with type 2 diabetes. According to Schott et al. (2017), CDSS improves medication prescribing for patients with type 2 diabetes mellitus. These findings have great implications for clinical practice.
Based on these findings, it is highly recommended that today’s healthcare organizations should integrate CDSS and use it to improve patient care, especially for older adults with type 2 diabetes mellitus. The rationale for this recommendation is that from the reviewed articles, it is evident that CDSS is an evidence-based practice intervention for improving healthcare quality in clinical practice settings. For example, CDSS has been proven to improve prescribing of medication for diabetes patients. This reduces medication errors and enhances patient safety (Schott et al., 2017). However, to ensure success, healthcare organizations must first address potential limitations and challenges associated with CDSS use. For instance, one can avoid alert fatigue by prioritizing critical alerts over non-critical ones (Schott et al., 2017). Some of the conditions under which a healthcare provider can allow an override of an alert include when the patient has previously tolerated the medication being overridden and when close monitoring of the patient will be done (Nanji et al., 2018). To monitor compliance, the healthcare provider can monitor compliance by connecting a CDSS to an electronic health records system (Schott et al., 2017). One of the factors that might contribute to continuous overrides is when a healthcare provider high level of experiential knowledge. However, examples of conditions when an override may be necessary are during formulary substitution and drug duplicate alerts.
References
Alford, D., Alexander, S., & Barr, R. (2018). Optimization of clinical decision support tools for the care of older adults with diabetes mellitus type 2. Computers Informatics Nursing: CIN, 36(6):259-264. doi: 10.1097/CIN.0000000000000452. PMID: 29877896.
Holbrook, A., Thabane, L., Keshavjee, K., Dolovich, L., Bernstein, B., Chan, D., Troyan, S., Foster, G., Gerstein, H., & COMPETE II Investigators (2009). Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial. CMAJ : Canadian Medical Association Journal = journal de l’Association medicale canadienne, 181(1-2), 37–44. https://doi.org/10.1503/cmaj.081272.
James T. L. (2021). Improving referrals to diabetes self-management education in medically underserved adults. Diabetes Spectrum: A Publication of the American Diabetes Association, 34(1), 20–26. https://doi.org/10.2337/ds20-0001
Nanji, K. C., Seger, D. L., Slight, S. P., Amato, M. G., Beeler, P. E., Her, Q. L., Dalleur, O., Eguale, T., Wong, A., Silvers, E. R., Swerdloff, M., Hussain, S. T., Maniam, N., Fiskio, J. M., Dykes, P. C., & Bates, D. W. (2018). Medication-related clinical decision support alert overrides in inpatients. Journal of the American Medical Informatics Association: JAMIA, 25(5), 476–481. https://doi.org/10.1093/jamia/ocx115
Schott, G., Martinez, Y. V., Ediriweera de Silva, R. E., Renom-Guiteras, A., Vögele, A., Reeves, D., Kunnamo, I., Marttila-Vaara, M., & Sönnichsen, A. (2017). Effectiveness and safety of dipeptidyl peptidase 4 inhibitors in the management of type 2 diabetes in older adults: a systematic review and development of recommendations to reduce inappropriate prescribing. BMC Geriatrics, 17(Suppl 1), 226. https://doi.org/10.1186/s12877-017-0571-8
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digital Medicine, 3, 17. https://doi.org/10.1038/s41746-020-0221-y