NURS 6051 Week 8 Assignment: Literature Review: The Use of Clinical Systems to Improve Outcomes and Efficiencies

Course: NURS 6051: Transforming Nursing and Healthcare Through Technology

Institution: Walden University

Assessment: Week 8 Assignment (Literature Review)

Length: 4 to 5 pages, double-spaced, excluding the title page and reference page.

Format: APA 7th Edition. Academic writing standards apply.

Minimum References: Four peer-reviewed research articles published within the last five years.

Assessment Context

Walden University NURS 6051 Week 8 requires a literature review on the application of clinical systems. Healthcare organizations adopt new technology only after evidence demonstrates a positive impact on patient outcomes or care efficiencies[reference:0]. Nurse informaticists and healthcare leaders build clinical system strategies on existing research, and this assignment asks you to consult that research and summarize what it shows[reference:1]. The paper must synthesize four peer-reviewed studies around a single type of clinical system, such as electronic health records, clinical decision support, telehealth platforms, or mobile nursing stations. The review does not advocate for a system in the abstract. It examines what the evidence says about outcomes, efficiencies, and lessons learned from implementation. A Distinguished response moves beyond summarizing each article in isolation and draws connections across the four studies to produce a synthesis that a leadership team could act on.

Instructions

Review the Learning Resources for the week. Reflect on how clinical systems affect outcomes and efficiencies within nursing practice and healthcare delivery. Conduct a search for recent research, published within the last five years, focused on the application of clinical systems. Select one type of clinical system as the focus of the review. Identify and select four peer-reviewed research articles that provide evidence to support the use of that system to improve outcomes or efficiencies. Complete the following requirements.

1. Introduction. State the purpose of the paper. Identify the clinical system under review. Explain why the system matters to nursing practice or healthcare delivery.
2. Article Identification. Identify the four peer-reviewed research articles you reviewed. Cite each in APA format.
3. Article Summaries. Summarize each study. Explain the improvement to outcomes, the improvement to efficiencies, and the lessons learned from the application of the clinical system that each article describes. Be specific and provide examples. Do not settle for general claims about efficiency. Name the metric, the setting, and the mechanism through which the system produced the result.
4. Synthesis and Conclusion. Synthesize the findings from the four articles. Identify patterns, contradictions, and gaps. Explain what the combined evidence suggests about the system’s value and its limits.
5. APA Formatting. Use APA format for in-text citations and the reference page. Include a title page. Use the Turnitin Drafts feature to check the match percentage before submission.

Grading Rubric

Scoring Criterion	Non-Performance	Basic	Proficient	Distinguished
Introduction and Purpose	Does not state a purpose or identify a system.	States a vague purpose or identifies a system without connecting it to outcomes.	States the purpose and identifies the clinical system with a clear connection to practice.	States a precise purpose, identifies the system, and frames the review around a specific outcome or efficiency problem.
Article Selection and Citation	Does not identify four peer-reviewed articles or cites them incorrectly.	Identifies fewer than four articles or provides incomplete APA citations.	Identifies four peer-reviewed articles and cites each in APA format.	Identifies four relevant, recent peer-reviewed articles and cites each in flawless APA format.
Summary of Evidence	Does not summarize the studies or summarizes them inaccurately.	Summarizes studies superficially without explaining outcomes, efficiencies, or lessons learned.	Summarizes each study and explains the improvement to outcomes, efficiencies, and lessons learned with some examples.	Summarizes each study with specific metrics, settings, and mechanisms, and explains lessons learned that a leadership team could apply.
Synthesis	Does not synthesize findings.	Offers a weak synthesis that repeats article summaries without connecting them.	Synthesizes findings and identifies patterns across the four studies.	Synthesizes findings, identifies patterns and contradictions, and explains what the combined evidence suggests about the system’s value and limits.
Written Communication	Frequent errors impede understanding.	Some errors affect clarity.	Clear and organized with minor errors.	Clear, logical, and mechanically flawless.
APA Formatting	Citations and references absent or incorrect.	Inconsistent APA formatting.	Correct APA citations and references.	Flawless APA formatting with accurate integration of sources.

Sample Answer: Literature Review on Clinical Decision Support Systems

Clinical decision support systems (CDSS) occupy a contested space in nursing informatics. Proponents frame CDSS as a mechanism for reducing medication errors and standardizing care; critics warn that poorly designed alerts produce fatigue and workarounds that erode safety gains. A 2025 rapid review of CDSS effects on hospital nursing decision-making found that workflow efficiency improved when alerts were context-sensitive and declined when systems generated excessive low-value warnings[reference:2]. The same review noted that patient outcome improvements depended less on the sophistication of the algorithm and more on how well the system fit existing nursing workflows. Studies of nursing informatics in critical care settings reached a similar conclusion. Shi et al. (2025) conducted a systematic review of seventeen studies and found that informatics tools reduced medication errors and improved glycaemic control in intensive care units, yet the authors cautioned that these gains were not uniform across settings[reference:3]. The evidence supports CDSS adoption when implementation accounts for workflow, training, and alert design, not as a standalone technology fix.

A second pattern emerges when the literature is read across settings. Mebrahtu et al. (2024) reviewed experimental and observational studies of CDSS effects on nursing and allied health professional performance and found modest but consistent improvements in adherence to clinical guidelines[reference:4]. The effect sizes were smaller than vendor claims typically suggest, which matters for leaders weighing procurement decisions. Wang et al. (2025) examined an artificial intelligence-assisted closed-loop mobile nursing information management system and reported improvements in nursing quality indicators and work efficiency, with the strongest gains in critical value reception and documentation timeliness[reference:5]. These findings do not contradict the CDSS literature. They reinforce a conclusion that the system’s value depends on the specific process it supports and the degree to which nurses trust its outputs. A review that treats CDSS as a single intervention will miss this variation. A review that distinguishes between alert types, care settings, and implementation strategies will produce a more useful synthesis.

Interpreting Mixed Evidence

A common student misconception holds that a literature review must produce unanimous support for the chosen system. The evidence on CDSS does not support that expectation, and a Distinguished paper acknowledges the mixed findings rather than suppressing them. Shi et al. (2025) found safety improvements in critical care, but the authors also reported variability across studies in how informatics interventions were defined and measured[reference:6]. That variability is not a flaw in the review; it is a finding. Leaders who read only the positive results will overestimate the system’s readiness for their setting. Leaders who read the variability will ask the right implementation questions before procurement.

Lessons for Implementation Leaders

Implementation lessons recur across the four studies. Three points merit attention in a leadership briefing:

• Alert fatigue is a design problem, not a user problem. Systems that fire too many low-specificity alerts train nurses to ignore them, which erodes the safety benefit the system was purchased to deliver[reference:7].
• Efficiency gains depend on workflow integration. Systems that require duplicate documentation or extra clicks produce time savings on one task and time losses on another, with no net gain[reference:8].
• Outcome measurement must be specified in advance. Studies that report “improved outcomes” without naming the metric or the comparison period cannot support a procurement decision[reference:9].

Framing the Review Question

Students searching for guidance on this assignment often ask how narrow the clinical system focus should be. The assignment requires one type of clinical system, and the evidence is strongest when the review treats that system as a category with internal variation rather than a monolith. A review of “clinical systems” in general will produce a shallow paper. A review of “CDSS for medication safety in acute care” will produce a focused synthesis that identifies the conditions under which the system works. The same logic applies to telehealth, electronic health records, or mobile nursing stations. The narrower the system definition, the more useful the synthesis.

The paper ultimately answers a practical question for a leadership audience. Does the evidence justify investment in this system, and under what conditions? A literature review that answers that question with evidence, acknowledges the limits of the evidence, and translates the findings into implementation considerations meets the Distinguished standard. A review that summarizes four articles and declares the system effective does not.

References

Mebrahtu, T. F., Skyrme, S., Randell, R., Andre, D., Ledward, A., King, H., & Thompson, C. (2024). Effects of computerised clinical decision support systems (CDSS) on nursing and allied health professional performance and patient outcomes: A systematic review of experimental and observational studies. Implementation Science. https://doi.org/10.1186/s13012-024-01372-w

Shi, Q., Wotherspoon, R., & Morphet, J. (2025). Nursing informatics and patient safety outcomes in critical care settings: A systematic review. BMC Nursing, 24(1), 546. https://doi.org/10.1186/s12912-025-03195-6

Wang, Q., Ding, X. T., Luo, Z. C., et al. (2025). Impact of artificial intelligence-assisted closed-loop mobile nursing information management on nursing quality indicators and work efficiency. Journal of Multidisciplinary Healthcare, 18, 3581–3591. https://doi.org/10.2147/JMDH.S512345

“Rage against the machine?”: The impact of clinical decision support systems on hospital nursing decision-making, workflow efficiency, and patient outcomes: A rapid review. (2025). Scilit. https://www.scilit.com/

Note: The fourth reference is a rapid review indexed on Scilit. Students should verify the final citation details against the Walden University Library database before submission.

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Literature Review: Clinical Decision Support Systems for Medication Safety in Acute Care

Student Name
Walden University
NURS 6051: Transforming Nursing and Healthcare Through Technology
Instructor Name
Date

Introduction

Clinical decision support systems (CDSS) are electronic tools embedded in electronic health records that provide clinicians with patient-specific recommendations at the point of care. In acute-care settings, these systems commonly flag potential medication errors, duplicate therapies, drug interactions, renal dosing concerns, and opportunities to stop harmful medications. The purpose of this literature review is to examine whether CDSS improves medication-related patient outcomes and care efficiency in hospitalized adults. The review focuses on four peer-reviewed studies published between 2022 and 2024 that evaluated CDSS in hospital settings.

Medication safety is a central concern in nursing practice because nurses administer, monitor, and document medications across every shift. A CDSS may reduce preventable harm by identifying risks before a medication reaches the patient; however, poorly designed alerts can interrupt workflow and contribute to alert fatigue. The evidence reviewed here suggests that CDSS can improve prescribing behavior and workflow when alerts are targeted, clinically meaningful, and supported by pharmacists or other trained reviewers. The findings also show that improved processes do not always translate immediately into fewer adverse events, which makes implementation planning essential.

Articles Reviewed

The following four peer-reviewed research articles form the evidence base for this review:

• McDonald, E. G., Wu, P. E., Rashidi, B., Goodwin Wilson, M., Bortolussi-Courval, É., Atique, A., … Lee, T. C. (2022). The MedSafer study: Electronic decision support for deprescribing in hospitalized older adults: A cluster randomized clinical trial. JAMA Internal Medicine, 182(3), 265–273. https://doi.org/10.1001/jamainternmed.2021.7429

• Wilson, F. P., Yamamoto, Y., Martin, M., Coronel-Moreno, C., Li, F., Cheng, C., … Testani, J. M. (2023). A randomized clinical trial assessing the effect of automated medication-targeted alerts on acute kidney injury outcomes. Nature Communications, 14, Article 2826. https://doi.org/10.1038/s41467-023-38532-3

• Dahmke, H., Fiumefreddo, R., Schuetz, P., De Iaco, R., & Zaugg, C. (2023). Tackling alert fatigue with a semi-automated clinical decision support system: Quantitative evaluation and end-user survey. Swiss Medical Weekly, 153, Article 40082. https://doi.org/10.57187/smw.2023.40082

• Chen, C.-Y., Chen, Y.-L., Scholl, J., Yang, H.-C., & Li, Y.-C. (2024). Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication. Computer Methods and Programs in Biomedicine, 243, Article 107869. https://doi.org/10.1016/j.cmpb.2023.107869

MedSafer Deprescribing Support

McDonald et al. (2022) evaluated the MedSafer electronic deprescribing tool in 11 Canadian acute-care hospitals. The study included 5,698 hospitalized adults aged 65 years or older who took five or more daily medications. The software generated individualized reports identifying medications that may no longer be appropriate, based on the patient’s home medications, prognosis, and frailty. Treating teams in the intervention group received these reports, whereas usual-care teams received standard medication reconciliation.

The primary outcome was adverse drug events within 30 days after discharge. The intervention did not produce a statistically significant reduction in adverse drug events: 4.9% of intervention patients experienced an event compared with 5.0% of usual-care patients, an adjusted risk difference of -0.8% (McDonald et al., 2022). Deprescribing did improve substantially. Clinicians deprescribed at least one potentially inappropriate medication in 55.4% of intervention patients, compared with 29.8% of control patients, an adjusted risk difference of 22.2 percentage points (McDonald et al., 2022). The system therefore changed prescribing behavior without causing more adverse drug withdrawal events.

The lesson for nursing and hospital leadership is that CDSS can make a clinical process more consistent even when the desired patient outcome takes longer to appear. MedSafer did not reduce adverse events during the study period, but it nearly doubled deprescribing activity. A leadership team should therefore measure both process indicators, such as medication reviews completed, and outcome indicators, such as readmissions or adverse drug events. The study also shows that decision support must fit into existing admission and discharge workflows; a report that clinicians do not open or act upon cannot improve care.

Targeted Alerts for Kidney Injury

Wilson et al. (2023) tested automated medication-targeted alerts in hospitalized adults with acute kidney injury (AKI). The pragmatic randomized trial included 5,060 patients who had AKI and active orders for nonsteroidal anti-inflammatory drugs, renin-angiotensin-aldosterone system inhibitors, or proton pump inhibitors. The CDSS alerted clinicians when a potentially nephrotoxic medication remained active in a patient with AKI. The comparison group received usual care without the automated alert.

The alert changed clinician behavior. Within 24 hours of randomization, clinicians discontinued a targeted medication in 61.1% of alert-group patients compared with 55.9% of usual-care patients, a relative risk of 1.08 (Wilson et al., 2023). The primary clinical outcome, a composite of AKI progression, dialysis, or death within 14 days, occurred in 23.1% of alert-group patients and 25.3% of usual-care patients. Although the direction favored the alert group, the difference was not statistically significant (relative risk 0.92; 95% confidence interval 0.83–1.01; Wilson et al., 2023).

The efficiency gain appeared in medication-review decisions rather than in a dramatic reduction in severe renal outcomes. Automated surveillance identified patients who needed attention and prompted clinicians to reconsider a medication order. For nursing practice, this matters because nurses often recognize worsening renal function, fluid-status changes, and medication-related risks before formal orders change. A CDSS can reinforce that clinical judgment, but it cannot replace it. The study also demonstrates an important limit: changing one prescribing decision may not be enough to alter a complex outcome such as kidney injury, which depends on hydration, sepsis, hemodynamics, and other medications.

Pharmacist Review and Alert Fatigue

Dahmke et al. (2023) examined a semi-automated CDSS at Cantonal Hospital Aarau in Switzerland. The system screened medication orders and sent high-priority alerts directly to physicians, while sending other potential medication problems to clinical pharmacists for review before physician notification. The researchers reviewed 10,556 alerts generated during 2021 and surveyed physicians about their experience with the system.

The design reduced unnecessary interruptions for physicians. Direct physician notifications accounted for 619 alerts, whereas pharmacists reviewed 2,231 alerts before forwarding them to physicians (Dahmke et al., 2023). Acceptance rates were 89.8% for direct physician alerts and 68.4% for pharmacist-reviewed alerts, producing an overall acceptance rate of 72.4%. Physicians received an average of 7.8 notifications per day, while pharmacists received 17.2 alerts per day. In the survey, 94.5% of physicians reported satisfaction or high satisfaction with the system, and alerts related to anticoagulant safety received the highest usefulness ratings (Dahmke et al., 2023).

The central lesson is that alert volume is an implementation problem, not merely a technology problem. Routing lower-priority alerts through pharmacists protected physician attention while preserving pharmacist expertise for medication review. Nurses can apply a similar principle by ensuring that interruptive alerts reach the clinician best positioned to act. A hospital that floods every user with every possible warning risks training staff to dismiss alerts. A hospital that filters, prioritizes, and assigns alerts appropriately is more likely to sustain attention and improve medication safety.

Machine Learning and Error Prevention

Chen et al. (2024) evaluated a machine-learning CDSS designed to identify wrong-drug errors and look-alike, sound-alike medication risks. The system used machine-learning methods to distinguish clinically meaningful medication risks from alerts that clinicians were likely to disregard. The study focused on reducing alert fatigue while maintaining protection against wrong-drug selection and similar-sounding medication names.

The study addressed a specific failure mode in electronic prescribing: clinicians may select the wrong drug from a search list, particularly when medication names look or sound alike. Machine learning allowed the system to prioritize alerts according to risk rather than presenting every possible warning with equal urgency. The authors reported that the system reduced alert fatigue, identified wrong-drug errors, and alerted users to look-alike, sound-alike medications (Chen et al., 2024). The practical mechanism was selective alerting: the system concentrated attention on higher-risk prescribing situations instead of generating a high volume of low-value warnings.

For nursing practice, the value of this approach lies in safer medication administration and verification. Nurses routinely perform the final check before a medication reaches a patient. A CDSS that flags a high-risk look-alike medication can support independent double-checks, barcode medication administration, and pharmacist review. However, the study also implies that artificial intelligence must be monitored after implementation. Algorithms can perform differently across patient populations, formularies, languages, and clinical workflows. Hospitals should audit override rates, false-positive alerts, and missed-error rates rather than assuming that a machine-learning system will remain accurate over time.

Synthesis of Findings

Across the four studies, CDSS appears most effective when it targets a defined medication-safety problem and fits the clinical workflow. MedSafer improved deprescribing by embedding individualized recommendations into hospital care; Wilson et al. improved discontinuation of potentially nephrotoxic medications through targeted alerts; Dahmke et al. improved alert acceptance by using pharmacist review; and Chen et al. reduced alert fatigue through risk-based machine-learning prioritization. In each case, the system changed a specific decision or workflow rather than offering general advice.

The evidence also reveals an important contradiction. Improved prescribing behavior did not always produce a measurable improvement in hard clinical outcomes. McDonald et al. (2022) found more deprescribing but no reduction in 30-day adverse drug events, while Wilson et al. (2023) found more medication discontinuation but no statistically significant improvement in the AKI composite outcome. These findings do not mean that CDSS lacks value. They indicate that a single alert or recommendation may be insufficient to change outcomes shaped by many clinical factors. Leaders should therefore avoid promising immediate reductions in mortality, dialysis, or adverse events based on CDSS adoption alone.

Efficiency findings are more consistent. Dahmke et al. (2023) showed that pharmacist triage reduced physician alert burden while maintaining a high overall acceptance rate. Chen et al. (2024) showed that machine-learning prioritization can reduce alert fatigue by limiting irrelevant warnings. These studies suggest that efficiency improves when the system filters information intelligently and assigns the right task to the right professional. Conversely, a CDSS that adds documentation steps, generates excessive alerts, or requires duplicate verification may consume nursing time without improving safety.

Implementation Lessons

The combined evidence supports several practical actions for healthcare organizations considering CDSS implementation:

• Define the clinical problem before selecting the technology, such as nephrotoxic medication continuation in AKI or look-alike medication selection.

• Measure both process and outcomes, including alert acceptance, medication changes, adverse drug events, documentation time, and clinician satisfaction.

• Use pharmacist, nursing, and physician input during alert design, testing, and post-implementation review.

• Limit interruptive alerts to high-risk, actionable situations and route lower-priority warnings to pharmacists or asynchronous review.

• Audit the system continuously for alert fatigue, false positives, overrides, and unintended workflow delays.

• Provide training that explains why an alert appears, what action is recommended, and how the nurse or prescriber should document the response.

These actions recognize that CDSS is a sociotechnical intervention. The technology matters, but workflow design, professional roles, training, and ongoing evaluation determine whether it improves care.

Conclusion

Clinical decision support systems can improve medication-related processes and workflow efficiency in acute care when organizations design them around specific clinical risks and real workflow demands. The reviewed studies show meaningful improvements in deprescribing, discontinuation of potentially harmful medications, alert acceptance, and identification of wrong-drug risks. They also show that process improvement does not guarantee immediate improvement in patient outcomes such as adverse drug events or AKI progression. Therefore, the strongest case for CDSS rests on targeted implementation, pharmacist and nursing collaboration, careful alert design, and continuous measurement. A hospital that treats CDSS as a supported clinical workflow, rather than a stand-alone software purchase, is more likely to achieve safer and more efficient medication management.

References

Chen, C.-Y., Chen, Y.-L., Scholl, J., Yang, H.-C., & Li, Y.-C. (2024). Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication. Computer Methods and Programs in Biomedicine, 243, Article 107869. https://doi.org/10.1016/j.cmpb.2023.107869

Dahmke, H., Fiumefreddo, R., Schuetz, P., De Iaco, R., & Zaugg, C. (2023). Tackling alert fatigue with a semi-automated clinical decision support system: Quantitative evaluation and end-user survey. Swiss Medical Weekly, 153, Article 40082. https://doi.org/10.57187/smw.2023.40082

McDonald, E. G., Wu, P. E., Rashidi, B., Goodwin Wilson, M., Bortolussi-Courval, É., Atique, A., Battu, K., Bonnici, A., Elsayed, S., Goodwin Wilson, A., Papillon-Ferland, L., Pilote, L., Porter, S., Murphy, J., Ross, S. B., Shiu, J., Tamblyn, R., Whitty, R., Xu, J., … Lee, T. C. (2022). The MedSafer study: Electronic decision support for deprescribing in hospitalized older adults: A cluster randomized clinical trial. JAMA Internal Medicine, 182(3), 265–273. https://doi.org/10.1001/jamainternmed.2021.7429

Wilson, F. P., Yamamoto, Y., Martin, M., Coronel-Moreno, C., Li, F., Cheng, C., Aklilu, A., Ghazi, L., Greenberg, J. H., Latham, S., Melchinger, H., Mansour, S. G., Moledina, D. G., Parikh, C. R., Partridge, C., Testani, J. M., & Ugwuowo, U. (2023). A randomized clinical trial assessing the effect of automated medication-targeted alerts on acute kidney injury outcomes. Nature Communications, 14, Article 2826. https://doi.org/10.1038/s41467-023-38532-3

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