• Use the hospital data set provided in Vila Health: Data Analysis. to analyze data to identify a health care issue or area of concern.
  • Include basic information about the health care setting, size, and specific type of care delivery related to the identified topic.
  1. Analyze data to identify a health care issue or area of concern.
    • Identify the type of data you are analyzing from your institution or from the Vila Health activity.
    • Explain why data matters. What does data show related to outcomes?
    • Analyze the dashboard metrics. What else could the organization measure to enhance knowledge?
    • Present dashboard metrics related to the selected issue that are critical to evaluating outcomes.
    • Assess the institutional ability to sustain processes or outcomes.
    • Evaluate data quality and its implications for outcomes.&
  2. Determine whether any adverse event or near-miss data needs to be factored in to outcomes and recommendations.
    • Examine the nursing process for variations or performance failures that could lead to an adverse event or near miss.
    • Identify trends, measures, and information needed to critically analyze specific outcomes.
    • Specify desired outcomes related to prevention of adverse events and near misses.
    • Analyze which metrics indicate future quality improvement opportunities.
  3. Develop a QI initiative proposal based on a selected health issue and supporting data analysis.
    • Determine benchmarks aligned to existing QI initiatives set by local, state, or federal health care policies or laws.
    • Identify any internal existing QI initiatives in your practice setting or organization related to the selected issue. Explain why they are insufficient.
    • Evaluate external national or international QI initiatives on the selected health issue with existing quality indicators from other facilities, government agencies, and nongovernmental bodies on quality improvement.
    • Define target areas for improvement and the processes to be modified to improve outcomes.
    • Propose evidence-based strategies to improve quality.
    • Analyze challenges that meeting prescribed benchmarks can pose for a health care organization and the interprofessional team.
  4. Communicate QI initiative proposal based on interdisciplinary team input to improve patient safety and quality outcomes and work-life quality.
    • Define interprofessional roles and responsibilities relating to data and the QI initiative.
    • Explain how to ensure all relevant interprofessional roles are fully engaged in this effort.
    • Identify how outcomes will be measured and data used to inform interprofessional team performance related to specific tasks.
    • Reflect on the impact of the proposed initiative on work-life quality of the interprofessional team.
    • Describe how the initiative enhances work-life quality due to improved strategies supporting efficiency.
  5. Determine communication strategies to promote quality improvement of interprofessional care.
    • Identify interprofessional communication strategies that will help to promote and ensure the success of the QI initiative.
    • Identify communication models, such as SBAR and CUS, to include in your proposal. 
      • SBAR stands for Situation, Background, Assessment, Recommendation.
      • CUS stands for “I am Concerned about my resident’s condition; I am Uncomfortable with my resident’s condition; I believe the Safety of the resident is at risk.”
    • Consult this resource for additional information about these fundamental evidence-based tools to improve interprofessional team communication for patient handoffs: 

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Running head: QUALITY IMPROVEMENT INITIATIVE PROPOSAL 1

Copyright ©2017 Capella University. Copy and distribution of this document are prohibited.

Data Analysis and Quality Improvement Initiative Proposal

Learner’s Name

Capella University

Quality Improvement for Interprofessional Care

Data Analysis and Quality Improvement Initiative Proposal

Month, Year

Comment [JS1]: Good job with the
submission. It follows the rubric. For

the most part is written in scholarly

voice. The submission is clear and

concise. References and citations are

used to support your opinion and

position with relevant evidence.

Please see my tracked changes for

areas of revision.

QUALITY IMPROVEMENT INITIATIVE PROPOSAL 2

Data Analysis and Quality Improvement Initiative Proposal

I. Introduction

Health care professionals are constantly striving to improve the quality of care and safety

provided to their patients. The culture of care quality and patient safety depends on a strong and

supportive work environment that promotes leadership, evidence-based practice, effective

communication, and interprofessionalism. Nurse leaders play a crucial role in establishing this

culture and directly influence quality outcomes across an organization.

II. Problems and Needs

The role of nurse leaders in maintaining the quality in the nursing and clinical

departments is discussed using the example of TrueWill General Hospital (TGH), a

multispecialty hospital in the United States. As part of an annual assessment of organizational

quality, the hospital’s quality management office completed its analysis of dashboard metrics for

the surgical units for the year 2016–2017. The office released the data in its Quality and Safety

Report 2016–2017. The surgical units’ data included adverse events and near misses and used

four quality indicators: length of stay (LOS) exceeding 7 days, patient readmission rates, pain

level between 7 and 10 for more than 24 hours, and patients with pressure ulcers.

III. Proposed Solution

The results of the analysis showed that three quality indicators—pain levels, readmission

rates, and pressure ulcers—performed below the hospital’s benchmarks (see Table 1 and

Appendix for data and descriptions of indicators and benchmarks). The connection between these

indicators and the services of the surgical units’ nurses will be discussed in this proposal for a

quality improvement initiative. The proposal will analyze the relational patterns between the

Copyright ©2017 Capella University. Copy and distribution of this document are prohibited.

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