Manage Discussion by Breanna Zabel

Reply from Breanna Zabel

Methodologies in Current Practice Environment

In my current role as an RN on a telemetry unit at Providence Medford Medical Center, several methodologies discussed in this week’s readings are used, particularly regarding patient safety and quality improvement (QI). One QI initiative I have been involved in is reducing hospital readmission rates for heart failure patients and CAUTI and CLABSI prevention. The heart failure readmission rates initiative utilized a pre- and post-metric study design, incorporating process and outcome measures.

Pre-metrics included the baseline readmission rates, average length of stay, and patient satisfaction scores. Post-metrics demonstrated a decrease in readmission rates by 15%, a slight reduction in the length of stay, and improved patient satisfaction due to better education and follow-up care processes (Shah, 2019).

The primary method used was the Plan-Do-Study-Act (PDSA) cycle, which involved planning interventions (educational sessions, enhanced discharge planning), implementing these interventions, studying the outcomes (tracking readmission rates and patient feedback), and acting based on the findings (refining and standardizing the improved discharge protocols) (Facchiano & Snyder, 2012).

Comparative Analysis of Statistics Types

Chapter 5 of our textbook delineates three distinct types of statistics: descriptive, enumerative, and analytic, each serving unique roles in healthcare quality and patient safety (Nash, 2019):

1. Descriptive Statistics describe and understand the characteristics of a specific patient population or health event. In practice, this might involve detailing the demographic and health characteristics of patients admitted with heart failure, helping to tailor care to this specific group.
2. Enumerative Statistics are employed to analyze data from a defined group within a health system, such as all patients treated in the cardiovascular department over a year. This type of statistics is crucial for understanding patterns and outcomes within specific cohorts but is limited when applied to broader, generalizable insights.
3. Analytic Statistics focus on understanding the processes that produce observed outcomes in a sample and predicting outcomes for a broader population. This statistical approach is vital for QI, as it allows us to infer how changes in process quality affect patient outcomes across various settings.

Analytic statistics, in particular, play a crucial role in advancing quality by enabling us to assess and refine processes based on robust, predictive insights rather than mere descriptions or limited cohort analyses.

Evaluating Quality Tools for Practice

Given the focus on improving patient outcomes and processes, three quality tools from the ASQ resource list are particularly relevant (American Society for Quality, 2022):

• Cause and Effect Diagram: Useful for identifying potential causes of a problem (like high readmission rates) by visually mapping out the relationship between effects and their causes. This diagram would include categories such as patient behavior, discharge process, and follow-up care, helping pinpoint intervention areas.
• Matrix Diagram: This tool would effectively examine and prioritize multiple factors and their relationships, such as the correlation between patient education methods and understanding discharge instructions. Providing a structured format to assess the strength of relationships assists in focusing efforts where the highest impact can be made.
• Relations Diagram: Helps to analyze the interconnections and influences among various factors involved in a process, such as those contributing to patient readmission. This diagram could trace how elements such as medication adherence, follow-up appointments, and patient education interact, highlighting key drivers of outcomes.

By leveraging these diagrams, we effectively break down the complex data into clear, understandable visuals. This clarity is crucial in breaking down complex information into accessible insights that are easy for all staff members to grasp. By presenting data in an understandable format, we facilitate a deeper understanding and engagement across our team. This enhanced comprehension makes for a greater buy-in from staff, encouraging active participation and collaboration in our quality and safety initiatives. As a result, when everyone understands the ‘why’ and the ‘how’ behind our actions, they are more likely to contribute enthusiastically and willingly, driving the success of our initiatives.

Conclusion

Integrating these methodologies and statistical approaches, along with the strategic use of quality tools, not only enhances our understanding of patient care dynamics but also guides practical improvements. This holistic approach is essential for advancing patient safety and healthcare quality in our practice (Leming-Lee et al., 2019; Steere et al., 2018).

References

American Society for Quality (ASQ). (2022). Quality toolsLinks to an external site..

Facchiano, L., & Snyder, C. H. (2012). Evidence-based practice for the busy nurse practitioner: Part one: Relevance to clinical practice and clinical inquiry processLinks to an external site.. Journal of the American Academy of Nurse Practitioners, 24(10), 579–586.

Leming-Lee, T. S., Polancich, S., & Pilon, B. (2019). The application of the Toyota production system LEAN 5S methodology in the operating room settingLinks to an external site.. Nursing Clinics of North America, 54(1), 53–79.

Nash, D. B. (2019). The Healthcare Quality Book: Vision, Strategy, and Tools, Fourth Edition: Vol. Fourth edition. pp. 127–163 AUPHA/HAP Book.

Shah, A. (2019). Using data for improvementLinks to an external site.. British Medical Journal, 364, I189.

Steere, L., Rousseau, M., & Durland, L. (2018). Lean Six Sigma for intravenous therapy optimization: A hospital use of lean thinking to improve occlusion management (PDF)Links to an external site.. Journal of the Association for Vascular Access, 23(1), 42–50

7 pts

Excellent

Provides two peer responses by day 6 of the discussion forum. Provides two peer posts on two different days. Demonstrates building on peer postings and extending the discussion. Questions posed are thoughtful and relevant. Includes substantial factually correct content which demonstrates evidence of comprehension, application, and/or appraisal. Includes evidence of self- reflective content which demonstrates views, beliefs, and/or feelings. Interaction demonstrates respect and sensitivity to others.

My original post:

Intensive care practice offers an opportunity to apply quality improvement methodologies in areas such as patient transfer from the Intensive Care Unit (ICU) to the Inpatient Unit (IU). This is due to the number of associated risks that present during the handoff of care between units, including lack of standardization, variability in the transfer process, compromised communication, and differences in care after transition. The Sigma Six methodology uses a DMAIC (Define, Measure, Analyze, Improve, and Control) approach to develop improved healthcare frameworks (Ahmed, 2019). The first step would be to identify patients who require transfer between units, analyze their specific requirements, and define the objectives and scope of improvement. Statistics will then determine what metrics will be used to evaluate improvement. This information will be used to define study objectives. This could be patient satisfaction, cost of service levels, or readmission to a higher level of care. An analysis would then be conducted to reveal issues in system processes or loopholes that may undermine patient care. Afterward, potential improvements are considered and implemented to mitigate challenges emerging in these stages of patient care. Finally, monitoring frameworks are implemented to ensure sustained improvement and report any outliers or problems that may arise.

Data is essential in analyzing systematic problems, defining problems, and inferring conclusions. Statistics like averages, ranges, and medians help to display data in ways that allow information to be organized (Ben‐Aharon et al., 2019). The average or mean represents the sum of total observations in a particular study divided by the number of total observations. At the same time, the range is the difference between the smallest and the most significant number in a set of observations. Conversely, the median can be described as the middle number given a set of observations. While all the statistics are computed in different ways, they are integral in identifying trends in certain areas and help summarize data ranging over long periods, easing the analysis process. They can also help analyze data points on a graph as they give a standard of comparison.

Tools such as an Excel control sheet, balance scorecard, and a quality plan would ensure success in improving the process of transferring patients from the ICU to the IU. Maintaining a balance scorecard would be essential to evaluate the multilevel performance of initiated changes (Raval et al., 2019). A quality plan would help to organize intervention mechanisms. An Excel control sheet would be vital in recording data at different intervention analysis points.

References

Ahmed, S. (2019). Integrating DMAIC approach of Lean Six Sigma and theory of constraints        toward quality improvement in healthcare. Reviews on Environmental Health, 34(4), 427-   434. https://doi.org/10.1515/reveh-2019-0003

Ben‐Aharon, O., Magnezi, R., Leshno, M., & Goldstein, D. A. (2019). Median survival or mean        survival: which measure is the most appropriate for patients, physicians, and        policymakers? The Oncologist, 24(11), 1469-1478.             https://doi.org/10.1634/theoncologist.2019-0175

Raval, S. J., Kant, R., & Shankar, R. (2019). Benchmarking the Lean Six Sigma performance          measures: a balanced score card approach. Benchmarking: An International   Journal, 26(6), 1921-1947. https://doi.org/10.1108/BIJ-06-2018-0160

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