Tools for Measuring Quality Paper

Tools for Measuring Quality Paper

Healthcare professionals are responsible for delivering quality care services amidst the prevailing challenges, including increased care costs, demographic dynamics, and prevalence of chronic conditions. Despite the overarching objective of providing quality care, there is no universal definition of quality care, considering the interplay between various dimensions and dimensions. According to the Agency for Healthcare Research and Quality (AHRQ, 2018b), the Institute of Medicine (IOM) provided one of the most influential frameworks for consolidating the domains of healthcare quality. The IOM framework encompasses six dimensions and attributes of care quality, including safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity (AHRQ, 2018b). Notably, the six domains narrow down to the need to avert harm, incorporate scientific knowledge in care practice, respect patients’ preferences and values, reduce waits, eliminate healthcare disparities, and reduce care costs. It is essential to note that healthcare organizations perform differently concerning these parameters of care quality. Therefore, it is crucial to incorporate tools for measuring quality to assess how institutions perform against the profound domains of care quality. Consequently, this assessment elaborates on three rate-based tools for measuring quality and establishes their relationship with patients’ safety and the overall cost of healthcare delivery. The paper uses a hypothetical case of Mercy Medical Center’s dashboard metrics for diabetes tests.

Rate-based Tools for Measuring Quality

Rate-based quality measurement tools leverage data about events in healthcare settings and express them as proportions or rates, including ratios or mean values for straightforward interpretation, dissemination, and utilization. Therefore, such tools necessitate utilizing nursing informatics that consolidates information and data regarding events and outcomes of concern (Agency for Healthcare Research and Quality, 2018a). According to Nash et al. (2019), health organizations can obtain data from various sources, including medical record reviews, administrative databases, patient surveys, and in-home and wearable technologies. In this sense, record reviews enable retrospective and prospective healthcare data collection. Nash et al. (2019) argue that the retrospective data collection approach entails identifying and selecting patients’ medical records after discharge from the hospital or clinic.

On the other hand, prospective data collection involves consolidating patients’ information during hospitalization. This approach has an advantage over the retrospective approach because it enables healthcare programs to capture data that is not routinely available from the administrative databases (Nash et al., 2019, p. 115). As a result, it is a profound dimension of capturing real-time threats to patient safety, including incidences of patient falls and their respective causes and risk factors.

On the other hand, administrative databases are ideal data sources for quality improvement because they consolidate information from care enrollment or eligibility, hospital and physician billing systems, health plan claims databases, medical record systems, and registries. According to Nash et al. (2019), data in administrative databases are profound for reporting on clinical quality, financial performance, and particular patient outcomes. As a result, these databases inform clinical and economic decisions. Finally, patient surveys and wearable technologies can provide valid, reliable, and relevant information regarding patients’ perceptions, experiences, and satisfaction with care processes. Therefore, they can provide opportunities for identifying areas of improvement and focus for healthcare professionals consistent with the IOM’s six domains of care quality.

Although there are plenty of options to consider when collecting clinical and administrative data, it is valid to contend that the data collection procedures are time-consuming, hectic, and complex. More so, data analysis and dissemination processes are susceptible to complexities depending on the quality of the collected data. As a result, healthcare professionals are responsible for utilizing appropriate tools for analyzing quality improvement data to aid interpretation, dissemination, and utilization. According to Nash et al. (2019), it is possible to analyze and present clinical and administrative data using three profound tools; run charts, control charts, and Pareto analysis charts.

Run Charts

Run charts are a profound example of statistical process control (SPC) charts that present data over time and enable the healthcare quality improvement team to identify variations in performance, process, and outcomes. According to Brady et al. (2017), run charts are simple data displays over time, with a median line indicating the central tendency. Developing a run chart involves determining the data of concern and the appropriate time for performance evaluation. For example, Mercy Medical Center, a reputed healthcare institution in Minnesota, conducted 234 eye tests on patients diagnosed with diabetes in 2020. The number of eye tests that year represented only 58.9%, considering the institution received 394 diabetic patients aged 40. To present this data on run charts, it is possible to plot the number of eye tests against the respective months. In this sense, if the institution conducted 30 eye tests in January, it is possible to plot this number against time.

After plotting the values of concern in the Y-axis against time (X-axis), it is essential to determine and draw a median line as a point of reference. According to Nash et al. (2019), a median is “the number that divides the data values in half when they are sorted from the smallest to the largest” (p. 133). In this sense, the median line in a run chart operates as the reference point to statistically present any process shifts during the plotted period. The median of the collected data is (n+1)/2, where n is the total collected data. After drawing the median line, it is possible to account for process variations, rendering run charts risk-adjusted tools for measuring quality in healthcare. Houseworth et al. (2022) argue that risk adjustment is achievable by identifying and including known or potential factors associated with the outcome of interest. In the case of Mercy Medical Center, the outcome of concern is the number of eye tests conducted in a year compared to the state and national benchmarks. As a result, determining a median line in a run chart is a statistical dimension accounting for the underlying reasons for process and outcome variations.

How to Externally Compare the Measurement to Other Similar Settings

Although Mercy Medical Center is a reputed healthcare organization in Minnesota, its 2020 statistics for eye tests were below the state and national benchmarks. According to the Agency for Health Research and Quality (AHRQ, n.d.), the national and state benchmarks for eye exams as a criterion for quality measurement are 75.2%, meaning that the institution did not match or surpass the national and state benchmarks for that quality indicator. In this sense, the national and state standards enable healthcare professionals to compare measures of quality care with top-performers to identify areas of improvement and benchmark appropriate approaches for improving care quality.

To come up with national and state benchmarks, various national healthcare organizations consolidate data on the six domains of care such as safety, effectiveness, patient-centeredness, timeliness, cost-effectiveness, and equity. According to Nash et al. (2019), the Medicare’s Hospital Value-based Purchasing (HVBP) program and Hospital Readmissions Reduction Program (HRRP) are among the initiatives administered by the Centers for Medicare and Medicaid Services (CMS) to establish national quality improvement efforts by applying data on process and outcome variations. In this sense, the CMS calculates two essential thresholds for each quality measure in the top performance score (TPS); the achievement threshold set by the 50th percentile (median performance) during a baseline period and the ‘benchmark” threshold at the mean performance of the top ten percent of hospitals during the baseline period (Nash et al., At this point, it is essential to differentiate between the actual rate of institutional benchmark and a percentile rank. For example, the actual value can be the mathematical value presented out of 100, while the percentile is the percent of values below a specific value.

Control Charts

Control charts present graphical data on how a process varies over time. Unlike run charts, control charts have an average of three lines: the central control line (median), the upper control limit, and the lower control limit (Nash et al., 2019, p. 86). The centerline (central control line) represents where the characteristic under study should fall in the absence of unusual sources of variability. On the other hand, the upper and lower control limits are three standard deviation distances from the center line on both sides and act as the warning lines, signifying the presence of unusual variations against the set control line. Another characteristic of a control chart is the ability to plot independent and control variables on the X and Y-axis, respectively. When using a control chart as a tool for measuring the performance of Mercy Medical Center regarding eye exams for diabetic patients, it is possible to plot the number of tests conducted on the Y-axis against time on the X-axis. Gupta & Kaplan (2020) contend that control charts provide more significant insights into data variations because control limits present distinctions between common and unique causes of variations. However, the effectiveness of the control chart relies massively upon the ability to calculate the mean and the standard deviations as measures of central tendency. In this sense, standard deviations are essential in determining the upper and lower control limits, while the mean is crucial for determining the position of the centerline. Establishing upper and lower control limits is essential in reducing confounding variables (Sachlas et al., 2019). As a result, control charts are risk-adjusted tools for measuring quality because they consider different risks and characteristics of items of concern.

Pareto Analysis Charts

The Pareto chart is the third tool for measuring quality, enabling healthcare professionals to prioritize the primary causes of process and outcome variations. Alkiayat (2021) contends that this chart is a bar graph with frequency on the left Y-axis, the percentage on the right side (Z-axis), and the contributing factors plotted in descending order by frequency on the X-axis. Further, this bar graph contains a line that presents the cumulative percentage of the elements. This line reaches the ≥80% mark and actualizes the 80-20 rule that accounts for ‘vital few’ and ‘trivial many’ (Alkiayat, 2021). Therefore, the purpose of a Pareto chart is to highlight the differences in magnitude and effects of various factors on a specific outcome or process of concern. A clinical scenario where this chart can measure quality is the proliferation of medication errors. While using Mercy Medical Center as an example, healthcare professionals can identify the causes of medication errors by evaluating the cumulative frequency of each risk factor consistent with incidences of medication mistakes. In this sense, it is possible to identify risk factors more likely to result in errors, enabling care providers to prioritize addressing them. Like run and control charts, Pareto charts are risk-adjusted tools that can reduce the confounding variables associated with the studied outcome.

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The Importance of these Measures to the Organization

While using Mercy Medical Center as an example and diabetes screening and medication errors as process and outcomes of concern, run charts, control charts, and Pareto analysis bar graphs can enable the organization to set strategic goals for the process and outcome improvement. Firstly, run and control charts can account for data variations over time by identifying fluctuations against the set centerlines. In determining the organizational performance in eye exams for diabetic patients, run and control charts can indicate the prevalence patterns for erroneous processes, enabling care providers to identify causes of variations and improve processes.

On the other hand, Pareto analysis charts can offer more straightforward opportunities for identifying the underlying causes of outcome variations over time compared to the other two quality measurement tools. Alkiayat (2021) contends that the Pareto quality improvement tool emphasizes the 80-20 rule of “vital few” and trivial many” that categorizes causative and contributing factors for specific outcomes based on their weights and frequencies. For example, it is essential to use this tool to identify the most profound causes of medication errors in Mercy Medical Center. The ability to categorize causes according to their weights and frequencies enables quality improvement teams to prioritize the vital few while defining measures to prevent the effects of trivial many.

The Relationship Between the Three Rate-based Quality Measurement Tool with Patient Safety, Cost of Poor Quality, and the Overall Cost of Healthcare Delivery

Undoubtedly, the three tools for measuring quality resonate with the determination to avert harm, reduce the cost of poor quality, and the overall cost of care delivery. Firstly, run and control charts can identify prevalent patterns of flawed processes by indicating variations in the process of concern. For example, they can locate fluctuations in eye exams conducted by healthcare professionals in Mercy Medical Center over time. This aspect enables the organization to compare its performance with state and national benchmarks, facilitating the element of organizational learning. Eventually, the plausibility of identifying areas of improvement can translate to improved processes consistent with the IOM’s six domains: care quality, including care safety, efficiency, effectiveness, and patient-centeredness. These factors can improve patient safety and address the problem of poor-quality care.

Equally, Pareto charts consistently improve patient safety and reduce care costs and losses incurred due to flawed processes. These tools identify risk factors and causes of unfavorable outcomes by categorizing them according to their weights and frequencies. When used as a tool for assessing the organizational performance in preventing medication errors, Pareto charts can identify causes and arrange them in a descending order to enable quality improvement teams to prioritize significant causes while keeping an eye on the “trivial many.” As a result, the tool allows care providers to focus on issues that result in unfavorable outcomes, safeguarding patient safety and reducing economic losses incurred due to flawed processes.

Conclusion

Care quality is a topic of significant impact in the current healthcare systems. Despite the absence of a universal definition of care quality, the Institute of Medicine (IOM) presented various dimensions that constitute the “quality” aspect of care, including safety, effectiveness, timeliness, efficiency, and patient-centeredness. Healthcare organizations must evaluate their processes and outcomes consistent with the IOM’s six domains of care quality. Notably, there are multiple tools for measuring quality. As a result, this assessment elaborates on three rate-based tools for measuring quality and establishes their relationship with patients’ safety and the overall cost of healthcare delivery.

References

Agency for Health Research and Quality. (n.d.). Minnesota: Diabetes quality measures compared to achievable benchmarks. Retrieved June 25, 2022, from https://nhqrnet.ahrq.gov/inhqrdr/Minnesota/benchmark/table/Diseases_and_Conditions/Diabetes

Agency for Healthcare Research and Quality. (2018a, November). Key driver 2: Implement a data-driven quality improvement process to integrate evidence into practice procedures. https://www.ahrq.gov/evidencenow/tools/keydrivers/implement-qi.html

Agency for Healthcare Research and Quality. (2018b, November). Six domains of health care quality. https://www.ahrq.gov/talkingquality/measures/six-domains.html

Alkiayat, M. (2021). A practical guide to creating a Pareto chart as a quality improvement tool. Global Journal on Quality and Safety in Healthcare. https://doi.org/10.36401/jqsh-21-x1

Brady, P. W., Tchou, M. J., Ambroggio, L., Schondelmeyer, A. C., & Shaughnessy, E. E. (2017). Quality improvement feature series article 2: Displaying and analyzing quality improvement data. Journal of the Pediatric Infectious Diseases Society, 7(2), 100–103. https://doi.org/10.1093/jpids/pix077

Gupta, M., & Kaplan, H. C. (2020). Measurement for quality improvement: Using data to drive change. Journal of Perinatology, 40(6), 962–971. https://doi.org/10.1038/s41372-019-0572-x

Houseworth, J., Kilaberia, T., Ticha, R., & Abery, B. (2022). Risk adjustment in-home and community-based services outcome measurement. Frontiers in Rehabilitation Sciences, 3. https://doi.org/10.3389/fresc.2022.830175

Nash, D. B., Joshi, M., Ransom, E. R., & Ransom, S. B. (Eds.). (2019). The healthcare quality book: Vision, strategy, and tools (4th ed.). Health Administration Press.

Sachlas, A., Bersimis, S., & Psarakis, S. (2019). Risk-Adjusted control charts: Theory, methods, and applications in health. Statistics in Biosciences, 11(3), 630–658. https://doi.org/10.1007/s12561-019-09257-z

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Assignment: Tools for Measuring Quality
How do we determine quality? Quality in other areas of our lives can be subjective, so as it relates to our nursing practice, how do we specifically ensure that quality is clearly defined and measurable?
Tools for measuring quality are used to assess the value measured, collected, or compared. These tools allow for subjectivity to be replaced with objectivity through data, formula, ranking, and analysis.

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For this Assignment, you will explore at least three rate-based measures of quality. You will deconstruct each measure to explore your understanding of

It, includes its importance and its impact on patient safety, the cost of healthcare, and the overall quality of healthcare.
To Prepare:
• Review the Learning Resources for this week, and reflect on tools for measuring quality in nursing practice.
• Select three rate-based measurements of quality that you would like to focus on for this Assignment.
o Note: These measurements must relate to some aspect of clinical or service quality that directly relates to patient care or the patient’s experience of care, and for the purposes of this Assignment, an analysis of staffing levels is not permitted.
o You can find useful information on quality indicators that are of interest to you on these websites and resources. You may choose only one of the three measures to be some form of patient satisfaction measure.
• Consider how the three rate-based measures (you will select) are defined, how the rates were determined or calculated, how the measures were collected, and how these measures are communicated to both internal and external stakeholders.
• Reflect on how the three rate-based measures (you will select) may relate to organizational goals for improved performance.
• Reflect on the three rate-based measures (you will select), and consider the importance of these measures on patient safety, cost of healthcare, and overall quality of healthcare.
The Assignment: (8–10 pages)
• Describe the three rate-based measures of quality you selected, and explain why.
• Deconstruct each measure to include the following:
o Describe the definition of the measure.
o Explain the numerical description of how the measure is constructed (the numerator/denominator measure counts, the formula used to construct the rate, etc.).
o Explain how the data for this measure are collected.
o Describe how the measurement is compared externally to other like settings, and differentiate between the actual rate and a percentile ranking. Be specific.
o Explain whether the measure is risk adjusted or not. If so, explain briefly how this is accomplished.
o Describe how goals might be set for each measure in an aggressive organization, which is seeking to excel in the marketplace. Be specific and provide examples.
• Describe the importance of each measure to a chosen clinical organization and setting.
o Using the websites and resources you can choose a hospital, a nursing home, a home health agency, a dialysis center, a health plan, an outpatient clinic, or a private office. A total population of patient types is also acceptable, but please be specific as to the setting. That is, if you are interested in patients with chronic illness across the continuum of care, you might home in a particular health plan, a multispecialty practice setting or a healthcare organization with both inpatient and outpatient/clinic settings.
o Note: Faculty appointments and academic settings are not permitted for this exercise. For all other settings, consult the Instructor for guidance. You do not need actual data from a given organization to complete this Assignment.
• Explain how each measure you selected relates to patient safety, to the cost of poor quality, and to the overall cost of healthcare delivery. Be specific and provide examples.

 

LEARNING RESOURCES:
Nash, D. B., Joshi, M. S., Ransom, E. R., & Ransom, S. B. (Eds.). (2019). The healthcare quality book: Vision, strategy, and tools (4th ed.). Health Administration Press.
• Chapter 4, “Data Collection” (pp. 107–26)
• Chapter 5, “Statistical Tools for Quality Improvement” (pp. 127–169)

https://www.ahrq.gov/evidencenow/tools/keydrivers/implement-qi.html

Please provide examples and Use headings for each question please and thank you.