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Quality and Efficiency Improvement Tools for Every Radiologist

Posted May 31, 2018 Updated Apr 10, 2023
By Alexei U. Kudla MD , Olga R. Brook MD
10 min read

In an era of value-based medicine, data-driven quality improvement is more important than ever to ensure safe and efficient imaging services. Familiarity with high-value tools enables all radiologists to successfully engage in quality and efficiency improvement. In this article, we review the model for improvement, strategies for measurement, and common practical tools with real-life examples that include Run chart, Control chart (Shewhart chart), Fishbone (Cause-and-Effect or Ishikawa) diagram, Pareto chart, 5 Whys, and Root Cause Analysis.

Introduction

In Radiology, quality improvement is the constant effort to improve performance, safety, and patient outcomes based on imaging services . Diagnostic imaging and image-guided procedures require a complex system of information, equipment, personnel, and decision-making that must be well integrated to provide patient care effectively and safely. Appropriate intervention at points of inefficiency or potential hazard can reduce costs and benefit patient care. Involving departmental and hospital leadership is essential to establish an organizational commitment to support these activities . Understanding the model for improvement, strategies for measurement, and practical quality improvement tools enables every radiologist to successfully engage in quality and efficiency improvement.

The Model for Improvement

Quality improvement is most effective when it is systematic, data-driven, continuous, and incorporated as a core responsibility of health-care professionals. It should employ a formal methodology and focus on system change. In contrast, informal improvement efforts are frequently sporadic, anecdotal, rarely data-driven, and implemented without an assigned responsible supervisor. Such patchwork improvements are challenging to integrate into a cohesive system and may lead to future inefficiencies, not initially anticipated.

The “Model for Improvement,” as outlined by the Institute of Healthcare Improvement, emphasizes project aims, designing measurements around the aims, and then testing small changes before enterprise-wide implementation. The process is then continued in a cycle of Plan-Do-Study-Act (PDSA) ( Table 1 ) .

TABLE 1

The Model for Improvement Specifies Aims, followed by Measurements Needed to Track Progress Toward the Specified Aims, and then Specific Ideas That Will Enable Us to Accomplish Our Aims

Aims What are we trying to accomplish?

  • State clear objectives—know exactly what you are trying to do

Measurements How will we know that a change is an improvement?

  • Measure processes and outcomes

Change ideas What change can we make that will result in improvement?

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Lean Management and Six Sigma

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Aim

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Measurement

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TABLE 2

Types of Measures, Relation to Aims, and Our Health Example

Aims Clear objectives

Outcome measures Assess progress toward the ultimate aim

Progress measures Learning during PDSA cycles

Balance measures Assess system improvement

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Figure 1, More data is not always better. In this example, a sample size of 10 did not clearly show the trend. When the sample size is increased to 30, a clear trend is seen (decreasing CT radiation exposure after an intervention was implemented in May). Further increasing the sample size from 30 to 50 or from 30 to 100 does not alter interpretation of the trend. Finding a balance that enables interpretation of trends while minimizing administrative burdens of data collection is essential for quality improvement.

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TABLE 3

Measurement for Improvement Versus Measurement for Clinical Research

Improvement Clinical research Aim Better process, system, outcomes New generalizable knowledge Observability Test observable Test blinded Bias Accept consistent bias Design to eliminate bias Sample size “Just enough” data “Just in case” data Hypothesis flexibility Changes as learning takes place Fixed hypothesis Testing strategy Small sequential tests One large test Data analysis Run chart or control chart Hypothesis tests ( t tests, etc)

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Data Presentation

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Specific Quality and Efficiency Improvement Tools

Run Chart

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Figure 2, How to make a Run chart. Place time along the x-axis and measurement data along the y-axis. Plot process measurement data and indicate the median as the centerline.

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When it Is Most Useful

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Figure 3, Examples of run charts with nonrandom variation illustrating the four rules (shift, trend, astronomical point, and too many runs).

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Figure 4, Measuring impact in our department. Data pre or post intervention can be directly compared to assess for change. In this case, an intervention aimed at reducing radiation exposure during chest computed tomography angiography examinations (scanner settings modified based on patient body mass index [BMI]) was implemented in May. The subsequent continuously decreasing 5 data points indicate a trend implying that nonrandom change from the initial baseline has been achieved.

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Control Chart (Shewhart Chart)

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Figure 5, Basic ingredients of a Control chart: mean, control limits, data over time.

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When it Is Most Useful

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Figure 6, Control chart and a successful intervention. The historical mean and control limits are extended, and new data are recorded after an intervention ( arrow ). The subsequent data point below the lower control limit ( red circle ) and two continuous data points within the outer third of the control limit ( orange circle ) indicate that the intervention had real impact. New mean and control limits illustrate the adjusted baseline, which has decreased by 24%. The new tighter control limits indicate more predictable performance. Monitoring to keep data within these new limits ensures the new baseline performance is sustained. (Color version of figure is available online.)

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Choosing a Run Chart or Control Chart

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Fishbone (Cause-and-Effect or Ishikawa) Diagram

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When it Is Most Useful

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Example

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Figure 7, Region of hypoenhancement in the left inferior kidney ( red arrow ) revealing the clinical diagnosis of pyelonephritis. Pericholecystic fluid and ascites adjacent to the cecum ( blue arrows ) are likely secondary to third spacing from cirrhosis, but are relevant distractors given provided clinical histroy of right upper quadrant pain. Normal appendix ( white circle ). (Color version of figure is available online.)

Figure 8, Fishbone (Cause-and-Effect or Ishikawa) diagram of potential contributors to the near-miss diagnostic error described above. Factors are not weighted or ranked but are categorized to help understand the overall process.

Figure 9, (a) Contribution table. (b) Pareto chart from our diagnostic “near-miss” example. Improvement efforts focusing on the most relevant contributors would be most likely to have a significant impact. Ranking contributors help prioritize interventions.

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Pareto Chart

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When it Is Most Useful

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Five Whys

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When it Is Most Useful

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Example

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Root Cause Analysis

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When it Is Most Useful

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Example

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An Integrated Approach

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Conclusions

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Acknowledgment

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References

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Academic Radiology, Volume 25, Issue 6
Journals General Radiology
This post is licensed under CC BY 4.0 by the author.
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