Adoption of PDSA Cycles in Healthcare: A Systematic Review

Key Takeaways:

• 73 papers explicitly adopted PDSA methodologies in their quality improvement efforts
• Only 7 studies regularly used data at intervals of monthly or more frequently
• Only 2 studies implemented a comprehensive use of the PDSA framework

INTRODUCTION

In contrast to ordering more tests and offering more services, there is an increasing emphasis on the quality of healthcare delivered. This is driven by the recognition that healthcare delivery itself can be dangerous. However, exploding costs are also driving new payment models that financially incentivize stakeholders to produce outcomes rather than simply offer more services. Quality improvement in healthcare is a broad subject, but our team has focused on the use of data (e.g., from the electronic health record system) and iteration. Of course, there are a multitude of methods and frameworks to encourage quality improvement iteration including Lean, Six Sigma, and Total Quality Management among others.

The Phrase Health team often adopts the Plan-Do-Study-Act (PDSA) framework given its simplicity. The name, PDSA, is an acronym for the steps in a circular process of iteration. The first step, “Plan”, involves activities like setting goals and establishing methods for data collection. The next step, “Do”, entails actually implementing the plan, collecting data, and identifying issues.The next step, “Study”, incorporates activities around the analysis of data and comparing it with the hypotheses from the planning stage. Finally, the “Act” step concludes by determining the need for an additional iteration or, if goals are met, incorporating the changes systemwide. Of course, the most important consideration is about pursuing additional iterations. Projects are bound to require multiple iterations and variations before meeting their desired goals.

WIth this PDSA methodology in mind, it raises the question of how PDSA has been implemented in the literature. In 2013, a team from London sought answers by conducting a systematic review of the literature. Their findings highlight the state of adoption of this methodology in quality improvement.

APPROACH

A systematic review of the literature requires a broad search of all available publications on a specific topic. This is a similar approach to the Journal Club article previously published on the effectiveness of interruptive electronic health record (EHR) alerts. In the case of the PDSA review, the team used the following search terms in a variety of article databases: 'PDSA', 'PDCA', 'Deming Cycle', 'Deming Circle', 'Deming Wheel' and 'Shewhart Cycle'.

A set of standardized inclusion principles were used and the articles were independently reviewed by three researchers. These are the criteria that they used:

• peer-reviewed journal (inclusion)
• PDSA was used in a healthcare setting to improve quality (inclusion)
• published in English (inclusion)
• Editorial letters (exclusion)
• Conference abstracts (exclusion)
• Opinion articles (exclusion)

The researchers attempted to map integral parts of the PDSA cycle into observable data elements that they could identify in a study. For example, some of the key parts included: “iterative cycles”, “small-scale testing”, and “documentation.” In the case of “iterative cycles,” the researchers could assess inclusion of “were multiple cycles used” or “when isolated cycles were used were future actions based on the 'Act' stage?”

Key Features of PDSA Framework

Table 2 from the original paper

It’s important to recognize that the researchers didn’t aim to evaluate if the PDSA approach was successful, but rather was PDSA used. As a result, the researchers acknowledged that this may introduce publication bias in the number of studies published given unsuccessful implementations are less likely to be published.

RESULTS

The researchers found 409 unique articles based on their search. After further investigation into the abstracts and/or full texts, the team settled on 73 papers that met the inclusion criteria. The researchers provided a broad set of descriptive statistics and findings from these available studies. Below, many of these have been mapped back to the key features of the authors' PDSA framework presented earlier. (All data derived from Table 2 from the original paper.)

Iterative Cycles

Description: To achieve an iterative approach, multiple PDSA cycles must occur. Lessons learned from one cycle link and inform cycles that follow. Depending on the knowledge gained from a PDSA cycle, the following cycle may seek to modify, expand, adopt or abandon a change that was tested

How Measured: Were multiple cycles used? Were multiple cycles linked to one another (ie, does the 'act' stage of one cycle inform the 'plan' stage of the cycle that follows)? When isolated cycles were used were future actions postulated in the 'act' stage?

Included: 49 studies

The authors found a total of 49 studies that referenced any iterative analysis. Of these, only 14 used successive iterations. Another 33 used unlinked cycles. The final 2 actually used the incorrect order of PDSA!

Prediction-Based Testing of Change

Description: A prediction of the outcome of a change is developed in the 'plan' stage of a cycle. This change is then tested and examined by comparison of results with the prediction.

How Measured: Was a change tested? Was an explicit prediction articulated?

Included: 47 studies

The PDSA cycle follows the tenants of the scientific method. During the 'plan' stage, a hypothesis is developed and then tested through the cycle. A total of 30 studies used a single cycle to test a change. Another 3 used the framework to simple analyze the data. Of those that actually used successive iterations, 8 iteratively tested their hypotheses. On the other hand, 5 of these iterative PDSA studies started with data collection cycles and then successive testing cycles.

Small-Scale Testing

Description: As certainty of success of a test of change is not guaranteed, PDSAs start small in scale and build in scale as confidence grows. This allows the change to be adapted according to feedback, minimizes risk and facilitates rapid change and learning

How Measured: Sample size per cycle? Temporal duration of cycles? Number of changes tested per cycle? Did sequential cycles increase scale of testing?

Included: 47 studies

The authors refer to "scale" in a few ways. These include number of included subjects, duration of study, and complexity. The size was difficult to compare as some studies reported this for the entire project. Only 27 studies included sample sizes for at least one PDSA cycle. Of the studies with a single PDSA cycle, the range was 7 to 2079 (mean=323.33, standard deviation=533.60). The first cycle of the iterative studies ranged from 1 to 34 (mean=16.75, standard deviation=11.47). A single PDSA cycle ranged from 2 weeks to 5 years (mean=11.91 months, standard deviation=12.81). For articles that mentioned successive cycles, total project lengths covered from 1 day to 4 years (mean=20.38 months, standard deviation=20.39). Finally, 22 of the articles tried to test more than one change in at least a single cycle.

Documentation

Description: Documentation is crucial to support local learning and transferability of learning to other settings

How Measured: How thoroughly was the application of the PDSA method detailed in the reports? Was each stage of the PDSA cycles documented?

Included: 73 studies

Since there needed to be some mentioned and detail about the cycles, all of the 73 included studies qualified for documentation. However, there were 16 publications that included no details around the PDSA cycles whatsoever. On the other hand, 47 articles described details from each stage of the PDSA cycle.

Data Over Time

Description: Data over time increases understanding regarding the variation inherent in a complex healthcare system. Use of data over time is necessary to understand the impact of a change on the process or outcome of interest.

How Measured: Was data collected over time? Were statistics used to test the effect of changes and/or understand variation?

Included: 47 studies

Of course, data can be either quantitative of qualitative. Objective quantitative data is more easily measured and easier to analyze change. Several studies used quantitative techniques. However, the researchers broke down the qualitative data assessment into four types of categories:

• Regular (n=15): 3+ data points at consistent intervals. Interestingly only 7 included data at intervals of at least monthly.
• Non-regular (n=16): before and after
• Single data point (n=8): single data element following the PDSA cycle
• No quantitive data (n=8)

Of note, none of the publications used statistical process control (SPC) methods to assess change. Only 11 used some statistical tests to assess the data.

The study also mentioned the geographic distribution. The studies came from: USA (n=46), UK (n=13), Canada (n=3), Australia (n=3), Netherlands (n=2). Additionally, the authors mention a broad array of clinical settings. However, the two most common were surgery (n=6) and pain management (n=6).

DISCUSSION

This systematic review was published in 2013, so the field has continued to evolve since then. However, there is a large amount of variation in the reporting of quality improvement efforts using PDSA. This is ironic because the reduction of variation is a key goal when aiming to deliver reliable results. Overall, the researchers found that assessing compliance with any part of the PDSA cycle was difficult because of a lack of standard reporting procedures.

Only two of the publications (bolded in references below) demonstrated all integral components of the PDSA framework. This shows a lack of rigor or available resources when applying the framework. Either way, it may reflect poorly on the results from these efforts. The authors note, “The theoretical framework presented in this paper highlights the complexity of PDSA cycles and the underpinning knowledge required for correct application.” As a result, the researchers warn about the interpretability of the results of many studies that reference PDSA as they may not truly adopt it appropriately.

One of the key features of the authors PDSA framework is “the use of data over time.” Surprisingly, only 7 papers used monthly or more frequent data as part of their PDSA approach. As data is key in evaluating effectiveness, it’s impossible to assess directionality of efforts without some available quantifiable information. Access to self-service data tools can be an important factor in encouraging quality improvement using PDSA or other frameworks.

EVALUATING QUALITY IMPROVEMENT USING PDSA

Phrase Health provides tools to analyze and manage EHR interventions. If tools like EHR alerts or order sets are part of your quality improvement efforts, the self-service access to dashboards can lower the bar to data accessibility. Additionally, Phrase Health enables quality improvement teams to construct entire projects using key driver diagrams to map out interventions, process measures, and outcome measures.

Journal Club Source

Taylor MJ, McNicholas C, Nicolay C, et al. Systematic review of the application of the plan–do–study–act method to improve quality in healthcare. BMJ Quality & Safety 2014;23:290-298.

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