Revamping Healthcare Using DMAIC And DFSS

Evidence pointing to the applicability of Six Sigma and related best practices within healthcare has been steadily mounting over the past few years. Primarily based on the implementation of the DMAIC process (Define, Measure, Analyze, Improve and Control), we’re seeing an ever-widening array of documented and publicized results… from improving turnaround time for patients to receive radiology exam reports, to reducing medication errors and infection rates …and even to literally changing the cultural fabric or DNA of an entire organization. But although interest and implementation are growing rapidly, current case studies represent the tip of the iceberg in terms of improving the system as a whole.

If you were to extrapolate the above-mentioned results and extend the DMAIC approach beyond those institutions currently pioneering the way, the impact would be measurable and impressive. It would not, however, represent a total solution and would not completely close the infamous quality chasm that continues to plague the industry. What’s missing? Is there another route we can take?

Profile of the DMAIC Approach in Healthcare

Once they’ve had a chance to see it in action, clinicians have generally embraced the DMAIC approach, since it builds on familiar concepts while adding a level of scientific rigor and sustainability lacking in other initiatives. As one practitioner put it, ‘being able to clearly Define, Measure, Analyze, Improve and Control ANYTHING in the healthcare environment represents a big leap forward.’

DMAIC has been an effective method for improving any process that has measurable response variables, which in healthcare may be classified within four primary groups:

As a technical strategy for process and quality improvement (and particularly when coupled with strong change management tools like change acceleration process [CAP] and Work-out), the DMAIC approach has been successful in driving a wide range of sustainable results, including:

◈ Medical error reduction and patient safety improvement

◈ Cost management and revenue enhancement

◈ Improvements in patient, physician and employee satisfaction

◈ Increase in capacity and throughput

◈ Improvements in supply chain management

◈ Reduction in cycle time for radiology reports

◈ Reduction in patient waiting times in ED

◈ Identification of market growth opportunities

◈ Development of internal leadership capabilities

◈ Streamlining and optimizing technologies and related workflow

◈ Achieving compliance and meeting regulatory requirements

Figure 1: Formula for Effective Results

Aligning Six Sigma healthcare projects with the fundamental objectives of the organization is one of the keys to success, and all projects must show a clear business case in order to gain the required allocation of time and resources.

A common application of DMAIC is shown below, illustrating cycle time for reporting radiology results:

Figure 2: Capability Analysis

The DMAIC approach has worked quite well for healthcare processes that have both measurable response variables and causal factors that are primarily controllable. However, since healthcare involves human behavior and a great deal of interaction between people, processes and technology, we often face a situation where critical issues are being driven by uncontrollable factors and require intervention at the design level.

Designing Healthcare for Six Sigma Excellence

Progress applying Six Sigma in healthcare has been steady and significant. The next wave of change, however, will likely go beyond DMAIC to involve the creation of new processes not bound by investments in archaic technologies, outmoded policies and procedures and other encumbrances inherent in the system.

To achieve the level of change described in the Institute of Medicine’s report, Crossing the Quality Chasm, and break through the 5 sigma “wall” that Mikel Harry referred to in Six Sigma, The Breakthrough Management Strategy Revolutionizing the World’s Top Corporations, healthcare organizations will need to do more than simply improve upon the current system. They will need to employ the DFSS or DMADV (Define, Measure, Analyze, Design and Verify) approach and build entirely new processes from the ground up. The design process gathers customer requirements and translates them into process specifications, then into system design requirements and finally into subsystem and process design requirements. As with DMAIC, DFSS involves a structured, five-phased approach and the application of rigorous statistical tools and techniques.

DFSS has been used by many industries for a myriad of purposes – to design new medical equipment, develop superior dishwashers, create better systems for customer relationship management and even to launch new businesses from concept to completion.

Since studies have shown that 80 percent of quality issues are linked to the design of a product or process, DFSS addresses this problem head-on and delivers long-term cost avoidance and customer satisfaction by ensuring the design clearly meets customer specifications and has been rigorously tested against possible defects.

Knowing When to Apply DMAIC or DFSS

As mentioned earlier, a typical DMAIC project will have measurable response variables, controllable factors and clear linkage with the overall business objectives. Some projects may obviously lend themselves to application of either DMAIC or DFSS. Sometimes, however, a Six Sigma team may be halfway through a DMAIC project only to discover that it should really be a DFSS project in order to meet the true objectives and satisfy customer requirements. At this point, the team will need to go back to the drawing board and take the project through each of the five phases of the DMADV process.

It’s important to consider the following questions in order to determine at the outset whether a particular project requires application of the DFSS approach:

◈ Is this a situation (perhaps involving new technology) where there is no existing process to build upon?

◈ For an existing process, to what extent does it meet customer expectations? Have DMAIC improvements been tried? With what measure of success?

◈ Does it require decreased variability alone or a radical shift in the mean?

◈ Does your organization have the flexibility to either continue or abandon legacy systems linked with this process?

◈ What new developments are planned that may affect the project? ( i.e., new clinical service line or facility renovation)

When an existing process is simply broken beyond repair or the “fix” is precluded by bureaucratic entanglement, DFSS may be a better path to follow. Healthcare organizations may also find it beneficial and refreshing to use DFSS as a chance to take ownership and design new systems that clearly meet customer expectations, instead of copying and perpetuating old processes.

One of the differences between DMAIC and DMADV can be found in the first two phases. The Define and Measure phases of a DMADV project may be summarized as a process of CTQ (critical to quality) flow-down. The Analyze phase can be summarized as a process of capability flow-up. In DMAIC, an understanding of causal factors on a specific process outcome is calculated mathematically, while in DMADV, a specific process may not even exist.

Figure 3: DFSS Process

When new processes, systems and structures are involved, the capability may be projected or forecasted using modeling. In healthcare, the models most relevant to a new service line are those involving capacity, patient queuing, provider resource allocation, and patient routing.

For a DMADV project around a new healthcare service line, the outcome of the Analyze phase will enable the team to:

1. Translate customer needs into specific service line features, service delivery system and service sub-system/process design specification

2. Match needs and requirements against a mathematical expression of existing or forecasted process capabilities

During the Design phase, an optimal design is selected and implemented based on merging the CTQ flow-down and the capability flow-up into one integrated design scorecard. Capability forecasting and analysis provides insight into how well design requirements will be met and the QFD (quality function deployment) translates this into customer satisfaction. The result is a formula for understanding customer impact associated with specific design alternatives and trade-offs.

Finally, in the Verify phase, the actual performance from a sub-system is measured against predicted performance by confirming customer satisfaction. Within manufacturing, this is done through component, sub-system, and system level testing. In healthcare, however, the opportunity to “test” segments of the service line may not exist. The key then becomes understanding the degree to which proper controls are operationalized to consistently yield predictable results.

During the Verify phase the team also has the opportunity to rethink existing systems and processes. If a hospital is planning to launch a new imaging center, for example, they may find that the existing patient registration process is not ideal for meeting customer expectations. Redesign of this process should prompt a customer-focused reevaluation of patient registration across the entire institution.