Assuring Quality
through
Total Quality
Management
 A word from Dr. Westgard |
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Quality assurance in healthcare is a modern MYTH - a Mighty Yearning (by the public), Testimony (by healthcare providers), and Hope (by all of us) that things will work okay if and when we need medical care. Current quality assurance practices mainly emphasize the assessment or measurement of quality, assuming (maybe hoping is a better word) that this interest and attention will work some magic to make quality happen. Unforunately, quality doesn't just happen! Production processes have to be carefully planned, monitored, and managed to assure quality is achieved.
Even the analytical quality of laboratory tests is being assumed today, rather than assured. [1] Laboratories assume that manufacturer's have solved all the problems with their testing processes. Programs in quality control and quality assurance are being reduced to the minimums needed to comply with regulatory and accreditation guidelines. Today the trend is towards new management practices, such as utilization control, outcome assessment, and compliance. But, have we really achieved the analytical quality that is needed? Do we even know what quality is needed for each of the tests we perform? If we haven't defined the quality that is needed, how can we assure that quality is being achieved by our testing processes?
Here's an assessment of what needs to be done if laboratories are to guarantee the quality of the test results they produce.
Total Quality Management, or TQM, has been implemented in many healthcare organizations during the last decade. The teachings of industrial quality gurus, such as Deming and Juran, have established new principles and processes for managing quality. Personally, I have found that Deming provides the principles for what needs to be done and that Juran describes the methodologies or processes for getting it done.
Juran's quality triology is particularly
important because it identifies quality planning as an integral
component of TQM [2]. We have illustrated how quality planning
can be integrated with other quality management functions using
the model shown in the accompanying figure [3]. This TQM framework
involves quality laboratory processes, quality control, quality
assessment, quality improvement, quality planning, and quality
goals.
As shown in the figure, these components work together to provide a quality management process that functions like a feedback loop. QLP defines the best way to get the work done. QC and QA measure how well the work is getting done. When problems are detected, QI determines the root causes, which can then be eliminated through QP, in this case actually re-planning the testing processes and implementing new and better ways of doing the work (which means making changes in QLP).
This quality management framework provides a process focus for management, in contrast to the typical control structure that is usually used to organize management activities. Through this framework, continuous improvement is built into the management process by cycling through the different quality functions. Customer focus is achieved by centering the framework on quality goals, customer requirements, and quality plans. It is important to recognize that quality assurance is the outcome of this whole quality management process, rather than being a component in the process.
Another important insight is that quality planning is a prerequisite to quality assurance! Unfortunately, quality planning is lacking in many laboratories. The highest priority for improving laboratory quality management is to formalize a mechanism for designing or building quality into the testing processes. The benefits will be the ability to provide objective specifications for the precision and accuracy needed for the analytical methods and the quality control needed to monitor the performance of those methods.
Industry has learned that to guarantee quality it is necessary to specify what performance is required and how to know if that performance is achieved. An operational definition is needed to tell people what to do and how to know if they're doing it right. For example, "answer the telephone within three rings" tells a person what to do and how to know if they're achieving the desired performance.
The important message here is that bench level specifications are needed to assure the quality of routine operations. For analytical quality, these operating specifications must describe the imprecision and inaccuracy that are allowable for the method and the control rules and number of control measurements that are necessary for routine QC. The laboratory cannot guarantee the quality of its product unless these operating specifications are achieved.
To establish operating specifications, industry has developed a technique called Quality Function Deployment (or QFD). This technique is used to translate the voice of the customer into the performance characteristics of the process AND a procedure for knowing if the process is working okay. For a laboratory test, the necessary steps include listening to the voice of the customer to understand how the test is used and the changes that are medically important, interpreting these customer needs to define quality requirements, such as the allowable total error for analytical quality or the clinical decision interval for clinical quality, and then translating these quality requirements into the operating specifications for the method, i.e., the imprecision and inaccuracy that are allowable and the QC that is necessary.
A simple example will help illustrates the key importance of the interpretation and translation steps. Anyone working in a laboratory has experienced the situation where a physician has ordered a stat or emergency test, then calls for the answer before the specimen has arrived in the laboratory. The physician is unhappy because the test result is not available and tells you to get your act together and do the test faster. Now, if you listen to the customer and do the test faster, you know that still won't satisfy the customer's needs. Doing the test faster doesn't solve the problem, which most likely is due to the acquisition of the specimen and transporting it to the laboratory. Listening to the voice of the customer doesn't mean doing what the customer says. Interpretation and translation are required to understand the problem and fix the process.
| It is easy to see how this planning approach can be used to achieve the desired turnaround time for a laboratory test. A quality requirement can be defined in the form of an allowable turnaround time on the basis of discussions with the users. The steps in the process can be identified from the point of ordering the test, acquiring the specimen, transporting the specimen, processing the specimen, analyzing a sample, reporting the result, and receiving the result. Portions of the allowable turnaround time can then be allocated to different steps in the process, establishing specifications for each of the steps, and allowing those steps to be monitored to be sure the desired performance can be achieved. |
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For analytical quality, the translation is more difficult. Quality-planning models are used to convert quality requirements into specifications for the imprecision and inaccuracy that are allowable and the QC that is necessary. These models are developed by identifying the various factors or steps in the process, allocating or budgeting a portion of the quality requirement to that factor or step, building in a QC check to assure the desired quality or performance is achieved for that factor or step, then balancing the budget for the whole process and monitoring (or controlling) that budget during routine operation [5].
The analytical factors that must be considered include the imprecision and inaccuracy of the method and the sensitivity of the QC procedure. Pre-analytical factors may include within-subject biological variation, sampling variation, and specimen stability. Portions of the allowable variation or allowable error for a test need to be allocated to these different factors, as well as a allocating a margin of safety to quality control the process [3,5]. For an analytical quality requirement in the form of an allowable total error, the allocations consider only analytical factors - the imprecision, inaccuracy, and QC. For a clinical decision interval requirement, a more complicated model is needed to handle both preanalytical and analytical factors.
The application of these models can be simplified by providing a graphical presentation, e.g, a chart of operating specifications (or OPSpecs chart) can be prepared for a stated quality requirement to show the inaccuracy that is allowable versus the imprecision that is allowable for different control rules and numbers of control measurements.
We need to make quality planning quick and easy. The time to be saved is often the time of the managers, supervisors, and lead scientists who have the responsibilities for selecting laboratory methods and QC procedures. The essential process, tools, and technology will be described in later lessons, and here's a quick preview of what's to come in the next several lessons:
The physician can give us information about how the test is used and interpreted. This information will most likely describe the change in a test result that would cause a change the diagnosis, treatment, or management of the patient - a medically important change. We in the laboratory have to translate the medically important change into specifications for operating our testing processes, i.e., the precision and accuracy that are allowable and the QC that is necessary. We shouldn't expect the physician to understand precision, accuracy, and QC and to be able to give us those specifications directly. We have to listen, interpret, and translate - that's our job in the laboratory.
Implementation of a quality planning process requires that laboratories build on a solid foundation of QC, QA, and TQM, rather than replacing these management practices with new practices, such as utilization review and outcome measurements. That's a real danger with trends in management practices - we may throw out some old but useful practices because we don't have the time to do both the old and the new. It's still important to improve the technical management of analytical processes to provide a solid baseline for utilization review and outcomes assessment.
