The science of Quality Improvement (sometimes referred to as Continuous Quality Improvement or Total Quality Management) has been applied for decades in manufacturing and other industries to reduce defects and errors1. Its application to healthcare, and to laboratory practice in particular, provides standard methods by which we can identify and remedy not only errors, but also inefficiencies and ineffectiveness in our practice. The ability to apply principles of Quality Improvement (QI) to evaluate systems performance is one of the five competencies defined by the Institute of Medicine as essential for all healthcare professionals (along with working in interprofessional teams, applying evidence-based practice, using informatics, and providing patient-centered care)2.

Quality improvement should not be confused with quality control or quality assurance. Quality control methods are applied to detect analytical errors as tests are performed. Quality assurance is defined by the CDC as “a range of activities that enable laboratories to achieve and maintain high levels of accuracy and proficiency”; specifically, the development of standard operating procedures (SOPs), tracking compliance with SOPs, and description of corrective actions when required3. In contrast, Quality Improvement evaluates the performance of a system, with the goal of designing interventions to improve that performance.

A Quality improvement project begins with the selection of a quality indicator: a measurable quantity which can serve as a gauge of performance related to the IOM quality aims (safe, timely, effective, efficient, patient-centered, and equitable). A number of standardized quality indicators for the pre-analytic phase of laboratory testing have been described, such as identification errors and hemolyzed or clotted samples; post-analytic quality indicators include incorrect reports or notification errors. Collecting information in standard formats for these measurements (harmonization) will allow data to be appropriately compared with available benchmarks4. Once a clear understanding of current performance is available, comparison with goals may reveal quality shortfalls or excessive errors (or near-misses).

When a specific quality indicator has been identified as a target for improvement, all individuals who participate in the system(s) responsible for the outcome should be asked to carefully analyze processes involved and to propose interventions for improvement. Although quality indicator data may be compared with published or benchmark data, the systems analysis works better if conducted on a local basis, due to individual variations5. A common methodology applied to QI is Plan-Do-Study-Act (PDSA)6; in this process, the system analysis is the Plan step. Plan a way to implement the intervention which is determined to be most likely to improve the system outcome. It is important to test only one intervention at a time, so that its effect can be clearly assessed.

The Do step includes the actual deployment of the new process and collection of subsequent quality indicator data for a specified period of time. Once data are available, they are evaluated to determine if the results have improved, worsened, or remained unchanged (the Study step). This analysis determines the Act step: the decision to keep the intervention, or to discontinue it. In either case, another intervention may then be Planned, and the PDSA cycle is repeated. The iterative nature of this process – to continue implementing and evaluating the effect of systems changes – leads to cumulative (and continuous) improvement.

Demonstration of all five competencies in laboratory practice involves considerable overlap, but quality improvement in particular is linked to interprofessional teamwork, evidence-based care, and use of informatics. Most opportunities for improvement which are addressed with QI are in the pre- or post-analytical phases of the testing process, so professionals from other disciplines must be involved (teamwork). Ideally, outcomes both before and after QI implementation can be compared with benchmark data or best practices (evidence-based care). Informatics can provide the data needed to establish the initial level of performance and again for evaluation after intervention.

We can probably agree that every laboratory could improve their practice in some manner. That improvement will require changes to whatever systems are in place; doing the same things in the same ways will not lead to better outcomes, no matter how well-intentioned we are! Quality Improvement offers a process by which we can initiate and evaluate change in an evidence-based manner, leading to better patient care.

  1. Nicolay CR, et al. Systematic review of the application of quality improvement methodologies from the manufacturing industry to surgical healthcare. Brit J Surg. 2011; 99: 324-335.
  1. Morris S, Otto CN, and Golemboski K. Improving patient safety and healthcare quality in the 21st century: competencies required of future Medical Laboratory Science practitioners. Clin Lab Sci 2013; 16(4):200 – 204.
  1. Centers for Disease Control and Prevention. Laboratory Quality Assurance and Standardization Programs. Retrieved from URL; Accessed 2016, April 23.
  1. Lippi G, et al. Preanalytical quality improvement: In pursuit of harmony. Clin Chem Lab Med 2015; 53(3): 357-370.
  1. Lippi G, et al. Preanalytical quality improvement: In quality we trust. Clin Chem Lab Med 2013; 51(1): 229-24.
  1. Institute for Healthcare Improvement, PDSA Cycles. Retrieved from URL ; Accessed 2016, April 23.

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