The CCLM contribution to improvements in quality and patient safety

Quality in laboratory medicine and the Journal: walking together

Quality in laboratory medicine is defined as "an unfinished journey", as the more essential the laboratory information provided, the more assured its quality should be. In the past decades, the Journal Clinical Chemistry and Laboratory Medicine has provided a valuable forum for garnering new insights into the analytical and extra-analytical phases of the testing cycle, and for debating crucial aspects of quality in clinical laboratories. The impressive number of papers published in the Journal is testimony to the efforts made by laboratory professionals, national and international scientific societies and federations in the quest to continuously improve upon the pre-, intra- and post-analytical steps of the testing cycle, thus enhancing the quality of laboratory information. The paper appearing in this special issue summarizes the most important and interesting contributions published in the Journal, thus updating our knowledge on quality in laboratory medicine and offering further stimuli to identify the most valuable measures of quality in clinical laboratories.

Something's Lost and Something's Gained: Seeing Reference Laboratory Quality from Both Sides, Now

Growing regulatory burdens, payment model changes, and increased complexity in laboratory medicine have contributed to an increased reliance on reference laboratories. Although reference laboratories often offer rapid, low cost, high quality testing, outsourcing laboratory tests can create quality and patient safety vulnerabilities particularly in the pre-analytic and post-analytic phases of the test cycle. Disconnects in governance, policy, and information technology between the reference laboratory and the referring provider conspire to increase risk. Laboratory leaders seeking to reduce risk and improve quality must ensure clear and collaborative oversight, monitor meaningful quality metrics, and integrate feedback from ordering providers.

Improving the Delivery of Healthcare through Clinical Diagnostic Insights: A Valuation of Laboratory Medicine through "Clinical Lab 2.0"

BACKGROUND

As healthcare payment and reimbursement begin to shift from a fee-for-service to a value-based model, ancillary providers including laboratories must incorporate this into their business strategy. Laboratory medicine, while continuing to support a transactional business model, should expand efforts to include translational data analytics, proving its clinical and economic valuation. Current literature in this area is limited.

CONTENT

This article is a summary of how laboratory medicine can support value-based healthcare. Population health management is emerging as a method to support value-based healthcare by aggregating patient information, providing data analysis, and contributing to clinical decision support. Key issues to consider with a laboratory-developed population health management model are discussed, including changing reimbursement models, the use of multidisciplinary committees, the role of specialists in data analytics and programming, and barriers to implementation. Examples of data considerations and value are given.

SUMMARY

Laboratory medicine is able to provide meaningful clinical diagnostic insights for population health initiatives that result in improved short- and long-term patient outcomes and drive cost-effective care. Opportunities include data analysis with longitudinal laboratory data, identification of patient-specific targeted interventions, and development of clinical decision support tools. Laboratories will need to leverage the skills and knowledge of their multidisciplinary staff, along with their extensive patient data sets, through innovative analytics to meet these objectives.

Comprehensive evaluation of the internal and external quality control to redefine analytical quality goals

Introduction

The aim of this work is to design a selection algorithm for total allowable error (TEa) source using a graphic tool that, by integrating internal (IQC) and external (EQC) quality control performances, enables the laboratory to evaluate which TEa source better fits the test analytical performance.

Materials and methods

Two analytical performance indicators (bias and Sigma metrics) were estimated for 23 biochemistry tests during 2016. Bias was estimated on the EQC, and Sigma metrics was calculated through the results obtained in the IQC. The Sigma metrics was charted as a function of the bias (TEa%). Following the proposed algorithm (considering the hierarchy in the Milan 2014 consensus), the TEa was evaluated depending on two areas. One area in the chart was defined as the objective area in which the used TEa is the appropriate one for the analytical performance of the test under evaluation. For any test located outside this area, a performance re-evaluation was required using another source of TEa.

Results

In 19 out of 23 evaluated tests, the resulting analytical performance allowed for the selection of biologic variability as TEa source. In the four remaining cases, TEa sources of lesser hierarchy were selected.

Conclusion

The graphic tool designed together with the proposed algorithm enabled the laboratory to standardize the selection procedure of the most appropriate TEa for the test analytical performance.

Sigma Metrics Across the Total Testing Process

Laboratory quality control has been developed for several decades to ensure patients' safety, from a statistical quality control focus on the analytical phase to total laboratory processes. The sigma concept provides a convenient way to quantify the number of errors in extra-analytical and analytical phases through the defect per million and sigma metric equation. Participation in a sigma verification program can be a convenient way to monitor analytical performance continuous quality improvement. Improvement of sigma-scale performance has been shown from our data. New tools and techniques for integration are needed.

Useful measures and models for analytical quality management in medical laboratories

The 2014 Milan Conference "Defining analytical performance goals 15 years after the Stockholm Conference" initiated a new discussion of issues concerning goals for precision, trueness or bias, total analytical error (TAE), and measurement uncertainty (MU). Goal-setting models are critical for analytical quality management, along with error models, quality-assessment models, quality-planning models, as well as comprehensive models for quality management systems. There are also critical underlying issues, such as an emphasis on MU to the possible exclusion of TAE and a corresponding preference for separate precision and bias goals instead of a combined total error goal. This opinion recommends careful consideration of the differences in the concepts of accuracy and traceability and the appropriateness of different measures, particularly TAE as a measure of accuracy and MU as a measure of traceability. TAE is essential to manage quality within a medical laboratory and MU and trueness are essential to achieve comparability of results across laboratories. With this perspective, laboratory scientists can better understand the many measures and models needed for analytical quality management and assess their usefulness for practical applications in medical laboratories.

Assessing quality on the Sigma scale from proficiency testing and external quality assessment surveys

BACKGROUND

There is a need to assess the quality being achieved for laboratory examinations that are being utilized to support evidence-based clinical guidelines. Application of Six Sigma concepts and metrics can provide an objective assessment of the current analytical quality of different examination procedures.

METHODS

A "Sigma Proficiency Assessment Chart" can be constructed for data obtained from proficiency testing and external quality assessment surveys to evaluate the observed imprecision and bias of method subgroups and determine quality on the Sigma scale.

RESULTS

Data for hemoglobin A1c (HbA1c) from a 2014 survey by the College of American Pathologists (CAP) demonstrates that approximately two-thirds of the examination subgroups provide only two-Sigma quality when evaluated against the CAP requirement of an allowable total error of 6.0%. The weighted averages were 1.46 Sigma for a survey sample with an assigned value of 6.49% Hb (average bias 2.31%, CV 2.87%), 1.45 Sigma at 6.97% Hb (average bias 2.29%, CV 2.81%), and 1.75 at 9.65% Hb (average bias 1.55%, CV 2.71%). Maximum biases for examination subgroups were 5.7%, 5.8%, and 4.1%, respectively.

CONCLUSIONS

Assessment of quality on the Sigma scale provides evidence of the analytical performance that is being achieved relative to requirements for intended use and should be useful for identifying and prioritizing improvements that are needed in the analytical quality of laboratory examinations. In spite of global and national standardization programs, bias is still a critical limitation of current HbA1c examination procedures.

Developing GRADE outcome-based recommendations about diagnostic tests: a key role in laboratory medicine policies

Harmonisation and risk management policies represent key-issues in modern laboratory medicine as they focus on a more patient-centred delivery of laboratory information based on the recognition of the importance of all steps of the total testing process (TTP) for assuring quality and patient safety. However, a further essential step in project aiming to improve the value of laboratory medicine becomes the assessment of the impact of testing on patient-important outcomes. The grading of recommendations assessment, development and evaluation (GRADE) evidence to decision (EtD) frameworks may provide a systematic and transparent approach for translating the best clinical evidence available into healthcare decisions and recommendations. GRADE is a tool appropriate not only for evaluating test accuracy but also for clinical impact, such as mortality, morbidity, symptoms, and quality of life and therefore it should be applied to the outcome research in laboratory medicine. The application of GRADE requires the recognition that a recommendation about the use of test results should result from a balance between the desirable and the undesirable consequences, including non-health related consequences such as resource utilisation, feasibility, acceptability, equity and other factors. GRADE EtDs, represents a fundamental step in projects designed to improve care quality. Patient-physician-laboratory feedback can be assured through the GRADE process, where the team developing the recommendations should include the “three-parties” representatives; clinicians, laboratorians and patient/consumers. This ensures that the laboratory-patient interaction should not be a one-way process only (information from laboratory to patient) but a two-way process, incorporating patient expectations and feedback.

Identifying risk in the use of tumor markers to improve patient safety

Background:

Tumor markers (TM) are a routine test that are not always used well, and can lead to unnecessary additional tests, which are not without risks for the patients. So, to implement appropriate strategies to improve the adequate use of TM and, therefore, improve patient safety, is required to analyze the use of TM, identifying risks and establishing if there are differences in their use as a function of their utility.

Methods:

The study was a descriptive, longitudinal, retrospective and systematic study in the area covered by the University Hospital of Padua. In the follow-up 2-year study, 23,059 analytical requests of TM, corresponding to 14,728 patients, were analyzed. For the level of statistical significance it applies an approximation of the normal law (Z statistic) and χ

Results:

Only 9196 requests (39.88%) out of a total of 23,059 on 5080 patients with neoplastic diseases have been classified as adecuate according to current guidelines. The number of requests per patient was variable (1.57±1.35). In patients with neoplastic diseases this increased to 1.80±1.68. The mean of number of TM per request was 2.4±1.73. The analysis showed an association between the number of requests and the type of marker used.

Conclusions:

The use of TM is variable, mainly of the follow-up markers, when they are used as screening. This inappropriate use, minimizes their utility favoring erroneous interpretations and increases the risk of damage to the patient. So it is essential to implement safe practices in the use of TM.

Quality control review: implementing a scientifically based quality control system

This review focuses on statistical quality control in the context of a quality management system. It describes the use of a 'Sigma-metric' for validating the performance of a new examination procedure, developing a total quality control strategy, selecting a statistical quality control procedure and monitoring ongoing quality on the sigma scale. Acceptable method performance is a prerequisite to the design and implementation of statistical quality control procedures. Statistical quality control can only monitor performance, and when properly designed, alert analysts to the presence of additional errors that occur because of unstable performance. A new statistical quality control planning tool, called 'Westgard Sigma Rules,' provides a simple and quick way for selecting control rules and the number of control measurements needed to detect medically important errors. The concept of a quality control plan is described, along with alternative adaptations of a total quality control plan and a risk-based individualized quality control plan. Finally, the ongoing monitoring of analytic performance and test quality are discussed, including determination of measurement uncertainty from statistical quality control data collected under intermediate precision conditions and bias determined from proficiency testing/external quality assessment surveys. A new graphical tool, called the Sigma Quality Assessment Chart, is recommended for demonstrating the quality of current examination procedures on the sigma scale.

Harmonization of pre-analytical quality indicators

Quality indicators (QIs) measure the extent to which set targets are attained and provide a quantitative basis for achieving improvement in care and, in particular, laboratory services. A body of evidence collected in recent years has demonstrated that most errors fall outside the analytical phase, while the pre- and post-analytical steps have been found to be more vulnerable to the risk of error. However, the current lack of attention to extra-laboratory factors and related QIs prevent clinical laboratories from effectively improving total quality and reducing errors. Errors in the pre-analytical phase, which account for 50% to 75% of all laboratory errors, have long been included in the ‘identification and sample problems’ category. However, according to the International Standard for medical laboratory accreditation and a patient-centered view, some additional QIs are needed. In particular, there is a need to measure the appropriateness of all test request and request forms, as well as the quality of sample transportation. The QIs model developed by a working group of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) is a valuable starting point for promoting the harmonization of available QIs, but further efforts should be made to achieve a consensus on the road map for harmonization.

Laboratory-associated and diagnostic errors: a neglected link

Clinical laboratories play a vital role in patient care, but many diagnostic errors are associated with laboratory testing. The past decades have seen sustained improvements in analytical performances but the error rates, particularly in pre- and post-analytical phases is still high. Although the seminal concept of the brain-to-brain laboratory loop has been described more than four decades ago, the awareness about the importance of extra-analytical aspects in laboratory quality is a recent achievement. According to this concept, all phases and activities of the testing cycle should be assessed, monitored and improved in order to decrease the total error rates and thereby improve patient safety. In the interests of patients, any direct or indirect negative consequence related to a laboratory test must be considered, irrespective of which step is involved and whether the error depends on a laboratory professional (e.g., calibration or testing error) or a non-laboratory operator (e.g., inappropriate test request, error in patient identification and/or blood collection). Data collected in various clinical settings demonstrate that many diagnostic errors are associated with laboratory testing. In particular, errors are due to inappropriate test request and/or result interpretation and utilization. Collaborations between laboratory professionals and other care providers, namely clinicians and nurses, are needed to achieve the goal of improved patient safety.