Error budget calculations in laboratory medicine: linking the concepts of biological variation and allowable medical errors

Clinical Decision Support systems: A step forward in establishing the clinical laboratory as a decision maker hubA CDS system protocol implementation in the clinical laboratory

Background

New tools for health information technology have been developed in recent times, such as Clinical Decision Support (CDS) systems, which are any digital solutions designed to help healthcare professionals when making clinical decisions. The study aimed to show how we have adopted a CDS system in the San Juan de Alicante Clinical Laboratory and facilitate the implementation of our protocol in other clinical laboratories. We have user experience and the motivation to improve healthcare tools. The improvement, measurement, and monitoring of interventions and laboratory tests has been our motto for years.

Materials and methods

A descriptive research was conducted. All stages in the design of the project are as follows: 1. Set up a multidisciplinary workgroup. 2. Review patients' data. 3. Identify relevant data from main sources. 4. Design the likely outcomes. 5. Define a complete integration scenario. 6. Monitor and track the impact. To set up this protocol, two new software systems were implemented in our laboratory: AlinIQ CDS v8.2 as Rule Engine, and AlinIQ AIP Integrated Platform v1.6 as Business Intelligence (BI) tool.

Results

Our protocol shows the workflow and actions that can be done with a CDS system and also how it could be integrated with other monitoring systems, as well as some examples of KPIs and their outcomes.

Conclusions

CDS could be a great strategic asset for clinical laboratories to improve the integration of care, optimize the use of laboratory tests, and add more clinical value to physicians in the interpretation of results.

Evaluating the effectiveness of a new student-centred laboratory training strategy in clinical biochemistry teaching

BACKGROUND

The error-proneness in the preanalytical and postanalytical stages is higher than that in the analytical stage of the total testing process. However, preanalytical and postanalytical quality management has not received enough attention in medical laboratory education and tests in clinical biochemistry courses.

METHODS/APPROACH

Clinical biochemistry teaching program aim to improve students' awareness and ability of quality management according to international organization for standardization 15,189 requirements. We designed a student-centred laboratory training program, according to case-based learning that included 4 stages: "establish an overall testing process based on the patient's clinical indicator, clarify principles, improve operational skills, and review process and continuous improvement". The program was implemented in our college during the winter semesters of 2019 and 2020. A total of 185 undergraduate students majoring in medical laboratory science participated in the program as a test group, and the other 172 students were set up as the control group and adopted the conventional method. The participants were asked to finish an online survey to evaluate the class at the end.

RESULTS/OUTCOMES

The test group had significantly better examination scores not only in experimental operational skills (89.27 ± 7.16 vs. 77.51 ± 4.72, p < 0.05 in 2019 grade, 90.31 ± 5.35 vs. 72.87 ± 8.41 in 2020 grade) but also in total examination (83.47 ± 6.16 vs. 68.90 ± 5.86 in 2019 grade, 82.42 ± 5.72 vs. 69.55 ± 7.54 in 2020 grade) than the control group. The results of the questionnaire survey revealed that the students in the test group better achieved classroom goals than those in the control group (all p < 0.05).

CONCLUSIONS

The new student-centred laboratory training program based on case-based learning in clinical biochemistry is an effective and acceptable strategy compared with the conventional training program.

Incidents in Molecular Pathology: Frequency and Causes During Routine Testing

CONTEXT.—

Errors in laboratory medicine could compromise patient safety. Good laboratory practice includes identifying and managing nonconformities in the total test process. Varying error percentages have been described in other fields but are lacking for molecular oncology.

OBJECTIVES.—

To gain insight into incident causes and frequency in the total test process from 8 European institutes routinely performing biomarker tests in non-small cell lung cancer and colorectal cancer.

DESIGN.—

All incidents documented in 2018 were collected from all hospital services for pre-preanalytical entries before the biomarker test, as well as specific incidents for biomarker tests.

RESULTS.—

There were 5185 incidents collected, of which 4363 (84.1%) occurred in the pre-preanalytical phase (all hospital services), 2796 of 4363 (64.1%) related to missing or incorrect request form information. From the other 822 specific incidents, 166 (20.2%) were recorded in the preanalytical phase, 275 (33.5%) in the analytical phase, and 194 (23.6%) in the postanalytical phase, mainly due to incorrect report content. Only 47 of 822 (5.7%) incidents were recorded in the post-postanalytical phase, and 123 (15.0%) in the complete total test process. For 17 of 822 (2.1%) incidents the time point was unknown. Pre-preanalytical incidents were resolved sooner than incidents on the complete process (mean 6 versus 60 days). For 1215 of 5168 (23.5%) incidents with known causes a specific action was undertaken besides documenting them, not limited to accredited institutes.

CONCLUSIONS.—

There was a large variety in the number and extent of documented incidents. Correct and complete information on the request forms and final reports are highly error prone and require additional focus.

Biological variation: Understanding why it is so important?

This Review will describe the increasing importance of the concepts of biological variation to clinical chemists. The idea of comparison to 'reference' is fundamental in measurement. For the biological measurands, that reference is the relevant patient population, a clinical decision point based on a trial or an individual patient's previous results. The idea of using biological variation to set quality goals was then realised for setting Quality Control (QC) and External Quality Assurance (EQA) limits. The current phase of BV integration into practice is using Patient-Based Real-Time Quality Control (PBRTQC) and Patient Based Quality Assurance (PBQA) to detect a change in assay performance. The challenge of personalised medicine is to determine an individual reference interval. The Athletes Biological Passport may provide the solution.

Utilization of biological variation data in the interpretation of laboratory test results - survey about clinicians' opinion and knowledge

Introduction

To interpret test results correctly, understanding of the variations that affect test results is essential. The aim of this study is: 1) to evaluate the clinicians’ knowledge and opinion concerning biological variation (BV), and 2) to investigate if clinicians use BV in the interpretation of test results.

Materials and methods

This study uses a questionnaire comprising open-ended and close-ended questions. Questions were selected from the real-life numerical examples of interpretation of test results, the knowledge about main sources of variations in laboratories and the opinion of clinicians on BV. A total of 399 clinicians were interviewed, and the answers were evaluated using a scoring system ranked from A (clinician has the highest level of knowledge and the ability of using BV data) to D (clinician has no knowledge about variations in laboratory). The results were presented as number (N) and percentage (%).

Results

Altogether, 60.4% of clinicians have knowledge of pre-analytical and analytical variations; but only 3.5% of them have knowledge related to BV. The number of clinicians using BV data or reference change value (RCV) to interpret measurements results was zero, while 79.4% of clinicians accepted that the difference between two measurements results located within the reference interval may be significant.

Conclusions

Clinicians do not use BV data or tools derived from BV such as RCV to interpret test results. It is recommended that BV should be included in the medical school curriculum, and clinicians should be encouraged to use BV data for safe and valid interpretation of test results.

Enabling Laboratory Medicine in Primary Care Through IT Systems Use

Important problems remain regarding the efficiency and quality of laboratory testing in primary care. In view of this, a significant function of electronic medical record (EMR) systems is to enable the practice of laboratory medicine by primary care physicians. The present study aims to deepen our understanding of the nature and extent of physicians' use of EMR and other laboratory information exchange systems for patient management and care within the laboratory testing process. We conducted a survey of 684 Canadian family physicians. Results indicate that physicians use 84 percent of the laboratory functionalities available in their EMR system. The two most important impacts are the ability to gain time in the post-analytical phase and to take faster action in this same phase as they follow-up on their patients' test results. Physicians who perceive to benefit most from their EMR use are those who make the most extensive use of their system. Extended use of an EMR system allows primary care physicians to better ascertain and monitor the health status of their patients, verify their diagnosis assumptions, and, if their system includes a clinical decision support module, apply evidence-based practices in laboratory medicine.

Laboratory testing in primary care: A systematic review of health IT impacts

INTRODUCTION

Laboratory testing in primary care is a fundamental process that supports patient management and care. Any breakdown in the process may alter clinical information gathering and decision-making activities and can lead to medical errors and potential adverse outcomes for patients. Various information technologies are being used in primary care with the goal to support the process, maximize patient benefits and reduce medical errors. However, the overall impact of health information technologies on laboratory testing processes has not been evaluated.

OBJECTIVES

To synthesize the positive and negative impacts resulting from the use of health information technology in each phase of the laboratory 'total testing process' in primary care.

METHODS

We conducted a systematic review. Databases including Medline, PubMed, CINAHL, Web of Science and Google Scholar were searched. Studies eligible for inclusion reported empirical data on: 1) the use of a specific IT system, 2) the impacts of the systems to support the laboratory testing process, and were conducted in 3) primary care settings (including ambulatory care and primary care offices). Our final sample consisted of 22 empirical studies which were mapped to a framework that outlines the phases of the laboratory total testing process, focusing on phases where medical errors may occur.

RESULTS

Health information technology systems support several phases of the laboratory testing process, from ordering the test to following-up with patients. This is a growing field of research with most studies focusing on the use of information technology during the final phases of the laboratory total testing process. The findings were largely positive. Positive impacts included easier access to test results by primary care providers, reduced turnaround times, and increased prescribed tests based on best practice guidelines. Negative impacts were reported in several studies: paper-based processes employed in parallel to the electronic process increased the potential for medical errors due to clinicians' cognitive overload; systems deemed not reliable or user-friendly hampered clinicians' performance; and organizational issues arose when results tracking relied on the prescribers' memory.

DISCUSSION

The potential of health information technology lies not only in the exchange of health information, but also in knowledge sharing among clinicians. This review has underscored the important role played by cognitive factors, which are critical in the clinician's decision-making, the selection of the most appropriate tests, correct interpretation of the results and efficient interventions.

CONCLUSIONS

By providing the right information, at the right time to the right clinician, many IT solutions adequately support the laboratory testing process and help primary care clinicians make better decisions. However, several technological and organizational barriers require more attention to fully support the highly fragmented and error-prone process of laboratory testing.

Assay Stability, the missing component of the Error Budget

Gaining a better understanding of Quality Control (QC) processes is a key requirement to improving performance and reducing patient risk. Detecting analytical error is dependent on a QC strategy that reliably detects a critical shift in a result away from the true value. Recently the concept of Six Sigma has been used by diagnostic laboratories to assess the performance of assays and to assist in the selection of QC rules. The sigma metric is one measure of an assay's ability to perform within specification. However an additional dimension to managing an assay is its stability in bias over time. The concept of long term stability is the same as measured QC drift (SE_drift) which is the effect of numerous calibrations, changes in reagent lots and other conditions i.e. a long term effect. This implies that the standard error budget is wrong because it is modelled on short term QC and misses this SE_drift stability component. We show that SE_drift provides a measure of Assay Stability that should be included in Quality Planning and that by including an allowance for this drift, determining target imprecision appropriate for matched QC algorithms that provide high error detection is as simple as dividing the Allowable Performance Specification by 4, 5 or 6.

Accuracy of conventional MRI for preoperative diagnosis of intracranial tumors: A retrospective cohort study of 762 cases

BACKGROUND

Conventional magnetic resonance imaging (MRI) is considered a valuable tool for preoperative diagnosis of intracranial tumors. We assessed its accuracy in the diagnosis of intracranial tumors in usual clinical practice.

MATERIALS AND METHODS

MRI reports of 762 patients who had undergone conventional brain MRI prior to surgery were retrospectively reviewed. A 4-grade scoring system was devised to establish diagnostic agreement. Each tumor type was compared with the corresponding pathological diagnoses by dichotomization. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated for the overall patient population as well as for each tumor type.

RESULTS

664 cases (87.1%) were tumor-positive, and 98 cases (12.9%) were tumor-negative. The most common tumor types were meningiomas, gliomas, pituitary adenomas and schwannomas. These four types together comprised 74.5% of all cases reviewed. Sensitivity and PPV for the overall population were 72.0-90.7% and 91.9-95.4%, respectively. Diagnostic accuracy differed among tumor types. Meningiomas, pituitary adenomas, schwannomas and cholesteatomas were more likely to be diagnosed correctly (sensitivities were 82.6-96.9%, 86.1-96.7%, 88.9-98.2% and 91.3-100.0%, respectively); while some other types like solitary fibrous tumors (SFTs) seemed difficult to identify. Gliomas tended to be confused with metastases, meningiomas with SFTs, and pituitary adenomas with craniopharyngiomas.

CONCLUSION

The accuracy of conventional MRI for diagnosing intracranial tumors is generally satisfactory but should not be too heavily relied upon, especially for certain tumor types. In cases of discrepancy, neurosurgeons are encouraged to confer with the reporting neuroradiologists to achieve optimal preoperative diagnoses.

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.

Evaluating the Connections Between Primary Care Practice and Clinical Laboratory Testing: A Review of the Literature and Call for Laboratory Involvement in the Solutions

Context.—Growing evidence has demonstrated a high frequency of quality gaps in laboratory medicine, with recent studies estimating that 15% to 54% of primary care medical errors reported by primary care physicians and staff are related to the testing process. However, there is lack of evidence-based performance metrics in the preanalytic and postanalytic phases of the testing pathway for primary care practices.

Objective.—To use results of the literature review to assist in the development of quality indicators that could improve preanalytic and postanalytic processes in primary care–based laboratory medicine.

Data Sources.—Literature in Ovid/MEDLINE from 2001 through 2011 was searched as a primary source of information. Ninety-five peer-reviewed and non–peer-reviewed publications were retrieved following title and abstract review and 10 articles were reviewed in their entirety by the authors. A systematic review of the literature was conducted regarding the connections between clinical laboratories and primary care offices and the resulting errors. Root causes of errors were categorized into 7 major themes: process failures, delays, communication gaps, errors in judgment and cognition, influence of minorities/language, practice culture, and lack of patient centeredness. Selected articles were evaluated for evidence quality using the Systematic Evidence Review and Evaluation Methods for Quality Improvement grading scale developed by the Centers for Disease Control and Prevention.

Conclusions.—The focused literature review documented 7 key error themes in the laboratory medicine/primary care testing process. Performance metrics related to these themes are proposed that deserve future study for evidence-based improvement.

Short-term interventions on wards fail to reduce preanalytical errors: results of two prospective controlled trials

Background

Preventing laboratory errors promotes patient safety and reduces the cost of unnecessary processing. The aim of this study was to test the effectiveness of two short-term interventions at reducing errors in the preanalytical stage of laboratory testing.

Methods

Error data were reviewed from inpatient wards at Bradford Royal Infirmary (BRI), Leeds General Infirmary (LGI) and St James’ University Hospital (SJUH) for 22 weeks. Two separate interventions lasted for two weeks. The outcome measures were inadequate tube and form labelling, incorrect tube selection and insufficient sample volume. Posters targeting these errors were created and displayed on inpatient wards in SJUH ( n = 48). BRI and LGI were control hospitals. Qualitative interviews were held with clinical staff to raise awareness of common errors, give advice and discuss error reduction ( n = 37). Ten weeks later, screensavers warning against labelling errors were displayed (LGI and SJUH). Quantitative error data, routinely collected by the laboratory, were used for analysis.

Results

There was no change in error rate or type at the intervention site(s) compared with the control(s). There were 7058 reported errors across three sites, of which 6623 were errors targeted by the interventions. The overall error rate remained stable on all three sites (analysis of variance, P = 1.0). When interviewing clinical staff, 29% thought that equipment was the main contributing factor to errors while 23% struggled with tube selection.

Conclusions

Despite enthusiasm on the part of the ward-based staff, both short-term interventions had no significant impact on preanalytical error rates. Most errors are due to human factors. These may be reduced with the introduction of an electronic ordering system.

Managing the pre- and post-analytical phases of the total testing process

For many years, the clinical laboratory's focus on analytical quality has resulted in an error rate of 4-5 sigma, which surpasses most other areas in healthcare. However, greater appreciation of the prevalence of errors in the pre- and post-analytical phases and their potential for patient harm has led to increasing requirements for laboratories to take greater responsibility for activities outside their immediate control. Accreditation bodies such as the Joint Commission International (JCI) and the College of American Pathologists (CAP) now require clear and effective procedures for patient/sample identification and communication of critical results. There are a variety of free on-line resources available to aid in managing the extra-analytical phase and the recent publication of quality indicators and proposed performance levels by the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) working group on laboratory errors and patient safety provides particularly useful benchmarking data. Managing the extra-laboratory phase of the total testing cycle is the next challenge for laboratory medicine. By building on its existing quality management expertise, quantitative scientific background and familiarity with information technology, the clinical laboratory is well suited to play a greater role in reducing errors and improving patient safety outside the confines of the laboratory.

Clinical quality indicators in laboratory medicine

Clinical laboratories have an important role in improving patient care. The past decades have seen enormous changes with unpredictable improvements in analytical performance, range of tests and capacity to manage large volumes of work. At the same time, there has been a dramatic fall in the rate of laboratory errors. However, there is now a growing awareness that the testing process includes the time before samples reach the laboratory and after reports have been printed and that these areas need to be included in the quality assessment of the total testing process. Laboratory quality should include a focus on patient safety and clinical effectiveness. Services should be patient-centred, timely, efficient and equitable, and finally, should be moulded to ensure optimal outcomes. There is a need to define quality indicators that will ensure there is appropriate choice and selection of tests, use of the appropriate assay standardization and the correct interpretation of the assay results at the appropriate time. These are the areas in which a quality laboratory can, and should, now involve itself.

A modified Delphi methodology to conduct a failure modes effects analysis: a patient-centric effort in a clinical medical laboratory

In this article, we describe the use of an information-gathering tool, the Delphi technique, to overcome issues encountered when conducting a failure modes effects analysis as part of a define, measure, analyze, implement, control study to improve the processes of a clinical medical laboratory. The study was conducted with the goals of reducing medical errors in the total testing process (TTP) in order to improve patient safety, patient satisfaction, and improve the overall quality of the health care services provided by the subject hospital while meeting its Joint Commission (JC) accreditation requirements. The study found that the Delphi technique was very useful in overcoming 4 barriers encountered in conducting a failure modes effects analysis in a hospital's clinical medical laboratory and in achieving those goals.

Medical Errors: Pre-Analytical Issue in Patient Safety

The last few decades have seen a significant decrease in the rates of analytical errors in clinical laboratories, while a growing body of evidence demonstrates that the pre- and post-analytical steps of the total testing process (TTP) are more error-prone than the analytical phase. In particular, most errors are identified in pre-pre-analytic steps outside the walls of the laboratory, and beyond its control. However, in a patient-centred approach to the delivery of health care services, there is the need to investigate, in the total testing process, any possible defect that may have a negative impact on the patient, 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). In the pre-analytic phase, the frequency of patient/specimens misidentification and the presence of possible causes of specimen rejection (haemolysis, clotting, insufficient volume, etc.) represent a valuable risk for patient safety. Preventing errors in the pre-analytical steps requires both technological developments (wristband, barcodes, pre-analytical workstations) and closer relationships with the clinical world to achieve an effective team-working cooperation. The most important lesson we have learned, therefore, is that laboratory errors and injuries to patients can be prevented by redesigning systems that render it difficult for all caregivers and in all steps of the total testing process to make mistakes.

Detecting Blood Laboratory Errors Using a Bayesian Network

Objectives: To detect errors in blood laboratory results using a Bayesian network (BN), to compare results with an established method for detecting errors based on frequency patterns (LabRespond) and logistic regression model. Methods: In Experiment 1 and 2 using a sample of 5,800 observations from the National Health and Nutrition Examination Survey dataset, large, medium and small errors were randomly generated and introduced to liver enzymes (ALT, AST, and LDH) of the dataset. Experiment 1 examined systematic errors, while Experiment 2 investigated random errors. The outcome of interest was the correct detection of liver enzymes as “error” or “not error.” With the BN, the outcome was predicted by exploiting probabilistic relationships among AST, ALT, LDH, and gender. In addition to AST, ALT, LDH, and gender, LabRespond required more information on related analytes to achieve optimal prediction. We assessed performance by examining the area under the receiver operating characteristics curves using a 10-fold cross validation method, as well as risk stratification tables. Results: In Experiment 1, the BN significantly outperformed both LabRespond and logistic regression in detecting large (both at p < 0.001), medium ( p = 0.01 and p < 0.001, respectively), and small ( p = 0.03 and, p = 0.05, respectively) systematic errors. In Experiment 2, the BN performed significantly better than LabRespond and multinomial logistic regression in detecting large ( p = 0.04 and p < 0.001, respectively) and medium ( p = 0.05 and p < 0.001, respectively) random errors. Conclusion: A Bayesian network is better at detection and can detect errors with less information than existing automated models, suggesting that Bayesian model may be an effective means for reducing medical costs and improving patient safety.

The detection and prevention of errors in laboratory medicine

The last few decades have seen a significant decrease in the rates of analytical errors in clinical laboratories. Evidence demonstrates that pre- and post-analytical steps of the total testing process (TTP) are more error-prone than the analytical phase. Most errors are identified in pre-pre-analytic and post-post-analytic steps outside of the laboratory. In a patient-centred approach to the delivery of health-care services, there is the need to investigate, in the TTP, any possible defect that may have a negative impact on the patient. 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/testing error) or non-laboratory operator (e.g. inappropriate test request, error in patient identification and/or blood collection). Patient misidentification and problems communicating results, which affect the delivery of diagnostic services, are recognized as the main goals for quality improvement. International initiatives aim at improving these aspects. Grading laboratory errors on the basis of their seriousness should help identify priorities for quality improvement and encourage a focus on corrective/preventive actions. It is important to consider not only the actual patient harm sustained but also the potential worst-case outcome if such an error were to reoccur. The most important lessons we have learned are that system theory also applies to laboratory testing and that errors and injuries can be prevented by redesigning systems that render it difficult for all health-care professionals to make mistakes.

Governance of preanalytical variability: travelling the right path to the bright side of the moon?

Medical errors can be traditionally clustered into 4 categories, which include errors of diagnosis, errors of treatment, errors of prevention, and an 'other miscellaneous' category. Owing to the volume and complexity of testing, and considering that laboratory error is defined as any defect from ordering tests to reporting results and appropriately interpreting and reacting on these, it is not surprising that mistakes in the total testing process occur with frequency, have connections to all four types of medical errors and represent a serious hazard for patient health. Throughout the laboratory diagnostics, preanalytical problems prevail. Moreover, the positive trends towards reduction of laboratory errors over the past decade, particularly those in the analytical phase, has little involved the preanalytical phase, which actually represents the most critical area to target. In particular, the high frequency of errors still attributable to processes external to the laboratory requires additional efforts for the governance of this mistreated phase of the total testing process, so that we can finally find the right path to progress from the dark to the bright side of the moon. As for any other type of medical errors, the most effective path to improvement is the implementation of a total quality management system, encompassing a multifaceted strategy for process and risk analysis, based on error prevention, detection, and management.

Exploring the iceberg of errors in laboratory medicine

The last few decades have seen a significant decrease in the rates of analytical errors in clinical laboratories, and currently available evidence demonstrates that the pre- and post-analytical steps of the total testing process (TTP) are more error-prone than the analytical phase. In particular, most errors are identified in pre-pre-analytic and post-post analytic steps outside the walls of the laboratory, and beyond its control. However, in a patient-centered approach to the delivery of health care services, there is the need to investigate any possible defect in the total testing process that may have a negative impact on the patient. In fact, 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 is caused by a laboratory professional (e.g., calibration or testing error) or by a non-laboratory operator (e.g., inappropriate test request, error in patient identification and/or blood collection). Data on diagnostic errors in primary care and in the emergency department setting demonstrate that inappropriate test requesting and incorrect interpretation account for a large percentage of total errors whatever the discipline involved, be it radiology, pathology or laboratory medicine. Patient misidentification and problems in communicating results, which affect the delivery of all diagnostic services, are widely recognized as the main goals for quality improvement. Therefore, some common problems affect diagnostic errors, although specific faults characterising errors in laboratory medicine should lead to preventive and corrective actions if evidence-based quality indicators are developed, implemented and monitored. The lesson we have learned is that each practice must examine its own total testing process to discover its weaknesses and identify appropriate remedies.

Does POCT reduce the risk of error in laboratory testing?

Point-of-care testing (POCT), the fastest growing segment of the current clinical laboratory testing market, is a rapid means for providing test results in different clinical settings. In theory, this tool eliminates some of the more problematic steps in the testing process, including specimen transport and result distribution. However, POCT has created new challenges, and sources of potential errors; moreover, while the upsurge in its use has generated concerns regarding the quality of test results, few data are available in the literature on errors with POCT. Nor are data available for the evaluation of errors, and the risk of errors in POCT based on all steps in the entire testing process, including test requesting and result utilization. According to a modified Kost model, which takes into account all steps of the testing process and latent conditions for error, POCT reduces errors and the risk of error in only a few steps of the testing process. There is therefore an urgent need for an evaluation of errors and risks of error in POCT that is based on the entire testing process and uses well-designed studies aiming to improve clinical outcomes and increase patient safety.

Measuring quality in anatomic pathology

This article focuses mainly on diagnostic accuracy in measuring quality in anatomic pathology, noting that measuring any quality metric is complex and demanding. The authors discuss standardization and its variability within and across areas of care delivery and efforts involving defining and measuring error to achieve pathology quality and patient safety. They propose that data linking error to patient outcome are critical for developing quality improvement initiatives targeting errors that cause patient harm in addition to using methods of root cause analysis, beyond those traditionally used in cytologic-histologic correlation, to assist in the development of error reduction and quality improvement plans.

Effect of Lean method implementation in the histopathology section of an anatomical pathology laboratory

Background:

In the USA, the lack of processes standardisation in histopathology laboratories leads to less than optimal quality, errors, inefficiency and increased costs. The effectiveness of large-scale quality improvement initiatives has been evaluated rarely.

Aim:

To measure the effect of implementation of a Lean quality improvement process on the efficiency and quality of a histopathology laboratory section.

Methods:

A non-concurrent interventional cohort study from 1 January 2003 to 31 December 2006 was performed, and the Lean process was implemented on 1 January 2004. Also compared was the productivity of the Lean histopathology section to a sister histopathology section that did not implement Lean processes. Pre- and post-Lean specimen turnaround time and productivity ratios (work units/full time equivalents) were measured. For 200 Lean interventions, a 5-part Likert scale was used to assess the impact on error, success and complexity.

Results:

In the Lean laboratory, the mean monthly productivity ratio increased from 3439 to 4074 work units/full time equivalents (p<0.001) as the mean daily histopathology section specimen turnaround time decreased from 9.7 to 9.0 h (p = 0.01). The Lean histopathology section had a higher productivity ratio compared with a sister histopathology section (1598 work units/full time equivalents, p<0.001) that did not implement Lean processes. The mean impact, success and complexity of interventions were 2.4, 2.7 and 2.5, respectively. The mean number of specific error causes affected by individual interventions was 2.6.

Conclusion:

It is concluded that Lean process implementation improved efficiency and quality in the histopathology section.

Errors in laboratory medicine and patient safety: the road ahead

The Institute of Medicine (IOM) report,

Clin Chem Lab Med 2007;45:700–7.

Quality indicators and specifications for the extra-analytical phases in clinical laboratory management

BACKGROUND

Quality management systems should cover all the steps involved in the overall testing and non-testing processes.

AIM

To identify quality indicators for extra-analytical processes in the clinical laboratory and to specify acceptability limits, in order to provide a useful tool for continuous improvement of laboratory service.

METHODS

A literature review by Medline search was performed using the keywords: Q-Tracks and Q-probes alone, and management, error, mistake, and indicator crossed with quality, laboratory and medicine. The indicators retrieved were organized according to the various laboratory processes. Their expression was standardized in relation to the total activity of each process reported in each paper reviewed. The magnitude of the errors reported was considered to be the current state of the art for the extra-analytical step and was proposed as the quality specification.

RESULTS

Examples of indicators and specifications for the pre-analytical process: Analytical request: Error in patient identification (0.08%), request unintelligible (0.1%).

SAMPLING

Requested but not collected (7%), redraws (2%). Transport and reception of samples: Inadequate transportation conditions (0.005%), hemolyzed sample (0.2%). Examples of indicators and specifications for the post-analytical process: Report validation: Test not performed (1.4%), test performed but not requested (1.1%). Intra-laboratory reports: Laboratory reporting errors (0.05%), delivery outside specified time (11%). Consulting service: average time to communicate critical values for inpatients (6 min).

CONCLUSIONS

These extra-analytical indicators and their specifications, expressed in a standardized manner, constitute a preliminary basis for comparison of individual laboratory performance with the purpose of improving laboratory quality.