The key incident monitoring and management system - history and role in quality improvement

A Practical Tool for Risk Management in Clinical Laboratories

Risk management constitutes an essential component of the Quality Management System (QMS) of medical laboratories. The international medical laboratory standard for quality and competence, International Standards Organization (ISO) 15189, in its 2012 version, specified risk management for the first time. Since then, there has been much focus on this subject. We authors aimed to develop a practical tool for risk management in a clinical laboratory that contains five major cyclical steps: risk identification, quantification, prioritization, mitigation, and surveillance. The method for risk identification was based on a questionnaire that was formulated by evaluating five major components of laboratory processes, namely i) Specimen, ii) Test system, iii) Reagent, iv) Environment, and v) Testing. All risks that would be identified using the questionnaire can be quantified by calculating the risk priority number (RPN) using the tool, failure modes, and effects analysis (FMEA). Based on the calculated RPN, identified risks then shall be prioritized and mitigated. Based on our collective laboratory management experience, we authors also enlisted and scheduled a few process-specific quality assurances (QA) activities. The listed QA activities intend to monitor new risk emergence and re-emergence of those previously mitigated ones. We authors believe that templates of risk identification, risk quantification, and risk surveillance presented in this article will serve as ready references for supervisors of clinical laboratories.

The preanalytical phase – from an instrument-centred to a patient-centred laboratory medicine

In order to guarantee patient safety, medical laboratories around the world strive to provide highest quality in the shortest amount of time. A major leap in quality improvement was achieved by aiming to avoid preanalytical errors within the total testing process. Although these errors were first described in the 1970s, it took additional years/decades for large-scale efforts, aiming to improve preanalytical quality by standardisation and/or harmonisation. Initially these initiatives were mostly on the local or national level. Aiming to fill this void, in 2011 the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) working group “Preanalytical Phase” (WG-PRE) was founded. In the 11 years of its existence this group was able to provide several recommendations on various preanalytical topics. One major achievement of the WG-PRE was the development of an European consensus guideline on venous blood collection. In recent years the definition of the preanalytical phase has been extended, including laboratory test selection, thereby opening a huge field for improvement, by implementing strategies to overcome misuse of laboratory testing, ideally with the support of artificial intelligence models. In this narrative review, we discuss important aspects and milestones in the endeavour of preanalytical process improvement, which would not have been possible without the support of the Clinical Chemistry and Laboratory Medicine (CCLM) journal, which was one of the first scientific journals recognising the importance of the preanalytical phase and its impact on laboratory testing quality and ultimately patient safety.

Review of current incidents and risk calculations used in the Royal College of Australasian Pathologists Key Incident Management and Monitoring Systems - a system that could be used by all Australasian medical laboratories, and easily adapted to worldwide use

Introduction

The Royal College of Pathologists of Australasia Quality Assurance Programs (RCPAQAP) Key Incident Monitoring and Management Systems (KIMMS) program has found that some existing Quality Indicators are too broad or not well defined. The risk matrix in use does not allow changes in incident Detection or Probability. In 2020, a review was performed: what issues should KIMMS include as Key Incidents and how could risk measurement be improved?

Materials and methods

Twenty-seven networked and stand-alone laboratories enrolled in KIMMS during 2020 were surveyed on 45 current and new indicators of risk in the total testing process. They were asked which indicators they considered were significant in causing patient harm. Existing risk matrices in use by members of the KIMMS Advisory Committee laboratories were reviewed regarding their size or structure (3x3 or 5x5) and the descriptions of consequences and probability.

Results

Thirteen participants indicated 21 indicators should be monitored, and the KIMMS Advisory committee added a further 13 (11 from the remaining 24 and 2 new). Of the five risk matrices reviewed, all consistently used a 5x5 matrix to estimate Consequences vs Probability of harm. The KIMMS advisory committee added a third parameter to the calculation of Risk, Detectability.

Conclusion

All 34 pre- and post- indicators should be monitored, covering all aspects of the total testing cycle other than analytical. The risk measurement can be improved by introducing a 5x5 risk matrix to evaluate harm (consequences x probability) and then evaluating risk by adding detectability; risk equals harm x detectability.

What factors are associated with improvements in productivity in clinical laboratories in the Asia Pacific Region?

INTRODUCTION

Clinical laboratories usually have a quality management system such as ISO 15189, which provides a framework for quality and competence to perform medical testing and internal systems such as audit and nonconformance to ensure consistent processes. However, organizations need to have access to internal procedures and external competitors' performance to improve their operations. These are often seen as commercial or areas where it is difficult to agree on an acceptable goal.

METHOD

In 2019, 1158 laboratories from 17 countries/regions in the Asia Pacific Region answered the survey, including 399 Chinese sites. The survey collected information on quality, turnaround time and productivity.

RESULTS

Median productivity for laboratories in the Asia Pacific Region not including Chinese sites was 25 samples/FTE/day for small laboratories (workload: <250 samples/day), 100 for medium-sized laboratories (workload: 251-1000 samples/day) and 220 for large laboratories (workload: >1001 samples/day). The parameters associated with increased productivity in some laboratories were automation, middleware, Lean Six Sigma quality improvement activities and International Accreditation.

CONCLUSION

This survey provides evidence of an association of quality improvement activities on laboratory productivity. There are differences in the effect of these activities in Chinese and non-Chinese laboratories in the Asia Pacific Region. The survey confirmed that the implementation of automation is associated with increased median productivity in all sites. Implementation of Lean Six Sigma and International Accreditation is associated with increased productivity in large laboratories.

Risk assessment of the total testing process based on quality indicators with the Sigma metrics

Background Evidence-based evaluation of laboratory performances including pre-analytical, analytical and post-analytical stages of the total testing process (TTP) is crucial to ensure patients receiving safe, efficient and effective care. To conduct risk assessment, quality management tools such as Failure Mode and Effect Analysis (FMEA) and the Failure Reporting and Corrective Action System (FRACAS) were constantly used for proactive or reactive analysis, respectively. However, FMEA and FRACAS faced big challenges in determining the scoring scales and failure prioritization in the assessment of real-world cases. Here, we developed a novel strategy, by incorporating Sigma metrics into risk assessment based on quality indicators (QIs) data, to provide a more objective assessment of risks in TTP. Methods QI data was collected for 1 year and FRACAS was applied to produce the risk rating based on three variables: (1) Sigma metrics for the frequency of defects; (2) possible consequence; (3) detection method. The risk priority number (RPN) of each QI was calculated by a 5-point scale score, where a value of RPN > 50 was rated as high-risk. Results The RPNs of two QIs in post-analytical phase (TAT of Stat biochemistry analyte and Timely critical values notification) were above 50 which required rigorous monitoring and corrective actions to eliminate the high risks. Nine QIs (RPNs between 25 and 50) required further investigation and monitoring. After 3 months of corrective action the two identified high-risk processes were successfully reduced. Conclusions The strategy can be implemented to reduce identified risk and assuring patient safety.

Integrating quality control and external quality assurance

Effective management of clinical laboratories relies upon an understanding of Quality Control and External Quality Assurance principles. These processes, when applied effectively, reduce patient risk and drive quality improvement. In this Review, we will describe the purpose of QC and EQA and their role in identifying analytical and process error. The two concepts are linked, and we will illustrate that linkage. Some EQA providers offer far more than analytical surveillance. They facilitate training and education and extend quality improvement and identify areas where there is potential for patient harm into the pre-and post-analytical phases of the total testing process.

Understanding and managing interferences in clinical laboratory assays: the role of laboratory professionals

The recently raised concerns regarding biotin interference in immunoassays have increased the awareness of laboratory professionals and clinicians of the evidence that the analytical phase is still vulnerable to errors, particularly as analytical interferences may lead to erroneous results and risks for patient safety. The issue of interference in laboratory testing, which is not new, continues to be a challenge deserving the concern and interest of laboratory professionals and clinicians. Analytical interferences should be subdivided into two types on the basis of the possibility of their detection before the analytical process. The first (type 1) is represented by lipemia, hemolysis and icterus, and the second (type 2), by unusual constituents that are not undetectable before analysis, and may affect the matrix of serum/plasma of individual subjects. Type 2 cannot be identified with current techniques when performing the pre-analytical phase. Therefore, in addition to a more careful evaluation and validation of the method to be used in clinical practice, the awareness of laboratory professionals should be raised as to the importance of evaluating the quality of biological samples before analysis and to adopt algorithms and approaches in the attempt to reduce problems related to erroneous results due to specific or non-specific interferences.

Failure Mode and Effect Analysis: Engineering Safer Neurocritical Care Transitions

BACKGROUND/OBJECTIVE

Inter-hospital patient transfers for neurocritical care are increasingly common due to increased regionalization for acute care, including stroke and intracerebral hemorrhage. This process of transfer is uniquely vulnerable to errors and risk given numerous handoffs involving multiple providers, from several disciplines, located at different institutions. We present failure mode and effect analysis (FMEA) as a systems engineering methodology that can be applied to neurocritical care transitions to reduce failures in communication and improve patient safety. Specifically, we describe our local implementation of FMEA to improve the safety of inter-hospital transfer for patients with intracerebral and subarachnoid hemorrhage as evidence of success.

METHODS

We describe the conceptual basis for and specific use-case example for each formal step of the FMEA process. We assembled a multi-disciplinary team, developed a process map of all components required for successful transfer, and identified "failure modes" or errors that hinder completion of each subprocess. A risk or hazard analysis was conducted for each failure mode, and ones of highest impact on patient safety and outcomes were identified and prioritized for implementation. Interventions were then developed and implemented into an action plan to redesign the process. Importantly, a comprehensive evaluation method was established to monitor outcomes and reimplement interventions to provide for continual improvement.

RESULTS

This intervention was associated with significant reductions in emergency department (ED) throughput (ED length of stay from 300 to 149 min, (p < .01), and improvements in inter-disciplinary communication (increase from pre-intervention (10%) to post- (64%) of inter-hospital transfers where the neurological intensive care unit and ED attendings discussed care for the patient prior to their arrival).

CONCLUSIONS

Application of the FMEA approach yielded meaningful and sustained process change for patients with neurocritical care needs. Utilization of FMEA as a change instrument for quality improvement is a powerful tool for programs looking to improve timely communication, resource utilization, and ultimately patient safety.

Continual improvement of the pre-analytical process in a public health laboratory with quality indicators-based risk management

Background Quality indicators (QIs) and risk management are important tools for a quality management system designed to reduce errors in a laboratory. This study aimed to show the effectiveness of QI-based risk management for the continual improvement of pre-analytical processes in the Kayseri Public Health Laboratory (KPHL) which serves family physicians and collects samples from peripheral sampling units. Methods QIs of pre-analytical process were used for risk assessment with the failure modes and effects analysis (FMEA) method. Percentages and risk priority numbers (RPNs) of QIs were quantified. QI percentages were compared to the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) performance specifications and RPNs were compared to risk level scale, and corrective actions planned if needed. The effectiveness of risk treatment actions was re-evaluated with the new percentages and with RPNs of predefined QIs. Results RPNs related to four QIs required corrective action according to the risk evaluation scale. After risk treatment, the continual improvement was achieved for performance and risk level of "transcription errors", for risk levels of "misidentified samples" and "not properly stored samples" and for the performance of "hemolyzed samples". "Not properly stored samples" had the highest risk score because of sample storage and centrifugation problems of peripheral sampling units which are not under the responsibility of the KPHL. Conclusions Public health laboratories may have different risk priorities for pre-analytical process. Risk management based on predefined QIs can decrease the risk levels and increase QI performance as evidence-based examples for continual improvement of the pre-analytical process.

Changes in error rates in the Australian key incident monitoring and management system program

Introduction

The Key incident monitoring and management system program (KIMMS) program collects data for 19 quality indicators (QIs) from Australian medical laboratories. This paper aims to review the data submitted to see whether the number of errors with a higher risk priority number (RPN) have been reduced in preference to those with a lower RPN, and to calculate the cost of these errors.

Materials and methods

Data for QIs from 60 laboratories collected through the KIMMS program from 2015 until 2018 were retrospectively reviewed. The results for each QI were averaged for the four-year average and coefficient of variation. To review the changes in QI frequency, the yearly averages for 2015 and 2018 were compared. By dividing the total RPN by 4 and multiplying that number by the cost of recollection of 30 AUD, it was possible to assign the risk cost of these errors.

Results

The analysis showed a drop in the overall frequency of incidents (6.5%), but a larger drop in risk (9.4%) over the period investigated. Recollections per year in Australia cost the healthcare industry 27 million AUD. If the RPN data is used, this cost increases to 66 million AUD per year.

Conclusions

Errors with a higher RPN have fallen more than those with lower RPN. The data shows that the errors associated with phlebotomy are the ones that have most improved. Further improvements require a better understanding of the root cause of the errors and to achieve this, work is required in the collection of the data to establish best-practice guidelines.

Informatics External Quality Assurance (IEQA) Down Under: evaluation of a pilot implementation

External quality assurance (EQA) provides ongoing evaluation to verify that laboratory medicine results conform to quality standards expected for patient care. While attention has focused predominantly on test accuracy, the diagnostic phases, consisting of pre- and post-laboratory phases of testing, have thus far lagged in the development of an appropriate diagnostic-phase EQA program. One of the challenges faced by Australian EQA has been a lack of standardisation or “harmonisation” resulting from variations in reporting between different laboratory medicine providers. This may introduce interpretation errors and misunderstanding of results by clinicians, resulting in a threat to patient safety. While initiatives such as the Australian Pathology Information, Terminology and Units Standardisation (PITUS) program have produced Standards for Pathology Informatics in Australia (SPIA), conformity to these requires regular monitoring to maintain integrity of data between sending (laboratory medicine providers) and receiving (physicians, MyHealth Record, registries) organisations’ systems. The PITUS 16 Informatics EQA (IEQA) Project together with the Royal College of Pathologists of Australasia Quality Assurance Programs (RCPAQAP) has created a system to perform quality assurance on the electronic laboratory message when the laboratory sends a result back to the EQA provider. The purpose of this study was to perform a small scale pilot implementation of an IEQA protocol, which was performed to test the suitability of the system to check compliance of existing Health Level-7 (HL7 v2.4) reporting standards localised and constrained by the RCPA SPIA. Here, we present key milestones from the implementation, including: (1) software development, (2) installation, and verification of the system and communication services, (3) implementation of the IEQA program and compliance testing of the received HL7 v2.4 report messages, (4) compilation of a draft Informatics Program Survey Report for each laboratory and (5) review consisting of presentation of a report showing the compliance checking tool to each participating laboratory.

Steady state errors and risk of a QC strategy

BACKGROUND

To minimise the risk of patient harm from results, laboratories should establish QC strategies and monitor the performance of assays in line with the analytical and clinical risk.

METHODS

Steady state errors were calculated from a distribution normalized for an Analytical Performance Specification expressed as Assay Capability (imprecision) minus Assay Stability (drift). Inverting this error rate gave QC run length containing one error. Multiplying by error detection of a critical shift gave a QC functional run length for stable and unstable situations. Suitability of this technique was examined using laboratory EQA imprecision and drift data against various analytical and clinical performance specifications.

RESULTS

Steady state errors and error detection, and hence QC functional run length, were dramatically affected by worsening imprecision, drift or changing performance specifications. For a single analyser type, laboratory steady state errors against RCPAQAP performance specification ranged over five orders of magnitude, with contributions from Assay Capability and Assay Stability varying by laboratory.

CONCLUSIONS

Steady state errors accumulate for all assays. Our functional QC run length based on steady state error rate adjusted for error detection of the QC algorithm, amounts to a risk approach using the first two elements of FMEA-like calculation and allows laboratories to examine the suitability of their combinations of QC run length, algorithm, workload and timing of QC challenges. An appropriate common performance specification is critical when assessing and comparing risk.

Extra-analytical quality indicators – where to now?

A large body of evidence collected in recent years demonstrates the vulnerability of the extra-analytical phases of the total testing process (TTP) and the need to promote quality and harmonization in each and every step of the testing cycle. Quality indicators (QIs), which play a key role in documenting and improving quality in TTP, are essential requirements for clinical laboratory accreditation. In the last few years, wide consensus has been achieved on the need to adopt universal QIs and common terminology and to harmonize the management procedure concerning their use by adopting a common metric and reporting system. This, in turn, has led to the definition of performance specifications for extra-analytical phases based on the state of the art as indicated by data collected on QIs, particularly by clinical laboratories attending the Model of Quality Indicators program launched by the Working Group “Laboratory Errors and Patient Safety” of the International Federation of Clinical Chemistry and Laboratory Medicine. Harmonization plays a fundamental role defining not only the list of QIs to use but also performance specifications based on the state of the art, thus providing a valuable interlaboratory benchmark and tools for continuous improvement programs.