Harmonizing the post-analytical phase: focus on the laboratory report

The final, post-analytical, phase of laboratory testing is increasingly recognized as a fundamental step in maximizing quality and effectiveness of laboratory information. There is a need to close the loop of the total testing cycle by improving upon the laboratory report, and its notification to users. The harmonization of the post-analytical phase is somewhat complicated, mainly because it calls for communication that involves parties speaking different languages, including laboratorians, physicians, information technology specialists, and patients. Recently, increasing interest has been expressed in integrated diagnostics, defined as convergence of imaging, pathology, and laboratory tests with advanced information technology (IT). In particular, a common laboratory, radiology and pathology diagnostic reporting system that integrates text, sentinel images and molecular diagnostic data to an integrated, coherent interpretation enhances management decisions and improves quality of care.

Tuberculosis Infection Control: Experiences and Considerations from a Web Based Tool Implementation

Tuberculosis (TB) remains a significant global health challenge. Indeed, according to the World Health Organization (WHO), TB is classified as the second most common cause of death worldwide due to a single infectious agent in 2022, following COVID-19. To effectively manage tuberculosis patients, it is necessary to ensure accurate diagnosis, prompt treatment initiation, and vigilant monitoring of patients' progress. In 2017, the TB Ge network was implemented and launched in two primary hospitals within the Liguria Region in Italy, with the main purpose to manage tuberculosis infections. This system, organized as a web-based tool, simplifies the manual input of patient's data and therapies, while automating the integration of test results from hospitals' Laboratory Information Systems (LIS), without requiring human intervention. The goal of this paper is to highlight the outcomes achieved through the implementation of the TB Ge network in a period seriously affected by the COVID-19 pandemia and outline future directions. More specifically, the aim is to extend its adoption to all hospitals in the Liguria Region, thus improving the management of tuberculosis infections across healthcare facilities.

Laboratory Data Timeliness and Completeness Improves Following Implementation of an Electronic Laboratory Information System in Côte d'Ivoire: Quasi-Experimental Study on 21 Clinical Laboratories From 2014 to 2020

BACKGROUND

The Ministry of Health in Côte d'Ivoire and the International Training and Education Center for Health at the University of Washington, funded by the United States President's Emergency Plan for AIDS Relief, have been collaborating to develop and implement the Open-Source Enterprise-Level Laboratory Information System (OpenELIS). The system is designed to improve HIV-related laboratory data management and strengthen quality management and capacity at clinical laboratories across the nation.

OBJECTIVE

This evaluation aimed to quantify the effects of implementing OpenELIS on data quality for laboratory tests related to HIV care and treatment.

METHODS

This evaluation used a quasi-experimental design to perform an interrupted time-series analysis to estimate the changes in the level and slope of 3 data quality indicators (timeliness, completeness, and validity) after OpenELIS implementation. We collected paper and electronic records on clusters of differentiation 4 (CD4) testing for 48 weeks before OpenELIS adoption until 72 weeks after. Data collection took place at 21 laboratories in 13 health regions that started using OpenELIS between 2014 and 2020. We analyzed the data at the laboratory level. We estimated odds ratios (ORs) by comparing the observed outcomes with modeled counterfactual ones when the laboratories did not adopt OpenELIS.

RESULTS

There was an immediate 5-fold increase in timeliness (OR 5.27, 95% CI 4.33-6.41; P<.001) and an immediate 3.6-fold increase in completeness (OR 3.59, 95% CI 2.40-5.37; P<.001). These immediate improvements were observed starting after OpenELIS installation and then maintained until 72 weeks after OpenELIS adoption. The weekly improvement in the postimplementation trend of completeness was significant (OR 1.03, 95% CI 1.02-1.05; P<.001). The improvement in validity was not statistically significant (OR 1.34, 95% CI 0.69-2.60; P=.38), but validity did not fall below pre-OpenELIS levels.

CONCLUSIONS

These results demonstrate the value of electronic laboratory information systems in improving laboratory data quality and supporting evidence-based decision-making in health care. These findings highlight the importance of OpenELIS in Côte d'Ivoire and the potential for adoption in other low- and middle-income countries with similar health systems.

A machine learning model for identifying systemic lupus erythematosus through laboratory information system and electronic medical record

OBJECTIVES

Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease. Its diagnosis poses significant challenges especially at early stages and in atypical cases. The aim of this study was to develop a machine learning model based on common laboratory tests that can aid SLE diagnosis.

METHODS

A standard protocol was developed to collect data of SLE and control immune diseases. A 10-fold cross-validation was performed in the modeling dataset (n=862), and an external dataset (n=198) was used for model validation. Machine learning algorithms were applied to construct a diagnostic model. Performance was evaluated based on area under the curve (AUC) values, F1-score, negative predictive value, positive predictive value, accuracy, sensitivity, and specificity.

RESULTS

The optimal model was based on a random forest algorithm with 10 clinical features. Thrombin time, prothrombin activity, and uric acid contributed most to the diagnostic model. The SLE diagnostic model showed sufficient predictive accuracy, with AUC values of 0.8286 in the validation dataset.

CONCLUSIONS

Our diagnostic model based on 10 common laboratory tests identified the patients with SLE with high accuracy. An online version of the model can potentially be applied in clinical settings for the differential diagnosis of SLE.

Working with the Electronic Health Record and Laboratory Information System to Maximize Ordering and Reporting of Molecular Microbiology Results

Molecular microbiology assays have a higher cost of testing compared to traditional methods and need to be utilized appropriately. Results from these assays may also require interpretation and appropriate follow-up. Electronic tools available in the electronic health record and laboratory information system can be deployed both preanalytically and postanalytically to influence ordering behaviors and positively impact diagnostic stewardship. Next generation technologies, such as machine learning and artificial intelligence, have the potential to expand upon the capabilities currently available and warrant additional study and development but also require regulation around their use in health care.

Improving regional medical laboratory center report quality through a report recall management system

OBJECTIVES

Currently, most medical laboratories do not have a dedicated software for managing report recalls, and relying on traditional manual methods or laboratory information system (LIS) to record recall data is no longer sufficient to meet the quality management requirements in the large regional laboratory center. The purpose of this article was to describe the research process and preliminary evaluation results of integrating the Medical Laboratory Electronic Record System (electronic record system) laboratory report recall function into the iLab intelligent management system for quality indicators (iLab system), and to introduce the workflow and methods of laboratory report recall management in our laboratory.

METHODS

This study employed cluster analysis to extract commonly used recall reasons from laboratory report recall records in the electronic record system. The identified recall reasons were validated for their applicability through a survey questionnaire and then incorporated into the LIS for selecting recall reasons during report recall. The statistical functionality of the iLab system was utilized to investigate the proportion of reports using the selected recall reasons among the total number of reports, and to perform visual analysis of the recall data. Additionally, we employed P-Chart to establish quality targets and developed a "continuous improvement process" electronic flow form.

RESULTS

The reasons for the recall of laboratory reports recorded in the electronic recording system were analyzed. After considering the opinions of medical laboratory personnel, a total of 12 recall reasons were identified, covering 73.05 % (1854/2538) of the recalled laboratory reports. After removing data of mass spectra lab with significant anomalies, the coverage rate increased to 82.66 % (1849/2237). The iLab system can generate six types of statistical graphs based on user needs, including statistical time, specialty labs (or divisions), test items, reviewers, reasons for report recalls, and distribution of the recall frequency of 0-24 h reports. The control upper limit of the recall rate of P-Chart based on laboratory reports can provide quality targets suitable for each professional group at the current stage. Setting the five stages of continuous process improvement reasonably and rigorously can effectively achieve the goal of quality enhancement.

CONCLUSIONS

The enhanced iLab system enhances the intelligence and sustainable improvement capability of the recall management of laboratory reports, thus improving the efficiency of the recall management process and reducing the workload of laboratory personnel.

Implementation of an HIV Case Based Surveillance Using Standards-Based Health Information Exchange in Rwanda

As Rwanda approaches the UNAIDS Fast Track goals which recommend that 95% of HIV-infected individuals know their status, of whom 95% should receive treatment and 95% of those on treatment achieve viral suppression, the country currently relies on an inefficient paper, and disjointed electronic, systems for case-based surveillance (CBS). Rwanda has established an ecosystem of interoperable systems based on open standards to support HIV CBS. Data were successfully exchanged between an EMR, a client registry, laboratory information system and DHIS-2 Tracker, and subsequently, a complete analytic dataset was ingested into MS-Power Business Intelligence (MS-PowerBI) for analytics and visualization of the CBS data. Existing challenges included inadequate workforce capacity to support mapping of data elements to HL7 FHIR resources. Interoperability optimization to support CBS is work in progress and rigorous evaluations on the effect on health information exchange on monitoring patient outcomes are needed.

A Quality Improvement (QI) initiative in diagnostic stewardship of urine culture in an era of Laboratory Information Systems (LIS)

Standard urine culture is the gold standard for diagnosing urinary tract infections (UTIs) but fails to differentiate true UTI from asymptomatic bacteriuria, which is important to prevent the overuse of antibiotics. Correlation with the presence or absence of pyuria can be helpful in giving a hint of the true situation. With the help of Laboratory Information System (LIS), patients' urinalysis reports can be conveniently accessed and compared simultaneously with appropriate reports. In our study, a quality improvement initiative was planned for appropriate reporting of urine culture and antimicrobial susceptibility testing using information obtained through LIS.

Initial and follow-up evaluations on cerebrospinal fluid involvement by hematologic malignancy

Central nervous system (CNS) involvement is a serious complication in hematologic malignancy, and early detection and management of CNS involvement in these cases significantly impact the prognosis. Currently, there is no consensus on the use of multiparametric flow cytometry (MFC) and conventional cytology (CC) testing for initial and follow-up cerebrospinal fluid (CSF) specimens to diagnose CNS involvement by hematologic malignancy. In our institution, after initial MFC and CC, two subsequent negative MFCs are required before discontinuing MFC. The aim of this study is to evaluate the outcome of this approach. CSF cytology and MFC reports were retrieved from Laboratory Information System, and data was reviewed. Between January 2020 and December 2021, 1789 CSF samples from 280 patients were submitted for CSF analysis. For those 517 CSF samples tested by both MFC and CC, 97 cases tested positive by both MFC and CC with 95% concordance. Eighteen cases were MFC + /CC - and 7 were MFC - /CC + . Thirty-six cases had initially positive MFCs followed by more than one MFC evaluation. Among those 36 cases, 22 cases (61.1%) converted to negative after the second follow-up sample, 9 cases (25%) were continuously positive for at least three samples, and 5 cases (13.9%) exhibited negative to positive conversion. Compared to negative CSF cases, positive CSFs had higher total nucleated cell count and higher total protein levels while red blood cells, glucose, and lactate dehydrogenase levels remained at comparable levels. The concordance between MFC and CC was excellent. The high incidence of positive MFCs on two or more follow-up samples and the high frequency of negative MFC to positive conversion indicate the necessity of repeated negative MFCs before discontinuing MFC. The fact that more than half of the positive cases converted to negative after the second CSF specimen and most follow-up positive cases can be detected by CC alone suggests it is adequate to use CC alone for follow-up CSF study after two consecutive negative MFCs.

Designing and validating an autoverification system of biochemical test results in Hatay Mustafa Kemal University, clinical laboratory

Introduction

Autoverification (AV) is a postanalytical tool that uses algorithms to validate test results according to specified criteria. The Clinical and Laboratory Standard Institute (CLSI) document for AV of clinical laboratory test result (AUTO-10A) includes recommendations for laboratories needing guidance on implementation of AV algorithms. The aim was to design and validate the AV algorithm for biochemical tests.

Materials and methods

Criteria were defined according to AUTO-10A. Three different approaches for algorithm were used as result limit checks, which are reference range, reference range ± total allowable error, and 2nd and 98th percentile values. To validate the algorithm, 720 cases in middleware were tested. For actual cases, 3,188,095 results and 194,520 reports in laboratory information system (LIS) were evaluated using the AV system. Cohen’s kappa (κ) was calculated to determine the degree of agreement between seven independent reviewers and the AV system.

Results

The AV passing rate was found between 77% and 85%. The highest rates of AV were in alanine transaminase (ALT), direct bilirubin (DBIL), and magnesium (Mg), which all had AV rates exceeding 85%. The most common reason for non-validated results was the result limit check (41%). A total of 328 reports evaluated by reviewers were compared to AV system. The statistical analysis resulted in a κ value between 0.39 and 0.63 (P < 0.001) and an agreement rate between 79% and 88%.

Conclusions

Our improved model can help laboratories design, build, and validate AV systems and be used as starting point for different test groups.

Efficient Visualization of Whole Slide Images in Web-based Viewers for Digital Pathology

CONTEXT.—

Wide adoption of digital pathology requires efficient visualization and navigation in Web-based digital slide viewers, which is poorly defined.

OBJECTIVE.—

To define and quantify relevant performance metrics for efficient visualization of cases and slides in digital slide viewers.

DESIGN.—

With a universal slide viewer used in clinical routine diagnostics, we evaluated the impact of slide caching, compression type, tile, and block size of whole slide images generated from Philips, Leica, and 3DHistech scanners on streaming performance on case, slide, and field of view levels.

RESULTS.—

Two hundred thirty-nine pathologists routinely reviewed 60 080 whole slide images over 3 months. The median time to open a case's slides from the laboratory information system was less than 4 seconds, the time to change to a slide within the case was less than 1 second, and the time to render the adjacent field of view when navigating the slide was less than one-quarter of a second. A whole slide image's block size and a viewer tile size of 1024 pixels showed best performance to display a field of view and was preferrable over smaller tiles due to fewer mosaic effects. For Philips, fastest median slide streaming pace was 238 ms per field of view and for 3DHistech, 125 ms. For Leica, the fastest pace of 108 ms per field of view was established with block serving without decompression.

CONCLUSIONS.—

This is the first study to systematically assess user-centric slide visualization performance metrics for digital viewers, including time to open a case, time to change a slide, and time to change a field of view. These metrics help to improve the viewer's configuration, leading to an efficient visualization baseline that is widely accepted among pathologists using routine digital pathology.

Validation of the Martin method to estimate low-density lipoprotein cholesterol concentrations in Japanese populations and a modified method for laboratory information system application

OBJECTIVES

High concentrations of low-density lipoprotein cholesterol (LDL-C) are a risk factor for cardiovascular disease. We validated the efficacy of the Martin method is useful in the estimation of LDL-C concentrations was validated in Japanese populations and derived a modified Martin method for easy laboratory information system applications.

METHODS

We created 3 subject groups, including 2664 health check-up participants registered with the Resource Center for Health Science, 29,806 clinical patients (A) in the Gifu University Hospital, and 113,716 clinical patients (B) in the Fujita Health University Hospital. Each method to estimate serum LDL-C concentrations (Friedewald formula, Martin method and modified Martin method) was validated by correlation analysis with serum LDL-C concentrations measured using a direct method.

RESULTS

The correlation coefficients with the direct method in terms of the Friedewald formula, Martin method, and modified Martin method were 0.963, 0.972 and 0.970 in the health check-up participants; 0.946, 0.962 and 0.961 in clinical patients A; and 0.961, 0.979 and 0.978 in clinical patients B, respectively. Concordance ratios with using the direct method in the Friedewald formula, Martin method and modified Martin method were 82.8%, 85.5% and 85.3% in the health check-up participants; 76.4%, 80.5% and 80.2% in clinical patients A; and 76.1%, 82.6% and 82.6% in clinical patients B, respectively.

CONCLUSION

Our results show that the Martin and modified Martin methods display good performance in terms of the estimation of LDL-C concentrations among triglyceride concentrations of a wide range, and they may thus be useful for estimating LDL-C concentrations.

An analysis of the vitamin D overtesting in a tertiary healthcare centre

Introduction

Vitamin D testing is excessively used in clinical practice, despite of the clinical guidelines statements against population screening for vitamin D deficiency. This study aimed to assess an annual number of performed 25-hydroxy vitamin D (25(OH)D) tests that were unsupported by the national guidelines for prevention, detection and therapy of vitamin D deficiency in adults and to calculate associated financial burden for the publicly funded healthcare.

Materials and methods

A representative sample of requested 25(OH)D tests in 2018 (N = 474) was formed after selection and randomisation of data set (N = 5298) collected from the laboratory information system database of the Clinical Department for Laboratory Diagnostics, the Clinical Hospital Centre Rijeka. Records were classified in two groups depending on associated medical condition(s) according to the national guidelines. An annual cost of the total and group specific vitamin D testing was calculated on the base of a single test price reimbursed by the Croatian Healthcare Insurance Fund (CHIF).

Results

Medical conditions with high-risk for vitamin D deficiency were detected in 43% (206/474) of vitamin D requests (group 1). Conditions not associated with vitamin D deficiency were detected in 57% (268/474) requests (group 2). A total cost of 25(OH)D testing for the CHIF was 58,729.50 EUR (25,523.79 EUR in the group 1 and 33,205.71 EUR in the group 2).

Conclusions

More than half of all 25(OH)D tests performed in the clinical laboratory represent avoidable cost for the public healthcare. Prevention of population screening by vitamin D testing is needed.

OUP accepted manuscript

Objective

Assess the effectiveness of providing Logical Observation Identifiers Names and Codes (LOINC®)-to-In Vitro Diagnostic (LIVD) coding specification, required by the United States Department of Health and Human Services for SARS-CoV-2 reporting, in medical center laboratories and utilize findings to inform future United States Food and Drug Administration policy on the use of real-world evidence in regulatory decisions.

Materials and Methods

We compared gaps and similarities between diagnostic test manufacturers’ recommended LOINC® codes and the LOINC® codes used in medical center laboratories for the same tests.

Results

Five medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (chi-square 45.0, 4 df, P < .0001).

Discussion

The five medical centers and three test manufacturers vary in how they organize, categorize, and store LIS catalog information. This variation impacts data quality and interoperability.

Conclusion

The results of the study indicate that providing the LIVD mappings was not sufficient to support laboratory data interoperability. National implementation of LIVD and further efforts to promote laboratory interoperability will require a more comprehensive effort and continuing evaluation and quality control.

Comparison of reference intervals derived by direct and indirect methods based on compatible datasets obtained in Turkey

BACKGROUND

Indirect derivation of reference intervals (RIs) from the laboratory information system (LIS) has been recently pursued. We aimed at evaluating the accuracy of indirectly predicted RIs compared to the RIs established directly from healthy subjects in the nationwide RI study in Turkey, targeting 25 major chemistry analytes.

METHODS

LIS data were retrieved from the laboratory that performed measurements for the direct study. They were cleaned by limiting to outpatients with age 18-65 years, and by allowing only one record per year per patient. Evaluated were four indirect methods of univariate approach: Hoffmann, Bhattacharya, Arzideh, and Wosniok methods. Power transformation of the LIS dataset was performed either using the power (λ) reported by the IFCC global RI study (the first two methods) or using a λ predicted (the last two).

RESULTS

Compared to the direct study dataset, the LIS dataset showed a variable degree of alterations in peak location and shape. Consequently, lower-side peak-shifts observed in sodium, albumin, etc. led to lowered RI limits, whereas higher-side peak-shift observed in triglyceride, low-density lipoprotein cholesterol, etc. led to raised RI limits. Overall, 72% (62-81) of the RI limits predicted by indirect methods showed significant biases from direct RIs. However, the biases observed in total cholesterol, lactic dehydrogenase, etc. were attributed to a higher-side age-bias in LIS dataset. After excluding them, the overall proportion of biased RIs was reduced to 47% (38-54).

CONCLUSION

To reduce prediction biases that remained after age adjustment, it is necessary to apply more rigorous data-cleaning before applying indirect methods.

Incomplete Filling of Laboratory Request Forms: A Look at the Practice at a Federal Medical Centre in South Eastern Nigeria

BACKGROUND

Laboratory request forms serve as a medium for communication between clinicians managing medical cases and the pathologist. Improperly filled forms impairs the ability of pathologists to generate sound and valuable reports. This research focused on finding out the extent to which doctors in Federal Medical Centre, Umuahia properly fill laboratory request forms.

METHODOLOGY

A total of 1,509 laboratory request forms directed to the electrolyte bench, already filled out by various doctors in the hospital between May and October, 2018 were retrospectively studied. The completeness of information supplied by requesting physician based on some parameters were analysed.

RESULTS

In descending order, the most frequently provided data were as follows; name of patient, 1,509[100.0%]; gender,973[64.5%]; provisional diagnosis,866[57.4%]; age,639[42.4%]; hospital number,428[28.4%]; clinical summary,47[3.1%]. 26[2%] forms were completed in their entirety per the 6 variables assessed.

CONCLUSION

Proper and adequate filling of laboratory request forms is very poor in this hospital. Therefore, continuous medical education on the need for adequate completion of request forms is absolutely necessary.

Implementing LOINC: Current Status and Ongoing Work at the Hannover Medical School

The Logical Observation Identifiers, Names and Codes (LOINC) is a common terminology used for standardizing laboratory terms. Within the HiGHmed consortium, LOINC is used as a central terminology for health data sharing across all university hospital sites. Therefore, linking the LOINC codes to the site-specific tests and measures is one crucial step to reach this goal. In this work we report our ongoing work in implementing LOINC to the laboratory information system, our challenges and lessons learned.

Microservice Architecture to Provide Medical Data Management for Decision Support

Efficient Interaction between Hospital information (HIS) and laboratory information systems (LIS) provide a smooth laboratory testing process and data consistency. The current software ecosystem can be characterized by its rapid changes that can lead to breaks in HIS-LIS interaction and problems with semantic interoperability of the systems. To avoid such problems developers can clusterize software applications into small, easily supportable functional units that can be changed on demand without effecting other pieces of software. This approach commonly referred to as microservice architecture. The goal the research is to develop a FHIR based microservice platform that connects HIS, LIS and a Clinical decision support system (CDSS) into unified information space. A microservice platform has been implemented and now is in the production operation processing around 15000 orders a day.

Algorithm to Estimate the Extended Turnaround Time Including Outpatient Waiting Time for Blood Specimen Collection when a Stand-alone Queue Ticket System not Connectable to Laboratory Information System Is Used

BACKGROUND

A queue ticket system (QTS) used in an outpatient phlebotomy clinic was unable to be directly integrated with the laboratory information system (LIS). To monitor patient's waiting time and extended turnaround time (TAT) as patient-centered quality indicators for outpatient laboratory services, we developed an algorithm to integrate data between the QTS and the LIS.

METHODS

Between June 1 to September 30, 2017, data files were exported from a QSYS-8000 (HION Tech, Seoul, Korea). Each calling event from the QTS data was matched to a barcode of test requests from the LIS if the following conditions were met: (1) time interval between "call time" from QTS and "barcode printing time" from LIS <90 s; (2) "Counter Number" from LIS="Counter Number" from QTS. Extended TAT was estimated as the interval between pulling the queue ticket and the reporting of the test result.

RESULTS

82.66%±3.14% of the barcodes from the LIS were matched to issued tickets. Median waiting time (mean±SD) was 6.5±5.3 min. Median extended TAT was 84.7±11.2 min for non-STAT and 53.0±6.4 min for STAT.

CONCLUSION

When a stand-alone QTS was used in the outpatient phlebotomy clinic, data from the QTS and the LIS were integrated using a novel algorithm we developed.

Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database

Objective

Standards such as the Logical Observation Identifiers Names and Codes (LOINC®) are critical for interoperability and integrating data into common data models, but are inconsistently used. Without consistent mapping to standards, clinical data cannot be harmonized, shared, or interpreted in a meaningful context. We sought to develop an automated machine learning pipeline that leverages noisy labels to map laboratory data to LOINC codes.

Materials and Methods

Across 130 sites in the Department of Veterans Affairs Corporate Data Warehouse, we selected the 150 most commonly used laboratory tests with numeric results per site from 2000 through 2016. Using source data text and numeric fields, we developed a machine learning model and manually validated random samples from both labeled and unlabeled datasets.

Results

The raw laboratory data consisted of >6.5 billion test results, with 2215 distinct LOINC codes. The model predicted the correct LOINC code in 85% of the unlabeled data and 96% of the labeled data by test frequency. In the subset of labeled data where the original and model-predicted LOINC codes disagreed, the model-predicted LOINC code was correct in 83% of the data by test frequency.

Conclusion

Using a completely automated process, we are able to assign LOINC codes to unlabeled data with high accuracy. When the model-predicted LOINC code differed from the original LOINC code, the model prediction was correct in the vast majority of cases. This scalable, automated algorithm may improve data quality and interoperability, while substantially reducing the manual effort currently needed to accurately map laboratory data.

Development and evaluation of a novel user interface for reviewing clinical microbiology results

Background

Microbiology laboratory results are complex and cumbersome to review. We sought to develop a new review tool to improve the ease and accuracy of microbiology results review.

Methods

We observed and informally interviewed clinicians to determine areas in which existing microbiology review tools were lacking. We developed a new tool that reorganizes microbiology results by time and organism. We conducted a scenario-based usability evaluation to compare the new tool to existing legacy tools, using a balanced block design.

Results

The average time-on-task decreased from 45.3 min for the legacy tools to 27.1 min for the new tool (P < .0001). Total errors decreased from 41 with the legacy tools to 19 with the new tool (P = .0068). The average Single Ease Question score was 5.65 (out of 7) for the new tool, compared to 3.78 for the legacy tools (P < .0001). The new tool scored 88 ("Excellent") on the System Usability Scale.

Conclusions

The new tool substantially improved efficiency, accuracy, and usability. It was subsequently integrated into the electronic health record and rolled out system-wide. This project provides an example of how clinical and informatics teams can innovative alongside a commercial Electronic Health Record (EHR).

Overinterpretation is common in pathological diagnosis of appendix cancer during patient referral for oncologic care

CONTEXT

Low-grade appendiceal mucinous neoplasm (LAMN) and appendiceal adenocarcinoma are known to cause the majority of pseudomyxoma peritonei (PMP, i.e. mucinous ascites); however, recognition and proper classification of these neoplasms can be difficult despite established diagnostic criteria.

OBJECTIVE

To determine the pathological diagnostic concordance for appendix neoplasia and related lesions during patient referral to an academic medical center specialized in treating patients with PMP.

DESIGN

The anatomic pathology laboratory information system was searched to identify cases over a two-year period containing appendix specimens with mucinous neoplasia evaluated by an outside pathology group and by in-house slide review at a single large academic medical center during patient referral.

RESULTS

161 cases containing appendix specimens were identified over this period. Forty-six of 161 cases (28.6%) contained appendiceal primary neoplasia or lesions. Of these, the originating pathologist diagnosed 23 cases (50%) as adenocarcinoma and 23 cases (50%) as LAMN; however, the reference pathologist diagnosed 29 cases (63.0%) as LAMN, 13 cases (28.3%) as adenocarcinoma, and 4 cases (8.7%) as ruptured simple mucocele. Importantly, for cases in which the originating pathologist rendered a diagnosis of adenocarcinoma, the reference pathologist rendered a diagnosis of adenocarcinoma (56.5%, 13 of 23), LAMN (39.1%, 9 of 23), or simple mucocele (4.3%, 1 of 23). The overall diagnostic concordance rate for these major classifications was 71.7% (33 of 46) with an unweighted observed kappa value of 0.48 (95% CI, 0.27-0.69), consistent with moderate interobserver agreement. All of the observed discordance (28.3%) for major classifications could be attributed to over-interpretation. In addition, the majority of LAMN cases (65.5%) had potential diagnostic deficiencies including over-interpretation as adenocarcinoma and lacking or discordant risk stratification (i.e. documentation of extra-appendiceal neoplastic epithelium).

CONCLUSIONS

Appendiceal mucinous lesions remain a difficult area for appropriate pathological classification with substantial discordance due to over-interpretation in this study. The findings highlight the critical need for recognition and application of diagnostic criteria regarding these tumors. Recently published consensus guidelines and a checklist provided herein may help facilitate improvement of diagnostic concordance and thereby reduce over-interpretation and potential overtreatment. Further studies are needed to determine the extent of this phenomenon and its potential clinical impact.

Implementation of Epic Beaker Clinical Pathology at Stanford University Medical Center

OBJECTIVES

To provide an account of implementation of the Epic Beaker 2014 clinical pathology module at Stanford University Medical Center and highlight strengths and weaknesses of the system.

METHODS

Based on a formal selection process, Stanford selected Epic Beaker to replace Sunquest as the clinical laboratory information system (LIS). The rationale included integration between the LIS and already installed Epic electronic medical record (EMR), reduction in the number of systems and interfaces, and positive patient identification (PPID). The build was significantly customized and included a first of its kind Epic-to-Epic interface. This was due to the clinical laboratory serving two hospitals (pediatric and adult) with independent instances of Epic.

RESULTS

Test turnaround times showed improvement from historical baselines, mostly because of the implementation of PPID. PPID also resulted in significant reduction in mislabeled specimens.

CONCLUSIONS

Epic 2014 Beaker clinical pathology is a viable LIS with adequate functionality for a large academic center. Strengths include PPID and integration with the EMR. Integration provides laboratory users with ready access to the patient's relevant clinical history to assist releasing of results and gives physician and nurse providers sophisticated add-on ordering and specimen collection workflows. Areas that could use further development include specimen aliquoting, quality control reporting, and maintenance tools.

Toward Automatic Reporting of Infectious Diseases

Accurate, complete, and timely disease surveillance data are vital for disease control. We report a national scale effort to automatically extract information from electronic medical records as well as electronic laboratory systems. The extracted information is then transferred to the centers of disease control after a proper confirmation process. The coverage rates of the automated reporting systems are over 50%. Not only is the workload of surveillance greatly reduced, but also reporting is completed in near real-time. From our experiences, a system sustainable strategy, well-defined working plan, and multifaceted team coordination work effectively. Knowledge management reduces the cost to maintain the system. Training courses with hands-on practice and reference documents are useful for LOINC adoption.

Mapping Local Codes to Read Codes

Background &amp; Objectives: Legacy laboratory test codes make it difficult to use clinical datasets for meaningful translational research, where populations are followed for disease risk and outcomes over many years. The Health Informatics Centre (HIC) at the University of Dundee hosts continuous biochemistry data from the clinical laboratories in Tayside and Fife dating back as far as 1987. However, the HIC-managed biochemistry dataset is coupled with incoherent sample types and unstandardised legacy local test codes, which increases the complexity of using the dataset for reasonable population health outcomes. The objective of this study was to map the legacy local test codes to the Scottish 5-byte Version 2 Read Codes using biochemistry data extracted from the repository of the Scottish Care Information (SCI) Store.

METHODS

Data mapping methodology was used to map legacy local test codes from clinical biochemistry laboratories within Tayside and Fife to the Scottish 5-byte Version 2 Read Codes.

RESULTS

The methodology resulted in the mapping of 485 legacy laboratory test codes, spanning 25 years, to 124 Read Codes.

CONCLUSION

The data mapping methodology not only facilitated the restructuring of the HIC-managed biochemistry dataset to support easier cohort identification and selection, but it also made it easier for the standardised local laboratory test codes, in the Scottish 5-byte Version 2 Read Codes, to be mapped to other health data standards such as Clinical Terms Version 3 (CTV3); LOINC; and SNOMED CT.

Achieving High Reliability in Histology: An Improvement Series to Reduce Errors

OBJECTIVES

Despite sweeping medical advances in other fields, histology processes have by and large remained constant over the past 175 years. Patient label identification errors are a known liability in the laboratory and can be devastating, resulting in incorrect diagnoses and inappropriate treatment. The objective of this study was to identify vulnerable steps in the histology workflow and reduce the frequency of labeling errors (LEs).

METHODS

In this 36-month study period, a numerical step key (SK) was developed to capture LEs. The two most prevalent root causes were targeted for Lean workflow redesign: manual slide printing and microtome cutting. The numbers and rates of LEs before and after interventions were compared to evaluate the effectiveness of interventions.

RESULTS

Following the adoption of a barcode-enabled laboratory information system, the error rate decreased from a baseline of 1.03% (794 errors in 76,958 cases) to 0.28% (107 errors in 37,880 cases). After the implementation of an innovative ice tool box, allowing single-piece workflow for histology microtome cutting, the rate came down to 0.22% (119 errors in 54,342 cases).

CONCLUSIONS

The study pointed out the importance of tracking and understanding LEs by using a simple numerical SK and quantified the effectiveness of two customized Lean interventions. Overall, a 78.64% reduction in LEs and a 35.28% reduction in time spent on rework have been observed since the study began.

Significant Reduction in Preanalytical Errors for Nonphlebotomy Blood Draws After Implementation of a Novel Integrated Specimen Collection Module

OBJECTIVES

Most preanalytical errors at our institution occur during nonphlebotomy blood draws. We implemented an electronic health record (EHR), interfaced the EHR to the laboratory information system, and designed a new specimen collection module. We studied the effects of the new system on nonphlebotomy preanalytical errors.

METHODS

We used an electronic database of preanalytical errors and calculated the number and type of the most common errors in the emergency department (ED) and inpatient nursing for 3-month periods before (August-October 2014) and after (August-October 2015) implementation. The level of staff compliance with the new system was also assessed.

RESULTS

The average monthly preanalytical errors decreased significantly from 7.95 to 1.45 per 1,000 specimens in the ED (P < 0001) and 11.75 to 3.25 per 1,000 specimens in inpatient nursing (P < 0001). The rate of decrease was similar for mislabeled, unlabeled, wrong specimen received and no specimen received errors. Most residual errors (80% in the ED and 67% in inpatient nursing) occurred when providers did not use the new system as designed.

CONCLUSIONS

Implementation of a customized specimen collection module led to a significant reduction in preanalytical errors. Improved compliance with the system may lead to further reductions in error rates.

Towards an evaluation framework for Laboratory Information Systems

INTRODUCTION

Laboratory testing and reporting are error-prone and redundant due to repeated, unnecessary requests and delayed or missed reactions to laboratory reports. Occurring errors may negatively affect the patient treatment process and clinical decision making. Evaluation on laboratory testing and Laboratory Information System (LIS) may explain the root cause to improve the testing process and enhance LIS in supporting the process. This paper discusses a new evaluation framework for LIS that encompasses the laboratory testing cycle and the socio-technical part of LIS.

METHODOLOGY

Literature review on discourses, dimensions and evaluation methods of laboratory testing and LIS. A critical appraisal of the Total Testing Process (TTP) and the human, organization, technology-fit factors (HOT-fit) evaluation frameworks was undertaken in order to identify error incident, its contributing factors and preventive action pertinent to laboratory testing process and LIS.

RESULT

A new evaluation framework for LIS using a comprehensive and socio-technical approach is outlined. Positive relationship between laboratory and clinical staff resulted in a smooth laboratory testing process, reduced errors and increased process efficiency whilst effective use of LIS streamlined the testing processes.

CONCLUSION

The TTP-LIS framework could serve as an assessment as well as a problem-solving tool for the laboratory testing process and system.

Artificial Neural Network Approach in Laboratory Test Reporting: Learning Algorithms

OBJECTIVES

In the field of laboratory medicine, minimizing errors and establishing standardization is only possible by predefined processes. The aim of this study was to build an experimental decision algorithm model open to improvement that would efficiently and rapidly evaluate the results of biochemical tests with critical values by evaluating multiple factors concurrently.

METHODS

The experimental model was built by Weka software (Weka, Waikato, New Zealand) based on the artificial neural network method. Data were received from Dokuz Eylül University Central Laboratory. "Training sets" were developed for our experimental model to teach the evaluation criteria. After training the system, "test sets" developed for different conditions were used to statistically assess the validity of the model.

RESULTS

After developing the decision algorithm with three iterations of training, no result was verified that was refused by the laboratory specialist. The sensitivity of the model was 91% and specificity was 100%. The estimated κ score was 0.950.

CONCLUSIONS

This is the first study based on an artificial neural network to build an experimental assessment and decision algorithm model. By integrating our trained algorithm model into a laboratory information system, it may be possible to reduce employees' workload without compromising patient safety.

Mining of hospital laboratory information systems: a model study defining age- and gender-specific reference intervals and trajectories for plasma creatinine in a pediatric population

BACKGROUND

The knowledge of physiological fluctuation and variation of even commonly used biochemical quantities in extreme age groups and during development is sparse. This challenges the clinical interpretation and utility of laboratory tests in these age groups. To explore the utility of hospital laboratory data as a source of information, we analyzed enzymatic plasma creatinine as a model analyte in two large pediatric hospital samples.

METHODS

Plasma creatinine measurements from 9700 children aged 0-18 years were obtained from hospital laboratory databases and partitioned into high-resolution gender- and age-groups. Normal probability plots were used to deduce parameters of the normal distributions from healthy creatinine values in the mixed hospital datasets. Furthermore, temporal trajectories were generated from repeated measurements to examine developmental patterns in periods of changing creatinine levels.

RESULTS

Creatinine shows great age dependence from birth throughout childhood. We computed and replicated 95% reference intervals in narrow gender and age bins and showed them to be comparable to those determined in healthy population studies. We identified pronounced transitions in creatinine levels at different time points after birth and around the early teens, which challenges the establishment and usefulness of reference intervals in those age groups.

CONCLUSIONS

The study documents that hospital laboratory data may inform on the developmental aspects of creatinine, on periods with pronounced heterogeneity and valid reference intervals. Furthermore, part of the heterogeneity in creatinine distribution is likely due to differences in biological and chronological age of children and should be considered when using age-specific reference intervals.

Laboratory utilization improvement through a computer-aided algorithm developed with general practitioners

BACKGROUND

One of the main duties of healthcare workers is to get an appropriate use of diagnostic and therapeutic tools. The aim of this study was to show how strategies can be designed and established in consensus with general practitioners (GPs) to reach an optimal laboratory test request.

METHODS

The laboratory serves a population of approximately 235,000 inhabitants, including nine primary care centers. GPs could request every test in profiles and individually. In meetings between the laboratory and GPs our request patterns were compared to other geographic regions, and we investigated the appropriateness of test requesting. The group devised strategies that consisted of removing tests from profiles [aspartate aminotransferase (AST), γ-glutamyltranspeptidase (GGT) and phosphate], removing tests from GPs requests (iron and transferrin), substituting tests (IgA antigliadin antibody), and measuring total bilirubin (tBil) only when the icteric index value was above 34.2 mmol/L (2 mg/dL). We analyzed every test request in the post- (years 2012-2013) and pre-intervention period (years 2010-2011), and the tBil measured in the post-intervention period. We studied if AST/alanine aminotransferase (ALT) and GGT/ALT achieved indicators targets and calculated the economic savings in the post-intervention period.

RESULTS

There was a significant drop in every test request in the post-intervention period. AST/ALT achieved the indicator target. GGT/ALT, never achieved the indicator goal. The strategies resulted in a savings of more than €34,000.

CONCLUSIONS

The strategies designed between the laboratory and the requesting clinicians and automatically established by using our laboratory information system were successful.

MI-Lab - A Laboratory Environment for Medical Informatics Students

Medical research and health care highly depend on the use of information technology. There is a wide range of application systems (patient administration system, laboratory information system, communication server etc.) and heterogeneous data types (administrative data, clinical data, laboratory data, image data, genomic data etc.). Students and researchers do not often have the possibility to use productive application systems of e.g. hospitals or medical practices to gain practical experiences or examine new components and technologies. Therefore, the aim of this project is to develop a dedicated laboratory environment for patient health care and clinical research. Essential application systems were identified and a suitable architecture was designed for this purpose. It is accompanied by a teaching plan that considers learning modules for bachelor and master degrees in medical informatics. We implemented the laboratory environment called MI-Lab with multiple free and open source software components. All components are installed on virtual machines and/or Docker containers. This modular architecture creates a flexible system which can be deployed in various scenarios. The preliminary evaluation results suggests that laboratory environments like MI-Lab work well in teaching practical aspects of medical informatics and are widely accepted by students.

System hazards in managing laboratory test requests and results in primary care: medical protection database analysis and conceptual model

OBJECTIVES

To analyse a medical protection organisation's database to identify hazards related to general practice systems for ordering laboratory tests, managing test results and communicating test result outcomes to patients. To integrate these data with other published evidence sources to inform design of a systems-based conceptual model of related hazards.

DESIGN

A retrospective database analysis.

SETTING

General practices in the UK and Ireland.

PARTICIPANTS

778 UK and Ireland general practices participating in a medical protection organisation's clinical risk self-assessment (CRSA) programme from January 2008 to December 2014.

MAIN OUTCOME MEASURES

Proportion of practices with system risks; categorisation of identified hazards; most frequently occurring hazards; development of a conceptual model of hazards; and potential impacts on health, well-being and organisational performance.

RESULTS

CRSA visits were undertaken to 778 UK and Ireland general practices of which a range of systems hazards were recorded across the laboratory test ordering and results management systems in 647 practices (83.2%). A total of 45 discrete hazard categories were identified with a mean of 3.6 per practice (SD=1.94). The most frequently occurring hazard was the inadequate process for matching test requests and results received (n=350, 54.1%). Of the 1604 instances where hazards were recorded, the most frequent was at the 'postanalytical test stage' (n=702, 43.8%), followed closely by 'communication outcomes issues' (n=628, 39.1%).

CONCLUSIONS

Based on arguably the largest data set currently available on the subject matter, our study findings shed new light on the scale and nature of hazards related to test results handling systems, which can inform future efforts to research and improve the design and reliability of these systems.

[Patient identification errors and biological samples in the analytical process: Is it possible to improve patient safety?]

BACKGROUND

Patient identification errors and biological samples are one of the problems with the highest risk factor in causing an adverse event in the patient.

OBJECTIVE

To detect and analyse the causes of patient identification errors in analytical requests (PIEAR) from emergency departments, and to develop improvement strategies.

MATERIAL AND METHODS

A process and protocol was designed, to be followed by all professionals involved in the requesting and performing of laboratory tests. Evaluation and monitoring indicators of PIEAR were determined, before and after the implementation of these improvement measures (years 2010-2014).

RESULTS

A total of 316 PIEAR were detected in a total of 483,254 emergency service requests during the study period, representing a mean of 6.80/10,000 requests. Patient identification failure was the most frequent in all the 6-monthly periods assessed, with a significant difference (P<.0001).

CONCLUSIONS

The improvement strategies applied showed to be effective in detecting PIEAR, as well as the prevention of such errors. However, we must continue working with this strategy, promoting a culture of safety for all the professionals involved, and trying to achieve the goal that 100% of the analytical and samples are properly identified.

[UNE-EN ISO 15189 accreditation of the preanalytical phase of a clinical laboratory]

OBJECTIVES

Description of the procedures followed in Catlab's pre-analytical area for UNE-EN ISO 15189 accreditation, and implementation of quality indicators to evaluate the standard requirements in the pre-analytical phase processes.

MATERIAL AND METHODS

Description of Catlab's 2 self-developed computer applications: Catlab Program Incidents (CPI) and Refrigerator Management Program (RMP). Both of them, among other applications, documentation and quality indicators, had enabled us to achieve UNE-EN ISO 15189 accreditation and have traceability in the pre-analytical phase.

RESULTS

Results of 4 quality indicators are shown. In the customers satisfaction measurement indicator, 97.3% clinicians value positively (quite/lot) different aspects of the laboratory. The indicator of pre-analytical incidents went from 7.2% in 2011 down to 4.4% in 2014. In the prompt transport of late arrival samples, 3 of the routes (33%) did not reach the acceptable target. And finally, the indicator of default opening time of sample coolers, 100% of the routes reached the desired objective in the second quarter of 2014.

CONCLUSIONS

The use of those applications, allowed us to design quality control indicators of the processes in the pre-analytical phase; from sample extraction to its analytical process in the laboratory, enhancing the collaboration with extraction sites, and allowing improvement actions to be established.

[Gender differences in the use of tumour markers]

INTRODUCTION AND OBJECTIVE

Gender is one of the factors that can influence the use of health resources. The use of tumour markers is widespread, due to the importance of these in monitoring cancer development. The aim of this study is to analyse the influence of gender on the use of tumour markers, and to investigate whether there are differences in their use.

MATERIAL AND METHODS

A longitudinal, retrospective and descriptive study, with a 2-year follow-up, was conducted in the catchment area of the University Hospital of Padua. An analysis was performed on 23,059 analytical requests for tumour markers. A descriptive and frequency analysis was performed on all variables. The statistical analysis was performed using Chi squared, Student t and Mann-Whitney U to test for significance.

RESULTS

The number of requests for women (1.5) was lower than men (1.6). In patients with tumour pathology, the number of requests was higher than in patients without tumour disease. In the analysis by disease and gender, the difference remained significant. As regards the number of tumour markers per request, the difference between genders was also significant: 2.13 in males versus 2.85 in women. Similar results were obtained when requests for tumour markers linked to gender-related diseases were eliminated.

CONCLUSIONS

There are differences in the use of tumour markers by gender with the number of requests for male patients being higher than for females. However, the number of tumour markers per request is greater in women than in men.

Prostate-specific antigen velocity is not better than total prostate-specific antigen in predicting prostate biopsy diagnosis

OBJECTIVES

1.) Identify whether prostate-specific antigen velocity improves the ability to predict prostate biopsy diagnosis. 2.) Test whether there is an increase in the predictive capability of models when Gleason 7 prostate cancers are separated into a 3+4 and a 4+3 group.

DESIGN AND METHODS

Calgary Laboratory Services' Clinical Laboratory Information System was searched for prostate biopsies reported between January 1, 2009 and December 31, 2013. Total prostate-specific antigen tests were recorded for each patient from January 1, 2007 to the most recent test before their recorded prostate biopsy. The data set was divided into the following three groups for comparison; benign, all prostate cancer and Gleason 7-10. The Gleason grade 7-10 group was further divided into 4+3 and 3+4 Gleason 7 prostate cancers. Prostate-specific antigen velocity was calculated using four different methods found in the literature. Receiver operator curves were used to assess operational characteristics of the tests.

RESULTS

4622 men between the ages of 40-89 with a prostate biopsy were included for analysis. Combining prostate-specific antigen velocity with total prostate-specific antigen (AUC=0.570-0.712) resulted in small non-statistically significant changes to the area under the curve compared to the area under the curve of total prostate-specific antigen alone (AUC=0.572-0.699). There were marked increases in the area under curves when 3+4 and 4+3 Gleason 7 cancers were separated.

CONCLUSIONS

Prostate-specific antigen velocity does not add predictive value for prostate biopsy diagnosis. The clinical significance of the prostate specific antigen test can be improved by separating Gleason 7 prostate cancers into a 3+4 and 4+3 group.

The impact of repeat-testing of common chemistry analytes at critical concentrations

BACKGROUND

Early notification of critical values by the clinical laboratory to the treating physician is a requirement for accreditation and is essential for effective patient management. Many laboratories automatically repeat a critical value before reporting it to prevent possible misdiagnosis. Given today's advanced instrumentation and quality assurance practices, we questioned the validity of this approach. We performed an audit of repeat-testing in our laboratory to assess for significant differences between initial and repeated test results, estimate the delay caused by repeat-testing and to quantify the cost of repeating these assays.

METHODS

A retrospective audit of repeat-tests for sodium, potassium, calcium and magnesium in the first quarter of 2013 at Tygerberg Academic Laboratory was conducted. Data on the initial and repeat-test values and the time that they were performed was extracted from our laboratory information system. The Clinical Laboratory Improvement Amendment criteria for allowable error were employed to assess for significant difference between results.

RESULTS

A total of 2308 repeated tests were studied. There was no significant difference in 2291 (99.3%) of the samples. The average delay ranged from 35 min for magnesium to 42 min for sodium and calcium. At least 2.9% of laboratory running costs for the analytes was spent on repeating them.

CONCLUSIONS

The practice of repeating a critical test result appears unnecessary as it yields similar results, delays notification to the treating clinician and increases laboratory running costs.

Graphical display of diagnostic test results in electronic health records: a comparison of 8 systems

Accurate display and interpretation of clinical laboratory test results is essential for safe and effective diagnosis and treatment. In an attempt to ascertain how well current electronic health records (EHRs) facilitated these processes, we evaluated the graphical displays of laboratory test results in eight EHRs using objective criteria for optimal graphs based on literature and expert opinion. None of the EHRs met all 11 criteria; the magnitude of deficiency ranged from one EHR meeting 10 of 11 criteria to three EHRs meeting only 5 of 11 criteria. One criterion (i.e., the EHR has a graph with y-axis labels that display both the name of the measured variable and the units of measure) was absent from all EHRs. One EHR system graphed results in reverse chronological order. One EHR system plotted data collected at unequally-spaced points in time using equally-spaced data points, which had the effect of erroneously depicting the visual slope perception between data points. This deficiency could have a significant, negative impact on patient safety. Only two EHR systems allowed users to see, hover-over, or click on a data point to see the precise values of the x-y coordinates. Our study suggests that many current EHR-generated graphs do not meet evidence-based criteria aimed at improving laboratory data comprehension.

[The Establishment and Reality of a Quality Management System--Chairmen's Introductory Remarks]

Compared with clinical test data, the main ditterence from other forms of medical information is the objectivity of clinical test data. The value of clinical testing is basically to offer objective and reliable clinical test data for clinical practice both timely and promptly. A quality management system supports those practices, and the mission of a clinical test department is to embody the principle of accuracy assurance and reflect it in routine clinical testing. Accuracy management was previously a system to maintain and manage accurate reading, but it has developed into a system to assure data quality. It is considered necessary to use knowledge and skills on accuracy assurance and actively ensure the reliability of data. We therefore suggest the need for "a shift from accuracy assurance to quality management".