Monitoring and root cause analysis of clinical biochemistry turn around time at an academic hospital

Identification and characterization of critical values in therapeutic drug monitoring: a retrospective analysis

Therapeutic drug monitoring (TDM) is a crucial clinical practice that improves pharmacological effectiveness and prevent severe drug-related adverse events. Timely reporting and intervention of critical values during TDM are essential for patient safety. In this study, we retrospectively analyzed the laboratory data to provide an overview of the incidence, distribution pattern and biochemical correlates of critical values during TDM. A total of 19,110 samples were tested for nine drug concentrations between January 1, 2019, and December 31, 2020. Of these, 241 critical values were identified in 165 patients. The most common critical values were vancomycin trough (63.4%), followed by tacrolimus trough (16.9%) and digoxin (15.2%). The primary sources of drug critical values were the department of general intensive care unit (ICU), cardiology, and surgery ICU. At baseline or the time of critical value, significant differences were found between the vancomycin, digoxin, and tacrolimus groups in terms of blood urea nitrogen (BUN), creatinine, N-terminal Pro-B-Type Natriuretic Peptide (NT-proBNP), and lymphocyte percentage, P < 0.05. Therefore, it is important to prioritize and closely monitor drug concentrations to reduce laboratory critical values during TDM.

Logistic analysis of delayed reporting of emergency blood potassium and comparison of improved outcomes

Potassium testing is an essential test in emergency medicine. Turnaround time (TAT) is the time between specimen receipt by the laboratory and the release of the test report. A brief in-laboratory TAT increases emergency department effectiveness. Optimizing processes to shorten TAT using other tools requires extensive time, resources, training, and support. Therefore, we aimed to find a convenient way to shorten TAT, identify risk factors affecting the timeliness of emergency potassium test reporting, and verify the intervention's effects. The dependent variable was emergency potassium reporting time > 30 or < 30 min. Logistic analysis was performed on monitorable factors, such as sex, age, potassium results, number of items, specimen processing time (including centrifugation and time before specimen loading), critical value ratio, instrument status, shift where the report was issued, specimen status, and work experience, as independent variables. In the multivariate analysis, work experience, instrument failure rate, and specimen processing time were risk factors for emergency blood potassium reporting exceeding 30 min. Improvement measures were implemented, significantly decreasing the timeout rate for acute potassium reporting. Our study confirms the usefulness of logistics in reducing the time required to report potassium levels in the emergency department, providing a new perspective on quality management.

Quality improvement of outpatient clinical chemistry tests through a novel middleware-laboratory information system solution

OBJECTIVES

Rapid and accurate laboratory tests are essential to support clinical decision-making. Despite the various efforts to control quality in the laboratory, our outpatient chemistry turnaround time (TAT) has deteriorated since 2018. Moreover, these difficulties have accelerated further due to the COVID-19 pandemic. Therefore, we aimed to improve laboratory work efficiency by identifying and eliminating the causes of reduced laboratory work efficiency.

DESIGN & METHODS

We surveyed to identify tasks that reduce work efficiency. Based on our survey, a new-concept of work assistance middleware linked to laboratory information system (LIS) was developed. The middleware supports test end-time prediction, automatic real-time TAT monitoring, and urgent test requests so that medical technologists can focus on their chemistry tests. The developed middleware was used for 6 months in laboratory and outpatient clinics, and its effectiveness was evaluated.

RESULTS

The median TAT for outpatient chemistry tests was reduced by 6.6 min, from 72.4 min to 65.8 min. And not only did the maximum TAT for the sample decrease from 353 min to 214 min, but the proportion of samples exceeding the TAT target (120 min) also decreased by 77%; from 2.00% in 2010 (1,905 out of 94,989 samples) to 0.46% in 2021 (453 out of 98,117 samples). 2,199 samples were urgently requested through middleware, and they were processed about 15% faster than other samples, effectively performing urgent tests. The test end-time prediction showed an error of 8.6 min in the evaluation using the MAE (Mean Absolute Error) index.

CONCLUSIONS

Through this study, the quality and efficiency of the laboratory were improved, and while reducing the workload of medical staff, it contributed to enhancing patient safety and satisfaction.

Reducing Laboratory Turnaround Time in Patients With Acute Stroke and the Lack of Impact on Time to Reperfusion Therapy

CONTEXT.—

Timely reperfusion improves the recovery of patients with acute ischemic stroke. Laboratory results are crucial to guide treatment decisions in patients when abnormal laboratory tests are suspected.

OBJECTIVE.—

To implement a new laboratory workflow for acute stroke patients and compare laboratory turnaround time (TAT) preimplementation and postimplementation.

DESIGN.—

We conducted a retrospective pre-post intervention study of patients with suspected acute stroke during the 4-month periods before and after the implementation of a new laboratory workflow process. The improvement process included relocating the specimen registration site, laboratory notification before specimen arrival, a color-coding system on tubes, timing at all processes, and eliminating the smear review if platelets were normal. TATs of the laboratory and door-to-clinical intervention times before and after the improvement process were compared.

RESULTS.—

Postintervention, median specimen transportation time decreased from 11 (interquartile range [IQR], 8.4-16.4) to 9 minutes (IQR, 6.3-12.8), P < .001. The intralaboratory and total TATs of complete blood cell count, coagulation tests, and creatinine significantly decreased (P < .001 for all). Blood drawn-to-laboratory reported time decreased from 43 (IQR, 36.0-51.5) to 33 minutes (IQR, 29.2-35.8, P < .001). However, door-to-needle time for thrombolysis and door-to-puncture time and door-to-recanalization time for mechanical thrombectomy were not statistically different (P = .11, .69, and .50, respectively).

CONCLUSIONS.—

The new laboratory workflow significantly decreased transportation time, TAT of individual tests, and the blood drawn-to-laboratory reported time. However, the time to treatment of acute ischemic stroke patients was not different between preimplementation and postimplementation.

Turnaround time prediction for clinical chemistry samples using machine learning

OBJECTIVES

Turnaround time (TAT) is an essential performance indicator of a medical diagnostic laboratory. Accurate TAT prediction is crucial for taking timely action in case of prolonged TAT and is important for efficient organization of healthcare. The objective was to develop a model to accurately predict TAT, focusing on the automated pre-analytical and analytical phase.

METHODS

A total of 90,543 clinical chemistry samples from Erasmus MC were included and 39 features were analyzed, including priority level and workload in the different stages upon sample arrival. PyCaret was used to evaluate and compare multiple regression models, including the Extra Trees (ET) Regressor, Ridge Regression and K Neighbors Regressor, to determine the best model for TAT prediction. The relative residual and SHAP (SHapley Additive exPlanations) values were plotted for model evaluation.

RESULTS

The regression-tree-based method ET Regressor performed best with an R² of 0.63, a mean absolute error of 2.42 min and a mean absolute percentage error of 7.35%, where the average TAT was 30.09 min. Of the test set samples, 77% had a relative residual error of at most 10%. SHAP value analysis indicated that TAT was mainly influenced by the workload in pre-analysis upon sample arrival and the number of modules visited.

CONCLUSIONS

Accurate TAT predictions were attained with the ET Regressor and features with the biggest impact on TAT were identified, enabling the laboratory to take timely action in case of prolonged TAT and helping healthcare providers to improve planning of scarce resources to increase healthcare efficiency.

Big Data in Laboratory Medicine—FAIR Quality for AI?

Laboratory medicine is a digital science. Every large hospital produces a wealth of data each day—from simple numerical results from, e.g., sodium measurements to highly complex output of “-omics” analyses, as well as quality control results and metadata. Processing, connecting, storing, and ordering extensive parts of these individual data requires Big Data techniques. Whereas novel technologies such as artificial intelligence and machine learning have exciting application for the augmentation of laboratory medicine, the Big Data concept remains fundamental for any sophisticated data analysis in large databases. To make laboratory medicine data optimally usable for clinical and research purposes, they need to be FAIR: findable, accessible, interoperable, and reusable. This can be achieved, for example, by automated recording, connection of devices, efficient ETL (Extract, Transform, Load) processes, careful data governance, and modern data security solutions. Enriched with clinical data, laboratory medicine data allow a gain in pathophysiological insights, can improve patient care, or can be used to develop reference intervals for diagnostic purposes. Nevertheless, Big Data in laboratory medicine do not come without challenges: the growing number of analyses and data derived from them is a demanding task to be taken care of. Laboratory medicine experts are and will be needed to drive this development, take an active role in the ongoing digitalization, and provide guidance for their clinical colleagues engaging with the laboratory data in research.

Newly implemented community CD4 service in Tshwaragano, Northern Cape province, South Africa, positively impacts result turn-around time

Background

The Northern Cape is South Africa’s largest province with an HIV prevalence of 7.1% versus a 13.5% national prevalence. CD4 testing is provided at three of five National Health Laboratory Service district laboratories, each covering a 250 km precinct radius. Districts without a local service report prolonged CD4 turn-around times (TAT).

Objective

This study documented the impact of a new CD4 laboratory in Tshwaragano in the remote John Taolo Gaetsewe district of the Northern Cape, South Africa.

Methods

CD4 test volumes and TAT (total, pre-analytical, analytical, and post-analytical) data for the Northern Cape province were extracted for June 2018 to October 2019. The percentage of CD4 results within the stipulated 40-h TAT cut-off and the median and 75th percentiles of all TAT parameters were calculated. Pre-implementation, samples collected at Tshwaragano were referred to Kimberley or Upington, Northern Cape, South Africa.

Results

Pre-implementation, 95.4% of samples at Tshwaragano were referred to Kimberley for CD4 testing (36.3% of Kimberley’s test volumes). Only 7.5% of Tshwaragano’s total samples were referred post-implementation. The Tshwaragano laboratory’s CD4 median total TAT decreased from 24.7 h pre-implementation to 12 h post-implementation (p = 0.003), with > 95.0% of results reported within 40 h. The Kimberley laboratory workload decreased by 29.0%, and testing time significantly decreased from 10 h to 4.3 h.

Conclusion

The new Tshwaragano CD4 service significantly decreased local TAT. Upgrading existing community laboratories to include CD4 testing can alleviate provincial service load and improve local access, TAT and efficiency in the centralised reference laboratory.

Weekly laboratory turn-around time identifies poor performance masked by aggregated reporting

Background

High-level monthly, quarterly and annual turn-around time (TAT) reports are used to assess laboratory performance across the National Health Laboratory Service in South Africa. Individual laboratory performances are masked by aggregate TAT reporting across network of testing facilities.

Objective

This study investigated weekly TAT reporting to identify laboratory inefficiencies for intervention.

Methods

CD4 TAT data were extracted for 46 laboratories from the corporate data warehouse for the 2016/2017 financial period. The total TAT median, 75th percentile and percentage of samples meeting organisational TAT cut-off (90% within 40 hours) were calculated. Total TAT was reported at national, provincial and laboratory levels. Provincial TAT performance was classified as markedly or moderately poor, satisfactory and good based on the percentage of samples that met the cut-off. The pre-analytical, testing and result review TAT component times were calculated.

Results

Median annual TAT was 18.8 h, 75th percentile was 25 h and percentage within cut-off was 92% (n = 3 332 599). Corresponding 75th percentiles of component TAT were 10 h (pre-analytical), 22 h testing and 1.6 h review. Provincial 75th percentile TAT varied from 17.6 h to 34.1 h, with three good (n = 13 laboratories), four satisfactory (n = 24 laboratories) and two poor performers (n = 9 laboratories) provinces. Weekly TAT analysis showed 12/46 laboratories (28.6%) without outlier weeks, 31/46 (73.8%) with 1-10 outlier weeks and 3/46 (6.5%) with more than 10 (highest of 20/52 weeks) outlier weeks.

Conclusion

Masked TAT under-performances were revealed by weekly TAT analyses, identifying poorly performing laboratories needing immediate intervention; TAT component analyses identified specific areas for improvement.

Using machine learning to develop an autoverification system in a clinical biochemistry laboratory

OBJECTIVES

Autoverification systems have greatly improved laboratory efficiency. However, the long-developed rule-based autoverfication models have limitations. The machine learning (ML) algorithm possesses unique advantages in the evaluation of large datasets. We investigated the utility of ML algorithms for developing an artificial intelligence (AI) autoverification system to support laboratory testing. The accuracy and efficiency of the algorithm model were also validated.

METHODS

Testing data, including 52 testing items with demographic information, were extracted from the laboratory information system and Roche Cobas^® IT 3000 from June 1, 2018 to August 30, 2019. Two rounds of modeling were conducted to train different ML algorithms and test their abilities to distinguish invalid reports. Algorithms with the top three best performances were selected to form the finalized ensemble model. Double-blind testing between experienced laboratory personnel and the AI autoverification system was conducted, and the passing rate and false-negative rate (FNR) were documented. The working efficiency and workload reduction were also analyzed.

RESULTS

The final AI system showed a 89.60% passing rate and 0.95 per mille FNR, in double-blind testing. The AI system lowered the number of invalid reports by approximately 80% compared to those evaluated by a rule-based engine, and therefore enhanced the working efficiency and reduced the workload in the biochemistry laboratory.

CONCLUSIONS

We confirmed the feasibility of the ML algorithm for autoverification with high accuracy and efficiency.

Therapeutic turnaround times for common laboratory tests in a tertiary hospital in Kenya

METHODS

We evaluated therapeutic TAT for a tertiary hospital in Western Kenya, using a time-motion study focusing specifically on common hematology and biochemistry orders. The aim was to determine significant bottlenecks in diagnostic testing processes at the institution.

RESULTS

A total of 356 (155 hematology and 201 biochemistry) laboratory tests were fully tracked from the time of ordering to availability of results to care providers. The total therapeutic TAT for all tests was 21.5 ± 0.249 hours (95% CI). The therapeutic TAT for hematology was 20.3 ± 0.331 hours (95% CI) while that for biochemistry tests was 22.2 ± 0.346 hours (95% CI). Printing, sorting and dispatch of the printed results emerged as the most significant bottlenecks, accounting for up to 8 hours of delay (Hematology-8.3 ± 1.29 hours (95% CI), Biochemistry-8.5 ± 1.18 hours (95% CI)). Time of test orders affected TAT, with orders made early in the morning and those in the afternoon experiencing the most delays in TAT.

CONCLUSION

Significant inefficiencies exist at multiple steps in the turnaround times for routine laboratory tests at a large referral hospital within an LMIC setting. Multiple opportunities exist to improve TAT and streamline processes around diagnostic testing in this and other similar settings.

Reducing delay in laboratory reports for outpatients from 16% to <3% at a non-profit hospital in New Delhi, India

In early 2013, several outpatients at Sitaram Bhartia Institute of Science and Research in New Delhi, India complained that their laboratory results were not ready at the promised time. We reviewed the data for 3 months and learnt that 16% of outpatient results were not ready when patients returned to receive them. We formed a multidisciplinary team to fix the problem. After conducting a time-and-motion study, process mapping and discussions the team identified two key problems: (1) the laboratory consultant did not have a set time to validate the results and (2) the reasons of delay in laboratory reports were not documented; this made it hard to identify and solve specific reasons. The team decided to set a fixed time for the consultant to verify results and to document reasons for delay in each case. The team used Plan-Do-Study-Act (PDSA) cycles to finalise the verification system and to set up the documentation system. Documentation led to the identification of new problems which were also solved using PDSA cycles. Delay in reports reduced significantly from 16% in March 2013 to less than 3% in a period of 4 months. We have sustained these gains for the past 5 years.

Factors Affecting Turnaround Time in the Clinical Laboratory of the Kathmandu University Hospital, Nepal

BACKGROUND

The turnaround time (TAT) as defined by most of the laboratories is the time interval between the specimens received in the laboratory to the time of reports dispatched with verification. Nearly 80% of hospital-attached clinical laboratories receive complaints about delayed TAT. Reporting in time is a crucial indicator of quality services along with accurate, precise and reliable reports, thus each clinical laboratory should identify affecting factors to eliminate them for the enhancement of quality services.

METHODOLOGY

Dhulikhel Hospital-Kathmandu University Hospital is a tertiary care hospital, where this observational descriptive study was conducted in 2017. Requested tests received on database in the Department of Clinical Biochemistry Laboratory along with test requisition form (TRF) were carefully screened for any possible error. When analysis of individual patient's tests was completed, results of individual parameters were entered in the database manually. TAT was calculated as a time period between specimens received to analysis completed. Once test analysis has completed it was immediately followed by verification.

RESULTS

A total of 36,108 patients' reports generated from the Department of Clinical Biochemistry Laboratory during study period were analyzed. Nearly 36% of reports exceeded the predefined TAT in case of stat tests, while around 7% of reports were out of predefined TAT in case of routine tests. Among prolonged TAT, around 75% of reports were delayed due to various extra analytical reasons and approximately 48% of total delayed reports were found only due to error by cash unit.

CONCLUSION

The major reasons of delayed laboratory reports were due to time consumed to fix the pre-analytical errors created by other departments rather than laboratory itself. Cash unit alone has the highest degree of error in total testing process and it is the most significant factor for prolonged TAT. However reasons for prolonged TAT may vary with hospital to hospital depending upon different factors.

National survey on turnaround time of clinical biochemistry tests in 738 laboratories in China

BACKGROUND

This survey was initiated to estimate the current status of turnaround time (TAT) monitoring of clinical biochemistry in China, provide baseline data for establishment of quality specifications and analyze the impact factors of TAT.

METHODS

738 laboratories were included. Questionnaires involved general information and data of related indicators of TAT during 1 week were provided to participating laboratories. Nine quality indicators were covered, which were medians, 90th and outlier rates of pre-examination, examination, and post-examination TAT. The 25th percentile, median, and 75th percentile of TATs were calculated as optimum, desirable, and minimum quality specifications. Percentages and sigma values were used to describe the outlier rates. Mann-Whitney and Kruskal-Wallis tests were used to identify the potential impacts of TAT.

RESULTS

Response rate of this survey was 46.44%. More than 50% of the laboratories indicated they had set up target TATs in three time intervals and monitored TATs generally. The post-examination TAT of most laboratories was 0min, while the pre-examination and examination TAT varied. Sigma values of outlier rates for 45%~60% of laboratories were above 4, while 15%~20% of labs whose sigma values were below 3. Group comparisons suggested nurse or mechanical pipeline transportation, link laboratory information system with hospital information system, and using computer reporting instead of printing report were related to shorter TATs.

CONCLUSIONS

Despite of the remarkable progresses of TATs in China, there was also room to improve. Laboratories should strengthen the construction of information systems, identify reasons for TAT delay to improve the service quality continuously.

Laboratory-based clinical audit as a tool for continual improvement: an example from CSF chemistry turnaround time audit in a South-African teaching hospital

INTRODUCTION

Timeliness of laboratory results is crucial to patient care and outcome. Monitoring turnaround times (TAT), especially for emergency tests, is important to measure the effectiveness and efficiency of laboratory services. Laboratory-based clinical audits reveal opportunities for improving quality. Our aim was to identify the most critical steps causing a high TAT for cerebrospinal fluid (CSF) chemistry analysis in our laboratory.

MATERIALS AND METHODS

A 6-month retrospective audit was performed. The duration of each operational phase across the laboratory work flow was examined. A process-mapping audit trail of 60 randomly selected requests with a high TAT was conducted and reasons for high TAT were tested for significance.

RESULTS

A total of 1505 CSF chemistry requests were analysed. Transport of samples to the laboratory was primarily responsible for the high average TAT (median TAT = 170 minutes). Labelling accounted for most delays within the laboratory (median TAT = 71 minutes) with most delays occurring after regular work hours (P < 0.05). CSF chemistry requests without the appropriate number of CSF sample tubes were significantly associated with delays in movement of samples from the labelling area to the technologist's work station (caused by a preference for microbiological testing prior to CSF chemistry).

CONCLUSION

A laboratory-based clinical audit identified sample transportation, work shift periods and use of inappropriate CSF sample tubes as drivers of high TAT for CSF chemistry in our laboratory. The results of this audit will be used to change pre-analytical practices in our laboratory with the aim of improving TAT and customer satisfaction.

National survey on intra-laboratory turnaround time for some most common routine and stat laboratory analyses in 479 laboratories in China

Introduction

To investigate the state of the art of intra-laboratory turnaround time (intra-TAT), provide suggestions and find out whether laboratories accredited by International Organization for Standardization (ISO) 15189 or College of American Pathologists (CAP) will show better performance on intra-TAT than non-accredited ones.

Materials and methods

479 Chinese clinical laboratories participating in the external quality assessment programs of chemistry, blood gas, and haematology tests organized by the National Centre for Clinical Laboratories in China were included in our study. General information and the median of intra-TAT of routine and stat tests in last one week were asked in the questionnaires.

Results

The response rate of clinical biochemistry, blood gas, and haematology testing were 36% (479 / 1307), 38% (228 / 598), and 36% (449 / 1250), respectively. More than 50% of laboratories indicated that they had set up intra-TAT median goals and almost 60% of laboratories declared they had monitored intra-TAT generally for every analyte they performed. Among all analytes we investigated, the intra-TAT of haematology analytes was shorter than biochemistry while the intra-TAT of blood gas analytes was the shortest. There were significant differences between median intra-TAT on different days of the week for routine tests. However, there were no significant differences in median intra-TAT reported by accredited laboratories and non-accredited laboratories.

Conclusions

Many laboratories in China are aware of intra-TAT control and are making effort to reach the target. There is still space for improvement. Accredited laboratories have better status on intra-TAT monitoring and target setting than the non-accredited, but there are no significant differences in median intra-TAT reported by them.