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

Root Cause Analysis: Unraveling Common Laboratory Challenges

Diverse errors occur in a pathology laboratory and manual mistakes are the most common. There are various advancements to replace manual procedures with digitized automation techniques. Guidelines and protocols are available to run a standard pathology laboratory. But, even with such attempts to reinforce and strengthen the protocols, the complete elimination of errors is yet not possible. Root cause analysis (RCA) is the best way forward to develop an error-free laboratory, In this review, the importance of RCA, common errors taking place in laboratories, methods to carry out RCA, and its effectiveness are discussed in detail. The review also highlights the potential of RCA to provide long-term quality improvement and efficient laboratory management.

Turnaround Time: An Efficacy Measure for Medical Laboratories

Turnaround time (TAT), which doctors frequently use as the benchmark for laboratory performance, is a typical way to communicate timeliness. It also acts as a quality indicator to evaluate the effectiveness and efficiency of the testing process and the satisfaction of clinicians and patients. TAT is the time from receipt of the sample in the laboratory to final delivery or dispatch of the report of said test. The TAT procedure can be broadly divided into three stages pre-analytical, analytical and post-analytical. There is variability in TAT according to different conditions like the volume of sample size, staff expertise, availability of adequate resources, distances of the hospital from the lab, and various sub-departments. To remove obstacles to optimizing TAT, we must take a practical approach. A workload reduction plan, proper stock management, specialized work assignments, and skilled staff retention are crucial strategies to reduce the setting's delayed TAT.

In-depth investigation of turn-around time of full blood count tests requested from a clinical haematology outpatient department in Cape Town, South Africa

Background

The performance of laboratories can be objectively assessed using the overall turn-around time (TAT). However, TAT is defined differently by the laboratory and clinicians; therefore, it is important to determine the contribution of all the different components making up the laboratory test cycle.

Objective

We carried out a retrospective analysis of the TAT of full blood count tests requested from the haematology outpatient department at Tygerberg Academic Hospital in Cape Town, South Africa, with an aim to assess laboratory performance and to identify critical steps influencing TAT.

Methods

A retrospective audit was carried out, focused on the full blood count tests from the haematology outpatient department within a period of 3 months between 01 February and 30 April 2018. Data was extracted from the National Health Laboratory Service laboratory information system. The time intervals of all the phases of the test cycle were determined and total TAT and within-laboratory (intra-lab) TAT were calculated.

Results

A total of 1176 tests were analysed. The total TAT median was 275 (interquartile range [IQR] 200.0–1537.7) min with the most prolonged phase being from authorisation to review by clinicians (median 114 min; IQR: 37.0–1338.5 min). The median intra-lab TAT was 55 (IQR 40–81) min and 90% of the samples were processed in the laboratory within 134 min of registration.

Conclusion

Our findings showed that the intra-lab TAT was within the set internal benchmark of 3 h. Operational phases that were independent of the laboratory processes contributed the most to total TAT.

Magnitude of delayed turnaround time of laboratory results in Amhara Public Health Institute, Bahir Dar, Ethiopia

BACKGROUND

Clinical decisions depend on timely laboratory result reporting. The timeliness is commonly expressed in turnaround time and serves as a quality improvement tool to assess the effectiveness and efficiency of the laboratory. According to the International Organization for Standardization (ISO) guidelines, each laboratory shall establish turnaround times for each of its examinations that reflect clinical needs, and shall periodically evaluate whether or not it is meeting the established turnaround times. Therefore, this study aimed to assess the TAT of laboratory results done in the reference laboratories of the Amhara Public Health Institute, Bahir Dar, Ethiopia.

METHODS

A retrospective cross sectional study was carried out from 01 January to 31 September 2018. Each patient sample was considered as a study unit. Nine months data were extracted from the sample tracking log and from the Laboratory Information System (LIS) database. Descriptive and summary statistics were calculated using SPSS version 20.0 statistical software.

RESULTS

A total of 34,233 patients samples were tested during the study period. Monthly average TAT ranged from 38.6 to 51.3 days for tuberculosis (TB) culture, 5.3 to 42.4 days for exposed infant diagnosis (EID) for HIV, 8.4 to 26 days for HIV 1 viral load, and 1.9 to 3.5 days for TB genexpert tests. Compared with the standard, 76.5% of the viral load, 68.1% of the EID for HIV and 53.8% of the TB genexpert tests had delayed TAT. Repeated reagent stock out, high workload, activities overlapping, and staff turnover were major reasons for the result delays.

CONCLUSIONS

There was a delayed turnaround time of laboratory results in APHI. HIV viral load, EID and TB genexpert results were the most affected tests. Workload reduction plan, proper stock management, specific work assignment and trained staff retention are important approaches to minimize the delayed TAT in the setting.

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.

Sample transportation – an overview

Transportation of blood samples is a major part of the preanalytical pathway and can be crucial in delaying laboratory results to the clinicians. A variety of aspects however makes sample transportation a complex, challenging and often overlooked task that needs thorough planning and dedicated resources. The purpose of this review is to outline the options available for this task and to emphasize the preanalytical aspects that need consideration in this process, e.g. performance specifications for sample transportation as stated in ISO standards 15189 and 20658, quality control of automated transportation systems, monitoring of sample integrity parameters and temperature surveillance in general and for external samplers in particular. All these are tasks that the laboratory must assure on a daily basis in terms of continuous quality control, and simultaneously the laboratory must remain alert to alterations in clinical demands (sample frequency, turn-around-times) and new regulations within this area (e.g. the recent General Data Protection Regulation from the EU).

Using laboratory data to categorise CD4 laboratory turn-around-time performance across a national programme

Background and objective

The National Health Laboratory Service provides CD4 testing through an integrated tiered service delivery model with a target laboratory turn-around time (TAT) of 48 h. Mean TAT provides insight into national CD4 laboratory performance. However, it is not sensitive enough to identify inefficiencies of outlying laboratories or predict the percentage of samples meeting the TAT target. The aim of this study was to describe the use of the median, 75th percentile and percentage within target of laboratory TAT data to categorise laboratory performance.

Methods

Retrospective CD4 laboratory data for 2015–2016 fiscal year were extracted from the corporate data warehouse. The laboratory TAT distribution and percentage of samples within the 48 h target were assessed. A scatter plot was used to categorise laboratory performance into four quadrants using both the percentage within target and 75th percentile TAT. The laboratory performance was labelled good, satisfactory or poor.

Results

TAT data reported a positive skew with a mode of 13 h and a median of 17 h and 75th percentile of 25 h. Overall, 93.2% of CD4 samples had a laboratory TAT of less than 48 h. 48 out of 52 laboratories reported good TAT performance, i.e. percentage within target > 85% and 75th percentile ≤ 48 h, with two categorised as satisfactory (one parameter met), and two as poor performing laboratories (failed both parameters).

Conclusion

This study demonstrated the feasibility of utilising laboratory data to categorise laboratory performance. Using the quadrant approach for TAT data, laboratories that need interventions can be highlighted for root cause analysis assessment.