A framework for implementing best laboratory practices for non-integrated point of care tests in low resource settings.

The method we respond to pandemics is still inadequate for dealing with the point of care testing (POCT) requirements of the next large epidemic. The proposed framework highlights the importance of having defined policies and procedures in place for non-integrated POCT to protect patient safety. In the absence of a pathology laboratory, this paradigm may help in the supply of diagnostic services to low-resource centers. A review of the literature was used to construct this POCT framework for non-integrated and/or unconnected devices. It also sought professional advice from the Chemical Pathology faculty, quality assurance laboratory experts and international POCT experts from the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC). Our concept presents a comprehensive integrated and networked approach to POCT with direct and indirect clinical laboratory supervision, particularly for outpatient and inpatient care in low-resource health care settings.

Assessing Third-Year Medical Students' Perspective on Point of Care Testing Boot Camp: From Bench to Bedside.

Background: Point-of-care testing (POCT), which is also known as bed side-testing, has been integrated into the healthcare system, offering faster results that can lead to improved patient outcomes. POCT was missing from the medical education curriculum in our institute. Objectives: The primary objective of this study was to describe the development and introduce POCT training for medical students in a medical college in Pakistan.Secondary objectives were to evaluate student performance on POCT content and to assess the impact of POCT training via students' feedback. Methodology: The boot camp experience was devised, directed, and facilitated by team constituting of Chemical Pathology faculty members, laboratory technologists and teaching assistants. The program included presentations, demonstrations of POCT instrument handling, supervised hands-on individual performance on glucometer using quality control specimens, competency assessment and sign off followed by interactive case-based discussions. A knowledge quiz via Kahoot was administered at the beginning and end of the experience and scores were compared statistically. Online evaluation and feedback were designed via virtual learning environment based on 10 questions regarding the program and methodology using on a five-point Likert Scale. Frequencies were generated and t-tests were employed to determine pre-post differences. Results: The boot camp was spread over 2 days and ran three hours each day with the third-year medical students class split into two groups (n=80). On knowledge evaluation, the mean group pre and post test scores were 45% and 95% respectively (p-value =< 0.05). On documented structured competency assessment form a score of 95 was achieved by 100% participants. Positive feedback of 4 or more was recorded on the Likert's scale by 100% respondents. Conclusion: This POCT boot camp experience can be used by other institutions and can be applied at different times during the medical school curriculum and other professional education programs. This bootcamp will be helpful to educate medical students, postgraduate trainees and field workers working in rural areas and in low resource settings to deliver reliable POC tests results. Future research should examine these students' competence in achieving POCT skills when they enter in clinical practice.

Best Practices in the Implementation of a Point of Care Testing Program: Experience From a Tertiary Care Hospital in a Developing Country.

Implementation of a structured Point of Care Test (POCT) program is challenging. Traditionally POCT was unregulated and the aim was to introduce a structured POCT program at our tertiary care hospital to ensure compliance with regulatory standards. The purpose of this article is to describe how a hospital in a developing country with limited resources has approached POCT program initiative. The benefits offered by such systems, including cost-effectiveness, robustness and the ability to generate reliable accurate POCT results in a short time, are appropriate to the clinical and social needs of the developing world.

Performance Evaluation of Cardiac Troponin I Assay: A Comparison Between the Point-of-care Testing Radiometer AQT90 FLEX and the Central Laboratory Siemens Advia Centaur Analyzer.

Background To validate the point of care testing (POCT) Trop-I analyzer and compare it with a central laboratory-based chemiluminescence immunoassay, in order to evaluate its performance for use in critical care areas. Moreover, for clinical decision-making, it is imperative to know the extent to which patient stratification will differ based on the analytic method being used. In particular, the aim of this study was to evaluate the analytical performance of the point-of-care analyzer and demonstrate the agreement with the central laboratory measurements in patients presenting to the emergency department (ED) with chest pain and suspected acute coronary syndrome (ACS). Methods This cross-sectional study was performed at the section of chemical pathology, department of pathology and laboratory medicine, the Aga Khan University (AKU), Karachi, from October to November 2017. Samples from patients and the quality control material of Trop-I were analyzed for imprecision, linearity, and method comparison on Advia Centaur (Siemens Diagnostics, CA, USA), and the AQT90 FLEX analyzer (Radiometer Medical ApS, Brønshøj, Denmark) with photometric detection at the section of chemical pathology, AKU. Statistical analysis was done using Microsoft Excel (Microsoft Corporation, Washington, United States) and EP Evaluator version 10.3.0.556 (Data Innovations, LLC, VT, US). Quantitative variables were represented in terms of mean ± SD. For precision, the computed SD was compared with allowable random error. Furthermore, Cohen's kappa was applied to observe the agreement between the two methods. Results The Trop-I Precision study on the POCT analyzer showed a coefficient of variation (CV) of 2.4% using a pooled patient sample with a mean Trop-I of 2.15 ± 0.05 ng/ml. Three standards ranging from 0.034 to 1.316 ng/ml were run in triplicate to verify accuracy and linearity. The allowable systematic error (SEa) was 10.0%. The maximum deviation for a mean recovery from 100% was 4.1%. All three of the mean recoveries were accurate and within the allowable error limits. The results were linear with slope 1.04, intercept 0.0. On a method comparison, Trop-I showed good agreement, yielding a kappa value of 0.95. Conclusion This study has validated the performance of a POCT Trop-I assay against a central laboratory immunoassay and found acceptable results. POCT assays for cTnI should be implanted in emergency settings to ensure the fast triage of patients with chest pain, as well as timely diagnosis.

Error identification in a high-volume clinical chemistry laboratory: Five-year experience.

INTRODUCTION: Quality indicators for assessing the performance of a laboratory require a systematic and continuous approach in collecting and analyzing data. The aim of this study was to determine the frequency of errors utilizing the quality indicators in a clinical chemistry laboratory and to convert errors to the Sigma scale. MATERIALS AND METHODS: Five-year quality indicator data of a clinical chemistry laboratory was evaluated to describe the frequency of errors. An 'error' was defined as a defect during the entire testing process from the time requisition was raised and phlebotomy was done until the result dispatch. An indicator with a Sigma value of 4 was considered good but a process for which the Sigma value was 5 (i.e. 99.977% error-free) was considered well controlled. RESULTS: In the five-year period, a total of 6,792,020 specimens were received in the laboratory. Among a total of 17,631,834 analyses, 15.5% were from within hospital. Total error rate was 0.45% and of all the quality indicators used in this study the average Sigma level was 5.2. Three indicators - visible hemolysis, failure of proficiency testing and delay in stat tests - were below 5 on the Sigma scale and highlight the need to rigorously monitor these processes. CONCLUSIONS: Using Six Sigma metrics quality in a clinical laboratory can be monitored more effectively and it can set benchmarks for improving efficiency.