ABSTRACT
Introduction: Quality indicators are important parameters to enhance the quality of the clinical laboratory services. Due to the extensive testing processes, errors cannot be completely avoided in a clinical laboratory. To minimize errors, however, adequate training, QC checks, and regular procedure evaluations are beneficial. Objective(s): The objective of the study was to establish and evaluate quality indicators on an ongoing basis as an effort to increase quality. Method(s): This retrospective study, different quality indicators in a molecular laboratory in northern Gujarat were assessed over the course of a year (September 2020-August 2021). Data of total 8176 samples were summarized. Each Quality indicator was examined at the end of the month after being divided into the pre, analytical, and post-analytical stages, respectively. Result(s): As summarization of total 8176 samples, we found a cumulative error rate for all quality indicators of 346 (4.23%). Preanalytical errors were the most common 180 (2.20%), followed by analytical errors 114 (1.39%), and post analytical errors 52 (0.63%). Conclusion(s): There is no question that by continuously striving to develop the outcome of these quality indicators through the adoption of corrective measures over time, the quality of laboratory services and patient care would be improved.Copyright © 2023, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.
ABSTRACT
Background: The Coronavirus disease 2019 (Covid-19) is caused by the virus SARS COV-2 and is declared as a global pandemic by WHO. It is known that in SARS COV-2 virus infection causes haematological changes and often present the potential to optimize the monitoring of infectious process or to indicate the suspicion of their severity. The present study was conducted to study the routine haematological parameters including NLR (Neutrophil lymphocyte Ratio) in Covid-19 patients and to assess their utility in identifying severity of the disease. Methods: This retrospective study was conducted on 257 Covid-19 RT PCR positive cases. The cases were divided into mild, moderate, and severe category as per MoHFW. Haematological parameters were measured by Fully Automated Hematology cell counter using blood sample collected in EDTA vacutainers. MS Excel was used for data analysis and ANOVA tests were applied to test statistical significance. P<0.05 was considered statistically significant. Results: Degree of severity of Covid-19 cases could be correlated with older age group. Most important haematological parameters noted in adults are Eosinopenia in 84%, Monocytopenia in 64%, NLR >3 in 18.32 %, and Leucopoenia (17.5%). Severe category showed higher proportion of NLR >3 in 40.6%, Neutrophilia in 31.2%, Leucocytosis in 28.2% and lymphocytopenia in 12.5%. Conclusions: Haematological parameters in Covid-19 positive cases could help to predict a patient risk and outcome in the Indian scenario that will provide guidance to subsequent clinical practice.
ABSTRACT
The most common alarming and dangerous disease in the world today is the coronavirus disease 2019 (COVID-19). The coronavirus is perceived as a group of coronaviruses which causes mild to severe respiratory diseases among human beings. The infection is spread by aerosols emitted from infected individuals during talking, sneezing, and coughing. Furthermore, infection can occur by touching a contaminated surface followed by transfer of the viral load to the face. Transmission may occur through aerosols that stay suspended in the air for extended periods of time in enclosed spaces. To stop the spread of the pandemic, it is crucial to isolate infected patients in quarantine houses. Government health organizations faced a lack of quarantine houses and medical test facilities at the first level of testing by the proposed model. If any serious condition is observed at the first level testing, then patients should be recommended to be hospitalized. In this study, an IoT-enabled smart monitoring system is proposed to detect COVID- 19 positive patients and monitor them during their home quarantine. The Internet of Medical Things (IoMT), known as healthcare IoT, is employed as the foundation of the proposed model. The least-squares (LS) method was applied to estimate the linear model parameters for a sequential pilot survey. A statistical sequential analysis is performed as a pilot survey to efficiently collect preliminary data for an extensive survey of COVID-19 positive cases. The Bayesian approach is used, based on the assumption of the random variable for the priori distribution of the data sample. Fuzzy inference is used to construct different rules based on the basic symptoms of COVID- 19 patients to make an expert decision to detect COVID-19 positive cases. Finally, the performance of the proposed model was determined by applying a four-fold cross-validation technique. © 2022 Tech Science Press. All rights reserved.
ABSTRACT
Telecommuting has become the norm for various organizations due to the impact of COVID-19 pandemic. Telecommuting has positive effects not only on expenses but also on productivity, work flexibility and quality of work. The adoption rate of telecommuting has gone exponentially high especially due to government policies such as social distancing and country-wide lockdown measures. Human resource managers over the globe have reported higher empathy, increased co-ordination, retention and engagement during the lockdown. Provided that, very minimal percentage of workforce operated from their home prior to the coronavirus, it is difficult for employees to adopt the proposed model without any prior training and guidance. This research paper aims to shed light on how the pandemic has impacted the way we work, concerns related to human resource management with main focus on training and development function of HR department. The paper also proposes a framework for training employees to adapt to telecommuting as well as back to office conduct through technological interventions such as Virtual Reality, Artificial Intelligence etc. The overall theoretical framework provides a basic training and development process to reskill and upskill the employees to combat the perennial skill shortages post pandemic. © 2021 IEEE.