Diagnostic Laboratories in India: Investigating Quality Characteristics, Productivity and Time of Reporting

Situational Analysis of Healthcare and Medical Diagnostic Testing Facility Availability in Selected Blocks of Muzaffarpur District, Bihar, India: A Cross-Sectional Study

INTRODUCTION

This study presents a comprehensive assessment of healthcare facilities, focusing on workforce composition, operational dynamics, diagnostic laboratory services, and accessibility considerations. The comparison between government and private healthcare sectors provides insights into service delivery and potential disparities. The study's rationale, objectives, and methodology are explored in the context of the Indian healthcare landscape.

METHODS

A cross-sectional analysis was conducted in Muzaffarpur district, Bihar, targeting selected urban and rural blocks. The study employed geolocation data to analyze accessibility to healthcare facilities. Data collection involved on-site visits, structured questionnaires, and consultation of the Indian Council of Medical Research (ICMR)'s framework. The assessment concentrated on the availability of tests offered by the LaBike platform, and workforce compositions were compared.

RESULTS

Government healthcare facilities exhibited a balanced distribution of doctors, nurses, and grassroot workers, reflecting comprehensive healthcare provisions. Private facilities, although featuring moderate doctor and nurse presence, lacked grassroot workers. Diagnostic test prevalence was evident, with core tests, such as CBC and blood glucose, available in over 85% of facilities. Government facilities provided tests free of charge, while private facilities showcased a diverse cost spectrum. Proposed interventions received strong support from both sectors, indicating the potential for innovative healthcare solutions. Accessibility analysis: Urban intervention and control sites demonstrated comparable accessibility, with facilities located within 2 km. In rural intervention and control sites, distances varied significantly. Mushahari, a rural intervention site, required participants to travel 6 km to the nearest facility, impacting healthcare access. By contrast, Marwan, a rural control site, featured a shorter distance of 3 km.

CONCLUSION

This study's comprehensive evaluation of healthcare facilities offers valuable insights into workforce dynamics, diagnostic services, and healthcare interventions in the context of government and private sectors. The findings underscore the significance of addressing workforce gaps and promoting equitable access to diagnostics. By informing evidence-based decision-making, this study contributes to the optimization of healthcare service delivery, aiming to enhance healthcare quality and accessibility for all.

An interpretable decision-support model for breast cancer diagnosis using histopathology images

Microscopic examination of biopsy tissue slides is perceived as the gold-standard methodology for the confirmation of presence of cancer cells. Manual analysis of an overwhelming inflow of tissue slides is highly susceptible to misreading of tissue slides by pathologists. A computerized framework for histopathology image analysis is conceived as a diagnostic tool that greatly benefits pathologists, augmenting definitive diagnosis of cancer. Convolutional Neural Network (CNN) turned out to be the most adaptable and effective technique in the detection of abnormal pathologic histology. Despite their high sensitivity and predictive power, clinical translation is constrained by a lack of intelligible insights into the prediction. A computer-aided system that can offer a definitive diagnosis and interpretability is therefore highly desirable. Conventional visual explanatory techniques, Class Activation Mapping (CAM), combined with CNN models offers interpretable decision making. The major challenge in CAM is, it cannot be optimized to create the best visualization map. CAM also decreases the performance of the CNN models. To address this challenge, we introduce a novel interpretable decision-support model using CNN with a trainable attention mechanism using response-based feed-forward visual explanation. We introduce a variant of DarkNet19 CNN model for the classification of histopathology images. In order to achieve visual interpretation as well as boost the performance of the DarkNet19 model, an attention branch is integrated with DarkNet19 network forming Attention Branch Network (ABN). The attention branch uses a convolution layer of DarkNet19 and Global Average Pooling (GAP) to model the context of the visual features and generate a heatmap to identify the region of interest. Finally, the perception branch is constituted using a fully connected layer to classify images. We trained and validated our model using more than 7000 breast cancer biopsy slide images from an openly available dataset and achieved 98.7% accuracy in the binary classification of histopathology images. The observations substantiated the enhanced clinical interpretability of the DarkNet19 CNN model, supervened by the attention branch, besides delivering a 3%-4% performance boost of the baseline model. The cancer regions highlighted by the proposed model correlate well with the findings of an expert pathologist. The coalesced approach of unifying attention branch with the CNN model capacitates pathologists with augmented diagnostic interpretability of histological images with no detriment to state-of-art performance. The model's proficiency in pinpointing the region of interest is an added bonus that can lead to accurate clinical translation of deep learning models that underscore clinical decision support.

What factors are associated with improvements in productivity in clinical laboratories in the Asia Pacific Region?

INTRODUCTION

Clinical laboratories usually have a quality management system such as ISO 15189, which provides a framework for quality and competence to perform medical testing and internal systems such as audit and nonconformance to ensure consistent processes. However, organizations need to have access to internal procedures and external competitors' performance to improve their operations. These are often seen as commercial or areas where it is difficult to agree on an acceptable goal.

METHOD

In 2019, 1158 laboratories from 17 countries/regions in the Asia Pacific Region answered the survey, including 399 Chinese sites. The survey collected information on quality, turnaround time and productivity.

RESULTS

Median productivity for laboratories in the Asia Pacific Region not including Chinese sites was 25 samples/FTE/day for small laboratories (workload: <250 samples/day), 100 for medium-sized laboratories (workload: 251-1000 samples/day) and 220 for large laboratories (workload: >1001 samples/day). The parameters associated with increased productivity in some laboratories were automation, middleware, Lean Six Sigma quality improvement activities and International Accreditation.

CONCLUSION

This survey provides evidence of an association of quality improvement activities on laboratory productivity. There are differences in the effect of these activities in Chinese and non-Chinese laboratories in the Asia Pacific Region. The survey confirmed that the implementation of automation is associated with increased median productivity in all sites. Implementation of Lean Six Sigma and International Accreditation is associated with increased productivity in large laboratories.

Integrating quality control and external quality assurance

Effective management of clinical laboratories relies upon an understanding of Quality Control and External Quality Assurance principles. These processes, when applied effectively, reduce patient risk and drive quality improvement. In this Review, we will describe the purpose of QC and EQA and their role in identifying analytical and process error. The two concepts are linked, and we will illustrate that linkage. Some EQA providers offer far more than analytical surveillance. They facilitate training and education and extend quality improvement and identify areas where there is potential for patient harm into the pre-and post-analytical phases of the total testing process.

Quality management and accreditation in laboratory hematology: Perspectives from India

Quality management (QM), including quality assurance and quality control, was developed in clinical laboratories in North America and Western Europe, but must be implemented worldwide to ensure accurate, reproducible, and clinically useful results. India, a middle income country with a population of over 1.34 billion, has limited budget allotted to health care. As yet accreditation for clinical laboratories is not mandatory, which contributes to challenges in implementing good laboratory practice. This review provides a summary of internationally laid down QM principles and their application in a middle income country like India.