Total Lab Automation with digital transformation journey: Instrumental in the transformation for diagnostic ecosystem

Total Lab Automation with digital transformation journey: Instrumental in the transformation for diagnostic ecosystem


The implementation of Total Lab Automation (TLA), Artificial Intelligence (AI), Cloud Computing, Machine Learning and the adoption of paperless workflows are instrumental in the transformation of the laboratory, specifically clinical validation, procedural efficiency, data handling, data analysis and much more. AI assists in calculating the risk stratification score from laboratory and clinical data using expert systems and evidence-based guidelines. Increasing cost containment pressures make the application of this technology highly accessible.

Rapid changes in the diagnostic sector together with parallel advances in TLA and digital transformation technology in diagnostic platforms have stimulated the evolution of approaches for Artificial Intelligence (AI) and robotic elements in the routine laboratory process flow. Laboratory processes must be streamlined to ensure the provision of reliable and timely test results and an appropriate alliance with the brain-to-brain loop, thereby improving the quality of care and patient safety.

The new laboratory strategy combines revenue generation with quality healthcare, through automation and artificial intelligence. The concept of the Lundberg loop, commonly known as the brain-to-brain loop, for laboratory testing originates from the brain of the primary care physician involved in the selection of the laboratory tests and culminates in the final reporting of the test result to the order doctor. Basically, there are pre-pre-exam, pre-exam, exam, post-exam and post-post-exam steps involved in the total process. Total laboratory automation and digitization of the total testing process brings process excellence to laboratory workflows.

There is a strong need to create a sustained technology policy and supply chain policy framework through a drive on innovation and the allocation of resources for the rapid development of the IVD sector. A strong diagnostic technology model must be formulated that will ensure that technologically advanced health care is available to all sections of the population.

We also need to bridge the gap between supply and demand through digitalisation. The challenges during the first and second wave of the Covid pandemic suggest that we focus on the opportunities and bridge the demand-supply gap. Engagement requires different levels of investment, both short-term and long-term, for incremental improvements in the diagnostic care domain.

Diagnosticians, laboratory personnel, and other key stakeholder groups in the laboratory, such as administrators, supply chain professionals, distributors, and the IVD industry, have responded to the challenge with initiatives to standardize and harmonize inventory management, resource allocation, cost of ownership, and codification using of digital technology to promote supply chain efficiency during this pandemic time.

Mainly three key points, such as codification, standardization and global harmonization, have helped to build efficiency, in relation to supply chain. Effectiveness depends on commercial excellence, demand management and compliance management systems to mitigate the impact of supply chain shortages. Digitization has helped us plug many parts that leak in the supply chain, and ultimately it has helped build trust with our patients.

The digital and technology transformation journey with total lab automation in diagnostic platforms has helped us achieve the following:

  • Reduction of time traps
  • Elimination of staff movement
  • Elimination of unnecessary transportation
  • Elimination of non-value-added (NVA) steps
  • Verify the Business Value Added (BVA) steps
  • Focus on Value Added (VA) steps
  • Reduction of complexity in workflow process
  • Decrease in error rates
  • Reduce the number of repetitions
  • Improving turnaround time (TAT)
  • Cost savings
  • Improved operational productivity and confidence, leading to customer satisfaction

Effectively managing these increasing pressures is critical to the sustainability of laboratory management, and requires new strategies to reduce the cost burden while maintaining quality. Increasing cost containment pressures make this technology highly accessible.

Hospitals can benefit from a digital and technology transformation journey, both operationally and clinically. Laboratory automation and digitization can help hospitals and laboratories deliver greater outcomes, increase stakeholder collaboration, and improve communication between the laboratory and administration. Timely reporting of diagnostic test results to clinicians and all stakeholders is essential for effective disease and public health management.

Artificial Intelligence (AI) helps calculate the risk stratification score from laboratory data and clinical data using expert systems and evidence-based guidelines. The unique Clinical Decision Support solution will help one standardize care in hospitals with a combination of informatics and change management.

For example, I am working with the Koita Center for Digital Health (KCDH) of IIT Bombay on two digital health projects for clinical decision support:

I) Implementation of a warning system for early diagnosis of acute kidney injury

II) Machine learning-based models and web-based tools to predict the mild, moderate and severe cases of Covid-19 based on routine laboratory biomarkers.

Thus, implementing digital transformation with TLA in the laboratory improves revenue, suggests patient-specific next steps, test utilization, improves quality, standardizes treatment protocols according to local and international guidelines, improves patient satisfaction, provides patient-specific interpretation and next steps, improves standardized care by tagging patients , apply risk algorithms and provide better interpretation.

Views expressed by Dr Barnali DasPrincipal Consultant, Kokilaben Dhirubhai Ambani Hospital and Medical Research Institute, Mumbai


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