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Total lab automation and digital transformation – Keys to SDG 2030 agenda

The United Nations 2030 Agenda of the Sustainable Development Goals (SDG) cover economic, social, and environmental dimensions of development. In this context, healthcare and diagnostic sectors translate this SDG global agenda into development plans and policies through innovation in laboratory processes implementing automation, integration, digital transformation, and carbon foot print reduction.

Total lab automation (TLA) is combining sustainable diagnostic innovation in laboratory medicine with quality healthcare, through automation, activity-based costing, LEAN design, and carbon footprint reduction through green lab concept. Rapid changes in diagnostic sector, coupled with parallel advances in laboratory automation and digital transformation technology in diagnostic platforms, have stimulated the evolution of approaches for artificial intelligence (AI) and robotic elements in routine laboratory process flow. Laboratory processes are streamlined to ensure provision of reliable and timely test results, appropriate alliance with brain-to-brain loop, thus enhancing quality of care and patient safety. The implementation of middleware, assisted clinical decision making, and adoption of paperless workflows are instrumental in the transformation of the laboratory, more specifically, influencing clinical validation, procedure efficiency, data handling, data analysis, and much more. AI helps in computing risk stratification score of laboratory data and clinical data, using expert system and evidence based guidelines. Increasing cost-containment pressures make the application of this technology highly approachable.

The concept of Lundberg loop, commonly known as the brain-to-brain loop for laboratory testing originates from the brain of the primary care physician who is involved in selecting the laboratory tests, and ends in the final reporting of the test result to the ordering physician. Basically, there are pre-pre examination, pre-examination, examination, and post-examination and post-post examination steps involved in the total process. Total lab automation and digitization of total testing process brings process excellence in the laboratory work flow.

There is a strong need to create a sustained technology policy and supply chain policy framework through a thrust on innovation and allocation of resources for fast-pacing the development of the IVD sector. We also need to bridge the supply–demand gap by digitization. The challenges during Covid pandemic suggest focusing on the opportunities and bridging the supply–demand gap. Involvements require different level of investments – both short-term measures and long-term measures for incremental improvements in diagnostic care domain.

Hospitals can benefit from digital and technology transformation journey, both operationally and clinically. Total lab automation and digitization can help our hospital and laboratory deliver greater outcomes, increase stakeholder collaboration, and enhance communication between lab and administration. Timely reporting of diagnostic test results to clinicians and all stakeholders is essential for effective disease and public health management. The Smart Core Laboratory in Kokilaben Dhirubhai Ambani Hospital & Medical Research Institute is focused on delivering results in the most efficient way, in terms of quality, cost, speed, and paperless digital transformation.

Artificial Intelligence (AI) helps in computing risk stratification score of laboratory data and clinical data, using expert system and evidence based guidelines. The unique clinical decision support solution can help you standardize care in your hospitals with a combination of informatics and change management.

For example, we are collaborating with Koita Centre for Digital Health (KCDH) of IIT Bombay on two digital health projects for clinical decision support.

Thus, implementation of digital transformation with TLA in the laboratory improves revenues, suggests patient specific next steps, tests utilization, improves quality, standardizes treatment protocols as per local and international guidelines, improves patient satisfaction, provides patient-specific interpretation and next steps, improves standardized care by flagging patients, applies risk algorithms, and provides better interpretation.

The author is also Chair, Association for Diagnostics & Laboratory Medicine (formerly American Association of Clinical Chemistry) India Section. 

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