Industry Context and Challenges
Clinical Trial Failure & Delays: ~85% of clinical trials face delays, primarily due to slow patient recruitment, which massively inflates costs and delays revenue generation.
Data Fragmentation & Quality: Scientific, clinical, and commercial data are locked in siloes across legacy systems, preventing the holistic, real-time insights required for AI model training.
AI Led Digital Transformation Challenges: Numerous pilots, lack of overarching digital strategy and implementations, lack of an aligned Strategic Workforce Planning (SWP) and change management / governance lead to only partial realization of benefits
LS-SACS Approach and Services
LS-SACS has introduced a comprehensive set of methodologies to ensure small, medium and large life sciences organizations realize the true benefits of Ai-led digital transformation in a timely manner.
Holistic Ai-led Digital Clinical Transformation Framework
Our digital clinical transformation approach is a comprehensive and rapid approach that guides the organization from a "best-in-class" strategy through a maturity assessment, business case development and finally a phased implementation roadmap.
Ai-led Digital Clinical Maturity Assessment (DCMA)
DCMA evaluates a company's AI driven digital clinical maturity across 10 core parameters that include strategy, phased implementation, operating model, strategic workforce planning (SWP) and risk / compliance. This allows companies to see their own current status (staff and external benchmarking), identify gaps and develop a customized and phased implementation plan for tangible benefits.
Roll-out and Expected Benefits
Holistic Ai-led digital clinical transformation approach can allow clinical operations to develop the optimal business case as well as an effective roll out / phased implementation plan
Selection of AI Pilots and Initiatives
Most functional areas within life sciences offer opportunities for leveraging AI, but to varying extents. AI and functional leaders need to select pilots / POCs or full initiatives using some of the following key criteria:
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Overall Enterprise AI strategy and desired outcomes (KPIs)
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Complexity of the process (e.g., manufacturing Vs regulatory submissions)
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Ease of implementation
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Availability of hybrid talent (AI and domain)
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Availability of established products, tools, libraries etc.,
Optimal order of digital clinical initiatives roll out
Estimated cost savings by trial process due to AI