In the medical field, clinicians often need to quickly access and understand patient case notes to make informed decisions. The sheer volume of data in these notes can be overwhelming, especially when time is critical. This is where Large Language Models (LLMs) can play a transformative role by generating summaries at varying levels of detail. In this blog post, we will explore how to define and implement these levels of abstraction to meet clinicians’ needs at different stages of their decision-making processes.
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Understanding the Levels of Clinical Decision Support Systems (CDSS)
AI for healthcare holds immense value for improving the patient outcomes. One key factor in this is the Clinical Decision Support Systems (CDSS). But how does one integrate the AI tools into complex healthcare workflows?
The CDSS adoption in healthcare happens through a series of progresses levels incrementally. Each level signifies an increase in the level of automation and decision-making support offered to clinicians. Each level builds upon the previous, enhancing capabilities and ensuring a safe transition from human-driven to AI-assisted and ultimately autonomous clinical support.
For AI enthusiasts, EHR system builders, and healthcare professionals, understanding these levels is crucial for developing, implementing, and leveraging CDSS effectively. Let’s look into what these levels are.
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Cenacle Clinical decision support system uses NLP, AI and ML to help the Doctors make informed decisions about each patient accurately, as well as identify the population level trends in discovering the hidden patterns for personalized medicine.
Continue readingBlockchain based EHR
Cenacle built a secure Electronic Health Records system based on Blockchain Distributed Ledger technologies to offer secure data access, auditing and decision support analysis.
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