CAREFUL LLM ASSESSMENT AND MANAGEMENT SYSTEM
In healthcare CLAMS is a synergy between PRIDAR, BS 30440, and DCB0129/160, it reduces the risk of an LLM whilst making it fit for purpose.
Motivation's in Healtchare
The healthcare sector is undergoing a transformation, with AI-driven solutions like Large Language Models (LLMs) playing a pivotal role in enhancing patient care and streamlining administrative tasks. They offer the potential to reduce a clinicians's administration time by 75%. A good example is the use of LLM's in the creation of clinical summaries from video and audio recordings or meeting transcriptions.
1. Comprehensive LLM Testing & Deployment with CarefulAI:
CLAMS emphasises the importance of training data, fine-tuning, and continuous feedback clinical feedback loops. By utilising existing data with PII removed, LLMs are trained on relevant, real-world clinical data, ensuring their outputs are contextually appropriate. Prompt engineering , potentially combined to supervised fine-tuning and or retrieval augmented generation techniques is used to benchmark transformers e.g. OpenAI, Anthropic, Lama etc. This ensures the right language model is updated and responds with the latest knowledge. Continuous feedback loops, incorporating tools like Gradio /Prodigy etc are used in the testing phase. This ensures that any discrepancies or errors in the LLM outputs are immediately flagged and rectified. Deployment is enabled by customers existing framework providers, in-line with existing or updated Information and Data Governance measures e.g. DPIA's, and Privacy and ROPA Agreements.
2. PRIDAR - Ensuring Fairness and Reducing Bias:
PRIDAR is a UK government recognised good practice framework that enables identification and mitigation of biases in AI systems occasioned by how they are, or are intended to be, deployed. By integrating CALMS with PRIDAR healthcare institutions can ensure that their LLMs are free from any inherent biases, ensuring that the clinical meeting summaries produced are fair, accurate, and representative of the actual discussions.
3. BS 30440 and DCB0129/160 - Ensuring Safety and Compliance:
BS 30440 is a standard that provides guidelines for the safety aspects of AI applications in healthcare. DCB0129/160, on the other hand, emphasises the importance of clinical risk management in the deployment of health IT systems. By adhering to these standards, healthcare institutions can ensure that their LLMs are not only safe but also compliant with the necessary regulations. CLAMS ensures that the clinical meeting summaries produced are not only accurate but also adhere to the necessary safety and compliance standards.
4. Saving Clinicians Time
CLAMS ensures that LLMs are trained, fine-tuned, monitored, and compliant with the necessary standards, healthcare institutions can automate the process of producing clinical meeting summaries. This means that clinicians no longer have to spend hours transcribing and summarising their discussions. Or having to deal with poorly transcribed events and the clinical and IG risks associated with these transcriptions. Instead, they can rely on the LLMs to produce accurate, concise, and compliant meeting summaries, allowing them to focus on what they do best - providing care to their patients.
Conclusion:
The integration of CLAMS with standards like PRIDAR, BS 30440, and DCB0129/160 offers a promising solution for healthcare institutions looking to streamline their administrative tasks. By ensuring the safety, reliability, and compliance of LLMs, healthcare institutions can automate the production of GP meeting summaries, saving clinicians' time and enhancing patient care. LLM Models and systems created using CarefulAI's approach are each given a unique CLAMS number. If you are asked to deploy a LLM in your organisation. Do ask for its CLAMS number which we can confirm as being an object of conformity.
In healthcare CLAMS is a synergy between PRIDAR, BS 30440, and DCB0129/160, it reduces the risk of an LLM whilst making it fit for purpose.
Motivation's in Healtchare
The healthcare sector is undergoing a transformation, with AI-driven solutions like Large Language Models (LLMs) playing a pivotal role in enhancing patient care and streamlining administrative tasks. They offer the potential to reduce a clinicians's administration time by 75%. A good example is the use of LLM's in the creation of clinical summaries from video and audio recordings or meeting transcriptions.
1. Comprehensive LLM Testing & Deployment with CarefulAI:
CLAMS emphasises the importance of training data, fine-tuning, and continuous feedback clinical feedback loops. By utilising existing data with PII removed, LLMs are trained on relevant, real-world clinical data, ensuring their outputs are contextually appropriate. Prompt engineering , potentially combined to supervised fine-tuning and or retrieval augmented generation techniques is used to benchmark transformers e.g. OpenAI, Anthropic, Lama etc. This ensures the right language model is updated and responds with the latest knowledge. Continuous feedback loops, incorporating tools like Gradio /Prodigy etc are used in the testing phase. This ensures that any discrepancies or errors in the LLM outputs are immediately flagged and rectified. Deployment is enabled by customers existing framework providers, in-line with existing or updated Information and Data Governance measures e.g. DPIA's, and Privacy and ROPA Agreements.
2. PRIDAR - Ensuring Fairness and Reducing Bias:
PRIDAR is a UK government recognised good practice framework that enables identification and mitigation of biases in AI systems occasioned by how they are, or are intended to be, deployed. By integrating CALMS with PRIDAR healthcare institutions can ensure that their LLMs are free from any inherent biases, ensuring that the clinical meeting summaries produced are fair, accurate, and representative of the actual discussions.
3. BS 30440 and DCB0129/160 - Ensuring Safety and Compliance:
BS 30440 is a standard that provides guidelines for the safety aspects of AI applications in healthcare. DCB0129/160, on the other hand, emphasises the importance of clinical risk management in the deployment of health IT systems. By adhering to these standards, healthcare institutions can ensure that their LLMs are not only safe but also compliant with the necessary regulations. CLAMS ensures that the clinical meeting summaries produced are not only accurate but also adhere to the necessary safety and compliance standards.
4. Saving Clinicians Time
CLAMS ensures that LLMs are trained, fine-tuned, monitored, and compliant with the necessary standards, healthcare institutions can automate the process of producing clinical meeting summaries. This means that clinicians no longer have to spend hours transcribing and summarising their discussions. Or having to deal with poorly transcribed events and the clinical and IG risks associated with these transcriptions. Instead, they can rely on the LLMs to produce accurate, concise, and compliant meeting summaries, allowing them to focus on what they do best - providing care to their patients.
Conclusion:
The integration of CLAMS with standards like PRIDAR, BS 30440, and DCB0129/160 offers a promising solution for healthcare institutions looking to streamline their administrative tasks. By ensuring the safety, reliability, and compliance of LLMs, healthcare institutions can automate the production of GP meeting summaries, saving clinicians' time and enhancing patient care. LLM Models and systems created using CarefulAI's approach are each given a unique CLAMS number. If you are asked to deploy a LLM in your organisation. Do ask for its CLAMS number which we can confirm as being an object of conformity.
