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Insightful answers to the question. The best answer is from Swapnil Madhav Chaukar. Well done! Answer from R Rajesh is also a recommended read. My 2 cents - The AI solution needs to be made a process document and like we revise process documents (e.g. - SIPOC, process maps, SOPs etc) after every process change, we need to revise the AI solution after every change!!

May I respectfully suggest these questions be answered from an "AI Solution Architect" persona rather than a MBB Here are the AI/ML techniques I would employ into the Agent, the implementation steps, evaluation strategy and ethical considerations AI|ML Techniques: Reinforcement Learning: The Agent can learn actions through trial and error, guided by human feedback as reward or penalties Active Learning: This involves selecting the most informative data points for human feedback, which will minimize the amount of labeled data needed while also maximizing learning efficiency. Human-in-the-loop (HITL): This integrates human feedback directly into the learning process, allowing for real-time adjustments and improvements. The Agent can capture all the human feedbacks and add it to the KB data, keeping it up to date and relevant. Transfer Learning: This technique adapts to changes without having to retrain from LLM scratch. It leverages the pre-trained models but by also fine-tuning them with new data and feedback. An interface, like a web browser, where people can provide their updates and the Agent will append the updated data into the KB file or database. The Agent will always pull from that updated KB, thus not becoming obsolete and misaligned.

If medical coders or auditors recognize any persistent issues, or if Business Excellence teams alter workflows (for instance, by modifying DRG validation checks or POA flagging protocols), those insights are frequently confined to process documentation, unless they are explicitly reintegrated into AI retraining or rule modifications. How to Ensure AI Evolves with the Process in Medical Coding - Practicality of Integrating AI into Improvement Cycles This is achievable only if AI is regarded as an ongoing participant in the process instead of a fixed tool. In Medical Coding, regular coding audits, patterns of errors, and feedback loops from inquiries can be systematically gathered. Feedback Mechanism utilize organized, documented, and classified AI error logs, accompanied by established retraining timelines and configuration modifications. MBB Role Act as stewards for AI process alignment, oversee AI feedback cycles, spearhead AI-impact PDSA/DMAIC initiatives, and promote governance for enhancements in AI.

When we talk about Continuous improvement, as a concept, we refer to a constant WIP mode of innovation, enhancement and incremental progress. Take all possible learnings and loop it back an input to further refine a product or process. VOC, VOB, error types, new data or new pattern or behavior of a certain process or a machine that can be studied, performance monitoring. Getting RCAs. Feeding it back into the system and closing the loop of a continuous self-learning improvement process with the help of Artificial Intelligence. Natural language programing Reinforcement Learning : AI agents can learn by interacting with end users and learning best practices from online forums and receiving feedback (rewards or penalties). Over a period of time, AI models or AI agents can improve their decision-making based on outcomes. Example: AI in customer service optimizing responses based on customer satisfaction scores. Also, in an AI agent environment if a customer rates a low score or not resolved on a survey. AI can ask customer if he or she wants to get redirected to additional support Utilize online libraries: System based or conventional training methods have a focused content and is periodically reviewed once or twice a year. Unlike traditional models trained once on a fixed dataset, online learning allows models to update continuously as new data arrives. Can prove to be extremely useful in dynamic environments like fraud detection system that can improve itself whenever a new fraud pattern emerges or email classification or query categorization. Optimized Human-in-the-Loop (HITL): AI backend can incorporate VOC of output or a human feedback to refine and improve performance. On a continuous basis which a key component of continuous improvement For example, customer service agents correcting AI-generated email drafts helps the model learn better phrasing, grammar, formatting, and tone. Use concepts like A/B Testing and Feedback Loops: A tried and tested AI system can test different strategies (e.g., email templates) and learn which ones perform better. Manual or online VOC and Feedback loops help the system adapt to changing customer behavior or business goals. e.g. In a Banking Email Customer Service Context: AI can learn from: VOC (NPS scores, complaints and RCA) Agent corrections to AI-suggested replies, check of all queries are answered in a multi query email. Frequency and Escalation patterns (e.g., which types of queries lead to dissatisfaction) Compliance checks (to avoid regulatory violations) Though there will be challenges to guarantee a continuous improvement on AI based models, if we study it enough it can be overcome. Challenges like Propagation of systematic Bias. For e.g. an AI model might be more favorable to a certain type of machine or high-performance shift timing, or certain region in terms of Sales etc. Distribution or pattern shift. Or drifting of parameters, Real world the situation changes dynamically so AI will have to be trained to Adapt. Failing which it will follow a fixed pattern and might not necessarily be effective. In manufacturing or Healthcare sector or if we speak from a Six sigma perspective AI can Conduct SPC if we feed it in initial stage. Analyze process deviations If we find some points or processes out of control, we will implement solutions to get the process in control, AI agent can learn from such corrective actions It can also Suggest process changes to reduce defects depending upon previous corrective actions taken by us or information available online. Would be better poised to predict process output or future failures or improvement opportunities