Keyz Health Medical AI

Navigating the Maze of Multimorbidity: We Need a more Intelligent Approach

Imagine trying to create a personalized care plan for someone managing diabetes, heart disease, and kidney disease – all at once. It's not just about treating each condition separately. These conditions interact, medications can clash, and the sheer complexity can be overwhelming for both patients and healthcare providers. This is the reality of multimorbidity, and it demands a more sophisticated, holistic approach to care planning.

Enter metaquestioning.

Metaquestioning: Thinking About Our Thinking to Find Better Answers

At its heart, metaquestioning is simply thinking/asking questions about questions. It's like having a conversation with yourself about the best way to ask questions to uncover deeper insights. Instead of just jumping into problem-solving, we take a step back and ask:

• What questions should we be asking?

• Are we asking the right questions to solve this complex problem?

• How can we refine our questions to get even better information?

In complex situations, especially in healthcare, metaquestioning becomes incredibly powerful. It helps us:

• Uncover hidden assumptions: We might be making assumptions without realizing it, which can limit our search for solutions. Metaquestioning helps us identify and challenge these.

• Structure complex problems: It breaks down overwhelming problems into manageable, question-driven steps.

• Iteratively refine our approach: It allows us to learn from each step, adjust our questions, and get closer to effective solutions.

A key principle in effective questioning, especially when exploring vast information spaces, is to aim for low mutual information between questions, particularly in iterative searches. Low mutual information means that each question in a series should ideally explore a somewhat different aspect of the problem or search space, rather than simply repeating or slightly rephrasing the same question. This is crucial for comprehensive exploration and avoiding redundant information.

AI, Metaquestioning, and PubMed: A Powerful Trio for Multimorbidity Care

Now, let's bring in the exciting part: AI and Large Language Models (LLMs). These technologies are incredible at processing information and finding patterns. When combined with metaquestioning, they become even more powerful tools for tackling complex problems like multimorbidity care.

And where do we find the best information for healthcare? PubMed. This is a massive database of biomedical literature – think of it as the world's library for medical research. It's packed with studies, guidelines, and expert knowledge, but navigating it effectively can be challenging, especially when dealing with the complexities of multimorbidity.

Here's where the magic happens: We can program AI/LLMs to use metaquestioning strategies to intelligently search PubMed and help health professionals design better care plans. Instead of just querying PubMed at face value, the AI, guided by metaquestioning, asks itself a series of increasingly sophisticated questions to help direct its set of chosen queries, and deeply explore the evidence used to tailor to a specific patient's needs.

A Guided Tour of Our AI Prompts: A General Agenda

To show you how this works in practice, let's give a general overview of the agenda behind the prompts we use to guide our AI/LLM. Think of these prompts as a structured conversation we're having with the AI, teaching it how to think like an expert nurse or medical researcher when designing a multimorbidity care plan using PubMed.

We use a framework called Meta-Layered Chain of Thought (ML-CoT). Think of "layers" as levels of questioning, each building upon the previous one to get deeper insights.

Our prompts are designed to guide the AI through a structured process, broadly covering these key agendas:

• Initial Metaquestioning Prompt: This prompt sets the stage for the entire process. Its agenda is to have the AI:

  • Assess the complexity of a given patient case and the available resources in PubMed.

  • Generate a hierarchy of questions across three meta-layers (First-Order, Second-Order, Third-Order) to guide a PubMed search for evidence-based care interventions.

  • Develop initial search queries for PubMed, designed to be iterative and progressively focused.

  • Establish a framework for reasoning and synthesizing information from PubMed to inform care plan development.

• Research Buddy Prompt: This prompt guides a separate AI agent, the "Research Buddy," to analyze individual PubMed papers retrieved by the Metaquestioning Agent. Its agenda is to:

  • Systematically analyze each PubMed paper to extract core information, relevant care intervention details, and insights related to condition interactions and multimorbidity.

  • Critically assess the applicability of each paper's findings to the specific patient case and home care context.

  • Summarize key takeaways from each paper, focusing on actionable care plan components and multimorbidity considerations, to inform the Metaquestioning Agent.

• Iterative Metaquestioning Prompt: This prompt is used in subsequent iterations, building upon the initial pass. Its agenda is to:

  • Re-assess the care planning context in light of newly retrieved PubMed papers and insights.

  • Refine the question hierarchy to focus on integrating and refining care interventions based on the new evidence.

  • Generate targeted search queries to further explore specific aspects of care plan components and implementation strategies.

  • Update the reasoning framework to reflect the iterative nature of the process and synthesize information across multiple passes.

By using these prompts in sequence, we create a workflow that leverages metaquestioning and AI to effectively navigate PubMed and aid health care professionals reason deeper when designing evidence-based, personalized care plans for individuals with multimorbidity. This iterative and layered approach ensures thoroughness, adaptability, and a focus on the complex interactions inherent in managing multiple health conditions.

Benefits of Metaquestioning in AI-Driven Multimorbidity Care Planning

By using metaquestioning with AI and PubMed, we can achieve:

• More Personalized and Effective Care Plans: The iterative questioning and deep PubMed exploration lead to care plans that are truly tailored to the individual patient's needs and the best available evidence.

• Improved Evidence-Based Practice: Metaquestioning ensures that care plans are grounded in the strongest possible evidence from PubMed, rather than relying on assumptions or outdated practices.

• Enhanced AI Reasoning and Problem-Solving: The metaquestioning framework guides the AI to think more strategically and effectively when tackling complex healthcare challenges.

• Holistic and Integrated Care: By explicitly focusing on condition interactions and integrated care models, metaquestioning helps create care plans that address the whole person, not just individual diseases.

• Better Navigation of PubMed: Metaquestioning provides a structured and efficient way to navigate the vastness of PubMed and extract the most relevant information.

• Comprehensive Exploration through Low Mutual Information: By emphasizing low mutual information in our iterative questioning and search strategies, we ensure a broader and more diverse exploration of the available evidence, minimizing redundancy and maximizing information gain.

Conclusion: A Smarter Future for Multimorbidity Care

Metaquestioning, combined with the power of AI and the wealth of knowledge in PubMed, represents a significant step forward in designing care plans for individuals with multimorbidity. It's about moving beyond simple solutions and embracing a more thoughtful, iterative, and evidence-driven approach. By guiding our AI to ask smarter questions, we can unlock a future where healthcare is more personalized, effective, and truly addresses the complexities of managing multiple health conditions. This is just the beginning of an exciting journey towards smarter, AI-assisted healthcare!