AI-Powered Stakeholder Communication
Towards Personalized Explanations for Health Simulations: A Mixed-Methods Framework for Stakeholder-Centric Summarization
This paper introduces a groundbreaking framework for translating complex simulation models into personalized, easily understandable summaries for diverse stakeholders. By leveraging Large Language Models (LLMs) tuned to specific audience needs—from clinicians to policymakers—this approach dismantles barriers to adoption, builds trust in data-driven decisions, and unlocks the full ROI of sophisticated modeling and simulation investments.
Executive Impact Analysis
Implementing a stakeholder-centric summarization framework moves beyond static dashboards to deliver hyper-relevant insights, accelerating decision-making and boosting the adoption of critical data models across the enterprise.
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Increase in Stakeholder Engagement
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Model Adoption & Trust Lift
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Faster Decision Velocity
Deep Analysis & Enterprise Applications
Explore the core components of the proposed framework, from contrasting traditional and modern approaches to understanding the iterative process for achieving personalized communication at scale.
In healthcare, this framework can translate complex epidemiological models for public health officials, explain treatment efficacy simulations to clinicians, and communicate patient journey models to hospital administrators, ensuring everyone acts on the same trusted data but with insights relevant to their role.
For risk and compliance, the framework can be adapted to explain complex financial stress-test simulations to regulators, summarize supply chain vulnerability models for executives, and detail cybersecurity threat models for board members, all in a language and style that drives action.
At its core, this research provides a blueprint for any organization needing to communicate complex data. It enables data science teams to become powerful storytellers, building bridges to business units and ensuring that investments in data modeling yield tangible, understood, and actionable results across the enterprise.
The Communication Chasm: Generic vs. Tailored
The research highlights a fundamental failure of traditional data communication: a 'one-size-fits-all' report serves no one well. The proposed framework directly addresses this by personalizing both the content and style of explanations.
| Feature | Legacy Approach (One-Size-Fits-All) | Stakeholder-Centric AI Framework |
|---|---|---|
| Audience | Assumes a single, generic, technical user. | Segmented by role, expertise, and needs (e.g., executive, clinical, patient). |
| Content | Overloads with raw data, statistics, and tables. | Delivers role-relevant insights, key takeaways, and actionable next steps. |
| Style | Dry, technical, and jargon-heavy. | Adapts tone: executive summaries, empathetic narratives, or clinical workflows. |
| Business Outcome |
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Enterprise Process Flow
1. Elicit Stakeholder Needs
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2. Decompose & Structure Model
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3. Generate Tailored Summaries via LLM
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4. Evaluate, Tune & Iterate
Core Technology: Preference Tuning
The framework's success hinges on aligning the LLM's output with human preferences. Using techniques like Direct Preference Optimization (DPO), the system learns from stakeholder feedback to refine its summaries, moving beyond simple accuracy to achieve genuine relevance and resonance.
95% Target Alignment with Stakeholder NeedsUse Case: Hospital Layout Optimization
A simulation model analyzes patient flow for a new hospital wing. Instead of a single dense report, the framework generates tailored outputs:
For the Hospital Administrator: An executive summary with bullet points on throughput metrics, staffing needs, and projected ROI.
For the Clinician: A workflow-oriented summary highlighting changes to ICU-to-ER proximity, average patient transit times, and operational logic.
For the Patient Advocate: An empathetic narrative describing a patient's journey, focusing on ease of navigation, accessibility, and reduced wait times.
The result is faster consensus and a final design that meets diverse stakeholder objectives because the data was communicated effectively to each group.
Calculate Your Personalization ROI
Estimate the potential annual savings by automating tailored report generation for your key stakeholders, reclaiming hours spent on manual summarization and accelerating data-driven decisions.
Estimated Annual Savings
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Productivity Hours Reclaimed
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Your Implementation Roadmap
Deploying a stakeholder-centric communication framework is a phased process, moving from initial discovery to a scalable, enterprise-wide solution.
Phase 1: Stakeholder Discovery & Needs Analysis
Identify key stakeholder groups and use a mix of surveys and interviews to map their specific informational needs, stylistic preferences, and decision-making workflows.
Phase 2: Tailored Generation Pilot
Select a high-value simulation model. Decompose its structure and outputs, and configure an LLM to generate initial tailored summaries for each identified stakeholder group.
Phase 3: Feedback & Optimization Loop
Deploy the pilot summaries and systematically collect feedback on their clarity, relevance, and utility. Use this data to fine-tune the LLM via preference optimization techniques.
Phase 4: Enterprise Rollout & Scaling
Develop a scalable architecture and governance model to extend the framework across other critical business models, establishing a new standard for data communication.
Unlock the Value of Your Data Models
Stop letting valuable insights get lost in translation. Let's discuss how a stakeholder-centric AI framework can build trust, accelerate decisions, and maximize the return on your data science investments.
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