Data Activation: Bridging the Gap Between Raw Information and LLM Capabilities

The Evolving Value of Data in the AI Era

Traditional views of data as a defensible moat have fundamentally changed. In 2019, Andreessen Horowitz noted in The Empty Promise of Data Moats that data advantages erode as datasets grow and competitors catch up. This observation remains relevant today. Simply possessing proprietary data is no longer sufficient. The critical differentiator lies in data activation: converting raw information into formats that large language models can effectively utilize to develop new capabilities.

Healthcare: A Critical Frontier for Data Activation

Recent developments highlight healthcare as a primary testing ground for data activation. According to OpenAI's January 2026 report:

• Over 5% of global ChatGPT interactions involve healthcare topics
• 25% of weekly users seek health-related information
• 40+ million people use ChatGPT daily for medical guidance

Major AI labs are responding rapidly. During a single week in January 2026:

1. OpenAI launched ChatGPT for Healthcare with institutional partners like Cedars-Sinai and Stanford Medicine
2. Anthropic introduced Claude for Healthcare featuring HIPAA-compliant infrastructure and medical database integrations

Despite this activity, OpenRouter data indicates healthcare remains the most fragmented domain, revealing both the complexity of medical data and limitations of general-purpose models.

Practical Approaches to Data Activation

Structured Reasoning Scaffolds

The Tables2Traces research demonstrates how transforming tabular patient data into contrastive reasoning traces boosts LLM performance. Their methodology:

1. For each patient record, identify similar patients with divergent outcomes (e.g., one survived, one deceased)
2. Use advanced LLMs to generate explanations for these outcome differences
3. Convert these explanations into training data for specialized models

Results showed 17%+ accuracy gains on MedQA evaluations. Crucially, naive table-to-text conversion reduced performance, proving the necessity of structured reasoning frameworks. This approach effectively unlocks the "potential energy" within medical data by creating cognitive scaffolds mirroring clinical decision-making.

Knowledge Graph Integration

EHR-R1 developed an alternative method called the thinking-graph pipeline:

1. Extract medical entities from longitudinal EHRs including unstructured notes
2. Quantify associations between entities
3. Map entities to UMLS medical ontologies
4. Use graph searches to identify relevant medical relationships
5. Generate reasoning chains using LLMs guided by these relationships

Their system achieved 30+ point improvements over GPT-4o on the EHR-Bench benchmark. Smaller fine-tuned models (8B parameters) reached 89.3% accuracy at 1/85th the cost of larger teacher models.

Persistent Challenges

Three significant hurdles remain:

1. Verification Gap: Synthetic traces lack clinical validation. Physicians consistently rate generated medical reasoning as inadequate

2. Faithfulness Problem: Traces often misrepresent actual model decision processes, creating inconsistency between explanations and outputs

3. Scalability Limits: Current methods show diminishing returns when applied to state-of-the-art models. Improvement demonstrations focus primarily on mid-tier systems

As Tables2Traces co-author Dr. Li noted: "We've proven the energy exists behind the dam. Now we must engineer better turbines to harness it."

Future Directions

The research community is exploring diverse activation methods:

• Reinforcement learning for longitudinal data sequencing
• Temporal modeling of patient journey patterns
• Hybrid symbolic-neural approaches

Healthcare's historically fragmented infrastructure may paradoxically accelerate progress by reducing legacy system constraints. Data activation represents more than technical optimization—it's becoming the core competency for organizations leveraging AI. The organizations that master transforming proprietary data into LLM-digestible insights will lead their respective fields.

Reference: Original analysis builds on Tables2Traces (arXiv:2501.07891) and EHR-R1 (arXiv:2503.11402) research.