The Huge Untapped Data Opportunity

• Across hospitals globally, the majority of clinical data remains unstructured: physician notes, narrative reports, imaging, etc.
• Estimates suggest that ~ 80% of healthcare data is unstructured, much of it never fully leveraged for research or care improvement. [1]
• Even among structured vs. unstructured clinical trial eligibility criteria, unstructured text is essential for resolving many criteria—for example, ~ 59-77% of eligibility requirements in some trials cannot be resolved without narrative clinical content. [2]

So the raw material is there. What’s been holding back adoption of NLP and automated extraction?

Key Barriers: Control, Configurability, Scalability & Overhead

• ‍Model Output Control: Clinicians demand transparency & oversight; black-box results reduce trust.
• ‍Site Specificity & Configurability: Every hospital uses different terminologies, abbreviations, documentation styles; a generic model often fails in local settings.
• ‍Compute & IT Overhead: Heavy infrastructure, integration burdens, and maintenance slow deployment and increase cost.
• ‍Validation & Retraining Lag: Without mechanisms for expert feedback and automatic model updates, NLP performance degrades or remains sub-optimal.
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Our Solution: HITL + Auto-Retraining + Lightweight Infrastructure

LynxCare’s approach (Rapid Expert Refinement™) addresses these directly:

1. ‍Processing – NLP models extract structured data from notes.
2. Validation & Control – Clinicians or experts review outputs via dashboards; they flag errors.
3. Annotation & Configurability – Corrections capture local style; the system is configured per site/specialty.
4. Auto-Retraining – Models retrain automatically on corrected outputs, continuously improving.
5. Monitoring – Performance dashboards alert when performance drifts.

This creates a system that is trustworthy, adaptable, and scalable with minimal infrastructure strain.

Evidence That the Numbers Support This

• In one landmark study with >216,000 hospitalized adult patients, deep learning models using both structured data and clinical notes reached high performance (AUROC ~0.93–0.94 for in-hospital mortality) across centers without needing extensive site-level harmonization. [3]
• The same study shows the volume of data: in that case, over 46 billion data points including clinical notes. [4]

These examples underscore that when you combine structured + unstructured data, with robust model design and oversight, you get much more powerful, generalizable insights.

Why This Matters for Healthcare‍

• More granular, actionable insights — capturing what matters in narrative text (symptoms, social determinants, clinical reasoning).
• Better patient care & research — eligibility, adverse event detection, early warning, etc.
• Scalable across hospitals — if your NLP solution is configurable and retrains automatically, you avoid one-off manual fixes per site.
• Lower overhead — no need for massive compute infrastructures or long IT projects; optimized algorithms + cloud or light local resources + expert feedback loops suffice.
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The Call to Action

Healthcare institutions cannot afford to let this data remain unused. By combining human oversight, automatic retraining, and efficiency in infrastructure, we can unlock the value of clinical data for better patient outcomes.

Want to see what this looks like in practice? We’re happy to demo a site-specific deployment and show how it improves accuracy, reduces validation time, and stays under control.

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[1] Managing Unstructured Big Data in Healthcare System - PMC

[2] arXiv:1502.04049 [cs.CY]

[3] Rajkomar, A., Oren, E., Chen, K. et al. Scalable and accurate deep learning with electronic health records. npj Digital Med1, 18 (2018). https://doi.org/10.1038/s41746-018-0029-1

[4] Rajkomar, A., Oren, E., Chen, K. et al. Scalable and accurate deep learning with electronic health records. npj Digital Med1, 18 (2018). https://doi.org/10.1038/s41746-018-0029-1