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RESEARCH PAPER
Systems and Methods for Judgment-Calibrated Longitudinal Patient Narrative Intelligence in AI-Driven Healthcare
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TECHNICAL FIELD

The present disclosure relates generally to artificial intelligence systems in healthcare, life sciences, and public health, and more particularly to human-centered AI systems for clinical decision support, safety monitoring, and regulatory oversight. Specifically, the disclosure addresses systems and methods for integrating longitudinal patient narrative intelligence with clinician judgment calibration, enabling AI systems to align with human risk interpretation while preserving defensible clinical decision-making.

BACKGROUND AND PROBLEM STATEMENT

Current State of Healthcare AI

Modern healthcare AI systems are optimized for:

  • Pattern detection

  • Risk prediction

  • Alert generation

  • Statistical signal identification

​

These systems typically operate on event-level data, producing outputs such as:

  • Risk scores

  • Severity flags

  • Confidence metrics

  • Alerts and recommendations

​

However, clinical decision-making does not operate on isolated events or probabilities. Instead, clinicians reason through longitudinal patient trajectories, weighing evolving evidence against contextual risk tolerance and responsibility.

Structural Limitations of Existing Systems

Existing systems suffer from two fundamental, unresolved limitations:

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Limitation 1 — Event-Centric AI Without Narrative Understanding

AI systems fail to synthesize patient data into coherent, evolving clinical narratives. As a result:

  • Clinicians manually reconstruct patient histories

  • Trajectory shifts are detected late

  • Contextual causality is obscured

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Limitation 2 — Static AI Confidence Without Judgment Alignment

AI systems present confidence and urgency uniformly, without learning how different clinicians interpret and act on risk. Consequently:

  • Alerts are overridden silently

  • Trust erosion occurs invisibly

  • AI outputs are ignored rather than improved

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These limitations create a human–AI cognition gap, resulting in poor adoption, increased safety risk, and lack of regulatory defensibility.

SUMMARY OF THE INVENTION

The present disclosure introduces a unified system that combines:

​

  1. Longitudinal Patient Narrative Intelligence (LPNI) — enabling continuous synthesis of patient data into evolving clinical narratives; and

  2. Clinical Judgment Calibration (CJC) — enabling AI systems to learn how human users interpret risk and adapt AI behaviour accordingly.

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Together, these components form a Judgment-Calibrated Narrative Intelligence System (JCNIS).

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The system enables:

​

  • Narrative-driven AI reasoning

  • Context-aware risk interpretation

  • Adaptive AI recommendations

  • Preservation of human clinical judgment

  • Defensible, auditable decision-making

SYSTEM OVERVIEW

High-Level Architecture​

Patient Data Streams
(labs, meds, AEs, notes, devices)
        ↓
Longitudinal Narrative Intelligence Engine
  - Baseline modeling
  - Trajectory detection
  - Inflection point identification
        ↓
Narrative State Representation
        ↓
Judgment Calibration Layer
  - User risk tolerance modeling
  - Context sensitivity
  - Historical decision patterns
        ↓
Adapted AI Recommendation
        ↓
Human Clinical Decision
        ↓
Feedback & Learning Loop

DETAILED DESCRIPTION OF SYSTEM COMPONENTS

1. Longitudinal Patient Narrative Intelligence Engine

​

The narrative intelligence engine is configured to:

  • Aggregate heterogeneous patient data across time

  • Identify baseline states and deviations

  • Detect trajectory shifts and inflexion points

  • Establish temporal relationships between events

​

Unlike traditional summarisation, the engine produces a dynamic narrative state representing:

  • What has changed

  • When it changed

  • Why it matters clinically

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This narrative state serves as the primary input for downstream decision support.

2. Narrative State Representation

The narrative state includes:

  • Temporal progression descriptors

  • Causal linkage hypotheses

  • Confidence bands around interpretation

  • Supporting evidence references

​

This representation allows AI reasoning to operate on clinical meaning, not raw data.

3. Clinical Judgment Calibration Layer

The judgment calibration layer is configured to:

  • Observe human responses to AI recommendations

  • Capture acceptance, modification, or override actions

  • Learn implicit risk tolerance patterns

  • Adjust AI framing, urgency, and recommendation thresholds

​

Calibration signals may include:

  • Decision timing

  • Override frequency

  • Rationale categories

  • Contextual modifiers (trial phase, patient severity)

4. Adapted Recommendation Generation

Based on the narrative state and calibrated judgment profile, the system dynamically adjusts:

  • Recommendation urgency

  • Confidence language

  • Evidence depth

  • Escalation behavior

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The AI output is therefore aligned with how the user historically judges risk, without reducing safety constraints.

EXEMPLARY USE CASES

Use Case 1 — Pharmacovigilance Safety Review

The system synthesizes patient-level narratives across reported adverse events, identifies meaningful trajectory shifts, and calibrates escalation behavior to reviewer judgment patterns, reducing alert fatigue while preserving regulatory defensibility.

Use Case 2 — Clinical Trial Monitoring

The system contextualizes deviations within expected trial trajectories and adapts AI scrutiny based on trial phase and medical monitor behavior, enabling earlier detection of true safety risks.

Use Case 3 — Chronic Disease Management

The system presents evolving patient stories rather than fragmented visits, while adapting recommendations to clinician risk tolerance and experience level.

ADVANTAGES OVER PRIOR ART

The disclosed system provides several technical advantages:

  1. Transforms AI reasoning from event-based to narrative-based

  2. Aligns AI behavior with human judgment patterns

  3. Reduces cognitive reconstruction burden

  4. Improves trust and adoption

  5. Preserves clinical accountability

  6. Enables defensible AI-assisted decisions

​

No existing system integrates longitudinal narrative intelligence with judgment calibration as a unified decision-support architecture.

IMPLEMENTATION CONSIDERATIONS

The system may be implemented as:

  • A standalone clinical decision support platform

  • A middleware layer over existing AI models

  • A regulatory-grade audit and safety system

  • A public-health decision intelligence platform

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The architecture is model-agnostic and compatible with rule-based, statistical, and machine-learning models.

POTENTIAL CLAIM DIRECTIONS (NON-LIMITING)

Future patent claims may include:

  • Methods for generating longitudinal narrative states

  • Systems for calibrating AI recommendations to human judgment

  • Feedback loops linking decision behavior to AI adaptation

  • Interfaces for narrative-driven clinical decision support

  • Storage and replay of narrative states at decision time

CONCLUSION

​

This disclosure defines a new category of healthcare AI systems—Judgment-Calibrated Narrative Intelligence—that bridges the gap between machine prediction and human clinical reasoning. By embedding narrative understanding and judgment alignment into AI architectures, the system enables safer, more trustworthy, and more defensible AI-assisted healthcare decisions.

FINAL INVENTION POSITIONING​

A system that continuously constructs patient narratives and calibrates AI recommendations to human clinical judgment, enabling accountable and trustworthy decision-making in healthcare.

Clinical Judgment Calibration Systems

Longitudinal Patient Narrative Intelligence

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