Patient Timeline Reconstruction

The core process of ordering discrete medical events—diagnoses, procedures, medications, and lab results—into a coherent chronological narrative. This involves resolving timestamps from heterogeneous sources like EHR encounters, claims data, and pharmacy records to establish a ground-truth sequence.

• Resolves partial or imprecise dates (e.g., 'Summer 2023')
• Handles relative timestamps (e.g., '3 months post-surgery')
• Creates a unified, sortable event stream for downstream logic

Evaluates complex temporal eligibility rules against the reconstructed timeline. This engine verifies that clinical events occurred within protocol-specified windows, such as washout periods, recent therapy exclusions, or disease progression intervals.

• Validates 'within X days of screening' criteria
• Checks for minimum durations between events
• Flags violations like recent prohibited medication use

Validates complex ordered logic by confirming that events occurred in a specific sequence. For example, ensuring a diagnosis of metastatic disease was followed by first-line chemotherapy before considering a second-line trial.

• Models causal and prerequisite relationships
• Detects missing events in an expected chain
• Supports branching sequence logic for complex protocols

Identifies missing data periods and logical inconsistencies within the assembled timeline. A gap in the record might indicate care received outside the network, while an anomaly—like a procedure date preceding a birth date—signals a data quality issue requiring resolution before eligibility can be determined.

• Flags data quality issues for human review
• Estimates impact of information gaps on eligibility confidence
• Prevents false positives from erroneous timestamps

The foundational extraction step that ingests unstructured clinical notes and structured fields from a patient's complete medical history across multiple encounters. This parser identifies and normalizes timestamped clinical facts to build the raw material for the timeline.

• Extracts dates from narrative text using medical NER
• Normalizes formats from HL7 v2, FHIR, and CDA sources
• Associates clinical events with precise or inferred timestamps

Cross-references the reconstructed medication timeline against a trial's prohibited medication list. This process verifies that excluded drugs were not administered within the protocol-defined exclusionary window, such as 'no systemic steroids within 14 days of enrollment'.

• Aligns prescription dates, durations, and refills
• Accounts for drug half-life and washout logic
• Flags potential protocol deviations automatically

The temporal ordering of discrete medical events from a patient's history to validate complex eligibility logic. This process arranges diagnoses, procedures, medication orders, and lab results into a chronologically accurate chain.

• Resolves relative timestamps (e.g., '2 weeks post-surgery')
• Validates event precedence (e.g., 'diagnosis must precede treatment')
• Handles imprecise dates with fuzzy temporal anchoring

Accurate sequencing prevents false inclusions where events occurred in the wrong order.

The AI capability to interpret and validate time-dependent clinical constraints against a patient's longitudinal record. This goes beyond simple date comparison to understand clinical logic like:

• Washout periods: 'No chemotherapy within 28 days'
• Disease progression windows: 'Documented progression within 6 months'
• Stability requirements: 'Stable dose of corticosteroids for 2 weeks'

Temporal reasoning engines evaluate Allen's interval algebra relationships (before, after, during, overlaps) between clinical events and trial milestones.

The automated extraction and structuring of clinical data from a patient's complete medical history across multiple encounters. This foundational step ingests:

• Structured data: EHR timestamps, lab values, coded diagnoses
• Unstructured data: Clinical notes, radiology reports, discharge summaries
• Disparate sources: Multiple health systems, pharmacies, claims databases

The output is a unified, timestamped patient profile that serves as the input for timeline reconstruction algorithms.

The process of converting parsed, structured eligibility criteria into executable database queries that can screen patient repositories. Timeline constraints are translated into temporal SQL or FHIR API calls:

• Converts 'within 30 days of enrollment' to date-range filters
• Generates interval joins between related clinical events
• Produces parameterized queries for real-time screening

This translation layer bridges the gap between human-readable protocols and machine-executable screening logic.

The automated extraction and structuring of complex free-text inclusion and exclusion requirements from clinical trial protocols. This upstream process identifies:

• Temporal expressions: 'within 4 weeks', 'at least 6 months prior'
• Clinical entities: medications, procedures, lab thresholds
• Logical operators: AND, OR, NOT relationships between criteria

Parsed criteria become the machine-readable rules that drive timeline validation, enabling direct comparison against reconstructed patient histories.

The systematic review of reasons why pre-screened patients failed to meet trial eligibility, with specific attention to temporal failures. This feedback loop:

• Identifies common timeline-related exclusion patterns
• Quantifies the impact of specific time-window constraints on enrollment
• Informs protocol amendment recommendations to widen eligibility windows

Timeline reconstruction failures are categorized by root cause—missing data, ambiguous timestamps, or genuinely unmet temporal criteria—to drive continuous improvement.