Evidence Synthesis

Automatically ingests and harmonizes data from disparate EHR modules—including problem lists, medication administration records, lab results, and narrative physician notes—into a single structured timeline. This process resolves source system fragmentation, ensuring that a radiology report from one vendor and a cardiology consult from another are unified into a coherent patient narrative without manual chart chasing.

Establishes the chronological and causal links between clinical events to demonstrate disease progression and treatment rationale. The system identifies that a HbA1c of 9.2% on March 3rd preceded a medication adjustment on March 10th, which then led to a follow-up result of 7.1% on June 15th. This temporal stitching is critical for proving medical necessity for ongoing therapy by showing the patient's trajectory over time.

Detects and flags conflicting information across sources—such as a problem list diagnosis of Type 2 Diabetes contradicted by a physician note stating 'no known history of diabetes' . The synthesis engine applies contextual weighting, prioritizing the most recent, authoritative, and specific source to resolve the conflict and present a single source of truth to the payer, preventing unnecessary denials based on ambiguous records.

Proactively analyzes the aggregated clinical picture against the specific documentation requirements of a target payer's medical policy. The system identifies missing pieces of evidence—such as a required conservative therapy trial or a missing imaging report—before the authorization is submitted. This transforms the synthesis from a passive aggregation task into an active authorization readiness assessment.

Condenses the synthesized, multi-source evidence into a concise, chronologically structured narrative summary tailored for a payer clinical reviewer. Using a large language model, the system generates a one-page synopsis that highlights the key clinical milestones, relevant lab trends, and failed first-line therapies, directly mapping the patient's journey to the medical policy criteria to accelerate manual review.

Transforms the unified clinical evidence into a machine-readable, standards-compliant format such as FHIR or a payer-specific JSON schema. This serialization enables direct, automated submission to a payer's API endpoint, bypassing manual portal entry. The output includes discrete, coded data elements with their provenance, ensuring the payer's rules engine can instantly consume and adjudicate the synthesized evidence.

The foundational NLP process that identifies and pulls discrete clinical data points from unstructured text. Evidence synthesis depends on high-quality extraction to function.

• Entity Recognition: Identifies diagnoses, procedures, medications, and lab results in physician notes.
• Contextual Parsing: Distinguishes between current medications and historical treatments.
• Source Identification: Tags the origin document (e.g., progress note, discharge summary) for each extracted fact.

The automated evaluation of a proposed service against payer-defined clinical criteria. Evidence synthesis provides the unified patient picture that makes this determination possible.

• Criteria Matching: Compares synthesized evidence against policy rules.
• Gap Identification: Flags missing documentation before submission.
• Confidence Scoring: Assigns a probability of meeting medical necessity standards.

The process of mapping extracted clinical terms to standard terminologies like SNOMED CT, RxNorm, and ICD-10-CM. Without normalization, evidence from different sources cannot be reconciled.

• Synonym Resolution: Maps 'high blood pressure' to SNOMED CT 38341003.
• Cross-Source Alignment: Ensures a medication mentioned in a note matches the same drug in a lab report.
• Semantic Equivalence: Identifies when two different phrases describe the same clinical concept.

An NLP technique that compares synthesized patient evidence against formal medical policy documents to identify if coverage criteria are met. Evidence synthesis creates the structured input this process requires.

• Policy Parsing: Extracts structured criteria from payer PDFs and bulletins.
• Evidence-to-Criteria Alignment: Maps each synthesized data point to a specific policy requirement.
• Rationale Generation: Produces a human-readable explanation of how the evidence satisfies each criterion.

The application of large language models to condense lengthy patient histories into a concise, chronologically coherent summary. This is the output format that evidence synthesis delivers to payer clinical reviewers.

• Temporal Ordering: Arranges clinical events in chronological sequence.
• Relevance Filtering: Includes only information pertinent to the requested service.
• Citation Grounding: Links each summarized claim back to its source document.

The automated process of comparing synthesized evidence against policy requirements to identify missing or insufficient documentation. This prevents denials by surfacing gaps before submission.

• Requirement Mapping: Identifies every data element a payer policy demands.
• Deficiency Flagging: Highlights specific missing labs, notes, or imaging results.
• Remediation Guidance: Suggests which documents to request from the provider to close each gap.