Skill UI

This workflow implements Abridge's end-to-end clinical documentation pipeline. It captures ambient audio from patient encounters, performs real-time transcription, utilizes generative AI to structure the medical notes (e.g., SOAP, H&P), generates required medical codes (ICD-10, CPT), and finally inserts the structured data directly into the EHR system. Designed for primary clinical encounters, minimizing manual charting time.

This comprehensive guide outlines the strict requirements and protocols for submitting papers to the IEEE International Conference on Data Mining (ICDM). It details the crucial differences between the Research and Applied tracks, emphasizing the triple-blind anonymity regime. Key points include adhering to the strict 10-page IEEE format (including references and appendices), avoiding identity leaks in the body or metadata, and understanding the single, non-negotiable submission deadline. Failure to follow these guidelines results in automatic rejection.

Provides crucial guidelines for structuring research papers submitted to ICDM. It details how the 10-page IEEE limit must accommodate the body, in-cap appendices, and references, while effectively offloading bulk material (code, full logs) to an external repository. Essential for avoiding rejection due to page overruns.

This skill offers a comprehensive project management framework for the end-to-end submission process of data mining research to major conferences like ICDM. It guides users through critical milestones, including topic selection, experimental design, abstract submission, and final camera-ready preparation. It emphasizes backward planning from the final deadline, addressing unique constraints such as no-rebuttal policies, short-paper contingencies, and strict page limits.

Provides decision support for mapping diagnoses and procedures extracted by OpenMed to standardized ICD-10-CM and ICD-10-PCS codes. This skill suggests candidate codes along with detailed clinical rationale, helping human coders narrow down options, route findings to specific chapters, and pre-fill encounter records. Note that the output is advisory and requires mandatory validation by a certified medical coder.

Converts raw OpenMed NER outputs (text spans) into structured, standards-compliant FHIR R4 resources (e.g., Condition, Observation, MedicationStatement). It provides mechanical helpers for building CodeableConcepts using major coding systems like SNOMED, LOINC, and ICD-10, ensuring high interoperability with EHR systems and registries.

This skill extracts crucial Social Determinants of Health (SDOH) — such as housing instability, food insecurity, and transportation barriers — from unstructured clinical notes. It maps the identified text spans to standardized ICD-10-CM Z-codes (Z55–Z65). It is essential for health-equity analytics, building risk stratification tools, and identifying coding gaps by converting narrative text into queryable, discrete data points.

This skill resolves entities extracted by OpenMed (e.g., Disease, Drug) to standardized Concept Unique Identifiers (CUIs) within the UMLS Metathesaurus. It is essential for cross-vocabulary normalization, allowing users to unify concepts (like SNOMED CT, ICD-10, RxNorm) to a single canonical CUI. Crucially, the process relies entirely on the user's own UTS API key, ensuring that the Metathesaurus content is never bundled or cached.

This skill maps clinical concept spans (disorders, anatomy, procedures, etc.) extracted by OpenMed to SNOMED CT concept IDs (SCTID). It performs complex, out-of-process terminology resolution using standard FHIR R4 endpoints ($lookup, $translate, ValueSet/$expand) against a user-supplied, licensed terminology server (e.g., Ontoserver, UTS). Ideal for generating rich, hierarchy-aware clinical codes, performing subsumption queries (ECL), or translating codes between clinical standards (like ICD-10 to SNOMED CT).

This skill facilitates the crucial grounding step for clinical coding by interacting with external FHIR terminology servers. It supports core operations such as `$validate-code`, `$expand` (for ValueSets), `$lookup`, and `$translate` (ConceptMaps). It ensures that raw text spans are converted into validated, coded `CodeableConcept` resources using industry standards like SNOMED CT, LOINC, and ICD-10, maintaining interoperability.