Pharmacogenomic Profile
The patient's genetic test results relevant to drug metabolism including gene variants, metabolizer phenotypes, and actionable drug-gene interactions.
Why This Object Matters for AI
AI pharmacogenomic decision support requires genetic data in the EHR; without profiles, AI cannot alert to gene-drug interactions affecting dosing.
Pharmacy Operations Capacity Profile
Typical CMC levels for pharmacy operations in Healthcare organizations.
CMC Dimension Scenarios
What each CMC level looks like specifically for Pharmacogenomic Profile. Baseline level is highlighted.
Pharmacogenomic information is not formally documented in the clinical record. Genetic test results that affect drug metabolism exist in laboratory reports or specialty genetics records, but are not linked to the patient's medication profile. Prescribers have no systematic way to know whether a patient has been genetically tested or what the results mean for drug selection and dosing.
None — AI cannot alert clinicians to gene-drug interactions or recommend genotype-guided dosing because no formal pharmacogenomic profiles exist in the clinical workflow.
Create formal pharmacogenomic profiles — document each patient's relevant genetic test results including gene variants (e.g., CYP2D6, CYP2C19), metabolizer phenotype classification, and actionable drug-gene interaction pairs in a structured clinical record.
Some pharmacogenomic results are documented in the EHR, but inconsistently. A genetic test may appear as a scanned PDF report from an external lab, or as a free-text note in the patient's chart. There is no standardized location or format for pharmacogenomic information. Different clinicians look in different places to determine whether a patient has been tested, and the clinical significance of results varies by who interprets them.
AI can identify that pharmacogenomic testing occurred from notes or scanned reports, but cannot systematically extract gene variants, phenotype classifications, or drug-gene interaction pairs because the information is not in a structured, queryable format.
Standardize pharmacogenomic profile documentation — implement structured records with coded gene identifiers (using HGNC nomenclature), star allele notation for variants, standardized metabolizer phenotype classifications (poor/intermediate/normal/rapid/ultrarapid), and CPIC guideline-referenced drug-gene interaction pairs.
Pharmacogenomic profiles follow a standardized format: coded gene identifiers (HGNC), star allele variant notation, metabolizer phenotype classification per CPIC guidelines, and documented drug-gene interaction pairs with clinical action recommendations. Every patient with pharmacogenomic testing has a consistently formatted profile. But profiles are standalone documents — not linked to the patient's active medication list, prescribing alerts, or clinical decision support rules.
AI can retrieve and interpret pharmacogenomic profiles, classify metabolizer phenotypes, and identify documented drug-gene interactions from standardized records. Cannot provide prescribing-time alerts or dose adjustment recommendations because the profile is not connected to the active medication ordering workflow.
Link pharmacogenomic profiles to clinical workflow — connect each patient's pharmacogenomic profile to the medication ordering system, clinical decision support rules, and active medication list so that gene-drug interactions trigger alerts at prescribing time.
Pharmacogenomic profiles connect to the clinical workflow. Each profile links to the patient's active medication list (flagging current medications affected by genetic variants), clinical decision support rules (triggering alerts when medications with known gene-drug interactions are ordered), and dose adjustment guidelines (providing genotype-specific dosing recommendations). A pharmacist can query 'show me all patients with CYP2D6 poor metabolizer status who are currently prescribed codeine-containing medications.'
AI can provide real-time pharmacogenomic decision support — alerting prescribers to gene-drug interactions at ordering time, recommending genotype-guided dose adjustments, and identifying patients on medications affected by their metabolizer phenotype.
Implement formal pharmacogenomic entity schemas — model each profile as a structured entity with typed relationships to gene databases, drug metabolism pathways, CPIC guideline versions, patient medication records, and clinical outcome measurements.
Pharmacogenomic profiles are schema-driven entities with full relational modeling. Each profile links to gene databases (providing variant frequency and functional impact), drug metabolism pathway models (showing multi-gene interaction effects), CPIC guideline versions (ensuring recommendations match current evidence), patient medication records, and clinical outcome measurements. An AI agent can navigate from any genetic variant to the complete prescribing, metabolic, and outcome context.
AI can autonomously manage pharmacogenomic-guided therapy — evaluating multi-gene interaction effects, recommending optimal drug selection from complete metabolic pathway analysis, monitoring outcomes against genotype-predicted response, and updating recommendations as CPIC guidelines evolve.
Implement real-time pharmacogenomic intelligence streaming — publish every new genetic test result, guideline update, and genotype-drug outcome event as it occurs for continuous pharmacogenomic decision support.
Pharmacogenomic profiles are real-time clinical intelligence streams. Every new genetic test result, CPIC guideline update, drug metabolism research finding, and genotype-outcome correlation updates the profile continuously. The pharmacogenomic record is a living decision support resource that evolves with both the patient's genetic testing history and the expanding evidence base for precision pharmacotherapy.
Fully autonomous pharmacogenomic intelligence — continuously integrating new genetic evidence, guideline updates, and outcome correlations in real-time, optimizing precision drug therapy as a comprehensive pharmacogenomic management engine.
Ceiling of the CMC framework for this dimension.
Capabilities That Depend on Pharmacogenomic Profile
Other Objects in Pharmacy Operations
Related business objects in the same function area.
Medication Record
EntityThe patient's comprehensive medication list including active prescriptions, historical medications, allergies, adverse reactions, and adherence patterns.
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IV Compounding Order
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Drug Shortage Record
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Antimicrobial Stewardship Record
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Medication Adherence Record
EntityThe tracked pattern of medication fills and refills including proportion of days covered, gaps in therapy, and intervention history.
Antibiogram
EntityThe institutional summary of antimicrobial susceptibility patterns showing local resistance rates by organism and antibiotic to guide empiric therapy.
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