Antibiogram
The institutional summary of antimicrobial susceptibility patterns showing local resistance rates by organism and antibiotic to guide empiric therapy.
Why This Object Matters for AI
AI antimicrobial recommendations require local resistance data; without antibiograms, AI cannot suggest appropriate empiric antibiotics for the institution.
Pharmacy Operations Capacity Profile
Typical CMC levels for pharmacy operations in Healthcare organizations.
CMC Dimension Scenarios
What each CMC level looks like specifically for Antibiogram. Baseline level is highlighted.
The institutional antibiogram does not exist as a formal document. Antimicrobial susceptibility patterns are known informally by infectious disease physicians and pharmacists from their clinical experience, but there is no published summary of local resistance rates by organism and antibiotic. Empiric antibiotic selection is based on individual clinician experience rather than institutional resistance data.
None — AI cannot recommend empiric antibiotic therapy based on local resistance patterns because no formal antibiogram exists to inform drug selection algorithms.
Create a formal institutional antibiogram — compile and publish a summary of antimicrobial susceptibility testing results showing percent susceptibility for each organism-antibiotic combination tested over the prior 12 months.
An institutional antibiogram is published annually as a static PDF or printed chart. It shows percent susceptibility for common organism-antibiotic combinations from the prior year's culture data. But the antibiogram is a snapshot document — not updated between annual publications, not broken down by clinical unit or specimen source, and not linked to the prescribing workflow where empiric antibiotic decisions are made.
AI can reference the annual antibiogram for general susceptibility guidance, but cannot provide unit-specific or specimen-source-specific resistance data, and cannot ensure recommendations reflect current (rather than last-year's) resistance patterns because the antibiogram is a static annual document.
Standardize antibiogram documentation with granular detail — implement structured antibiogram records with organism-antibiotic percent susceptibility by clinical unit, specimen source, patient population (ICU vs general ward, pediatric vs adult), and confidence interval based on isolate count, with documented methodology per CLSI guidelines.
The antibiogram follows a standardized format with granular detail: percent susceptibility by organism-antibiotic combination, stratified by clinical unit, specimen source, and patient population. Isolate counts and confidence intervals are documented. Methodology follows CLSI guidelines. The antibiogram provides detailed, reliable resistance intelligence. But it remains a standalone reference document — not linked to prescribing workflows, clinical decision support, or patient-specific culture results.
AI can provide detailed susceptibility guidance stratified by unit and specimen source from the standardized antibiogram. Cannot integrate antibiogram recommendations into prescribing-time decision support or correlate institutional patterns with individual patient culture results because the antibiogram is not connected to clinical workflows.
Link the antibiogram to clinical workflows — connect antibiogram data to the prescribing system's clinical decision support, individual patient culture results, and antimicrobial stewardship review processes so that resistance intelligence is available at the point of antibiotic prescribing.
The antibiogram connects to clinical workflows. Prescribing decision support references antibiogram data when clinicians order empiric antibiotics for specific organisms. Individual patient culture results are contextualized against institutional resistance patterns. Stewardship review processes use antibiogram trends to identify shifts in resistance. A clinician ordering empiric therapy for a urinary tract infection sees unit-specific antibiogram guidance alongside the order entry screen.
AI can provide antibiogram-guided empiric therapy recommendations at prescribing time, contextualize individual culture results against institutional patterns, and flag emerging resistance trends for stewardship attention.
Implement formal antibiogram entity schemas — model the antibiogram as a structured entity with typed relationships to microbiology culture records, prescribing patterns, stewardship interventions, patient outcome measurements, and regional/national surveillance data.
The antibiogram is a schema-driven entity with full relational modeling. Each organism-antibiotic susceptibility entry links to the underlying culture records, prescribing pattern analysis, stewardship intervention history, patient outcome measurements, and regional/national surveillance comparisons (NHSN, state health department data). An AI agent can navigate from any antibiogram entry to the complete microbiological, prescribing, and outcome context.
AI can autonomously manage empiric antibiotic selection — integrating institutional antibiogram data with regional resistance trends, prescribing patterns, stewardship outcomes, and patient-specific factors for comprehensive antimicrobial decision support.
Implement real-time antibiogram intelligence streaming — publish every new culture susceptibility result, resistance trend shift, and prescribing pattern change as it occurs for continuous antibiogram intelligence.
The antibiogram is a real-time resistance intelligence stream. Every new culture susceptibility result updates institutional resistance rates immediately. Emerging resistance trends are detected as they develop, not after annual compilation. The antibiogram reflects the live state of institutional antimicrobial resistance, continuously updated as new microbiological evidence accumulates.
Fully autonomous antibiogram intelligence — continuously updating resistance patterns from real-time culture results, detecting emerging resistance before it becomes endemic, and optimizing empiric therapy recommendations as a comprehensive resistance surveillance engine.
Ceiling of the CMC framework for this dimension.
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