Infectious Diseases and Antimicrobial Stewardship
Infectious diseases combine clinical complexity with rich data: microbiology cultures, genomic sequencing, antimicrobial susceptibility testing, and population-level epidemiology. AI enhances antimicrobial stewardship through real-time bug-drug mismatch detection, though alert fatigue remains a challenge. Genomic surveillance platforms like Nextstrain automate phylogenetic analysis, tracking pathogen evolution in real time. But outbreak prediction remains limited to short-term nowcasting. Long-range epidemic forecasting consistently fails. This chapter examines what works in stewardship, surveillance, and diagnostics.
After reading this chapter, you will be able to:
- Evaluate AI systems for antimicrobial stewardship and antibiotic selection
- Understand outbreak prediction and genomic surveillance AI applications
- Assess rapid pathogen identification and resistance prediction tools
- Navigate AI applications in infection control and hospital epidemiology
- Recognize the role of AI in pandemic preparedness and response
- Apply evidence-based frameworks for infectious diseases AI adoption
Part 1: Antimicrobial Stewardship AI
The Stewardship Imperative
Antimicrobial resistance kills an estimated 1.27 million people globally each year (Murray et al., 2022). Stewardship programs reduce inappropriate antibiotic use by 10-30% and decrease C. difficile infections (CDC Core Elements, 2019). AI augments stewardship by processing microbiology data at scale.
Decision Support Applications
Current AI stewardship tools:
- Bug-drug mismatch detection
- Real-time alerts when prescribed antibiotic lacks coverage for cultured organism
- Integration with lab information systems
- Performance: High sensitivity but alert fatigue with low positive predictive value
- Duration of therapy monitoring
- Flags antibiotic courses exceeding guideline recommendations
- Supports de-escalation and IV-to-oral conversion
- Empiric therapy recommendations
- Antibiotic selection based on local antibiogram, patient factors, indication
- Updates dynamically as resistance patterns shift
IDSA/SHEA Stewardship Guidelines
The joint IDSA/SHEA guidelines on antimicrobial stewardship programs (Barlam et al., 2016) provide the framework for AI implementation:
Core recommendations:
- Preauthorization and prospective audit with feedback remain gold standards
- Clinical decision support should be integrated into EHR workflows
- Local antibiograms should drive empiric therapy recommendations
- Stewardship interventions require ID or pharmacy oversight
Implications for AI:
- AI tools should support, not replace, prospective audit processes
- Automated recommendations require human stewardship review
- Local validation essential: national models may not reflect institutional resistance patterns
- Alert frequency must balance sensitivity with actionability
2024 Update: SHEA released updated guidance emphasizing informatics infrastructure for stewardship, including standardized data elements and interoperability requirements (SHEA Position Statement, 2024).
Implementation Evidence
| Study | Intervention | Outcome |
|---|---|---|
| Beeler et al., 2023 | Bug-drug mismatch alerts | 22% reduction in discordant therapy |
| Timbrook et al., 2017 | Rapid diagnostic + stewardship CDS | 2.5 day reduction in antibiotic duration |
| Rittmann et al., 2022 | ML antibiogram integration | 18% improvement in empiric coverage |
Alert Fatigue in Stewardship
The fundamental challenge: stewardship alerts compete with dozens of other clinical alerts. Studies show clinicians override 49-96% of antibiotic alerts (Slight et al., 2013).
Mitigation strategies:
- Tiered alerting (hard stops for critical mismatches, soft alerts for duration)
- Pharmacist review before alert delivery
- Bundled recommendations rather than individual alerts
Part 2: Genomic Surveillance and Phylogenetic Analysis
The Genomic Surveillance Revolution
COVID-19 transformed pathogen genomics from research tool to clinical necessity. The infrastructure built for SARS-CoV-2 surveillance now applies to other pathogens.
Key Platforms
Nextstrain:
The Nextstrain platform (Hadfield et al., 2018) provides real-time genomic epidemiology through:
- Automated phylogenetic tree construction
- Geographic and temporal visualization
- Clade and variant assignment
- Open-source analysis pipelines
Nextstrain processes thousands of sequences daily for pathogens including SARS-CoV-2, influenza, Ebola, and monkeypox.
GISAID:
The Global Initiative on Sharing All Influenza Data (GISAID) hosts the largest repository of SARS-CoV-2 sequences (>16 million as of 2024). Machine learning classification systems automatically assign:
- Pango lineages
- WHO variant designations
- Clade membership
Africa CDC Pathogen Genomics Initiative:
Regional surveillance demonstrates AI-augmented genomics at scale. The Nextstrain Africa CDC builds track pathogen evolution across the continent, with automated quality control and lineage assignment (Tegomoh et al., 2022).
Automated Phylogenetic Analysis
AI accelerates phylogenetic workflows:
- Sequence quality control
- Automated detection of frameshifts, premature stops, contamination
- Flagging for manual review
- Lineage assignment
- Pangolin (SARS-CoV-2), Nextclade, custom classifiers
- Milliseconds per sequence vs. hours for manual curation
- Transmission cluster detection
- Genetic distance thresholds for outbreak definition
- Integration with epidemiologic data
- Recombination detection
- Identification of mosaic genomes
- Critical for SARS-CoV-2 and influenza
Resistance Gene Detection
Whole-genome sequencing enables genotypic resistance prediction:
Current capabilities:
| Pathogen | Resistance Prediction | Clinical Use |
|---|---|---|
| M. tuberculosis | INH, RIF, FQ resistance | Clinical (Cepheid, Hain) |
| S. aureus | mecA for MRSA | Clinical |
| Enterobacteriaceae | Carbapenemase genes | Clinical for CPE |
| N. gonorrhoeae | Research stage | Not yet clinical |
Limitations:
- Novel resistance mechanisms not in databases
- Phenotype-genotype discordance (expression levels, epistasis)
- Turnaround time still exceeds culture-based AST for most pathogens
Part 3: Healthcare-Associated Infection Surveillance
AI in Infection Prevention
Infection preventionists face increasing surveillance requirements with limited staffing. AI promises to automate case finding and risk stratification.
SHEA/APIC Position on Digital Surveillance
SHEA guidance on digital health in infection prevention (Haessler et al., 2023) addresses AI applications:
Endorsed applications:
- Automated HAI surveillance from EHR data
- Real-time syndromic surveillance dashboards
- Hand hygiene monitoring systems (with privacy considerations)
- Antimicrobial use tracking and benchmarking
Implementation requirements:
- Validation against manual chart review (sensitivity, specificity, PPV)
- Transparent algorithms with audit capability
- Integration with existing infection control workflows
- Privacy protections for patient and staff data
Cautions:
- AI predictions should not replace clinical judgment for isolation decisions
- False positives consume investigation resources
- Equity review required (do algorithms perform equally across patient populations?)
HAI Risk Prediction Evidence
| Infection Type | Model Performance | Clinical Impact |
|---|---|---|
| Surgical site infection | AUC 0.72-0.85 | Targeted surveillance studies ongoing |
| CAUTI | AUC 0.68-0.78 | Mixed results on prevention |
| CLABSI | AUC 0.70-0.82 | Bundle compliance improvement when coupled with alerts |
| C. difficile | AUC 0.65-0.75 | Enhanced isolation protocols studied |
Key challenge: Predicting HAI is inherently difficult because it requires distinguishing colonization from infection and accounting for interventions that occur after prediction.
Automated Surveillance Systems
Commercial systems (Premier, Theradoc, Vigilanz) incorporate:
- NHSN criteria-based case finding
- Antibiotic exposure tracking
- Line and device day counting
- Automated reporting generation
Evidence: Automated systems achieve 85-95% sensitivity compared to manual review but with lower specificity (Woeltje et al., 2016).
Part 4: Rapid Diagnostics and Resistance Prediction
AI-Enhanced Microbiology
MALDI-TOF Mass Spectrometry:
Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) MS revolutionized clinical microbiology. AI pattern recognition provides species identification in minutes rather than days (Seng et al., 2009).
Current performance: - 95-99% accuracy for common pathogens - Lower accuracy for unusual species, mixed cultures - FDA-cleared (Vitek MS, MALDI Biotyper)
Gram Stain AI:
Computer vision analysis of Gram stain images:
- Research accuracy: 80-93% for morphology and Gram reaction
- Challenges: slide quality variation, mixed flora, unusual organisms
- Limited clinical deployment; most systems in validation phase
Resistance Prediction from Spectral Data:
Emerging research uses MALDI-TOF spectra to predict: - MRSA vs. MSSA - Carbapenemase production - Extended-spectrum beta-lactamase (ESBL)
Performance varies widely (AUC 0.65-0.95) and remains mostly research-stage.
Molecular Diagnostics AI
Syndromic panels (BioFire, Verigene) use algorithmic interpretation:
- Multiplexed pathogen detection
- Automated resistance gene reporting
- Integration with stewardship pathways
Clinical impact: Syndromic panels reduce time to appropriate therapy by 24-48 hours (Beal et al., 2018) but may increase broad-spectrum antibiotic use if not coupled with stewardship.
Part 5: Outbreak Prediction and Response
What Works: Nowcasting
Nowcasting estimates current disease burden from incomplete, lagged data. AI approaches:
- CDC FluSight: Ensemble models forecast influenza 1-4 weeks ahead
- COVID-19 Forecast Hub: Aggregated models for hospitalizations and deaths
- Performance: Short-term forecasts reasonably accurate; accuracy degrades beyond 2-4 weeks
Key insight: Nowcasting outperforms traditional surveillance by 1-2 weeks, valuable for resource planning.
What Doesn’t Work: Long-Range Prediction
Despite substantial investment, AI cannot reliably predict:
- When and where novel outbreaks will emerge
- Pandemic timing or severity
- Long-range seasonal disease patterns
Why prediction fails:
- Rare events (low base rate problem)
- Complex, stochastic determinants (human behavior, weather, pathogen evolution)
- Data quality and timeliness limitations
- Non-stationary dynamics (patterns change over time)
Syndromic Surveillance
AI-powered syndromic surveillance:
- Emergency department chief complaint monitoring
- OTC medication sales tracking
- Social media and search trend analysis
Performance: Anomaly detection works for large outbreaks. Sensitivity for small, localized events remains poor.
Part 6: Professional Society Positions
IDSA Position on Digital Health
The Infectious Diseases Society of America has addressed AI in multiple contexts:
Antimicrobial stewardship: - AI decision support should align with IDSA stewardship guidelines - Local validation required before deployment - Human oversight of AI recommendations is essential
Telemedicine and digital consultations (IDSA, 2022): - Remote ID consultation supports stewardship in facilities without on-site ID - AI triage for teleconsultation requires careful implementation - Documentation and liability considerations apply to AI-assisted consultations
Genomic diagnostics: - WGS-based diagnostics require interpretive expertise - AI interpretation tools should not replace ID physician review - Turnaround time for genomic results must meet clinical needs
CDC and WHO Positions
CDC Core Elements of Hospital Antibiotic Stewardship Programs (2019) recommend decision support systems but do not specifically address AI. Updated guidance anticipated.
WHO Global Action Plan on Antimicrobial Resistance includes surveillance and diagnostics strengthening but limited AI-specific guidance. The WHO AWaRe classification (Access, Watch, Reserve antibiotics) informs AI stewardship tools.
Clinical Scenarios
Case: A 68-year-old man with bacteremia is receiving piperacillin-tazobactam. Blood cultures grow E. coli susceptible to ceftriaxone, ampicillin-sulbactam, and TMP-SMX. The stewardship AI generates a “de-escalation recommended” alert for the attending physician.
The alert states: “Current therapy: piperacillin-tazobactam. Organism susceptible to narrower-spectrum agents. Consider de-escalation to ceftriaxone.”
Question: How should the ID consultant approach this alert?
Discussion
Key considerations:
- Source control: Has the source of bacteremia been identified and addressed?
- Clinical trajectory: Is the patient improving on current therapy?
- Polymicrobial risk: Are cultures from other sites pending?
- Patient factors: Allergies, prior resistant organisms, immunocompromise?
The AI’s limitation: The alert is based solely on the susceptibility report. It lacks context about clinical status, imaging findings, or pending workup.
Appropriate response:
- Review the chart before acting on the alert
- Document rationale if continuing broad-spectrum therapy
- De-escalate when clinically appropriate, not simply because of an alert
Case: A 45-year-old woman with recurrent UTIs has a positive urine culture. The lab performs rapid molecular testing that detects blaCTX-M (ESBL gene). Conventional susceptibilities are pending.
The EHR displays: “ESBL gene detected. Consider carbapenem therapy pending phenotypic susceptibilities.”
Question: How should this information guide empiric therapy?
Discussion
Genotype-phenotype considerations:
- Gene detection confirms ESBL production likely but expression levels vary
- Carbapenems are reliable for ESBL-producers but represent overtreatment if alternatives work
- Cephalosporins may fail but MIC testing will clarify
- Nitrofurantoin and fosfomycin may have activity regardless of ESBL status
The nuanced approach:
- For severe infection: carbapenem empirically while awaiting phenotypic susceptibilities
- For uncomplicated cystitis: can await full susceptibilities given low severity
- Review prior cultures and susceptibility patterns
Case: The infection prevention team identifies three patients with carbapenem-resistant K. pneumoniae (CR-KP) on the same unit over two weeks. The hospital has WGS capability. Genomic analysis shows the isolates are within 5 SNPs of each other.
Question: What does the genomic data contribute to the investigation?
Discussion
Interpretation of genetic distance:
- <10 SNPs for K. pneumoniae strongly suggests recent transmission
- The three cases are likely part of a transmission cluster
- Transmission may have occurred on the unit or in a shared exposure setting
What genomics does not tell you:
- Who was the index case
- The specific transmission route (hands, environment, devices)
- Whether there are undetected carriers
Investigation steps:
- Enhanced contact precautions for known cases
- Review common exposures (procedures, staff, equipment)
- Consider point prevalence surveillance for additional carriers
- Environmental cultures if transmission route unclear
Case: An ID consultant receives a page because a “sepsis AI alert” fired on a 72-year-old patient with pneumonia on the medicine floor. The patient’s vital signs are stable, and lactate is normal. The AI alert cites elevated white count and recent antibiotic initiation as risk factors.
Question: How should the ID consultant interpret and respond to this alert?
Discussion
Context on sepsis prediction AI:
Sepsis prediction models have significant limitations (see Critical Care chapter): - High false positive rates (>80% in some implementations) - Low PPV: many alerts do not represent true sepsis - The Epic Sepsis Model, widely deployed, showed 33% sensitivity and 67% specificity in external validation (Wong et al., 2021)
This specific case:
- Stable vital signs and normal lactate are reassuring
- Antibiotic initiation and elevated WBC are expected for pneumonia treatment
- The alert adds little to clinical assessment
Appropriate response:
- Brief chart review to confirm clinical stability
- No action required based solely on the AI alert
- Continue planned pneumonia management
- Document assessment if institution requires alert acknowledgment
Key Takeaways for ID Specialists
Antimicrobial stewardship AI:
- AI tools identify de-escalation opportunities; clinical judgment determines appropriateness
- Local validation essential: national resistance data may not reflect your institution
- Alert fatigue is real: advocate for tiered, high-value alerts
Genomic surveillance:
- Phylogenetic analysis confirms or refutes transmission clusters
- Automated lineage assignment enables rapid variant tracking
- Genotypic resistance prediction is fast but requires phenotypic confirmation
Outbreak prediction:
- Nowcasting (1-4 week forecasts) provides actionable lead time
- Long-range prediction remains unreliable
- AI augments surveillance; it does not predict novel emergence
HAI prevention:
- Automated surveillance improves case finding sensitivity
- Risk prediction models require prospective validation
- Alert fatigue affects infection prevention as it does other clinical domains
Professional standards:
- IDSA/SHEA guidelines apply to AI-assisted stewardship
- Human oversight of AI recommendations is required
- Local validation and equity review are essential before deployment