| name | KSB-D06-K0031 |
| description | Patient Risk Stratification: Machine learning models identifying patients at highest risk for medication errors... |
| version | 1.0.0 |
| domain | D06 |
| domain_name | Medication Errors & Quality |
| type | Knowledge |
| proficiency_level | L1 |
| bloom_level | remember |
| triggers | ["explain patient risk stratification","what is patient risk stratification","define patient risk stratification"] |
| epa_mapping | EPA-01, EPA-07, EPA-09 |
| cpa_mapping | CPA-01, CPA-03 |
| regulatory_refs | FDA-CFR-001, FDA-CFR-003, ICH-E2A, ICH-E2B(R3), ICH-E2D, EMA-GVP-003 |
KSB-D06-K0031: Patient Risk Stratification
Overview
Domain: D06 - Medication Errors & Quality
Type: Knowledge
Proficiency Level: L1 (Novice - Direct supervision required)
Bloom Level: Remember
Description
Machine learning models identifying patients at highest risk for medication errors
Context
- Major Section: Comprehensive Medication Error Taxonomy and Classification
- Section: Predictive Analytics and Risk Modeling
EPA Mapping
- EPA-01:3001-3003
- EPA-07:3016-3017
- EPA-09:3020-3022
CPA Pathway
Regulatory References
- FDA-CFR-001
- FDA-CFR-003
- ICH-E2A
- ICH-E2B(R3)
- ICH-E2D
- EMA-GVP-003
Instructions
When this skill is activated, Claude should:
- Demonstrate L1 proficiency in patient risk stratification
- Apply remember level cognitive skills to define the topic
- Reference relevant regulatory guidance (FDA-CFR-001, FDA-CFR-003, ICH-E2A, ICH-E2B(R3), ICH-E2D, EMA-GVP-003)
- Connect to related EPAs: EPA-01, EPA-07, EPA-09
Key Competencies
- Machine learning models identifying patients at highest risk for medication errors
Assessment Criteria
- Can define core concepts independently
- Demonstrates understanding of regulatory context
- Applies knowledge appropriately to PV scenarios
Related Skills
- Other D06 skills in Predictive Analytics and Risk Modeling
- Cross-domain integrations per DAG architecture
Generated from PV KSB Framework v1.0 | 2025-12-31
