
Healthcare Cdss Patterns
- 4.5k installs
- 238k repo stars
- Updated August 5, 2026
- affaan-m/everything-claude-code
healthcare-cdss-patterns is an agent skill for implement pure-function cdss modules for drug interactions, dose validation, and clinical scoring like news2.
About
The healthcare-cdss-patterns skill 臨床意思決定支援システム(CDSS)パターン、医学的推論、およびエビデンスベースの実装。. Patterns for building Clinical Decision Support Systems that integrate into EMR workflows. CDSS modules are patient safety critical zero tolerance for false negatives. - Implementing drug interaction checking - Building dose validation engines - Implementing clinical scoring systems (NEWS2, qSOFA, APACHE, GCS) - Designing alert systems for abnormal clinical values - Building medication order entry with safety checks - Integrating lab result interpretation with clinical context The CDSS engine is a pure function library with zero side effects. Input clinical data, output alerts. This makes it fully testable. 1. checkInteractions(newDrug, currentMeds, allergies) Checks a new drug against current medications and known allergies. Returns severity-sorted InteractionAlert[]. Uses DrugInteractionPair data model. 2. validateDose(drug, dose, r 1. checkInteractions(newDrug, currentMeds, allergies) — Checks a new drug against current medications and known allergies. Returns severity-sorted InteractionAlert[]. Uses DrugInteractionPair data model. 2. validateDose(drug, dose, route, weight, age, renalFunction) — Va.
- Implementing drug interaction checking
- Building dose validation engines
- Implementing clinical scoring systems (NEWS2, qSOFA, APACHE, GCS)
- Designing alert systems for abnormal clinical values
- Building medication order entry with safety checks
Healthcare Cdss Patterns by the numbers
- 4,463 all-time installs (skills.sh)
- +221 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #151 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
- Security screen: LOW risk (skills.sh audit)
- Data as of Aug 5, 2026 (Skillselion catalog sync)
healthcare-cdss-patterns capabilities & compatibility
- Capabilities
- implementing drug interaction checking · building dose validation engines · implementing clinical scoring systems (news2, qs · designing alert systems for abnormal clinical va · building medication order entry with safety chec
- Use cases
- api development · testing
What healthcare-cdss-patterns says it does
Patterns for building Clinical Decision Support Systems that integrate into EMR workflows. CDSS modules are patient safety critical — zero tolerance for false negatives.
The CDSS engine is a **pure function library with zero side effects**. Input clinical data, output alerts. This makes it fully testable.
2. **`validateDose(drug, dose, route, weight, age, renalFunction)`** — Validates a prescribed dose against weight-based, age-adjusted, and renal-adjusted rules. Returns `DoseValidationResult`.
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| Installs | 4.5k |
|---|---|
| repo stars | ★ 238k |
| Security audit | 3 / 3 scanners passed |
| Last updated | August 5, 2026 |
| Repository | affaan-m/everything-claude-code ↗ |
How do I implement pure-function cdss modules for drug interactions, dose validation, and clinical scoring like news2 with documented agent guidance?
Implement pure-function CDSS modules for drug interactions, dose validation, and clinical scoring like NEWS2.
Who is it for?
Developers who need backend & apis help during build work.
Skip if: Skip when the task falls outside Backend & APIs scope described in SKILL.md.
When should I use this skill?
Implement pure-function CDSS modules for drug interactions, dose validation, and clinical scoring like NEWS2.
What you get
Completed backend & apis workflow aligned with SKILL.md steps and validation.
- CDSS interaction check logic
- Dose validation rules
- Clinical score and alert designs
By the numbers
- Implementing drug interaction checking
- Building dose validation engines
- Implementing clinical scoring systems (NEWS2, qSOFA, APACHE, GCS)
Files
Healthcare CDSS Development Patterns
Patterns for building Clinical Decision Support Systems that integrate into EMR workflows. CDSS modules are patient safety critical — zero tolerance for false negatives.
When to Use
- Implementing drug interaction checking
- Building dose validation engines
- Implementing clinical scoring systems (NEWS2, qSOFA, APACHE, GCS)
- Designing alert systems for abnormal clinical values
- Building medication order entry with safety checks
- Integrating lab result interpretation with clinical context
How It Works
The CDSS engine is a pure function library with zero side effects. Input clinical data, output alerts. This makes it fully testable.
Three primary modules:
1. `checkInteractions(newDrug, currentMeds, allergies)` — Checks a new drug against current medications and known allergies. Returns severity-sorted InteractionAlert[]. Uses DrugInteractionPair data model. 2. `validateDose(drug, dose, route, weight, age, renalFunction)` — Validates a prescribed dose against weight-based, age-adjusted, and renal-adjusted rules. Returns DoseValidationResult. 3. `calculateNEWS2(vitals)` — National Early Warning Score 2 from NEWS2Input. Returns NEWS2Result with total score, risk level, and escalation guidance.
EMR UI
↓ (user enters data)
CDSS Engine (pure functions, no side effects)
├── Drug Interaction Checker
├── Dose Validator
├── Clinical Scoring (NEWS2, qSOFA, etc.)
└── Alert Classifier
↓ (returns alerts)
EMR UI (displays alerts inline, blocks if critical)Drug Interaction Checking
interface DrugInteractionPair {
drugA: string; // generic name
drugB: string; // generic name
severity: 'critical' | 'major' | 'minor';
mechanism: string;
clinicalEffect: string;
recommendation: string;
}
function checkInteractions(
newDrug: string,
currentMedications: string[],
allergyList: string[]
): InteractionAlert[] {
if (!newDrug) return [];
const alerts: InteractionAlert[] = [];
for (const current of currentMedications) {
const interaction = findInteraction(newDrug, current);
if (interaction) {
alerts.push({ severity: interaction.severity, pair: [newDrug, current],
message: interaction.clinicalEffect, recommendation: interaction.recommendation });
}
}
for (const allergy of allergyList) {
if (isCrossReactive(newDrug, allergy)) {
alerts.push({ severity: 'critical', pair: [newDrug, allergy],
message: `Cross-reactivity with documented allergy: ${allergy}`,
recommendation: 'Do not prescribe without allergy consultation' });
}
}
return alerts.sort((a, b) => severityOrder(a.severity) - severityOrder(b.severity));
}Interaction pairs must be bidirectional: if Drug A interacts with Drug B, then Drug B interacts with Drug A.
Dose Validation
interface DoseValidationResult {
valid: boolean;
message: string;
suggestedRange: { min: number; max: number; unit: string } | null;
factors: string[];
}
function validateDose(
drug: string,
dose: number,
route: 'oral' | 'iv' | 'im' | 'sc' | 'topical',
patientWeight?: number,
patientAge?: number,
renalFunction?: number
): DoseValidationResult {
const rules = getDoseRules(drug, route);
if (!rules) return { valid: true, message: 'No validation rules available', suggestedRange: null, factors: [] };
const factors: string[] = [];
// SAFETY: if rules require weight but weight missing, BLOCK (not pass)
if (rules.weightBased) {
if (!patientWeight || patientWeight <= 0) {
return { valid: false, message: `Weight required for ${drug} (mg/kg drug)`,
suggestedRange: null, factors: ['weight_missing'] };
}
factors.push('weight');
const maxDose = rules.maxPerKg * patientWeight;
if (dose > maxDose) {
return { valid: false, message: `Dose exceeds max for ${patientWeight}kg`,
suggestedRange: { min: rules.minPerKg * patientWeight, max: maxDose, unit: rules.unit }, factors };
}
}
// Age-based adjustment (when rules define age brackets and age is provided)
if (rules.ageAdjusted && patientAge !== undefined) {
factors.push('age');
const ageMax = rules.getAgeAdjustedMax(patientAge);
if (dose > ageMax) {
return { valid: false, message: `Exceeds age-adjusted max for ${patientAge}yr`,
suggestedRange: { min: rules.typicalMin, max: ageMax, unit: rules.unit }, factors };
}
}
// Renal adjustment (when rules define eGFR brackets and eGFR is provided)
if (rules.renalAdjusted && renalFunction !== undefined) {
factors.push('renal');
const renalMax = rules.getRenalAdjustedMax(renalFunction);
if (dose > renalMax) {
return { valid: false, message: `Exceeds renal-adjusted max for eGFR ${renalFunction}`,
suggestedRange: { min: rules.typicalMin, max: renalMax, unit: rules.unit }, factors };
}
}
// Absolute max
if (dose > rules.absoluteMax) {
return { valid: false, message: `Exceeds absolute max ${rules.absoluteMax}${rules.unit}`,
suggestedRange: { min: rules.typicalMin, max: rules.absoluteMax, unit: rules.unit },
factors: [...factors, 'absolute_max'] };
}
return { valid: true, message: 'Within range',
suggestedRange: { min: rules.typicalMin, max: rules.typicalMax, unit: rules.unit }, factors };
}Clinical Scoring: NEWS2
interface NEWS2Input {
respiratoryRate: number; oxygenSaturation: number; supplementalOxygen: boolean;
temperature: number; systolicBP: number; heartRate: number;
consciousness: 'alert' | 'voice' | 'pain' | 'unresponsive';
}
interface NEWS2Result {
total: number; // 0-20
risk: 'low' | 'low-medium' | 'medium' | 'high';
components: Record<string, number>;
escalation: string;
}Scoring tables must match the Royal College of Physicians specification exactly.
Alert Severity and UI Behavior
| Severity | UI Behavior | Clinician Action Required |
|---|---|---|
| Critical | Block action. Non-dismissable modal. Red. | Must document override reason to proceed |
| Major | Warning banner inline. Orange. | Must acknowledge before proceeding |
| Minor | Info note inline. Yellow. | Awareness only, no action required |
Critical alerts must NEVER be auto-dismissed or implemented as toast notifications. Override reasons must be stored in the audit trail.
Testing CDSS (Zero Tolerance for False Negatives)
describe('CDSS — Patient Safety', () => {
INTERACTION_PAIRS.forEach(({ drugA, drugB, severity }) => {
it(`detects ${drugA} + ${drugB} (${severity})`, () => {
const alerts = checkInteractions(drugA, [drugB], []);
expect(alerts.length).toBeGreaterThan(0);
expect(alerts[0].severity).toBe(severity);
});
it(`detects ${drugB} + ${drugA} (reverse)`, () => {
const alerts = checkInteractions(drugB, [drugA], []);
expect(alerts.length).toBeGreaterThan(0);
});
});
it('blocks mg/kg drug when weight is missing', () => {
const result = validateDose('gentamicin', 300, 'iv');
expect(result.valid).toBe(false);
expect(result.factors).toContain('weight_missing');
});
it('handles malformed drug data gracefully', () => {
expect(() => checkInteractions('', [], [])).not.toThrow();
});
});Pass criteria: 100%. A single missed interaction is a patient safety event.
Anti-Patterns
- Making CDSS checks optional or skippable without documented reason
- Implementing interaction checks as toast notifications
- Using
anytypes for drug or clinical data - Hardcoding interaction pairs instead of using a maintainable data structure
- Silently catching errors in CDSS engine (must surface failures loudly)
- Skipping weight-based validation when weight is not available (must block, not pass)
Examples
Example 1: Drug Interaction Check
const alerts = checkInteractions('warfarin', ['aspirin', 'metformin'], ['penicillin']);
// [{ severity: 'critical', pair: ['warfarin', 'aspirin'],
// message: 'Increased bleeding risk', recommendation: 'Avoid combination' }]Example 2: Dose Validation
const ok = validateDose('paracetamol', 1000, 'oral', 70, 45);
// { valid: true, suggestedRange: { min: 500, max: 4000, unit: 'mg' } }
const bad = validateDose('paracetamol', 5000, 'oral', 70, 45);
// { valid: false, message: 'Exceeds absolute max 4000mg' }
const noWeight = validateDose('gentamicin', 300, 'iv');
// { valid: false, factors: ['weight_missing'] }Example 3: NEWS2 Scoring
const result = calculateNEWS2({
respiratoryRate: 24, oxygenSaturation: 93, supplementalOxygen: true,
temperature: 38.5, systolicBP: 100, heartRate: 110, consciousness: 'voice'
});
// { total: 13, risk: 'high', escalation: 'Urgent clinical review. Consider ICU.' }Related skills
Forks & variants (1)
Healthcare Cdss Patterns has 1 known copy in the catalog totaling 1.4k installs. They canonicalize to this original listing.
- affaan-m - 1.4k installs
How it compares
healthcare-cdss-patterns is an agent skill for implement pure-function cdss modules for drug interactions, dose validation, and clinical scoring like news2, not a generic alternative.
FAQ
Who is healthcare-cdss-patterns for?
Developers using Backend & APIs workflows with agent-guided SKILL.md steps.
When should I use healthcare-cdss-patterns?
Implement pure-function CDSS modules for drug interactions, dose validation, and clinical scoring like NEWS2.
Is healthcare-cdss-patterns safe to install?
Review the Security Audits panel on this page before installing in production.