
Healthcare CDSS Patterns
FreeImplement clinical decision support systems seamlessly.
Free · Opens the source repo
What Healthcare CDSS Patterns does
The Healthcare CDSS Patterns skill provides a structured approach for developing Clinical Decision Support Systems (CDSS) that are crucial in enhancing patient safety within Electronic Medical Record (EMR) workflows. This skill focuses on integrating essential functionalities such as drug interaction checking, dose validation, and clinical scoring systems, ensuring that healthcare providers can make informed decisions based on real-time data. Given the high stakes involved in clinical settings, the CDSS modules are designed with a zero tolerance for false negatives, making them critical components in patient care.
At its core, the CDSS engine operates as a pure function library, meaning it takes clinical data as input and produces alerts without any side effects. This design not only enhances testability but also ensures that the system can be reliably integrated into existing EMR interfaces. The skill encompasses three primary modules: checking drug interactions, validating prescribed doses, and calculating clinical scores like NEWS2. Each module is meticulously crafted to return actionable results that can directly influence clinical decisions, thereby improving patient outcomes.
Healthcare developers and designers will find this skill particularly useful when implementing features that require rigorous validation and alerting mechanisms. Whether you are building systems for drug interaction checks, dose validation engines, or clinical scoring systems, this skill provides the foundational patterns and functions necessary to achieve these goals. By leveraging these patterns, developers can ensure that their applications comply with clinical guidelines and enhance the safety and efficacy of patient care processes.
When to use it
Use this skill when developing healthcare applications that require robust mechanisms for drug interaction checking, dose validation, or clinical scoring systems.
When not to use it
This skill may not be suitable for applications outside the healthcare domain or for those not requiring stringent patient safety measures.
What you can build with it
Drug Interaction Checking
Utilize the drug interaction checking module to ensure new prescriptions do not conflict with existing medications or allergies.
Dose Validation
Implement the dose validation engine to confirm that prescribed doses are appropriate based on patient-specific factors such as weight and age.
Clinical Scoring Implementation
Use the clinical scoring module to assess patient vitals and provide risk scores that guide clinical actions.
How to install Healthcare CDSS Patterns
View source1. Install with the skills CLI
npx skills add affaan-m/ecc/healthcare-cdss-patterns --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by affaan-mHealthcare 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:
checkInteractions(newDrug, currentMeds, allergies)— Checks a new drug against current medications and known allergies. Returns severity-sortedInteractionAlert[]. UsesDrugInteractionPairdata model.validateDose(drug, dose, route, weight, age, renalFunction)— Validates a prescribed dose against weight-based, age-adjusted, and renal-adjusted rules. ReturnsDoseValidationResult.calculateNEWS2(vitals)— National Early Warning Score 2 fromNEWS2Input. ReturnsNEWS2Resultwith 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.' }
Frequently asked questions about Healthcare CDSS Patterns
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