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healthcare-cdss-patterns

affaan-m/ecc

Clinical Decision Support System patterns: drug interactions, dose validation, and clinical scoring for EMR integration.

What is healthcare-cdss-patterns?

Patterns for building patient-safety-critical Clinical Decision Support Systems that integrate into EMR workflows. Provides pure-function libraries for drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), and alert severity classification with zero tolerance for false negatives.

  • Drug interaction checking against current medications and allergies with severity classification
  • Dose validation accounting for weight, age, renal function, and route of administration
  • Clinical scoring systems (NEWS2, qSOFA, APACHE, GCS) with risk stratification and escalation guidance
  • Alert severity classification (critical, major, minor) with appropriate UI blocking behavior
  • Pure function architecture with zero side effects for full testability
  • Integration patterns for medication order entry and lab result interpretation in EMR workflows

How to install healthcare-cdss-patterns

npx skills add null --skill healthcare-cdss-patterns
Prerequisites
  • Understanding of clinical pharmacology and drug interactions
  • Familiarity with dose adjustment principles (weight-based, age-adjusted, renal-adjusted)
  • Knowledge of clinical scoring systems (NEWS2, qSOFA) or access to clinical specifications
  • EMR system with medication order entry and alert display capabilities
Claude Code
Cursor
Windsurf
Cline

How to use healthcare-cdss-patterns

  1. 1.Install the skill and review the three primary modules: checkInteractions, validateDose, and calculateNEWS2
  2. 2.Populate your drug interaction database with bidirectional interaction pairs including severity, mechanism, and clinical effect
  3. 3.Define dose validation rules for each drug including weight-based limits, age brackets, renal adjustment thresholds, and absolute maximums
  4. 4.Implement the NEWS2 scoring function using Royal College of Physicians specification tables
  5. 5.Integrate CDSS functions into your EMR UI: critical alerts block actions with non-dismissable modals, major alerts require acknowledgment, minor alerts display as inline notes
  6. 6.Configure audit logging to capture all override reasons when clinicians bypass critical alerts
  7. 7.Write comprehensive test suites with 100% pass rate, including bidirectional interaction detection and missing-data blocking scenarios

Use cases

Good for
  • Implementing medication order entry with real-time safety checks before prescription
  • Detecting drug-drug interactions and cross-reactive allergies during medication reconciliation
  • Validating pediatric and renal-adjusted doses based on patient demographics
  • Calculating National Early Warning Score 2 from vital signs to trigger escalation protocols
  • Building alert systems that block critical safety issues while allowing minor warnings to proceed
Who it's for
  • EMR/EHR developers integrating clinical safety modules
  • Healthcare software engineers building medication management systems
  • Clinical informaticists designing decision support workflows
  • Hospital IT teams implementing CDSS at scale
  • Developers working on patient safety-critical healthcare applications

healthcare-cdss-patterns FAQ

What happens if a required patient parameter (like weight) is missing for dose validation?

The validator BLOCKS the dose with a 'weight_missing' factor and returns valid: false. This is intentional—missing safety-critical data must never silently pass.

Are interaction checks bidirectional?

Yes, required. If Drug A interacts with Drug B, then Drug B must also be detected as interacting with Drug A. Both directions must be tested.

How should critical alerts be displayed in the UI?

Critical alerts must be non-dismissable modals that block the action entirely. They must never be toast notifications or auto-dismissed. Clinicians must document an override reason in the audit trail to proceed.

What clinical scoring systems are included?

The skill provides NEWS2 (National Early Warning Score 2) as a primary example. The pattern supports other systems like qSOFA, APACHE, and GCS using the same pure-function architecture.

What testing standard applies to CDSS?

Zero tolerance for false negatives. Test pass rate must be 100%. A single missed interaction or undetected safety issue is a patient safety event.

Full instructions (SKILL.md)

Source of truth, from affaan-m/ecc.


name: healthcare-cdss-patterns description: Clinical Decision Support System (CDSS) development patterns. Drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), alert severity classification, and integration into EMR workflows. metadata: origin: Health1 Super Speciality Hospitals — contributed by Dr. Keyur Patel version: "1.0.0"

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

SeverityUI BehaviorClinician Action Required
CriticalBlock action. Non-dismissable modal. Red.Must document override reason to proceed
MajorWarning banner inline. Orange.Must acknowledge before proceeding
MinorInfo 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 any types 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.' }