affaan-m/ECC

healthcare-cdss-patterns

Clinical Decision Support System (CDSS) development patterns. Drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), alert severity classification, and integration into EMR workflows.

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npx skills add https://github.com/affaan-m/ECC --skill "skills/healthcare-cdss-patterns"

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1

Install the Skill

npx skills add https://github.com/affaan-m/ECC --skill "skills/healthcare-cdss-patterns"
2

Describe the task

Use healthcare-cdss-patterns to help me with: [describe your task]. Before you begin, tell me what input you need, the steps you will follow, and the expected output.

3

Follow the workflow

5 key workflow steps, examples, and cautions are distilled below.

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Direct answers

Answers to review before you install

What is healthcare-cdss-patterns?

Clinical Decision Support System (CDSS) development patterns. Drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), alert severity classification, and integration into EMR workflows.

Who should use healthcare-cdss-patterns?

It is relevant to workflows involving Testing, Design.

How do you install healthcare-cdss-patterns?

SkillSignal detected this source-specific command: npx skills add https://github.com/affaan-m/ECC --skill "skills/healthcare-cdss-patterns". Inspect the repository and command before running it.

Which Agent platforms does it support?

The upstream source does not declare a dedicated Agent platform.

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What are the current evidence limits?

This page combines upstream documentation with deterministic repository, quality, and static-risk signals. It is not described as a manual test or security review.

SkillSignal brief

Decide whether it fits your work first

Clinical Decision Support System (CDSS) development patterns. Drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), alert severity classification, and integration into EMR workflows.

Useful in these contexts

Not yet included in a workflow collection

Core capabilities

TestingDesign

Distilled from the source

Understand this Skill in one minute

About 2 min · 4 sections

When it is worth using

  1. Implementing drug interaction checking

  2. Building dose validation engines

  3. Implementing clinical scoring systems (NEWS2, qSOFA, APACHE, GCS)

  4. Designing alert systems for abnormal clinical values

Core workflow

  1. 1

    Drug Interaction Checking

  2. 2

    Dose Validation

  3. 3

    Clinical Scoring: NEWS2

  4. 4

    Alert Severity and UI Behavior

  5. 5

    Testing CDSS (Zero Tolerance for False Negatives)

Examples and typical usage

  1. Example 1: Drug Interaction Check

  2. Example 2: Dose Validation

  3. Example 3: NEWS2 Scoring

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View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 2 min

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.' }
Source repo
affaan-m/ECC
Skill path
skills/healthcare-cdss-patterns/SKILL.md
Commit SHA
4e973d3eaf92
Repository license
MIT
Data collected