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Healthcare Cdss Patterns

  • 1.4k installs
  • 238k repo stars
  • Updated August 5, 2026
  • affaan-m/ecc

This is a copy of healthcare-cdss-patterns by affaan-m - installs and ranking accrue to the original listing.

healthcare-cdss-patterns is an agent skill that rapidly implements reliable clinical decision support features such as drug interaction checking, dose validation, and clinical scoring inside medical applications.

About

healthcare-cdss-patterns is an ECC agent skill at version 1.0.0 contributed from Health1 Super Speciality Hospitals for building Clinical Decision Support Systems integrated into EMR workflows. CDSS modules are patient-safety critical with zero tolerance for false negatives. The skill covers drug interaction checking, dose validation engines, clinical scoring systems including NEWS2, qSOFA, APACHE, and GCS, alert systems for abnormal clinical values, medication order entry safety checks, and lab result interpretation hooks. Developers reach for healthcare-cdss-patterns when implementing evidence-based clinical logic in hospital or health-tech backends rather than generic CRUD APIs. Use it when alerts, scoring, and medication safety checks must meet clinical reliability standards.

  • Pure function library with zero side effects for full testability
  • Implements checkInteractions, validateDose, and clinical scoring modules
  • Supports NEWS2, qSOFA, APACHE, GCS scoring systems
  • Severity-sorted InteractionAlert and DoseValidationResult models
  • Zero tolerance for false negatives in patient-safety critical code

Healthcare Cdss Patterns by the numbers

  • 1,367 all-time installs (skills.sh)
  • +83 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/affaan-m/ecc --skill healthcare-cdss-patterns

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Listed on Skillselion
Installs1.4k
repo stars238k
Last updatedAugust 5, 2026
Repositoryaffaan-m/ecc

How do you implement clinical decision support in EMR?

Rapidly implement reliable clinical decision support features such as drug interaction checking, dose validation, and clinical scoring inside medical applications.

Who is it for?

Backend developers building EMR-integrated clinical decision support with drug checks, dose validation, and scoring systems where patient safety demands zero false negatives.

Skip if: General wellness apps or non-clinical projects without medication safety, clinical scoring, or regulated healthcare decision logic requirements.

When should I use this skill?

The user mentions CDSS, drug interaction checking, dose validation, clinical scoring, NEWS2, qSOFA, medication order entry safety, or EMR clinical alerts.

What you get

Drug interaction modules, dose validation rules, clinical scoring implementations, and abnormal-value alert systems.

  • drug interaction module
  • dose validation rules
  • clinical scoring implementation

By the numbers

  • Version 1.0.0 contributed from Health1 Super Speciality Hospitals
  • Documents 4 clinical scoring systems: NEWS2, qSOFA, APACHE, and GCS

Files

SKILL.mdMarkdownGitHub ↗

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.' }

Related skills

How it compares

Choose healthcare-cdss-patterns over generic backend skills when implementing regulated clinical decision logic with medication safety and scoring requirements.

FAQ

What clinical features does healthcare-cdss-patterns cover?

healthcare-cdss-patterns covers drug interaction checking, dose validation engines, clinical scoring systems such as NEWS2, qSOFA, APACHE, and GCS, abnormal-value alerts, and medication order entry safety checks integrated into EMR workflows.

Why is false-negative tolerance zero in healthcare-cdss-patterns?

healthcare-cdss-patterns treats CDSS modules as patient-safety critical because missed drug interactions, dosing errors, or unalerted abnormal values can directly harm patients, requiring zero tolerance for false negatives in clinical logic.

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