Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
affaan-m avatar

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)
At a glance

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
From the docs

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.
SKILL.md
The CDSS engine is a **pure function library with zero side effects**. Input clinical data, output alerts. This makes it fully testable.
SKILL.md
2. **`validateDose(drug, dose, route, weight, age, renalFunction)`** — Validates a prescribed dose against weight-based, age-adjusted, and renal-adjusted rules. Returns `DoseValidationResult`.
SKILL.md
npx skills add https://github.com/affaan-m/everything-claude-code --skill healthcare-cdss-patterns

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs4.5k
repo stars238k
Security audit3 / 3 scanners passed
Last updatedAugust 5, 2026
Repositoryaffaan-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

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

Forks & variants (1)

Healthcare Cdss Patterns has 1 known copy in the catalog totaling 1.4k installs. They canonicalize to this original listing.

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.

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.