đź“š CPOE Systems Intermediate 15 min read
How to Build Evidence-Based Order Sets for Common Clinical Conditions
Comprehensive guide to developing, implementing, and maintaining clinical order sets that improve care quality, reduce variability, and ensure adherence to evidence-based guidelines.
✍️
Dr. Michael Chen, MD, MPH
Order sets transform evidence-based clinical guidelines into actionable, one-click order bundles that ensure consistent, high-quality care. Well-designed order sets improve guideline adherence from 45-60% to 85-95%, reduce ordering errors by 73%, and save clinicians an average of 12 minutes per admission. This guide shows you how to build effective order sets for your organization.
Understanding Order Sets
An order set is a pre-defined collection of orders for a specific clinical scenario:
Common Order Set Types:
- Admission Order Sets: CHF admission, pneumonia admission, COPD exacerbation
- Procedure Order Sets: Pre-operative orders, cardiac catheterization prep
- Protocol Order Sets: Sepsis bundle, stroke alert, STEMI protocol
- Post-Operative Order Sets: Post-CABG, post-hip replacement
- Condition Management: Diabetes management, anticoagulation protocol
JustCopy.ai includes 200+ pre-built, evidence-based order sets for common conditions, all customizable by the platform’s 10 AI agents to match your organization’s protocols and formulary.
Order Set Architecture
// Order set data structure
interface OrderSet {
// Metadata
id: string;
name: string;
description: string;
version: string;
lastUpdated: Date;
// Clinical context
condition: string;
specialty: string[];
setting: 'inpatient' | 'outpatient' | 'emergency' | 'ICU';
icd10Codes: string[];
// Evidence basis
evidenceLevel: 'A' | 'B' | 'C';
guidelines: Reference[];
lastReviewed: Date;
reviewDueDate: Date;
// Usage
isActive: boolean;
requiresCustomization: boolean;
estimatedTimeToComplete: number; // minutes
// Structure
sections: OrderSetSection[];
// Metadata for tracking
usageCount: number;
outcomeTracking: OutcomeMetrics;
}
interface OrderSetSection {
id: string;
name: string;
description?: string;
sequence: number;
// Section items
items: OrderSetItem[];
// Conditional display
displayCondition?: Condition;
}
interface OrderSetItem {
id: string;
sequence: number;
// Order details
orderType: 'medication' | 'lab' | 'imaging' | 'nursing' | 'diet' | 'activity' | 'consult';
orderTemplate: any; // Type-specific order template
// Item behavior
isRequired: boolean;
isSelectedByDefault: boolean;
allowModification: boolean;
// Conditional logic
displayCondition?: Condition;
enableCondition?: Condition;
// Documentation
rationale?: string;
evidenceReference?: Reference;
// Customization hints
customizationPrompts?: CustomizationPrompt[];
}
interface CustomizationPrompt {
field: string;
prompt: string;
defaultValue?: any;
validationRules?: ValidationRule[];
suggestions?: any[];
}JustCopy.ai’s order set builder uses this comprehensive structure, with AI agents helping design conditional logic, evidence linking, and customization points automatically.
Step 1: Selecting Conditions for Order Sets
High-Impact Opportunities
// Analyze where order sets provide most value
class OrderSetOpportunityAnalyzer {
async identifyOpportunities() {
// Analyze admissions data
const admissions = await db.admissions.aggregate([
{
$group: {
_id: '$primary_diagnosis',
count: { $sum: 1 },
avgLOS: { $avg: '$length_of_stay' },
avgCost: { $avg: '$total_cost' },
readmissionRate: { $avg: '$readmitted_30day' }
}
},
{ $sort: { count: -1 } },
{ $limit: 50 }
]);
const opportunities = [];
for (const admission of admissions) {
// Calculate potential impact
const impact = this.calculateImpact(admission);
// Check if clinical guidelines exist
const guidelines = await this.findGuidelines(admission._id);
// Check current order set existence
const existingOrderSet = await db.orderSets.findOne({
condition: admission._id
});
if (!existingOrderSet && guidelines.length > 0 && impact.score > 0.7) {
opportunities.push({
condition: admission._id,
volume: admission.count,
avgLOS: admission.avgLOS,
avgCost: admission.avgCost,
readmissionRate: admission.readmissionRate,
availableGuidelines: guidelines,
estimatedImpact: impact,
priority: this.calculatePriority(admission, impact)
});
}
}
return opportunities.sort((a, b) => b.priority - a.priority);
}
calculateImpact(admission) {
let score = 0;
// High volume = more impact
if (admission.count > 100) score += 0.3;
// High cost = cost reduction opportunity
if (admission.avgCost > 15000) score += 0.2;
// High LOS = efficiency opportunity
if (admission.avgLOS > 5) score += 0.2;
// High readmission rate = quality opportunity
if (admission.readmissionRate > 0.15) score += 0.3;
return {
score,
estimatedAnnualSavings: this.estimateSavings(admission, score)
};
}
}JustCopy.ai’s analytics identify order set opportunities automatically, analyzing your organization’s data to prioritize high-impact conditions.
Step 2: Gathering Evidence
Evidence-Based Order Set Development
// Guideline research and compilation
class GuidelineCompiler {
async compileEvidence(condition: string): Promise<EvidenceBase> {
// Search professional society guidelines
const guidelines = await this.searchGuidelines(condition, {
sources: [
'ACC/AHA',
'ADA',
'IDSA',
'CHEST',
'ATS',
'NCCN',
'ACOG',
'AAP'
]
});
// Search systematic reviews
const reviews = await this.searchCochraneLibrary(condition);
// Search clinical trials
const trials = await this.searchClinicalTrials(condition);
// Compile evidence
return {
guidelines: this.rankGuidelines(guidelines),
systematicReviews: reviews,
keyTrials: trials,
// Synthesize recommendations
recommendations: await this.synthesizeRecommendations(
guidelines,
reviews,
trials
),
// Evidence quality assessment
qualityRating: this.assessEvidenceQuality(guidelines, reviews, trials)
};
}
async synthesizeRecommendations(guidelines, reviews, trials) {
const recommendations = [];
// Extract all recommendations
for (const guideline of guidelines) {
for (const rec of guideline.recommendations) {
recommendations.push({
source: guideline.name,
recommendation: rec.text,
strength: rec.classOfRecommendation, // I, IIa, IIb, III
evidenceLevel: rec.levelOfEvidence, // A, B, C
references: rec.supportingTrials
});
}
}
// Group by topic
const grouped = this.groupRecommendations(recommendations);
// Resolve conflicts
const reconciled = await this.reconcileConflicts(grouped);
return reconciled;
}
}JustCopy.ai’s AI agents research clinical guidelines automatically, synthesizing recommendations from multiple sources to build evidence-based order sets.
Step 3: Designing the Order Set
Example: Heart Failure Admission Order Set
// Complete heart failure admission order set
const heartFailureAdmissionOrderSet = {
id: 'hf-admission-v3.2',
name: 'Heart Failure Admission',
description: 'Comprehensive admission orders for patients with acute decompensated heart failure',
version: '3.2',
condition: 'Heart Failure',
icd10Codes: ['I50.1', 'I50.20', 'I50.21', 'I50.22', 'I50.23', 'I50.9'],
specialty: ['cardiology', 'internal-medicine', 'hospitalist'],
setting: 'inpatient',
// Evidence basis
evidenceLevel: 'A',
guidelines: [
{
name: 'ACC/AHA Heart Failure Guidelines 2022',
url: 'https://www.ahajournals.org/...',
sections: ['Acute HF Management', 'GDMT Initiation']
}
],
sections: [
{
id: 'admission-orders',
name: 'Admission Orders',
sequence: 1,
items: [
{
sequence: 1,
orderType: 'nursing',
orderTemplate: {
type: 'admission',
service: 'cardiology',
acuity: 'intermediate-care',
isolation: 'none'
},
isRequired: true,
isSelectedByDefault: true,
allowModification: true
},
{
sequence: 2,
orderType: 'nursing',
orderTemplate: {
type: 'vitals',
frequency: 'Q4H',
parameters: ['BP', 'HR', 'RR', 'SpO2', 'Weight'],
specialInstructions: 'Daily standing weight at same time'
},
isRequired: true,
isSelectedByDefault: true,
rationale: 'Monitor response to diuresis and hemodynamic status'
},
{
sequence: 3,
orderType: 'nursing',
orderTemplate: {
type: 'intake-output',
frequency: 'Q8H',
strictIO: true,
fluidRestriction: {
enabled: true,
maxDailyIntake: 2000 // mL
}
},
isRequired: true,
isSelectedByDefault: true,
rationale: 'Monitor fluid balance during diuresis'
},
{
sequence: 4,
orderType: 'diet',
orderTemplate: {
type: 'cardiac',
sodiumRestriction: '2-gram',
fluidRestriction: '2-liter',
consistency: 'regular'
},
isRequired: true,
isSelectedByDefault: true,
allowModification: true
},
{
sequence: 5,
orderType: 'activity',
orderTemplate: {
level: 'up-as-tolerated',
restrictions: ['Avoid Valsalva'],
fallPrecautions: true
},
isRequired: true,
isSelectedByDefault: true
}
]
},
{
id: 'diagnostic-studies',
name: 'Diagnostic Studies',
sequence: 2,
items: [
{
sequence: 1,
orderType: 'lab',
orderTemplate: {
panel: 'BMP',
priority: 'routine',
frequency: 'Daily',
durationDays: 3
},
isRequired: true,
isSelectedByDefault: true,
rationale: 'Monitor electrolytes and renal function during diuresis'
},
{
sequence: 2,
orderType: 'lab',
orderTemplate: {
panel: 'CBC',
priority: 'routine',
frequency: 'Once'
},
isRequired: false,
isSelectedByDefault: true
},
{
sequence: 3,
orderType: 'lab',
orderTemplate: {
test: 'BNP',
priority: 'stat'
},
isRequired: true,
isSelectedByDefault: true,
rationale: 'Baseline BNP for diagnosis and treatment response monitoring',
evidenceReference: {
study: 'PRIDE Study',
finding: 'BNP >100 pg/mL has 90% sensitivity for HF diagnosis'
}
},
{
sequence: 4,
orderType: 'lab',
orderTemplate: {
test: 'Troponin I',
priority: 'stat'
},
isRequired: false,
isSelectedByDefault: true,
rationale: 'Rule out ACS as precipitant'
},
{
sequence: 5,
orderType: 'imaging',
orderTemplate: {
modality: 'Chest X-ray',
views: ['PA', 'Lateral'],
portability: 'portable-if-needed',
priority: 'routine'
},
isRequired: true,
isSelectedByDefault: true,
rationale: 'Assess pulmonary congestion'
},
{
sequence: 6,
orderType: 'imaging',
orderTemplate: {
modality: 'Echocardiogram',
type: 'transthoracic',
indication: 'Assess LV function and valve disease',
priority: 'routine'
},
isRequired: false,
isSelectedByDefault: true,
displayCondition: {
type: 'no-recent-echo',
timeframe: '6-months'
},
rationale: 'Assess ejection fraction to guide therapy'
}
]
},
{
id: 'medications',
name: 'Medications',
sequence: 3,
items: [
{
sequence: 1,
orderType: 'medication',
orderTemplate: {
drug: 'Furosemide',
route: 'IV',
doseCustomization: true,
frequency: 'BID'
},
isRequired: true,
isSelectedByDefault: true,
customizationPrompts: [
{
field: 'dose',
prompt: 'Enter furosemide dose (typical: 40-80 mg)',
defaultValue: 40,
validationRules: [
{ type: 'range', min: 20, max: 200 }
],
suggestions: [
{
condition: 'patient.homeDosingFurosemide > 0',
suggestedDose: 'patient.homeDosingFurosemide * 2',
rationale: 'IV dose typically 2x home oral dose'
},
{
condition: 'patient.creatinine > 2.0',
suggestedDose: 80,
rationale: 'Higher dose needed with renal impairment'
}
]
}
],
rationale: 'IV diuresis for volume overload',
evidenceReference: {
guideline: 'ACC/AHA 2022',
recommendation: 'Class I, Level B',
text: 'Loop diuretics recommended for all patients with HF and volume overload'
}
},
{
sequence: 2,
orderType: 'medication',
orderTemplate: {
drug: 'Potassium Chloride',
dose: 20,
doseUnit: 'mEq',
route: 'PO',
frequency: 'BID'
},
isRequired: false,
isSelectedByDefault: false,
displayCondition: {
type: 'lab-value',
test: 'potassium',
operator: '<',
value: 4.0
},
rationale: 'Replete potassium during diuresis'
},
{
sequence: 3,
orderType: 'medication',
orderTemplate: {
drugClass: 'ACE-Inhibitor',
options: ['Lisinopril', 'Enalapril', 'Ramipril'],
route: 'PO',
frequency: 'Daily'
},
isRequired: false,
isSelectedByDefault: true,
displayCondition: {
type: 'and',
conditions: [
{ type: 'ejection-fraction', operator: '<', value: 40 },
{ type: 'not-on-medication', drugClass: 'ACE-Inhibitor' }
]
},
customizationPrompts: [
{
field: 'drug',
prompt: 'Select ACE inhibitor',
suggestions: [
{
drug: 'Lisinopril',
startingDose: 2.5,
doseUnit: 'mg',
rationale: 'Most commonly used, once daily dosing'
}
]
},
{
field: 'dose',
prompt: 'Starting dose (typical: 2.5-5 mg)',
validationRules: [
{
type: 'renal-check',
message: 'Start low dose if Cr >2.0 or eGFR <30'
}
]
}
],
rationale: 'GDMT for HFrEF - mortality benefit',
evidenceReference: {
guideline: 'ACC/AHA 2022',
recommendation: 'Class I, Level A',
trials: ['SOLVD', 'CONSENSUS'],
benefit: '16% relative risk reduction in mortality'
}
},
{
sequence: 4,
orderType: 'medication',
orderTemplate: {
drugClass: 'Beta-Blocker',
options: ['Carvedilol', 'Metoprolol Succinate', 'Bisoprolol'],
route: 'PO',
frequency: 'BID'
},
isRequired: false,
isSelectedByDefault: false,
displayCondition: {
type: 'and',
conditions: [
{ type: 'ejection-fraction', operator: '<', value: 40 },
{ type: 'patient-stable', euvolemic: true },
{ type: 'not-on-medication', drugClass: 'Beta-Blocker' }
]
},
rationale: 'Start after diuresis complete - GDMT for HFrEF',
evidenceReference: {
guideline: 'ACC/AHA 2022',
recommendation: 'Class I, Level A',
trials: ['MERIT-HF', 'CIBIS-II', 'COPERNICUS'],
benefit: '34% relative risk reduction in mortality'
}
},
{
sequence: 5,
orderType: 'medication',
orderTemplate: {
drug: 'Spironolactone',
dose: 25,
doseUnit: 'mg',
route: 'PO',
frequency: 'Daily'
},
isRequired: false,
isSelectedByDefault: false,
displayCondition: {
type: 'and',
conditions: [
{ type: 'ejection-fraction', operator: '<', value: 35 },
{ type: 'not-on-medication', drug: 'Spironolactone' },
{ type: 'lab-value', test: 'potassium', operator: '<', value: 5.0 },
{ type: 'lab-value', test: 'creatinine', operator: '<', value: 2.5 }
]
},
rationale: 'Mineralocorticoid receptor antagonist for severe HFrEF',
evidenceReference: {
guideline: 'ACC/AHA 2022',
recommendation: 'Class I, Level A',
trials: ['RALES'],
benefit: '30% relative risk reduction in mortality'
}
}
]
},
{
id: 'consults',
name: 'Consultations',
sequence: 4,
items: [
{
sequence: 1,
orderType: 'consult',
orderTemplate: {
service: 'Cardiology',
urgency: 'routine',
reason: 'Heart failure management optimization'
},
isRequired: false,
isSelectedByDefault: false,
displayCondition: {
type: 'or',
conditions: [
{ type: 'ejection-fraction', operator: '<', value: 30 },
{ type: 'new-diagnosis-hf' },
{ type: 'refractory-symptoms' }
]
}
},
{
sequence: 2,
orderType: 'consult',
orderTemplate: {
service: 'Heart Failure Nurse',
urgency: 'routine',
reason: 'Patient education and discharge planning'
},
isRequired: false,
isSelectedByDefault: true,
rationale: 'HF nurse education reduces 30-day readmissions by 25%'
}
]
},
{
id: 'patient-education',
name: 'Patient Education',
sequence: 5,
items: [
{
sequence: 1,
orderType: 'nursing',
orderTemplate: {
type: 'education',
topics: [
'Heart Failure Disease Process',
'Daily Weight Monitoring',
'Fluid and Sodium Restriction',
'Medication Compliance',
'When to Call Doctor'
],
method: 'teach-back',
documentComprehension: true
},
isRequired: true,
isSelectedByDefault: true,
rationale: 'Patient education improves adherence and reduces readmissions'
}
]
}
],
// Outcome tracking
outcomeTracking: {
measures: [
'Length of stay',
'30-day readmission rate',
'GDMT compliance at discharge',
'BNP reduction',
'Weight loss',
'Patient education completion'
]
}
};JustCopy.ai’s order set builder guides you through creating comprehensive, evidence-based order sets with conditional logic, customization prompts, and outcome tracking built in.
Step 4: Building Conditional Logic
// Dynamic order set behavior
class OrderSetEngine {
async evaluateItem(item: OrderSetItem, patient: Patient, context: any): Promise<ItemEvaluation> {
// Check display condition
if (item.displayCondition) {
const shouldDisplay = await this.evaluateCondition(
item.displayCondition,
patient,
context
);
if (!shouldDisplay) {
return { display: false, enable: false };
}
}
// Check enable condition
if (item.enableCondition) {
const shouldEnable = await this.evaluateCondition(
item.enableCondition,
patient,
context
);
return { display: true, enable: shouldEnable };
}
return { display: true, enable: true };
}
async evaluateCondition(condition: Condition, patient: Patient, context: any): Promise<boolean> {
switch (condition.type) {
case 'lab-value':
const labValue = await this.getLatestLab(patient, condition.test);
return this.compareValue(labValue, condition.operator, condition.value);
case 'ejection-fraction':
const echo = await this.getLatestEcho(patient);
return this.compareValue(echo.ef, condition.operator, condition.value);
case 'not-on-medication':
const onMed = patient.medications.some(m =>
(condition.drug && m.name === condition.drug) ||
(condition.drugClass && m.drugClass === condition.drugClass)
);
return !onMed;
case 'no-recent-echo':
const lastEcho = await this.getLatestEcho(patient);
if (!lastEcho) return true;
const daysSince = this.daysBetween(new Date(), lastEcho.date);
const timeframeMonths = parseInt(condition.timeframe);
return daysSince > (timeframeMonths * 30);
case 'and':
for (const subcondition of condition.conditions) {
const result = await this.evaluateCondition(subcondition, patient, context);
if (!result) return false;
}
return true;
case 'or':
for (const subcondition of condition.conditions) {
const result = await this.evaluateCondition(subcondition, patient, context);
if (result) return true;
}
return false;
default:
return true;
}
}
}JustCopy.ai’s conditional logic engine dynamically shows/hides order set items based on patient-specific data, ensuring every patient receives appropriate, personalized care.
Step 5: Pilot Testing
// Order set pilot program
class OrderSetPilot {
async runPilot(orderSet, pilotGroup) {
// Deploy to pilot users
await this.deployToPilot(orderSet, pilotGroup);
// Track usage
const usage = await this.monitorUsage(orderSet, durationDays=30);
// Gather feedback
const feedback = await this.collectFeedback(pilotGroup);
// Analyze outcomes
const outcomes = await this.analyzeOutcomes(orderSet);
return {
usage: {
totalUses: usage.count,
avgTimeToComplete: usage.avgCompletionTime,
customizationRate: usage.customizations / usage.count,
abandonmentRate: usage.abandoned / usage.count
},
feedback: {
satisfaction: feedback.avgSatisfaction,
easeOfUse: feedback.easeOfUse,
timeSavings: feedback.perceivedTimeSavings,
suggestions: feedback.improvements
},
outcomes: {
guidelineAdherence: outcomes.adherenceRate,
completeness: outcomes.completenessRate,
errors: outcomes.errorRate,
lengthOfStay: outcomes.avgLOS,
readmissions: outcomes.readmissionRate
},
recommendations: this.generateRecommendations(usage, feedback, outcomes)
};
}
generateRecommendations(usage, feedback, outcomes) {
const recommendations = [];
// High customization rate = needs better defaults
if (usage.customizationRate > 0.7) {
recommendations.push({
type: 'revise-defaults',
message: '70% of users modify defaults - consider revising default values',
action: 'review-customization-patterns'
});
}
// High abandonment = too complex or time-consuming
if (usage.abandonmentRate > 0.2) {
recommendations.push({
type: 'simplify',
message: '20% abandonment rate - order set may be too complex',
action: 'reduce-required-items-or-streamline'
});
}
// Low satisfaction = usability issues
if (feedback.satisfaction < 7.0) {
recommendations.push({
type: 'improve-usability',
message: 'Low satisfaction score - gather detailed feedback on pain points',
action: 'conduct-user-interviews'
});
}
return recommendations;
}
}JustCopy.ai tracks order set usage automatically, providing analytics on adoption, customization patterns, and outcomes to guide continuous improvement.
Step 6: Maintenance and Updates
// Order set lifecycle management
class OrderSetMaintenance {
async maintainOrderSets() {
const orderSets = await db.orderSets.find({ isActive: true });
for (const orderSet of orderSets) {
// Check if review is due
if (orderSet.reviewDueDate < new Date()) {
await this.flagForReview(orderSet, 'scheduled-review-due');
}
// Check for guideline updates
const guidelineUpdates = await this.checkGuidelineUpdates(orderSet);
if (guidelineUpdates.length > 0) {
await this.flagForReview(orderSet, 'guideline-updated', guidelineUpdates);
}
// Check for formulary changes
const formularyChanges = await this.checkFormularyChanges(orderSet);
if (formularyChanges.length > 0) {
await this.flagForReview(orderSet, 'formulary-changed', formularyChanges);
}
// Analyze usage patterns
const lowUsage = await this.checkUsageRate(orderSet);
if (lowUsage) {
await this.flagForReview(orderSet, 'low-usage-retire-candidate');
}
// Check outcome data
const poorOutcomes = await this.checkOutcomes(orderSet);
if (poorOutcomes) {
await this.flagForReview(orderSet, 'poor-outcomes-needs-revision');
}
}
}
async checkGuidelineUpdates(orderSet) {
const updates = [];
for (const guideline of orderSet.guidelines) {
const latestVersion = await guidelineRegistry.getLatestVersion(guideline.name);
if (latestVersion.version > guideline.version) {
updates.push({
guideline: guideline.name,
currentVersion: guideline.version,
latestVersion: latestVersion.version,
changesSummary: latestVersion.majorChanges,
impactsOrderSet: await this.assessImpact(orderSet, latestVersion.changes)
});
}
}
return updates;
}
}JustCopy.ai’s AI agents monitor order sets automatically, alerting your team when guidelines change, formularies update, or usage patterns suggest revisions needed.
Best Practices
1. Keep Order Sets Concise
Good: 8-12 orders, focused on essentials Bad: 40+ orders, trying to cover every possibility
2. Smart Defaults
// Intelligent default selection
function selectDefaults(orderSet, patient) {
for (const item of orderSet.items) {
// If most patients need this, select by default
if (item.usageRate > 0.85) {
item.isSelectedByDefault = true;
}
// If patient-specific criteria met, select
if (item.selectionCriteria) {
item.isSelectedByDefault = evaluateCriteria(
item.selectionCriteria,
patient
);
}
}
}3. Allow Customization
Every order should be modifiable - clinicians need flexibility for patient-specific care.
4. Link to Evidence
Include rationale and evidence references so clinicians understand the “why” behind each order.
5. Regular Review
Establish 12-month review cycle minimum, sooner if guidelines change.
ROI Analysis
500-Bed Hospital:
Investment:
- JustCopy.ai order set platform: [Contact for pricing]
- Clinical informaticist time: 120 hours
- Physician champion time: 80 hours
Returns (Annual):
- Time savings: 12 min/admission Ă— 25,000 admissions = 5,000 hours = $750k
- Improved guideline adherence: $2.1M (better outcomes, reduced complications)
- Reduced ordering errors: $800k (fewer ADEs)
- Reduced length of stay: $1.2M (0.3 day reduction Ă— 25,000 admissions)
- Improved Core Measure performance: $500k (quality incentives)
Total Benefit: $5.35M annually ROI: 600-800% in first year
Conclusion
Evidence-based order sets transform clinical guidelines from aspirational documents into practical, one-click tools that ensure consistent, high-quality care. Well-designed order sets improve outcomes, reduce errors, save time, and make it easy for clinicians to do the right thing.
JustCopy.ai makes order set development effortless, with 10 AI agents handling evidence research, conditional logic design, EHR integration, and continuous maintenance automatically. The platform includes 200+ pre-built order sets ready to customize and deploy.
Ready to build evidence-based order sets? Explore JustCopy.ai’s order set builder and discover how automated development can deliver comprehensive, customized order sets in days, not months.
Better care, one click at a time. Start with JustCopy.ai today.
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