Healthcare & Life Sciences

Vantage enables healthcare organizations and life sciences companies to consolidate clinical, operational, and financial data across disparate systems — from EMRs and lab systems to clinical trial databases. Build workflows that automate data reconciliation, monitor patient outcomes, and generate regulatory-grade documentation while maintaining strict audit trails.

Reconcile Clinical Trial Data Across Sites and Systems

Automatically reconcile electronic data capture (EDC) records against lab results and adverse event reports.

Scenario: A CRO (Contract Research Organization) managing a Phase III trial across 40 sites needs nightly reconciliation between the EDC system, central lab, and safety database to catch discrepancies before they delay the submission timeline.

Workflow Steps:

1. Schedule Trigger — Run nightly at 11 PM
2. Database Query (PostgreSQL) — Extract EDC submissions: subject ID, visit number, visit date, assessments completed, data entry status
3. Database Query (MSSQL) — Pull central lab results: subject ID, visit number, specimen date, analyte panel, results, reference ranges, flags
4. Database Query (PostgreSQL) — Pull adverse event (AE) and serious adverse event (SAE) reports from the safety database
5. Join — Reconcile EDC records with lab data on subject ID + visit number; link AEs to the corresponding visit
6. Data Validation — Apply reconciliation rules:
   • Missing lab results for expected visit assessments
   • Lab specimen dates > 3 days from visit date (protocol deviation?)
   • Out-of-range lab values not recorded as AEs in the EDC
   • Duplicate entries (same subject, same visit, same assessment)
   • Missing informed consent dates
7. Multi-Conditional — Route by discrepancy type:
   • Clean records → Aggregation (enrollment counts, endpoint metrics by cohort and site) → Dashboard Output
   • Data discrepancies → Write Excel (discrepancy listing by site and query type) → Send Email to clinical data management team
   • Safety signal (out-of-range lab not reported as AE) → AI Enrichment (classify severity: Grade 1–5, assess relationship to study drug) → Send Email to Medical Monitor (urgent) + Create Issue (Jira) for safety review board
8. Aggregation — Calculate trial metrics:
   • Enrollment: screened, enrolled, randomized, completed, withdrawn by site
   • Data quality: discrepancy rate per site, query resolution time
   • Protocol deviations by type and severity
9. Dashboard Output — Populate:
   • Metric Tile — Enrollment rate, screen-fail rate, query backlog count
   • Gantt Tile — Trial timeline milestones: first patient in, last patient in, database lock, submission
   • Comparison Tile — Site performance: enrollment vs. target by site
   • Table Tile — Query backlog by site with aging (days open)
   • Bar Tile — Discrepancies by category and site
   • Pivot Tile — Visit completion matrix: Subject × Visit (shows missing visits)
10. Write PDF — Generate weekly data quality report for the sponsor

Key Nodes: Schedule Trigger, Database Query (PostgreSQL, MSSQL), Join, Data Validation, Multi-Conditional, Aggregation, AI Enrichment, Write Excel, Write PDF, Create Issue (Jira), Send Email, Dashboard Output

Monitor Hospital Capacity and Optimize Patient Flow

Track bed census, ED throughput, OR utilization, and staffing levels to optimize resource allocation.

Scenario: A hospital operations center needs real-time visibility into capacity across all departments to manage patient flow, prevent ED boarding, and ensure adequate staffing.

Workflow Steps:

1. Schedule Trigger — Run every 2 hours
2. Database Query (MSSQL) — Pull bed census from the ADT (Admit/Discharge/Transfer) system: department, bed type, occupancy status, patient acuity, expected discharge date
3. Database Query (PostgreSQL) — Pull ED metrics: patients in waiting room, patients in treatment, average wait time, LWBS (left without being seen) count
4. Database Query (MSSQL) — Pull OR utilization: scheduled cases, actual start/end times, turnover time, cancellation count
5. Aggregation — Roll up by department and acuity level:
   • Occupancy rate: occupied beds / total beds
   • Available beds by type (ICU, med-surg, telemetry, pediatric)
   • ED throughput: arrivals/hour, discharges/hour
   • OR utilization: scheduled hours / available hours
6. Computed Column — Calculate:
   • Projected occupancy: current census + expected admissions - expected discharges
   • ED boarding time: time from admit decision to bed assignment
   • Staffing ratio: patients per nurse by department
7. Filter — Alert conditions:
   • Department occupancy > 90%
   • ED wait time > 60 minutes
   • ED boarding > 4 hours
   • Staffing ratio above safe threshold
8. Multi-Conditional — Route by severity:
   • Occupancy > 95% or ED boarding > 4 hours → Send Message (Teams) to chief nursing officer + Send Email to bed management coordinator + Dashboard Output (critical alert)
   • Occupancy 90–95% → Send Message (Teams) to charge nurses + Dashboard Output (warning)
9. AI Enrichment — Predict capacity pressure: "Based on admission patterns, the Medical-Surgical unit will reach 98% occupancy by 6 PM. Recommend expediting 3 pending discharges on 4-West."
10. Dashboard Output — Populate:
    • Stat Tile — Overall hospital occupancy rate
    • Bar Tile — Occupancy by department (color-coded: green < 85%, yellow 85-95%, red > 95%)
    • Metric Tile — ED wait time, boarding time, LWBS rate
    • Predictive Insights Tile — 24/48/72-hour bed demand forecast
    • Gantt Tile — OR schedule with utilization gaps
    • Line Tile — ED volume trend (hourly for past 72 hours)
    • Pivot Tile — Department × Shift staffing matrix

Key Nodes: Schedule Trigger, Database Query (MSSQL, PostgreSQL), Aggregation, Computed Column, Filter, Multi-Conditional, AI Enrichment, Send Message, Send Email, Dashboard Output

Manage Pharmacy Inventory and Medication Safety

Monitor medication inventory, track prescription patterns, and detect potential drug interactions.

Scenario: A hospital pharmacy director needs to track medication usage, predict stockout risks for critical drugs, and monitor prescribing patterns for potential safety issues.

Workflow Steps:

1. Schedule Trigger — Run every 4 hours
2. Database Query (PostgreSQL) — Pull pharmacy inventory: drug name, NDC, on-hand quantity, par level, reorder point, expiration date, unit cost
3. Database Query (MSSQL) — Pull dispensing data: drug, quantity dispensed, prescriber, patient department, timestamp
4. Aggregation — Calculate usage metrics:
   • Average daily usage by drug
   • Days of supply remaining: on_hand / avg_daily_usage
   • Cost per patient day by drug class
5. Computed Column — Calculate:
   • Drugs expiring within 90 days: potential waste
   • Critical drug coverage: hours of supply for emergency drugs
   • Budget variance: actual drug spend vs. formulary target
6. Filter — Flag critical conditions:
   • Days of supply < 7 for any formulary drug
   • Days of supply < 48 hours for critical/emergency drugs
   • Drugs expiring within 30 days with remaining inventory > $500
7. AI Enrichment — Analyze prescribing patterns: detect unusual spikes, off-formulary prescribing trends, and potential drug interaction risks
8. Multi-Conditional — Route by alert type:
   • Critical drug shortage → Send Email to pharmacy director + Send Message (Teams) to procurement + Dashboard Output (Event Monitor Tile)
   • Formulary deviation → Send Email to P&T committee chair
   • Expiration risk → Dashboard Output (List Tile) for rotation/redistribution
9. Dashboard Output — Populate:
   • Table Tile — Inventory status: drug, on-hand, par, days of supply, expiration
   • Metric Tile — Drug budget utilization %, critical shortage count
   • Bar Tile — Top 20 drugs by cost
   • Line Tile — Drug expenditure trend over 12 months
   • Event Monitor Tile — Active shortage alerts

Key Nodes: Schedule Trigger, Database Query (PostgreSQL, MSSQL), Aggregation, Computed Column, Filter, AI Enrichment, Multi-Conditional, Send Email, Send Message, Dashboard Output

Analyze Patient Outcomes and Track Quality Measures

Track clinical quality measures, patient outcomes, and readmission rates for regulatory reporting and improvement.

Scenario: A quality improvement team needs to monitor CMS (Centers for Medicare & Medicaid Services) quality measures, track 30-day readmission rates, and identify improvement opportunities.

Workflow Steps:

1. Schedule Trigger — Run weekly
2. Database Query (MSSQL) — Pull patient encounter data: admission date, discharge date, DRG, primary diagnosis, procedures performed, discharge disposition
3. Database Query (PostgreSQL) — Pull clinical outcomes: mortality, complications, hospital-acquired infections, falls, pressure injuries
4. Database Query (PostgreSQL) — Pull readmission data: patients readmitted within 30 days, readmission diagnosis, originating department
5. Join — Merge encounters with outcomes and readmission data
6. Aggregation — Calculate quality metrics:
   • 30-day readmission rate by DRG and department
   • Hospital-acquired infection rate per 1,000 patient days
   • Fall rate per 1,000 patient days
   • Average length of stay vs. expected by DRG
   • Mortality index: observed / expected
7. Computed Column — Calculate CMS quality measure compliance scores
8. AI Enrichment — Identify patterns: "30-day readmission for CHF increased from 18% to 24% in Q4, concentrated in patients discharged from 3-North. Root cause analysis suggests a correlation with a change in discharge education protocol implemented on October 15th."
9. Dashboard Output — Populate:
   • Line Tile — Quality measure trends over 12 months with target threshold lines
   • Comparison Tile — This quarter vs. same quarter last year for each measure
   • BI Intelligence Tile — AI-generated quality narrative for the monthly board report
   • Table Tile — Individual measure detail with status: met/not met
   • Metric Tile — Overall quality composite score
   • Bar Tile — Readmission rate by DRG (top 10)
   • Predictive Insights Tile — Projected measure compliance for the reporting period
10. Write PDF — Generate quarterly quality report for the medical executive committee

Key Nodes: Schedule Trigger, Database Query (MSSQL, PostgreSQL), Join, Aggregation, Computed Column, AI Enrichment, Write PDF, Dashboard Output

Example Dashboard: Hospital Operations Dashboard

Build this dashboard to give hospital administrators, nursing leadership, and operations teams real-time visibility into capacity, patient flow, and quality metrics.

Row 1 — Hospital Vitals

Row 2 — Capacity & Patient Flow

Row 3 — Quality & Safety

Row 4 — Staffing & Clinical Operations

Row 5 — Pharmacy & Predictions

Getting Started

To build healthcare workflows:

1. Connect your clinical databases — Add your EMR, lab, pharmacy, and ADT database connections under Integrations
2. Start with capacity monitoring — Build an hourly bed census workflow to get real-time visibility
3. Add quality measures — Schedule weekly quality metric calculations with automated trend detection
4. Automate reporting — Use Write PDF to generate regulatory submissions and board reports on schedule
5. Set up clinical alerts — Multi-Conditional routing for critical results, capacity thresholds, and safety signals