HEALTHCARE AND PHARMACOGENOMICS
Role: Business Analyst (Health Informatics)
Warfarin, a widely used anticoagulant, requires precise dosing due to narrow therapeutic windows and patient-specific risks. Incorrect dosing can lead to life-threatening complications such as stroke or bleeding events
The healthcare organisation aimed to enhance patient safety by developing an AI-assisted decision support dashboard that consolidates clinical, laboratory, and patient history data to help clinicians make more informed dosing decisions
The goal was to enhance dose accuracy, reduce review time, and support evidence-based decision-making by utilising a data-driven digital tool
Clinicians faced significant limitations with the previous manual review process
Patient data was spread across multiple clinical systems
Dosage decisions required manual cross-checks of INR values, medication history, and clinical notes
High risk of inconsistent dosing decisions between providers
No real-time analytics or trend monitoring for high-risk patients
Limited ability to detect early signs of unsafe dosing patterns
This created clinical inefficiencies and increased patient risk.
I served as the bridge between clinical teams, data scientists, and IT analysts, ensuring that requirements aligned with clinical workflows, safety standards, and technical feasibility
Conducted interviews with clinicians, pharmacists, lab leads, and IT teams
Mapped roles and data touchpoints across the current Warfarin review process
Captured clinical pain points and decision-making criteria
Identified key data sources (INR history, dosage records, comorbidities, lab results)
Defined business rules and functional requirements for dashboard views
Documented clinical decision dependencies (red flags, thresholds, alerts)
Collaborated with data scientists to translate clinical logic into data models
Mapped AS-IS Warfarin management pathway
Designed TO-BE workflow incorporating AI-supported recommendations
Created dashboard wireframes showing: Patient risk scoring, INR trend visualisations, Recommended dose ranges, Flags for unsafe conditions, Alert notifications
Defined user personas for different clinical roles (GP, nurse, pharmacist)
Built dashboard prototypes in Power BI
Created data visualisations for trend tracking, alerts, and risk stratification
Presented prototypes to clinical teams for iteration and validation
Developed UAT test scripts based on clinical scenarios
Coordinated user testing with nurses, pharmacists, and prescribers
Logged defects, usability concerns, and improvements for iteration
Documented operating procedures and dashboard guidelines
Produced training materials for clinical staff
Gathered feedback to support ongoing model improvement
Power BI
Excel
SQL (data extraction support)
Miro
Confluence
EHR/clinical system data feeds
Reduced clinician review time by 40% through consolidated data views
Improved Warfarin dose safety through alert-based recommendations
Enabled the identification of high-risk patients earlier using trend analysis
Standardised the decision-making process across clinical roles
Increased confidence in dosing decisions through AI-supported insights
Delivered an analytics framework that can scale to other medications and chronic conditions