Revolutionary scalable solution combining automation with human-in-the-loop validation for HCP data
The Challenge
The client faced significant hurdles managing the ever-evolving landscape of healthcare professional (HCP) data. They needed to balance data accuracy with operational efficiency while leveraging diverse data sources. Ensuring consistent engagement from the sales team in data validation without overburdening them also posed a challenge.
Our Solution
Agilisium implemented a comprehensive solution to streamline HCP data management, ensuring data accuracy, operational efficiency, and compliance. This solution focuses on the following key aspects:
Hybrid Automation Model
Blending human expertise with automation for efficient data validation.
Intelligent Data Triage System
Differentiating between key and non-key HCP profiles to prioritize data processing.
High -Touch Review Process
Targeted review process involving both data stewards and sales representatives for accurate data validation.
Digital Sign-Off Mechanism
Ensuring accountability, accuracy and reduced manual interventions.
Streamlined Workflow for Non-Key HCPs
Simplified technical workflow to ensure seamless master data processing for non-key HCPs.
The Outcomes
This solution not only resolved immediate data management challenges but also set a new industry standard by combining automation with human-in-the-loop validation. This scalable solution is adaptable to various healthcare markets and regulatory environments, promising significant ROI and competitive advantage in an increasingly data-driven healthcare landscape.
75% Reduction in Manual Data Review Time
Automation allowed data stewards to focus on higher-value tasks, increasing productivity.
60% Increase in Sales Team Engagement
Ensured active participation in data validation and improved data accountability.
40% Faster Time-to-Market
Enhanced efficiency in updating HCP information, resulting in faster market readiness.
99% Data Accuracy for Key HCPs
An improvement from the initial 85%, improving overall data reliability.