AWS-Powered Physician Engagement Intelligence

Situation

A leading pharmaceutical company’s medical technology business units required deeper visibility into physician influence, scientific contribution and professional development to guide engagement and investment strategy. Key Opinion Leaders (KOLs) and fellows data were scattered across numerous internal and external systems, making it difficult to build a unified picture of healthcare professionals activities across the US, EMEA and APAC regions.

Problem

• KOLs and fellows data fragmented across multiple disconnected systems 

• Inconsistent identifiers led to duplicate and unreliable physician records 

• Heavy manual effort and time required to aggregate and validate information 

• Limited visibility into physician influence, collaboration and engagement history 

• Delays in identifying high-value KOLs slowing down strategic investment planning 

• Reliance on intuitive decision-making leading to investment misallocation and suboptimal return on investment (ROI) 

Solution

• Built a centralized, cloud-based platform to unify KOLs, fellows and engagement data 

• Automated data ingestion, transformation and quarterly refresh cycles to eliminate manual effort 

• Applied intelligent fuzzy-matching techniques to resolve duplicate and inconsistent physician identities 

• Introduced KOL ranking models based on scientific contribution, publications and clinical activity 

• Enabled longitudinal tracking of fellows to surface emerging healthcare influencers early 

• Delivered interactive dashboards offering a 360-degree view of KOLs and fellows profiles 

Key Components:

Scalable Data Processing: AWS EC2 powered the ingestion, transformation and reporting  workflows, processing KOLs, fellows and engagement data from multiple internal and external healthcare sources at scale. 

Centralized Data Storage: Amazon S3 provided centralized, durable storage for raw and curated healthcare datasets, supporting flexible retention and downstream processing. 

Reporting-Optimized Querying: Amazon Redshift served as the enterprise data warehouse, enabling fast, optimized querying for reporting and dashboard consumption. 

Intelligent Identity Resolution: Fuzzy-matching algorithms running on AWS infrastructure standardized physician and fellow identities across sources, eliminating duplicate records and improving data trust. 

Automated Orchestration: Control-M managed scheduling and quarterly refresh cycles across the AWS environment, ensuring reliable, hands-off pipeline execution. 

Actionable Insights: Qlik Sense dashboards, powered by curated Redshift datasets, delivered business-friendly Physician and Fellow Card views with visibility into KOL ranking, scientific contribution and engagement trends. 

Together, these AWS services transformed fragmented, manually maintained physician data into a governed, centralized and insight-ready platform – empowering global business units to identify KOLs faster, track emerging clinical leaders, engage and invest with greater precision and agility. 

Mu-PDNA: Demonstrate a hypothesis-driven approach to break down large, ambiguous problem statements into manageable analytical questions, enabling systematic resolution of the overarching business objective. 

Impact

• 3.7x annualized cost savings through enablement of targeted engagement efforts 

• 30+ engagements successfully completed, enabling sustained business value delivery 

• 40+ views delivered (2021-current), expanding data-driven decision-making access across business units 

• Eliminated manual aggregation and reconciliation of KOLs and fellows data 

• Enabled earlier identification of emerging KOLs and future healthcare influencers