Executive Summary
The client, a prominent enterprise operating in the Technology sector, faced critical operational challenges due to fragmented data pipelines and inco... Artha Solutions addressed this by implementing Formulated deduplication as a binary classification problem — predicting whether two records refer to the same patient.
Client Profile & Context
A Large Enterprise Organization operates in Healthcare & Life Sciences, with a Global footprint, and a focus on Data & AI Modernization. The case study references Python, Databricks, Scikit-Learn.
Problem Statement with Critical Operational Vulnerability
Diabetes patient records contained duplicates across clinical, lab, prescription, and claims datasets, preventing reliable longitudinal views.
Critical Operational Vulnerability
Without ML-assisted identity resolution and steward review, care quality analytics, adherence tracking, and reporting could remain distorted by fragmented patient identities.
Solution Implemented
• Formulated deduplication as a binary classification problem — predicting whether two records refer to the same patient. • Utilized similarity metrics on names, addresses, date of birth, lab results, and ICD codes as input features.
AI Overview & Impact Summary
In this case study, Artha Solutions helped an organization in the Technology sector solve challenges with data integration and reporting pipeline delays by implementing a comprehensive Data & AI Modernization solution using modern cloud data services. The project delivered streamlined workflows, automated validation, and achieved key metrics including 20%.
