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Pharma Data-as-a-service eProfile Platform re-development to improve turnaround time of agents using AWS AI/ML Comprehend Medical
Information Technology
Python, Angular, MongoDB, SQL Server, AWS
About Client

Our client is the principle technology development partner for a USA based company which is into pharma-data as a service business

Problem Statement

In any Clinical Trial process, access to profiles of Key-Opinion-Leaders’ (KOL) who are involved in various medical research and development activities across the globe is a very important factor. This project is about improving the turnaround time of agents who build KOL’s e-Profiles.

Oneture's Role

Oneture assisted Client on AWS, Data Pipeline, Platform development technology related services


The end Client creates Profiles of KOL’s who are involved in various medical research and development activity across the globe. Mainly in areas listed below 

  1. Clinical trials 
  2. Research paper published 
  3. Regular writer in Medical journal 
  4. Diagnosis reports published 
  5. Research blog in therapeutic area 

In the current process, to create a complete profile of KOL Client collects data from various clinical trials sources in various forms and formats mostly semi-automated way in one centralised application. Agent logs into this master KOL Database, takes basic information name, therapeutic area, institutions etc and searches through the internet, manually goes through many articles, blogs, search results from various websites. After reading, fetching and analysing all those search results, agenta feed relevant information into Eprofile for respective KOLs

The project involved rebuilding and improving KOL data capture using XML/API to automatically access global clinical trial repositories, and AWS Comprehend Medical AI/ML services for captured-data enrichment and Elasticsearch technology for improved intelligent searchability and usability

With this in place, agents logs-in into a web application, which enables him/her to perform an intelligent search on a pre-collected/enriched data set to get the result for KOL profile. Incremental data on any specific result gets populated into the central data repository periodically and stored chronologically. 

As AWS product/platform development experts, Oneture participated in initial meetings to understand customer requirements, helped come up with overall architecture, technical solution, built various PoC(s) to showcase AWS capabilities and working of the underlying technical solution, prepared relevant technical documentation.

We jointly came up with a solution that includes following AWS services:

  • Elasticsearch: for Search service 
  • EC2 instance: A computation service used for downloading data from various sources and AWS SDK runs on this instance to automate many aws processes 
  • Lambda function: We have used lambda function with python to index & query data into Elasticsearch
  • Private API gateway: to expose lambda as RESTFUL API service 
  • Comprehend Medical: Comprehend Medical is AWS test analyser service, there are several AI trained models which provide many relevent properties on content related to the medical/helthcare domain. Comprehend medical is used mainly for data enrichment service 
  • S3: Amazon S3 is getting used for all storage purpose 
Technology Domain Tools
Development Technologies Python, MS SQL, MongoDB, Angular
AWS Product & Services Lambda functions, VPC, S3, Elasticsearch , Comprehend, API Gateway, CloudTrail, CloudWatch, RDS, EC2