Clinical Data Analytics/Legacy Modernization for Major Healthcare Information Services Company

Business Problem/ Scope of Work

Business Problem:  Sponsors & Contract Research Organizations (CROs) need proactive Risk Assessment and Mitigation.

Clinical Research Associates are burdened with a very logistically challenging job that includes large amounts of searching, organizing, planning and follow-up.

No clear leader in the clinical data repository space.
 

BDF Product Reference (Japan Mainframe Re-Platform [MFRP]):

The goal of this project is to migrate legacy programs running on Japan Mainframe to process the wholesale transactions and surrounding referential data (outlet, product and distributor) into the modern technology platform. 

Business Solution

A new SaaS-based clinical data analytics platform to harnesses structured and unstructured data into a single, standardized ecosystem for interrogation. 

Clinical Data Analytics Suite Modules — Providing flexibility to meet a range of customer needs Single solution supporting clinical operations and data management via CDAS.

BDF Product Reference (Mainframe Re-Platform [MFRP]):

MFRP is a project that migrates Core business processes implemented in the IBM mainframe to the open-source technology based BigData platform.

Throughout the development, we will work closely with Big Data Factory (BDF) component teams and try to integrate the components that would fits into the timeline.

Technical Solution

CDAS/CDP Project : 

  • CDAS enables clients to rapidly ingest, standardize, integrate data and generate insights from variety of clinical single data eco system.
  • Because CDAS enables the interoperability of systems, clients achieve shorter time to insight, increase the value of their data by making right decisions at the right time, and reduce cost of ownership of R&D focused operational and strategic business processes.
  • The Digital Management Suite (DTMS) is a set of IT platforms that will provide commercial products to customers for complete end to end management of clinical trials.
  • DTMS consists of the clinical Trial Management System (CTMS), Risk based Monitoring (RBM), Patient Engagement (PE) and Clinical Research Associate Central (CRA-C).
  • The DTMS IT Platforms utilize the Clinical Data Analytics Suite (CDAS) for data management which includes data capture, processing and analytics
     

BDF Product Reference (Japan Mainframe Re-Platform [MFRP]):

The Big Data Factory is a singular platform for modernizing our technology and infrastructure.  Bringing together the latest concepts and practices from Big Data: Auto scaling and containerized infrastructure,  Automation through DevOps, Hadoop & Map/Reduce, machine learning and predictive analytics, event driven and reactive architectures, stream processing, and service-orientation.  The BDF program aims to consolidate and hasten transforming data into insights, and insights into actions.  Going beyond descriptive statistics and "what happened" in healthcare markets, BDF will enable our clients, in conjunction with our internal product and consulting teams, to predict metrics and prescribe the right marketing strategies. 

Technologies/ Skills Used

CDAS/CDP Project :

  1. Worked for different work steams (Clinops, Patient Engagement, DTMS-CTMS, Ops) and played a key role in designing models/tables.
  2. Created separate data models/tables in Hive.
  3. Used ERWIN 2021R2 tool to create data models.
  4. Used BIG DATA TECHNOLOGIES (HADOOP, HIVE, SCALA, SPARK), AWS, DATA LAKE , SNOWFLAKE, AIRFLOW, GIT, GIT-Hub, HUE, DBeaver, etc.,
  5. Worked with Github/Git repo as change control tool and created merge requested for the scripts.
  6. Deployed the DDLs into different environments as needed and also supported in upgrading environments for every release.
  7. Assisted/helped in creating a database compare tool which will consider the latest DDLs available in Git repo for a particular release (From S3 bucket location) and compare them with target environment. Based on the delta, the tool will generate upgrade scripts. This saved lot of time and effort because of this automation tool we developed and helped us to deploy DDL more effective way.
  8. Worked on CDISC models and domain tables for Patient Engagement and created several tables in Hive environment.
     

BDF Product Reference (Japan Mainframe Re-Platform [MFRP]):

  1. Analyzed the data and mapped them to corresponding GDM tables.
  2. Updated mapping document with the transformation logics for Lift & Shift and also for new authored system scenarios.
  3. Identified bad data from source system and discussed with Japan Team to make sure our understanding is correct.
  4. Used ERWIN 2021R2 tool to create data models.
  5. Used BIG DATA TECHNOLOGIES (HADOOP, HIVE, SCALA, SPARK), AWS, DATA LAKE , SNOWFLAKE, AIRFLOW, GIT, GIT-Hub, HUE, DBeaver, etc.,
  6. Created Mainframe copybooks and  identified equivalent data types on target tables.
  7. Worked on GDM models (using 3NF) and added new tables in GDM as we proceed on our analysis on new source tables from Japan Mainframe system.Participated in discussion on Data Classifications, Data Governance, GDM discussions, Metadata Management and so on.
  8. Created and delivered source to target mapping documents on time.

Customer Success Outcomes

Harness clinical research data 

  • Streamline data collection from any source into a single ecosystem, and rapidly curate data for stakeholder use

Machine-augmented insights 

  • Generate smarter insights by applying sophisticated ML analysis to subject data, risk management, and more

Transform clinical outcomes

  • Embed data-driven intelligence in business workflows that automate manual tasks and empower stakeholders.