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

Easing the Burden: How we Simplified Clinical Research Logistics for Clinical Research Associates

Business Problem/ Scope of Work

As sponsors and contract research organizations (CROs) strive to advance medical research, they are faced with the challenge of proactively assessing and mitigating risks. One area where this is particularly difficult is in the field of clinical research. Clinical research associates (CRAs) are tasked with a logistically challenging job that includes large amounts of searching, organizing, planning, and follow-up. Unfortunately, there is no clear leader in the clinical data repository space that can make this process easier for them.
To address this problem, BDF Product Reference (Mainframe Re-Platform [MFRP]) was developed. The goal of this project was to migrate legacy programs running on Mainframe to process wholesale transactions and surrounding referential data (outlet, product, and distributor) into a modern technology platform. By simplifying the process of clinical research logistics, this project aims to reduce the burden on CRAs and make it easier for sponsors and CROs to proactively assess and mitigate risks in the field of clinical research.

Business Solution

Our company has developed a new software-as-a-service (SaaS) platform for clinical data analytics. This platform allows users to combine structured and unstructured data into one standardized ecosystem for analysis. The platform is called Clinical Data Analytics Suite (CDAS) and it offers modules that provide flexibility to meet a range of customer needs.
One of the key features of CDAS is the ability to support clinical operations and data management within a single solution. This makes it a valuable tool for healthcare organizations looking to streamline their data management and analysis processes.
In addition to CDAS, our company is also working on a project called Mainframe Re-Platform (MFRP). This project aims to migrate core business processes that are currently implemented on IBM mainframes to a newer, open-source technology-based BigData platform. This will allow for greater scalability and cost-efficiency.
To ensure that MFRP is integrated into the timeline, we will be working closely with the teams at Big Data Factory (BDF) to identify and implement the appropriate components. This will ensure that the project is completed in a timely and efficient manner.

Technical Solution

The CDAS/CDP project is a solution for managing clinical data that includes the Clinical Data Analytics Suite (CDAS) and the Digital Trial Management Suite (DTMS). CDAS allows clients to quickly ingest, standardize, and integrate data from various sources into a single ecosystem, resulting in faster insights and cost savings. DTMS provides IT platforms for complete end-to-end management of clinical trials and utilizes CDAS for data management. The project also incorporates the Big Data Factory (BDF) platform for modernizing technology and infrastructure, which aims to consolidate data and turn insights into action.

Technologies/ Skills Used

The CDAS/CDP project is a solution for managing clinical data that includes the Clinical Data Analytics Suite (CDAS) and the Digital Trial Management Suite (DTMS). In order to develop this solution, various technologies were used, such as ERWIN 2021R2 tool, Hive, Hadoop, Scala, Spark, AWS, Data Lake, Snowflake, Airflow, Git, GitHub, HUE, DBeaver, etc. These technologies were used for different work streams such as Clinops, Patient Engagement, DTMS-CTMS, Ops, and played a key role in designing models and tables.
For example, separate data models and tables were created in Hive, and Git and GitHub were used as change control tools to create merge requests for scripts. An automation tool was also developed to deploy DDLs into different environments, which saved time and effort. Additionally, CDISC models and domain tables were created for Patient Engagement in the Hive environment.
The Big Data Factory (BDF) is also a key component of the CDAS/CDP project. BDF is used for modernizing technology and infrastructure, and in this project, it was used for analyzing and mapping data to corresponding GDM tables. The data was analyzed, mapped and updated mapping document with the transformation logics for Lift & Shift and also for new authored system scenarios. Technologies like Hadoop, Hive, Scala, Spark, AWS, Data Lake, Snowflake, Airflow, Git, GitHub, HUE, DBeaver etc were used for creating data models, Mainframe copybooks, and for creating new tables in GDM.

Customer Success Outcomes

The CDAS/CDP project aims to help customers harness their clinical research data by streamlining the data collection process and rapidly curating it for use by stakeholders. By bringing together data from various sources into a single ecosystem, the project allows for more efficient and effective data management.
One of the key outcomes of the project is the ability to generate smarter insights through the application of sophisticated machine learning analysis. This can be applied to subject data, risk management, and other areas, providing a more comprehensive and accurate understanding of the data.
In addition to generating insights, the project also aims to transform clinical outcomes by embedding data-driven intelligence in business workflows. This automation of manual tasks empowers stakeholders and helps organizations make more informed decisions, leading to improved clinical outcomes. Overall, the CDAS/CDP project is designed to help organizations gain more value from their clinical data and make more informed decisions.

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