Case Study Endocrinology (Diabetes mellitus)
Geography: N/A
Type Service: N/A
Background/Client Requirement:
Our client a leading provider of in-vitro diagnostics and a global supplier of transformative, innovative solutions across major therapeutic areas. Committed to develop new medicines and diagnostics that make life better. Client collaborated with Techsol Life Sciences to conduct a clinical trial focused on Diabetes Empowerment of Subjects with Type 2 Diabetes Mellitus.
Project Objectives:
The scope of the study is to evaluate diabetes empowerment, health literacy, diabetes treatment satisfaction, and user experience. The study population assess the outcome characteristics for 8-weeks without the blood glucose monitoring devices followed by a 12-week observational phase using the blood glucose monitoring devices, active test strips and a lancing device and lancets. Diabetes empowerment is measured by the Diabetes Empowerment Scale-Short Form (DES-SF) questionnaire.
Challenges:
Converting Fast Healthcare Interoperability Resources (FHIR) data for Clinical Data Interchange Standards Collaboration (CDISC) standards can indeed present several challenges, primarily due to differences in structure, Study of meaning, and intended use between the two standards.
- Inconsistent Data Structure: The clinical data management finds complexity in transforming FHIR data provided by the sponsor, often in an incorrect format, into the required CDISC standard format.
- Missing or Incomplete Data: Sponsors provide FHIR data to Clinical Data Management (CDM), which include missing or incomplete data, complicating the conversion process.
- Terminologies: FHIR resources can have different terminologies from CDISC, making mapping complex when dealing with custom/local codes, ensuring collaboration equivalence and accuracy.
- Data Cleaning: Converting data from FHIR to CDISC standards is work-intensive, involving extensive data cleaning, organization, error correction, and terminology alignment. Manual execution is time-consuming and error-prone.
- Tools and Resources: Ensuring the availability of tools with the necessary features and capabilities to efficiently and accurately convert FHIR data to CDISC standards is a significant challenge, especially given limited resources.
- Training and Education: This is first of its kind requirement, with no readily available training materials and resources available to guide internal team to develop/validate the tools and the data. Explaining data in JSON format can be challenging. Healthcare professionals, IT teams, and developers should be briefed on FHIR and CDISC standards, data models, and terminology, and develop skills and manage data exchange effectively.
- Standardization Plan: A standardization plan need to be developed to transform FHIR data into a uniform format, ensuring consistent use of CDISC data elements, terminology, and coding schemes across the dataset.
Techsol Solution/Approach:
- To convert FHIR data to CDISC standards successfully, a comprehensive approach is needed that tackles technical and organizational aspects.
- Clinical Data Management develops transformation guidelines for convert FHIR data into the required standard format with version control, ensuring the integrity and accuracy of the transformed data. The process follows standardized formats with CDISC based on the mapping document, providing a robust verification process.
- Techsol clinical data management approached by implementing with data validation checks to identify inconsistencies, missing values or discrepancies within the FHIR data. This can involve checks for data integrity, completeness, and adherence to standards.
- Terminology Mapping: Map the terminology used in the FHIR standard to CDISC standard data vocabularies such as ICD-10 (International Classification of Diseases), develop comprehensive terminology mappings to ensure consistency and interoperability across datasets.
- Tools and Resources: Techsol’s Clinical Data Management team have developed In-house tool to export the data output with CDISC standards in human readable format and it is easy to analysis.
- Training and Education: We have nominated qualified resource to self-learn the FHIR data standards and developed training materials and documents for software development and also provided trainings, guidance, including manuals and power point presentations for internal team and QC/QA teams.
- Data Cleaning: Implemented robust data quality checks at various stages of the data management process. This includes both API’s checks using software tools and manual checks by experienced data management team and medical experts.
- Team collaboration: Regular meetings and discussions helped to identify challenges early on and find effective solutions collectively.
- Standardization Plan: The implementation of In-house software tools, adhering to industry-standard formats and protocols, has been instrumental in maintaining data consistency and promoting interoperability.
Project Achievements:
The clinical data management and QC teams successfully met their deadlines, earning positive feedback from the sponsor. Open communication within the team, created a collaborative atmosphere that facilitated the seamless exchange of information, which is an essential factor for the success of the study. Stakeholders and investigators commended the teams performance, acknowledging its vital role in carrying out the study with exceptional efficiency and effectiveness.
The Outcome:
Techsol’s continued support in clinical data evaluation, combined with the application of industry best practices, ensured that the study outcomes align with the client’s strategic goals and objectives, solidifying its position as a leader in diabetes care innovation and digital health solutions. The outcomes emphasize key aspects such as data quality, timelines, goal attainment, and complication management. Each of these factors ensured the validity and reliability of study findings and ultimately advancing medical knowledge and improving patient care.