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In healthcare, data mapping refers to gathering information from various sources and consolidating it into a centralized database. It enables healthcare providers to efficiently access patient data and make informed decisions about their treatment.
Healthcare data mapping typically utilizes specialized software that links databases or IT systems. This software maps the data using key attributes such as patient names, medical record numbers, and insurance details. Healthcare providers can quickly and effortlessly access the data adequately mapped.
This article will discuss how data mapping in healthcare works, including STDM, the mapping process, benefits, and challenges below.
Understanding SDTM
The Standard Data Tabulation Model (SDTM) was created by the Clinical Data Interchange Standards Consortium (CDISC). The SDTM provides a standard format for data handlers submitting clinical trial data to regulatory agencies.
The SDTM has two main goals:
- To Promote Electronic Data Submissions: The SDTM makes it easier for organizations and data handlers to submit data in a standard format, making it easier for regulatory agencies to review.
- To Improve Data Consistency: Using the same standards and definitions across studies and clinical trials means more accurate and consistent results.
SDTM mapping is a crucial aspect of clinical trials. It ensures the collected data is organized and easily readable for analysis. SDTM mapping made easy for researchers to take data from sources that use non-CDISC structure https://blog.formedix.com/the-sdtm-mapping-process-simplifiedand map it into a single, unified repository.
The Process Of Data Mapping
The data mapping process in a healthcare setting involves several stages that provide a comprehensive overview of the data. The steps include the following:
- Identify Datasets To Be Mapped
The first step in data mapping is identifying the datasets that need mapping. This involves selecting databases, systems, or other repositories to provide the source information.
It also involves checking the data sources, such as patient records, billing systems, electronic health records (EHRs), and laboratory results.
- Identify Corresponding SDTM Datasets
The next step is determining the corresponding SDTM datasets by comparing the source information to the CDISC standard data models and choosing the most suitable SDTM model for each data set.
- Collect Metadata Of The Datasets And Corresponding SDTM Datasets
Metadata refers to data that describes other data. In healthcare settings, metadata describes the structure and content of a dataset. It helps researchers analyze the data more quickly.
- Map Variables In The Dataset
To ensure consistent data across various sources, collecting metadata such as variable names, types, lengths, and other relevant information for each dataset is essential. This information should then be used to map the dataset variables to the corresponding SDTM domain variables as part of the data mapping process.
- Create Custom Domains
Custom domains can be created when there is no appropriate SDTM domain for the data. This ensures that all data is represented in a consistent format, making it easier for researchers to understand and analyze the data.
The custom domains are created per the CDISC rules and regulations. When creating the domains, you should check that no published domains are available under which the data collected can be mapped.
Benefits Of Data Mapping In Healthcare
Data mapping in healthcare settings offers several advantages, including the following:
- Increased Data Accuracy: By mapping data to an SDTM model, researchers can ensure that data is consistent and accurate across multiple sources. It reduces errors and inconsistencies, leading to more reliable results.
- Improved Efficiency: The process of data mapping allows for data to be collected quickly and efficiently, reducing the need for manual processing that can take up valuable time and resources.
- Streamlined Processes: Data mapping helps streamline the data collection process by assisting organizations in identifying areas where data is duplicated or missing. In turn, data mapping helps researchers improve their data collection practices.
- Bridge Interoperability Gap: Leveraging CDISC standards helps organizations bridge the interoperability gap between different systems, allowing them to share data more quickly and efficiently.
Challenges Of Data Mapping In Healthcare
Despite the many benefits of data mapping in healthcare settings, researchers must still address the following challenges:
- Data Quality: The quality of data collected can affect the accuracy of results, so it is essential to ensure the mapped data is high quality and reliable.
- Cost Of Implementation: Implementing data mapping in healthcare settings is expensive, as it typically involves purchasing specialized software and training staff to use them. These initial and other initial clinical trial costs can be significant barriers to implementing data mapping regulations.
- Lack of Standardization: There still needs to be a universal standard for data mapping in healthcare settings, which can cause confusion and inconsistencies when mapping datasets from different sources.
Conclusion
Data mapping is crucial in healthcare settings as it helps ensure that data is accurate and consistent across multiple sources. By using standard data models such as SDTM, researchers can improve the efficiency of their data collection and analysis processes. However, some challenges still need to be addressed to make data mapping more widely adopted and efficient in healthcare settings.
