Data mapping in clinical trials
Data mapping is the process of connecting a data field from one source to a data field in another source. This reduces the potential for errors, helps standardize your data, and makes it easier to understand Clinical Data management:
Clinical Data Management (CDM) is a critical phase in clinical research, which leads to the generation of high-quality, reliable, and statistically sound data from clinical trials. This helps to produce a drastic reduction in time from drug development to marketing. Team members of CDM are actively involved in all stages of clinical trials right from inception to completion. They should have adequate process knowledge that helps maintain the quality standards of CDM processes
CDM is the process of collection, cleaning, and management of subject data in compliance with regulatory standards. The primary objective of CDM processes is to provide high-quality data by keeping the number of errors and missing data as low as possible and gathering maximum data for analysis.[1] To meet this objective
Mapping can also facilitate the exchange of data. A common example is the transmission of electronic data using the Health Level 7 (HL7) standard. The content of the different HL7 messages has to be defined or mapped from a database. Finally, terminologies, as well as classifications and coding systems, can be mapped between each other. An example that may soon become relevant to everyone in healthcare is mapping between ICD-9-CM and ICD-10-CM.
Data mapping in healthcare is ubiquitous and will become more obvious as data currently in paper records increasingly converts to electronic format. Its accuracy is vital because there is an opportunity for error at each relay point in the system.
The elements of data mapping
Designing a data map begins with identifying the source of (the database, data set, or terminology being mapped from) and the target (the database, data set, or terminology being mapped to). A basic data map is shown below. Versions of both the source and target should be tracked, and there must be a mechanism to reflect the version history of the map.
Data mapping basics
Data mapping matches from a source to a target so that the two may exchange data meaningfully. Common sources and targets include databases, data sets, standards, and terminologies. Unidirectional mapping goes from the source to the target. Bidirectional maps translate in both directions. Not all maps can be bidirectional; for instance, when multiple and differing terms in the source map to a single term in the target.
In some maps, a database can act as a translation key from one source to the next, providing additional information needed to map the information.
Maps may be unidirectional (mapping only from the source to the target) or bidirectional (also mapping back from the target to the source). Not all maps can be bidirectional. For example, the map from SNOMED to ICD-9-CM cannot be reversed, since it is common for many detailed and different SNOMED concepts to map to a single ICD-9-CM code. Reversing the map is not possible because one ICD-9-CM code would point to many different SNOMED concepts. Likewise, the map from the old UB92 billing standard to the new 837i standard cannot be bidirectional. The map from the payer classification structure in the old format cannot be mapped back on some of the fields once translated into the format of the new transaction. This is the case in payer classification. In the old format, charity care was an option, whereas, in the new format, charity care is either mapped to unknown or self-pay. There is no option for charity care in the new transaction format. Mapping projects must carefully consider the data source and the intended use of the data for both primary and secondary purposes in order to ensure accuracy.
Once a data map has been created and validated, it will need to be tested to determine if it is "fit for purpose." This testing is crucial for ensuring the map meets the needs for which it was created. End users of the data must be involved in this process. For example, if the purpose of a data map is to exchange lab results between systems, both the sender and receiver must be involved in determining whether or not data are being exchanged accurately. Once a map is used within other software applications, processes external to the map can affect the data, particularly if the software uses the information in an interpretive way, such as the ICD-9-CM codes translated into the risk of mortality or severity of illness groupings. Frequently in this type of mapping project, in the event the random sample did not identify mapping errors, the use of the information will trigger further investigation. It is important for healthcare professionals to closely examine electronic information. In the event they see something that appears to be inaccurate, they need to question the information. Should a data map introduce questionable information, the investigator may need to track backward, up the stream of information, to determine where the discrepancy began. Finally, the data map must be maintained and updated. This is an often-overlooked part of data mapping. Many believe that once a map is created it is valid forever. This is not so, since data reporting requirements, standards, databases, and terminology, and classification systems change all the time. In fact, some organizations using data maps have staff titled "map managers" employed to manage and update maps. An inadequately maintained map is sometimes worse than a no map because it has the potential to transmit the wrong data, subsequently introducing errors.
References
Student Name: Sameena Anjum
Student ID: 020/012023
Qualification: Pharm. D
e-Mail ID: sameenaanjum80@gmail.com
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