Improve Data Linkage & Collaboration with Laboratory Software for Clinical Research

Laboratory Software for Clinical Research Enhances the Accuracy of Data Linkage

For a long time, clinical researchers have relied on primary data collection as the sole source of data. In the last few years, there has been a progressive shift towards methods that are more secure and less costly in terms of time and finances as well. The COVID-19 pandemic highlighted the usefulness of secondary data sources in providing an adequate public health emergency response. With this in mind, there’s a need to explore secondary data sources that can be harnessed to provide useful data for high-level clinical research projects and at the same time, enhance collaboration among stakeholders. 

Data linkage is a technique that allows researchers to link information from disparate sources (primary and secondary sources) to create richer data sets. This entails bringing together data from administrative sources and past clinical records from different databases that can be linked to one individual and examining that data to provide a comprehensive picture of the patient’s journey. It means combining data from clinical research with real-world data. In summary, data linkage has been defined as the process of linking information from two or more data sources, trying to find the data that belongs to the same individual, and making sense of the linked data.

How is Data Linkage Done?

Data linkage is done by first assigning a unique identification number to each individual on a data set and then linking all data that is related to that unique ID over time. Strict practices are put in place to ensure that the data remains anonymous and cannot be traced back to the individual by third parties. At the same time, the information remains in its source and is not transferred to another source; access to the information also remains unchanged. This ensures that the data remains secure throughout the linkage journey. 

Data linkage involves three key stakeholders as follows:

The data custodian: This is the original owner of the data source who is responsible for the collection and dissemination of the data. The custodian is often the primary source of the clinical data and will provide the linked clinical or administrative data to the researcher in an anonymized format.

Clinical researcher: This is the person or organization that needs data for comparative research. They have to liaise with the custodians so that they can access primary sources of data.

Data Linkage Units: These are service providers that provide data linkage services. They liaise with data custodians and create linkages between data sets that can be used by researchers. 

Importance of Data Linkage

Take the example of a clinical study sponsor who is struggling to determine the long-term safety and efficacy of a particular intervention involving a novel product. In such a case, the sponsor will be required to track longitudinal data to meet the regulatory requirements under Emergency Use Approval (EUA). Linking data in clinical research helps sponsors gain insights into safety outcomes and long-term effectiveness even after the trial has been completed without incurring many costs. This is useful in mitigating patient risk and also characterizing the risk versus benefit of treatments across different patient groups.

Linked data provides information that is not only representative of a small sample but the entire population. Linking administrative and clinical datasets provides an opportunity for researchers to better understand the health of a population. Clinical research stakeholders can leverage linked data to predict the long-term safety of interventions and improve the standard of care. 

Laboratory Software for Clinical Research Ensures Quality of Linked Data Sources

Data linkage is useful in clinical research. However, it is liable to different types of errors that may occur during the linking process. Errors may arise when some records are missing, hence some parts of the data fail to match. Errors may also arise due to linkages to inaccurate data (false negatives) or when it is linked to unrelated records (false positives). When manual systems are used, transcription errors are also likely to occur. 

Laboratory Software for Clinical Research, also known as Laboratory Information Management System (LIMS), enables laboratories to automate their workflows. Laboratory software for clinical research eliminates transcription errors and manages the metadata that is linked to samples to ensure that the data stored and transferred is accurate. Laboratory software for clinical research further enables the anonymization of PHIs and supports compliance with regulatory requirements. Cloud-hosted laboratory software for clinical research promotes real-time access and collaboration among stakeholders.


Data linkage enables clinical researchers to leverage data sources without having to spend extra time and money. This information can be used to predict the long-term safety and efficacy of interventions. Laboratory software for clinical research supports the process of data linkage by boosting the accuracy of the data linkage and enabling collaboration among stakeholders. 


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