Data enrichment benefits to epidemiology

The process of appending or otherwise augmenting gathered data with relevant context gained from extra sources is known as data enrichment. It’s done by appending various attributes and values from different data sets to one or more data sets.

Allows you to collect useful data

Enriched data describes important data that is relevant to the problem and scenario at hand and that may be shared across stakeholders such as epidemiologists, health care workers, researchers, and other shareholders. This might include data such as travel history, vaccination status or prior infection data. Enriched data allows you to address more than one research question at a time.

Enhances data quality and accuracy

Good data enrichment techniques validate the information and ensure that the data is current. Allows for extensive data collection by linking data from numerous sources, as well as improved validation of exposures and research endpoints. This may involve combining different research databases, hospital and community data, health records and patient self-reports.

It conserves time

You’d think that adding more data would take longer. Data enrichment, on the other hand, reduces the amount of time and effort required to complete a task by allowing for AI-assisted automation. Using historical medical records as a baseline can shorten multi-year study timelines. Due to the link to continuous medical records data, it also reduces timelines for follow-up studies

Provides Deeper insights

Provides a more solid evidence base for addressing specific research goals. It also allows for analysis to be carried out in parallel with studies based on continuous medical records data. The addition of patient-reported data, also allows researchers to generate more patient-centred insights.

Additionally, through existing data sources, insights can be gained earlier, informing feasibility, generating new research questions, and providing information on the path to diagnosis, disease characteristics, or treatment patterns. Furthermore, clinicians who do not have robust research infrastructures can participate in studies, resulting in more representative populations than those seen at research-savvy centres. Because existing clinical data alone are frequently insufficient to fully establish the value of treatment intervention, the ability of enriched studies to supplement existing data with patient-reported information opens up new avenues for greater impact.