Data quality system for anomaly detection in healthcare survey studies

Our client needed a robust solution to ensure the quality of data collected during their research studies, especially those involving surveys conducted by non-specialist interviewers. Human error and potential interviewer bias could compromise the integrity of the data. To address this, it was essential to implement a quantitative analysis and statistical anomaly detection methodology to identify and mitigate data quality issues.

We developed an additional component to the client’s existing survey system that aggregated and analyzed the collected data to detect anomalies. We seamlessly integrated it into their infrastructure and used open-source big data solutions to perform statistical analysis and generate detailed reports. These reports were then presented to the research teams, allowing them to quickly identify and correct data anomalies.

Implementing this solution significantly improved data quality standards within the research organization. By ensuring more reliable and accurate data, the organization was able to produce more credible and impactful research.