In the pharmaceuticals industry, pharmacovigilance practice—or drug safety surveillance—aims to ensure the safety and effectiveness of drugs and to quickly detect and report on any adverse events that may arise with patient use of those medicines. To ensure patient safety, proactive pharmacovigilance requires unencumbered access to a broad range of data originating from clinical trials and all the way through the post-marketing drug lifecycle. Sourced from various databases within the enterprise including published literature, consumer communications via email, social media and websites, drug- safety data is found in a wide variety of formats, both structured and unstructured.
Collecting and trying to quickly achieve critical insights from diverse data sets presents a challenge for many organizations within the healthcare ecosystem as information tends to be sequestered in data silos, and is then further limited by incompatible formats and database technologies. Traditional solutions call for the use of relational database systems that, because of their rigid nature, are ill-suited to handle variable data types, such as literature or digital consumer communications. As a result, a vast amount of important drug safety data is difficult to access, making it next to impossible to quickly achieve the critical insight required for timely reporting of adverse events. And the consequences of this are alarming. According to Harvard University’s Edmond J. Safra Center for Ethics, approximately 128,000 deaths per year are attributed to adverse drug reactions in the United States and up to 200,000 in the Europe. It’s clear that a faster and more responsive approach to data integration and analysis is needed in pharmacovigilance practice.
We address data access and other challenges in our recent Xtalks webinar, How to Leverage Real World Data to Achieve Faster Insights for Better Adverse Event Detection and Reporting. Presented by MarkLogic’s Imran Chaudhri, Chief Architect for Healthcare and Life Sciences, and hosted by Bill Fox, Chief Strategy Officer for Healthcare and Life Sciences, in this webinar, we explore how:
Every organization wants to use all of the data it has access to. This is the only responsible way to implement a pharmacovigilance strategy. Multi-structured (structured and unstructured) data stands in the way of this goal. This data can be completely unstructured, such as social media reports or research published in PDFs, or it can be variously structured through website-based input forms. With a traditional RDBMS or even a traditional data lake, this data can be notoriously difficult to process, integrate and correlate. It requires too much manual intervention to be accessible in time. The huge quantity of this data increases the time between reporting and usability.
The MarkLogic® Operational Data Hub (ODH) solves the problem of multi-structured data by leveraging two key technologies: a multi-model database engine and semantic relationships between data. As a multi-model database, it is able to quickly import and integrate records across different data silos, including multi-structured and unstructured data. By shortening the ETL process, data becomes available for use more quickly.
Making the data available is just the first step. Powerful semantic technology provides incredible flexibility for modeling the relationships between entities. It uses a data format called RDF triples to store semantic graph data. This improves data integration and forms a strong foundation for applications that use connected data.
MarkLogic customers have successfully leveraged semantics in advanced search apps, fraud detection, knowledge graphs and drug discovery, among other applications.
Even with the ability to create strong connections between records, there is a staggering amount of data to process for pharmacovigilance. Finding the signals in the noise can be a challenge. AI and machine learning are particularly useful in this scenario. However, AI requires highly curated and accurate data to be successful, and traditional approaches make this curation difficult, expensive and time-consuming.
With an ODH platform powered by semantics, you can find information of interest in real time. This reduces the number of needed machine learning cycles, making AI more efficient and useful. With real-time semantic search and filtering, users can provide the right data to machine-learning algorithms. A powerful document store also maintains and operationalizes machine-learning results, making them readily available for reviewers and researchers.
By making it easier to detect signals and evaluate adverse events, the operational data hub helps improve drug safety. By reducing risk, it lightens the burden of regulatory compliance. MarkLogic can use data for multiple purposes quickly, meaning that special platforms aren’t needed for every new kind of report required. Drug safety information is readily available.
Data silos isolate important information across the pharma organization. This fragmentation causes compatibility and reliability issues that slow down reporting and the detection of adverse events. The data hub approach makes all of an organization’s data available by breaking down data silos. An operational database provides a complete view of data from every source, department and operation. This eliminates the fragmentation of data—advancing signal detection and powering regulatory reporting.
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