Technological advances have dramatically increased the range of tools that can be used to identify, generate and manage electronic data with the potential of providing compelling insights into effectiveness and safety of interventions. Such data include digital social media (e.g. audio, video and images) that exist in a computer-readable format on websites, web pages, blogs, vlogs, social networking sites, internet forums, chat rooms, health portals, etc. To this list, can be added the data collected through mobile and other device applications such as wearable technology. By 2020, it is estimated that the amount of data recorded on digital media will reach 44 zettabytes (http://wikibon.org/blog/big-data-statistics/) 9% of which will be related to healthcare, of which half will be related to drugs.
Marketing Authorisation Holders (MAHs) are legally obliged to screen web sites under their management for potential reports of suspected ADRs and assessed whether they qualify for reporting and MAHs are encouraged to use their websites to facilitate ADR data collection. Cases from the Internet should also be handled as unsolicited reports and evaluated and reported in a similar way. Social media is already being used to provide insights into the patient’s perception of the effectiveness of drugs and for the collection of patient reported outcomes (PROs) as discussed in Web-based patient-reported outcomes in drug safety and risk management: challenges and opportunities? (Drug Saf. 2012;35(6):437-46).
While offering the promise of new research models and approaches, this rapidly evolving marketplace presents equal challenges. Without strong and systemic processes to manage new devices, simply keeping up to date and evaluating their suitability for a study will be challenging. There is currently no defined strategy or framework in place in order to meet the standards around data validity, generalizability, and likely regulatory acceptance for using this type of data. Current tools and solutions for analyzing unstructured data, especially for pharmacoepidemiology and drug safety research, are becoming available. A framework for use is evolving and proven models will emerge that ensure data content, rigor, and quality matched to intended use, as well as accompanying methods and solutions to validate these data. In the meantime, the following factors should be considered for data source and devices using this media:
The technology should be evaluated for reliability and “sameness” of outputs/inputs from the device.
Processes should be defined through which data can be validated e.g. originating from the individual as opposed to someone else using the device, and the accuracy of the device reading.
The completeness of data capture.
Data privacy and accessibility for longitudinal datasets in large populations.
Data warehousing requirements to securely store the volume of data potentially received from wearable devices.
When analysing unstructured data for pharmacoepidemiology and drug safety research, the following factors should be considered:
|Annex 1.||Guidance on conducting systematic revies and meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes|