An adverse event (AE) can be any unfavorable and an unintended sign (including an abnormal laboratory finding), symptom, or disease associated with the use of a medicinal product or device. AE’s categorized as serious (results in death, illness requiring hospitalization, events deemed life-threatening), must be reported to regulatory authorities immediately, whereas non-serious adverse events are documented and reported and sent to the regulatory authority.
When an HCP or a consumer files a complaint, it generally comes through one of four main channels — phone, web, fax, or analog post (mail). Increasingly, big data sources from healthcare payers along with conventional searches of medical literature are playing a role. Some drug or medical device manufacturers may also be actively monitoring social media posts and listening for product-related AEs. It is the role of the AE intake team to collect and normalize reported information.
Here’s what we heard from executives responsible for the intake-side of PV operations.
“[Intake] Automation is our biggest objective. We have some tools to manage [automation], but there are many gaps.”
“I can’t measure (Intake) performance since so many processes are done manually.”
“We need to eliminate fax and manual data entry. Vendors provide AE forms on paper which must be manually data-entered.”
“Our Intake is outsourced to a third-party … We use Outlook to collect AEs … Outlook is unstable, i.e., mailbox size limits, auto-purge, accidental deletion, etc., and not very secure.”
“The majority of AEs are inbound call-center based. We want to move to true omni-channel intake.”
“We want to automate intake for high-volume, low-value cases, the majority (>70 percent) of which are non-serious.”
“We need better visibility to field-level diligence as many of these processes happen outside of the system.”
So how do we fix this? We fix this through automation, allowing the flow of intake to follow a more digital path. Have digital robots monitor intake channels for digital normalization and initial level triage. Allow systems to open and read attachments and forms that recognize priority AEs for faster processing. Provide better visibility into process streams and throughputs so we can measure peaks and valleys, productivity, and process viability. Automate upstream reporting and integration to systems like Argus and ArisGlobal.
The Safety System Singularity
Solving the automation challenge requires innovative thinking and new approaches. Conventional PV systems remain mired in monolithic architectures and require major customization to adapt to technologies like optical character recognition, natural language processing, and semantic coding and classification. Even basic integration is a challenge for popular systems like Argus, which offer limited ability to integrate beyond the exchange of E2B files — creating a proverbial black hole for AE cases.
This limits needed innovation for the benefit of not only efficiency improvements but also more proactive patient safety. Innovation is at the core of the biopharmaceutical industry with pharmacovigilance sorely in need of innovation. Reported AE volumes are rising at 10-20 percent annually. This means that without innovation, PV operations will soon be unable to scale to meet demands. Furthermore, the valuable resources currently spent on low-value manual efforts in PV would be better applied to more effectively managing risk-benefit oversight, improving big data surveillance, and pharmacogenomics.
Escaping the Gravity of Conventional Wisdom
There is innovation in pharmacovigilance; you just need to look beyond the status quo. Companies like C3i and Agios Biopharma are asking the right questions and setting the stage for innovation. C3i manages patient services for their biopharma customers holistically, automating case intake as an integrated part of their patient engagement services. By connecting patient assistance/access, medical information, health coaching/care management, and adverse events and product complaint management, they keep the patient at the forefront, ensuring that an adverse event is understood as part of the patient experience, not something separate to be dropped into a safety system and forgotten.
Being patient-centric helps keep the risk-benefit equation front and center. Likewise, Agios Biopharma is augmenting advanced text analytics and natural language processing to speed and augment their abilities to detect and understand adverse events. They can rapidly convert unstructured text from an as-reported call center, social media, and literature cases to quickly identify adverse events in conjunction with MedDRA and WHODrug coding to accelerate case processing. This enables faster, automated intake, assessment, and an understanding of adverse events from more sources without needing additional resources.
While C3i and Agios are solving different problems within PV, they do have one thing in common. They need to free their data and processes from the black hole of conventional safety systems. They realized that to fully take advantage of this sort of innovation, both companies needed a more flexible, configurable, and adaptable PV platform.