Are patient outcomes getting lost in safety data complexity?

Proactivity around drug safety is assumed in life sciences, yet the reality of adverse event (AE) capture and reporting in a real world/post marketing context is rarely straightforward. In many ways, deriving consistent patient information and reliable, actionable insights has never been harder. Disparate and proliferating channels, and rising volumes of adverse event reports (due both to advanced new treatments, and growing public awareness around how to report side-effects), have magnified the AE case burden.

Until the industry gains control of the problem, it risks missing a huge opportunity. This includes the chance to leverage AI as a means to richer intelligence. Although the technology is generally good at combining data and creating order from complexity, AI can only work with what it has. Expecting it to turn bad or incomplete data into something richer, on the other hand, is more dubious. That isn’t to say AI can’t help patient safety teams; but that it could also be beneficial where paid professionals are being tasked with capturing AE data from patients.

Case volumes are increasing, but substance is often lacking

Comprehensive safety monitoring is critical to patient outcomes, so it is fairly alarming that opportunities to capture important new insights are not being leveraged fully, because those doing formal capture have not been equipped with the right tools.

Very often, AE reports are patchy, incomplete and difficult to follow up. Disjointed means of reporting, and inconsistency in what and how much is captured, render findings hard to combine in a way that is meaningful – e.g. as the basis for actionable intelligence, blended with data distilled from scientific journals, online forums and so on.

Anecdotally, large pharma organisations suggest that only around 10% of attempts at information follow-up (to fill in gaps in the narrative) are successful once initial details of any side-effects have been reported. Once that opportunity has passed, it’s usually too late. If this problem could be overcome more systematically, richer insights and better decisions would be a safer bet. One option is that regulators should exert more influence around patient safety data capture, in the interests of establishing the richest possible understanding of each patient’s experience.

Reinforcing the burden of responsibility

The case for proactively improving original patient safety data capture is particularly strong where the facilitator is a paid third party – perhaps a specialty pharmacist, or a service provider contracted to deliver a patient support program (a PSP vendor). As it stands, those partners’ main vehicles for receiving and registering adverse event notifications are typically an email address, paper form, or Word document, resulting in a painful process of manual data amalgamation and reconciliation for someone. When gaps in the narratives are found, it is generally too late to do anything about it – a gap that AI can’t fill retrospectively without risk of hallucination.

With so much claimed in life sciences about improving patient centricity, it is mystifying that only some pharma companies and regulators (relied on to uphold quality and safety), formally insist on the capture of complete and high-quality data first time.

Better data would provide a much clearer picture of adverse events and what may be contributing to them (such as drug interactions, pre-existing conditions). Consider, for instance, the high volumes of incoming data ready to be recorded around weight-loss drugs traditionally associated with diabetes treatment – those targeting GLP-1 and/or GIP receptors to control appetite. Possible side-effects range from digestive issues to reduced muscle and bone mass. The opportunity to capture this information widely and draw trend information from it is rich and important, so assigned (and especially paid) professionals should be doing all they can to improve the consistency and value of this activity.

Until the authorities mandate that better data is captured at source wherever possible, starting with paid professionals, pharma safety functions and their patients will be no better off. It should be a priority to empower respective actors with the right tools for the job, as well as a sense of accountability for the quality and onward value of the data being captured.

The AI opportunity: guiding better data capture

Given the complex, multi-channel landscape through which relevant patient safety data can flow now, it follows that strategies and approaches for improvements must be geared to standardisation and consistency. AI can help here, by prompting good and comprehensive data capture up front – for instance guiding the user to provide additional information. AI could also be used to tailor and optimise the digital experience, e.g. for each inputter’s persona (e.g. patient, health professional, pharmacist), their likely medical knowledge, their native language, the device being used, and so on.

In the modern age, sorting through reams of data sent by email and then trying to chase down missing details is inadequate – given that more effective alternatives are available. It is inefficient, ineffective and costly, and it serves no one. An optimised digital experience for the reporter, with pertinent questions or prompts to capture all of the preferred detail, has been shown in pharma company deployments to enable 70% overall improved efficiency, including reduced follow-up.

With the current pace of drug innovation, the pharma industry can’t afford to sacrifice any safety data point. As personalised medicine continues to grow as a proportion of pharma pipelines, and as smart devices do more to track individual’s health, the data collected will become increasingly patient-specific and critical. Improving practices now will help the pharma industry gear up for that future.

Building a holistic picture of a product or treatment and its impact in the market must begin with capturing more via the earliest patient feedback. That means employing the right tools for the given situation, and that includes AI where it can help foster greater data richness from the outset. Companies will also need to join up systems and overcome data silos, to enable more insights to flow into all the relevant downstream systems, where they can be analysed and actioned without the need for manual data re-entry. All of this will support more accurate triaging and onward decision-making, simultaneously boosting productivity and elevating patient outcomes.