Why PV signal teams need to leverage RWD and Advanced Cognitive Computing

Art Brown, director of product marketing, LifeSphere Clinical, ArisGlobal

Signal Detection One of the most critical activities within pharmacovigilance (PV) is the process of searching for and identifying safety signals from a variety of data sources. PV teams typically monitor safety signals for postmarket products by reviewing scientific and medical literature, data obtained from clinical trials, and individual case safety reports (ICSRs).

ICSRs collect data to support reporting of adverse events (AEs), product problems and customer complaints associated with regulated products. The four key elements of a valid ICSR include:

  • An identifiable patient.
  • An identifiable reporter.
  • A suspicious drug
  • An adverse event.

Suppose a disproportionate number of adverse events emerge for a product. In that case, highly trained risk physicians or safety scientists analyze the cases and other available evidence to confirm or refute a causal relationship between the adverse events and the suspect drug. This complex and time-consuming process requires several manual steps and depends on basic signal detection software which has been criticized by some as having a maturity gap.

While ICSRs do indeed indicate potential problems, ICSRs do not immediately address PV reporting teams. Signal and risk physicians sometimes have to process thousands of cases and refer to external sources of medical literature to make a decision. Another challenge? ICSRs do not always provide enough detailed information to PV reporting teams to draw a definitive conclusion about patient reactions to specific treatments. ICSRs are inherently delayed. Reporting and risk physicians only get visibility when patients or healthcare professionals report an adverse event (not all adverse events are reported).

PV signal teams must balance increasing workloads with maintaining regulatory compliance. To help achieve this balance, life science organizations must adopt operational efficiency improvements such as advanced cognitive computing to handle rising case volumes, increased signal noise, and antiquated manual tasks.

Advanced cognitive computing: the key to efficiency

Life science organizations that don’t implement technology designed to improve efficiency and analyze data without overburdening security teams will fall behind in the industry. Advanced cognitive computing capabilities, including machine learning (ML), improve operational performance, support increased compliance, and increase control by streamlining workflows, maximizing clinician signal and output.

The new innovation enables advanced cognitive computing to process data from a variety of disparate sources, accelerate the analysis of a case series, a cluster of clinical cases involving patients undergoing similar treatments, and produce high-quality signal assessments. Advanced knowledge graphs, in particular, help PV reporting teams focus and generate insights for goal prioritization in drug discovery and post-marketing settings.

Advanced cognitive computing offers exciting opportunities for PV signal teams to increase their strategic contributions and catapult growth. In the coming years, more drug sponsors and contract research organizations (CROs) will use these technologies to accelerate time to understanding, identify risks faster, and unlock new uses for existing medicines.

Embrace Real World Data (RWD)

To improve patient safety, PV teams need to leverage real-world data (RWD) AND ICSR data. RWD reflecting a larger group of patients can systematically add further valuable context to the cues found in reported AE datasets. Drug developers collect RWD from:

  • Electronic Health Record (EHR): Digital patient records with information on medical history, treatment plans, diagnoses, vaccination dates, allergies and more.
  • Claims and billing activity: Health services data, population coverage, and patient prescribing patterns.
  • Product and disease registries: Data collections defined by a particular condition, disease or exposure.
  • Patient Generated Data: Information collected directly from a patient via mobile and wearable devices that provides real-time health updates to medical teams.
  • Social media: Posts posted by patients on forums or social media platforms, including unsolicited first-hand data about a treatment or drug.

RWD facilitates faster risk detection and gets closer to the patient, helping organizations avoid over-reliance on ICSR. Not only can RWD add value to safety signals, RWD enables organizations to uncover correlations between drugs and benefits, opening the door to drug reuse. For example, if researchers identify a correlation between a drug and a reduction in blood pressure for hypertensive patients, they have unlocked a new way for their organization to help patients AND drive revenue.

While the life sciences industry has been slower to adopt RWD (only 32% of life sciences organizations connect to RWD to drive drug development), regulators recognize the value of RWD sources and are leading the way in innovating signal detection approaches using RWD.

Take the Sentinel Initiative, an FDA-created national electricity system designed to answer questions about approved medical products such as vaccines, drugs, and medical devices. The system generates computer programs for analyzing electronic health data and studying the relationships and patterns in electronic health records and medical billing information.

Advanced cognitive computing and RWD will redefine the status quo for pharmacovigilance teams around the world. Life science organizations must embrace these innovations to keep pace with the growing volumes of security signaling, streamlining the process of security reporting and analysis, and becoming an industry leader for the benefit of mankind.

About Art Brown

Art Brown is the director of product marketing, data and analytics at ArisGlobal. Art has 18 years of experience, primarily serving in healthcare IT SaaS solutions, PBM/pharma services, and care delivery at major healthcare systems and companies such as CVS Health. He applies his expertise to help life sciences organizations understand the true value that SaaS-based solutions can bring to their organizations and ultimately the patients they serve.

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