In 2016, the National Center for Biotechnology Information reported 197,000 deaths of patientswho were admitted due to adverse drug reactions (ADRs) in Europe. Not only so, the report mentioned that an alarming 5% of hospital admissions that year was related to ADRs. Statistics like these, along with evolving, highly complex drug safety regulations, are propelling pharmaceutical and biotech companies to adopt pharmacovigilance (PV) practices, which are proactive, risk-averse, and patient-centric.
To begin with, this would require rethinking medical coding and terminology management which form the cornerstones of successful clinical trials. Typically, companies rely on the accuracy of the pharmacological and biometric data pertaining to seriousness and severity of events in relation to their product portfolios—to help therapeutic area leads, biostatisticians, and medical reviewers accelerate their decision-making process.
Given the depth and breadth of existing medical databases, clinicians will need systems that can provide drug safety features and deliver insights, which can be further integrated with safety systems such as Argus and Integrated Patient Safety Platform.
Recalibrating Medical Coding Practices
Beyond standardized medical coding which depends on datasheet analytics, clinical documentation improvement (CDI), and product query optimization, there is a pressing need to improve form and structure of the way medical terms are perceived and visualized. Most medical reviewers depend on decision-making tools supported by Medical Dictionary for Regulatory Activities (MedDRA) and WHO Drug Dictionary (WHO DD) standards. These analytical tools, however, do not accommodate instances of product-specific customized solutions wherein understanding and using these standards can be a challenge. As an intermediate solution, signal detection and signal management processes for relating reference safety information (RSI) or auto-labels can help.
In turn, this can help the biometrics team receive structured datasets and integrate the same into clinical data repositories, factoring in medical coding highlights, standard and custom dictionary objects, along with product query mapping. A 360-degree reach across drug safety, medical coding, biometrics, regulatory, and labelling programs is needed to comprehensively capture the correct labelling information at the right time.
Going Beyond Existing Standards
While analytics and business intelligence capabilities may not be readily available, pharma companies can take this opportunity to collaborate with medical coders, technology solution providers, and subject matter experts as well as utilize AI capabilities to derive solution with enhanced PV.
For starters, a cloud-based solution can be deployed to develop and maintain standard dictionary queries as well as a datasheet to facilitate information extraction. This would enable global PV operations by managing and aligning custom and standard objects such as Standard MedDRA Queries and Standardized Drug Groupings and consolidate into a process-riven framework. This framework would be capable of visually navigating standardized and customized dictionary queries for MedDRA and WHO DD. A cloud-based solution can support multiple versions of these dictionaries, while only the current active version would be available for supporting daily operations.
The Art, Science, and Technology of Terminology Management
Since time it has been depicted that Terminology Management such as medical coding is more of an art than science, however, there are contradictions. The terminology management seeks an artistic license or at least some interpretation to create datasheets and product queries, and customize queries and groupings, as computers are not good at interpretation unlike the experts.
We see a collaboration of tools and technologies such as artificial intelligence, robotic automation, and digital technologies tapped by experts of this field to make sense from Medically Equivalent Terms, Rare Diseases, Events of Interest, and Medication of Interest, which are sliced and diced into custom objects that pass through a process framework. The outcome of these endeavors can address not only the listedness aspect but also seriousness, expectedness, and causality that will help in the decision-making value chain. The pharmaceutical industry itself is expanding at an incomprehensible pace, and this transformation will complement growth in a positive way with three dimensions of technology – Migration, Management, and Integration, bringing value to the structure and form to the traditional way we perform Terminology Management.
As the industry is undergoing technological evolution, do you think Connected Terminology Management would play a role in transforming areas such as drug safety, biometrics, labelling, and regulatory affairs?