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Highlights
- Knowledge management (KM) plays a key role across the pharmaceutical product lifecycle; agile and efficient KM enables organizations to accelerate drug approval and improve the patient experience.
- Regulatory associates spend up to 20%–35% of their time looking for data and knowledge to execute their work. KM will empower them with regulatory intelligence in real time.
- Knowledge management has three objectives―achieve product realization, establish, and maintain state of control, and facilitate continual improvement.
- The use of digital technologies and agile methodologies will improve knowledge management effectiveness, in turn enabling transformation of the pharma regulatory affair processes.
In this article
Adopting growth strategies in pharma
Pharma organizations need strategies for organizational transformation to stay ahead, get drugs approved faster, and improve patient experience.
This can be achieved by leveraging agile and efficient knowledge management, as it plays a vital role across the pharmaceutical product lifecycle. It improves product development and helps in its maintenance and sustenance.
Within the publication of ICH Q10 in the biopharmaceutical industry―Pharmaceutical Quality System (PQS), knowledge management as an enabler has three objectives; achieve product realization, establish and maintain state of control, and facilitate continual improvement. Knowledge management is a systematic approach to acquire, analyze, store, and disseminate information related to products, manufacturing processes and components. Table 1 represents various drivers and objectives that are achieved through knowledge management in pharma.
Drivers and objectives of knowledge management in pharma
Knowledge management in regulatory affairs
The pharma industry heavily invests in the development of new medicinal products, to improve patient outcomes.
To meet this objective with reduced cycle time and cost, enhanced quality and compliance, and improved success rates of the product approvals, a robust regulatory strategy—one driven by regulatory intelligence—is required.
Typically, regulatory associates spend 20% to 35% of their time looking for data and knowledge to execute their work. Table 2 demonstrates how KM can empower them with accurate and up to date information.
Dimensions for knowledge management in pharma regulatory affairs
Few examples of leveraging knowledge management in regulatory affairs (RA) are illustrated below:
Knowledge requirement use cases in pharma regulatory affairs
Knowledge management processes in regulatory affairs can be transformed by leveraging various themes driven by capabilities such as automated, intelligent, agile, and on-cloud, as shown in Figure 1.
The knowledge management framework for regulatory affairs
Knowledge management in regulatory affairs is expected to capture information from both external and internal sources. This information is then curated, analyzed, and synthesized for insights to help:
Carry out impact assessment
Disseminate knowledge
Embed into processes
Monitor performance
This is followed by a feedback loop to knowledge management, which goes back into building and improving knowledge for further use, enhancing the quality in RA organizations.
Knowledge management processes
Knowledge management processes
To enable the knowledge management framework as laid out in Figure 2, the key themes that leverage various concepts to drive the business value are as follows:
Driving business value
A. Smart data management
Knowledge can be captured faster by a foundational layer of standard aligned data models with built-in relationships across various data domains leveraging the knowledge graphs (KG). The use of taxonomies and ontologies and common vocabularies can enable standardization and organization of information for faster search and retrieval. Any new added information gets automatically synthesized, mapped to the model, defined into data domains, gets stored in linked knowledge graphs and get contextualized faster by use of semantic technologies.
Rapid synthesis of knowledge from source and real-time synchronization promotes high confidence in the data and rapid sharing. For example, product information data—its behavior, safety profile, patient type, and the like—can be established faster through connections and information flow between the connected domains.
B. Intelligent and automated curation of knowledge
Curation of captured knowledge can be done in an agile and efficient manner by leveraging digital twin and human intervention in a loop. Fully intelligent automated curation is possible in cases where no expert opinion is required, making updated knowledge readily available for compliance and risk management in regulatory affairs.
C. Intelligence-driven automated content creation
Regulatory knowledge output is produced as content as part of standard operating procedures (SOP), submission dossiers, and the like. With connected and related data, a content block for document can be generated. Technologies like NLG (neural language generation) can enable automated narratives—leveraging the data elements or using content template with metadata tags that are substituted with the actual data. The advantage of such a concept is that for any underlying change in data, the content will get autonomously updated in the next version, ensuring data integrity.
D. Smart engagement channels
Information captured can be made useful if it is available at right time for right stakeholders. Smart channels can be created to disseminate the knowledge based on the level of impact, for example, new regulatory guidance, new safety signals, and the like, can be notified through action channel - a directed path for high priority areas that demand urgent attention and can perform risk assessment and execute actions without human intervention. Automated dissemination of knowledge in near real time can minimize the risk delays and non-compliance.
E. Automated and intelligent operations
Once knowledge is captured, analyzed, and disseminated, it needs to be embedded into the processes as intelligence, to manage risk and enable efficient and faster regulatory operations. For example, a regulatory change is identified to trigger an SOP change request, generate the next version of the SOP with the changes, and release it for approval.
F. Improved performance and visibility of processes
Connected digital threads can be used to capture events as they occur across regulatory teams, service providers, and affiliates to rapidly identify delays and monitor deadlines (for example, sending a response to health authorities). Process twins can be leveraged to identify workload vs. capacity assessments, dynamically schedule resources, and specify workflows.
The knowledge graphs enable an easy construct of the 360-degree view and identify opportunities for improvement. One can find answers faster to queries like higher throughputs, quality of the submissions, and so on, and others like compliance index (indicating of how well an organization is meeting the health authorities' deadline). Knowledge management can drive continual improvement by analyzing and providing insights on metrics, and benchmarking.
G. Incentivization
Tacit regulatory knowledge is heavily SME-based and is most difficult to capture. The culture of knowledge creation and sharing can be facilitated through digital mechanisms (surveys, forums, and industry collaborations). Leveraging certain digital workplace concepts and creating regulatory communities of practices can help capture such knowledge. The curated data from regulatory agency guidance can be posted there for feedback, opinions, and comments. These can be analyzed to detect sentiments and apprehensions to determine priority for action. Experts can respond, react, and help in generating data and earn reward points. Hence, organizations can build knowledge by incentivization.
Achieving probability of regulatory success
In an agile environment where fit to use knowledge is in high demand, the pharma regulatory industry has acknowledged its dependence on knowledge management to achieve probability of regulatory success.
With robust knowledge, organizations can carry operations efficiently, save cost, time, and enhance productivity. To meet futuristic scalable KM needs, modernization of knowledge management requires support from today’s emerging digital concepts and technologies.
