Industry
Life Sciences
Patient-centered Care Models Taking Center Stage in Life Sciences
Healthcare
Patient-centered Care: Health and Wellness Redefined
HIGHLIGHTS
- Technological advancements, process evolution, regulatory changes, and the need for streamlined activities have greatly influenced the pharmacovigilance individual case safety report (PV-ICSR) process.
- The increased complexity of adverse events (AE) data, and reliance on real-world data (RWD) have only added to the PV-ICSR burden.
- What is needed is a reimagined framework for continuous PV-ICSR improvement through a balanced approach to achieve coordinated selection and application of advanced technologies, calibrated to the maturity and specific needs of the ICSR process.
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ICSR’s changed outlook
Technological advancements, process evolution, and regulatory changes aimed at streamlining processes, among others, have hugely impacted the pharmacovigilance-individual case safety report (PV-ICSR) practice.
The increased complexity of adverse events’ (AE) data, regulatory requirements, and reliance on real-world data (RWD) have introduced new challenges in the ICSR process. As safety and regulatory landscapes evolve, so, too, has the maturity of the ICSR process and supporting technologies.
While emerging technologies such as artificial intelligence (AI), automation, and data-driven methods can enhance efficiency, integrity, and compliance, they are themselves evolving in maturity, impacting their usability. Addressing these dynamics requires a well-coordinated approach that ensures synergy in patient safety initiatives, control on risk, and regulatory compliance. This calls for an overhaul of the ICSR processes to incorporate more digital capabilities. Consequently, this means the framework to implement PV systems has to be redrawn.
The challenges
Data now flows in from diverse sources—electronic health records (EHRs), social media, and wearable technology, for instance.
Maintaining data consistency and accuracy in safety reporting has thus become paramount for ensuring patient safety. But this endeavor faces challenges. Some of them are:
- Data quality and completeness: Variability in the quality of reports from different regions and sources continues to complicate accurate signal detection.
- Global regulatory requirements: Navigating the complex web of region-specific reporting rules adds a layer of difficulty, with new jurisdictions and guidelines being added regularly.
- Integration of new data sources: Leveraging RWD and patient data from non-traditional sources presents new opportunities, and poses technical challenges.
- Timeliness and accuracy in translations: Ensuring timely and consistent translations of AE reports from multiple languages remains a challenge with PV expanding globally.
- Inconsistent global standards: Compliance with different regional regulations is a challenge, with regulatory bodies continuously updating guidelines on AE reporting.
- Manual effort and human error: Manual processes such as case translations, intake and triage, data entry, and narrative writing increase the chances of errors and inefficiencies.
- Volume and complexity: AE data comes from various sources, including electronic health records (EHRs), wearable technology, and social media, making the management of these large data sets complex.
Digital – a crucial KPI
In addition to traditional key performance indicators (KPIs) like timeliness and data accuracy, organizations are now prioritizing metrics that reflect their own technological capabilities as well.
These new metrics (see Figure 1) reflect the industry’s shift towards more digital-first approaches, where process optimization is as much about adopting new technology as it is about maintaining the right compliance.
New metrics for a digital-first approach
New metrics for a digital-first approach
The top areas for improvement and KPIs are:
Automation and AI integration:
PV departments are seeing highest returns by automating labor-intensive and manual ICSR tasks, including:
- Translation: As the need for centralization increases globally, accurate and timely translation of ICSRs becomes crucial. AI-driven translation tools can translate medical terms and adverse events in real-time, eliminating bottlenecks caused by manual translation processes in human-in-the-loop model. Customers noted that AI tools can reduce translation time by up to 60% while maintaining high accuracy and ensuring compliance with local regulations.
- AI-powered case processing: AI and machine learning (ML) algorithms can be trained to detect patterns in large volumes of AE data, improve signal detection accuracy, and reduce the need for manual oversight. According to customer case studies, automating case processing can reduce case cycle time by 30-40%, thus improving overall efficiency. They demonstrated that the time to process ICSRs could be cut from 14 days to nine days or lesser through advanced automation.
- Intake and full data entry: Leveraging the advancements in automation, including Generative AI (GenAI) powered with natural language processing (NLP) and ML, along with optical character recognition (OCR), can automate the extraction of AE data from unstructured documents. For example, automation can achieve over 65% increased intake efficiency with over 50% reduction in data entry efforts, freeing up PV teams for more value-added activities like signal detection and risk analysis.
- Narrative writing: Narrative writing is often one of the most time-consuming of all activities. By leveraging LLMs and AI to generate narratives based on structured approaches, organizations can cut narrative drafting time by 25-30%. These drafts are then reviewed and refined by medical writers to ensure compliance and clarity. Customers’ NLP-based solutions helped clients reduce narrative generation time significantly, leading to faster ICSR submission without compromising quality.
- Follow-up questions: Timely follow-up questions are critical for gathering missing information in AE reports. Data-driven algorithms can prioritize cases based on severity and risk, generating questions that are most relevant. Automating follow-up queries can reduce the case closure time by up to 20%.
Technology and process integration strategy
Successful implementation of the above innovations requires a cohesive strategy that aligns business processes with the latest technological trends. The benefits of such a strategy are many:
- Hybrid AI-automation models: Combining AI and human oversight ensures the right balance between automation and the nuanced decision-making required in pharmacovigilance.
- Real-time monitoring systems: Modern pharmacovigilance processes need to adopt real-time monitoring and alert systems that use AI-driven analytics to detect safety signals as soon as they arise.
- Compliance with emerging global regulations: In 2024, new regulatory frameworks like the EU’s upcoming AI Act have impacted the way AI tools can be used in regulated industries, prompting companies to revise their AI and automation strategies to remain compliant.
- Automation strategy: Prioritize automation of manual tasks as an ongoing activity based on the value derived from, and the complexity of, automation.
For instance, some innovative initiatives reduced ICSR processing time by automating case prioritization and data entry tasks. By integrating machine learning (ML) algorithms, they optimized case handling efficiency, reducing the need for manual oversight by 40%, and cutting case processing time from two weeks to less than 10 days. The industry experience in PV process transformation showed that companies implementing AI-based automation saw:
- 35% reduction in total case cycle time
- 20% increase in case throughput
- 25% reduction in data entry errors, improving data accuracy
- Up to 50% reduction in manual intervention for case translations and narrative writing, enhancing productivity.
Revamped ICSR process
Implementing the above changes needs a reimagined framework for continuous improvement in PV systems.
The framework should lay particular focus on the strategic implementation of emerging technologies (Figure 2).
Centralized coordination of projects: Experience shows that a nodal program serves as a dynamic platform for identifying, assessing, and integrating new technologies, understanding risks and the initial limitations in addressing pharmacovigilance process needs while ensuring necessary attention to high-priority requirements.
Evaluation and adoption of technology: Regular evaluation and adoption of technology options allow comprehensive assessment and implementation of tools that streamline workflows, optimize resource utilization, and enhance reporting capabilities.
Iterative proof of concept and pilot studies: This validates technology before full-scale deployment, allowing risk mitigation, refinement of solutions, and increased confidence in automation and AI-driven processes.
Feedback loop for continuous improvement: Lessons learned from technology implementation are systematically assessed and used to refine workflows.
Focus on incremental improvements: It minimizes disruptions while enabling targeted enhancements.
By embedding these continuous improvement mechanisms, pharmacovigilance systems can maintain agility, drive operational excellence, and enhance patient safety outcomes in an increasingly complex regulatory landscape.
A framework to integrate emerging technologies
A framework to integrate emerging technologies
Precision pharmacovigilance: Case study
A cognitive end-to-end case processing approach helped a UK pharma company process over a million cases yearly with the right quality and compliance. An integrated strategy reduced the cycle time significantly and helped save cost throughout the PV value chain. The per case cost quotient improved by over 30-40% by case type.
What the future holds
A framework that incorporates emerging technologies will not only save costs but also streamline processes for greater efficiencies.
The reimagined framework for continuous improvement in PV processes, particularly for ICSR management, offers a strategic and technology-driven approach to address current inefficiencies. By leveraging a centralized coordination system, through iterative development, and by integrating emerging technologies such as AI and automation, this framework will pave the way for incremental yet impactful improvements.
