[NicoElNino/Adobe Stock]
Leading safety teams are seeing up to 80% efficiency gains on key workflows by investing in touchless case processing, automating pharmacovigilance processes across intake and assessment, review, full data entry, medical review, quality review and submission.
A new opportunity in AI and ML
The life sciences industry is rapidly growing in scale and complexity, with organizations generating increasingly large volumes of safety data. The pharmaceutical industry, healthcare providers and consumers have reported more than 18.6 million adverse events to the U.S. Food and Drug Administration (FDA) in the last 10 years, 216% more than the prior 10 years.
Safety case data is submitted in both structured and unstructured formats from a variety of sources. It is the role of safety teams to gather these submissions, extract relevant information and populate that data into the safety database — which is then made available for downstream consumption to inform signal and risk management, overall performance management and cross-domain inquiries.
When performed manually, these processes can often be tedious and error-prone and require multiple stages of manual reviews and quality checks. Many life sciences companies still rely on these outdated manual methods, which only makes it harder for them to maintain efficient and consistent compliance.
Fortunately, a solution exists. Life sciences organizations are adopting automation through artificial intelligence (AI) and machine learning (ML) to significantly accelerate end-to-end case processing, improve consistency and drive transformational safety outcomes.
What to look for in automation
Automation through AI and ML speeds up case intake by extracting safety case attributes from structured and unstructured fields, improves data quality through visual hints and consistency checks and eliminates repetitive tasks from employee workloads.
For true efficiency gains and optimal return on investment, however, safety teams need to be careful in selecting what automation to use and how to implement it in their organizations.
Key considerations to keep in mind include:
- Data quality: The data used to train and validate automation models must be high-quality to ensure model quality and trustworthiness.
- Variety of data: Consider the variety of fields, formats and sources of data used to build and train the model so it accounts for all variations.
- Explainable: Automation cannot be a black box; safety teams need to be able to readily communicate results with stakeholders and maintain a clear audit trail for compliance.
- Model training: Building an automation model requires extensive training, and so many teams save time and effort using models that are pre-trained or require minimal training.
- Model validation: Similarly, automation models must be thoroughly validated prior to going live, and so many teams are getting started sooner with pre-validated models.
- Continuous improvement: As models undergo continual training and are exposed to new data, they should gain incremental insights and become more powerful over time.
Not all safety platforms offer proven, pre-validated automation in production. However, those that do offer established models trained on industry-relevant data give organizations, from the smallest startup to the largest enterprise, the power to fully leverage the advantages of automation in pharmacovigilance.
The value of automation in pharmacovigilance
Life sciences organizations with pragmatic, strategic approaches to implementing automation can see an incredible return on value.
Key benefits automation can provide include:
- Time and cost savings: Automation enables safety teams to be exponentially more efficient, saving life sciences organizations both time and labor costs by streamlining and accelerating daily repetitive tasks throughout the safety lifecycle.
- Process consistency: Automation brings a new level of consistency and quality to safety operations, including reducing the probability of human error, improving the quality of manually entered data and simplifying otherwise complex workflows.
- Better resource utilization: Rather than replace human work, automation empowers professionals to make better decisions and do their jobs better, while also enabling organizations to shift human resources to higher-value initiatives.
- Scalability: Finally, as organizations expand in case volumes, products in market and globalized footprints, automation provides an invaluable way to enable cost-efficient scalability to ensure their safety operations can readily grow at the same rate.
Unlocking strategic value through automation in pharmacovigilance
For teams that are able to properly build, train and validate automation models that leverage AI and ML, there is immense potential not only to reduce time and costs spent processing increasingly high safety case volumes, but also to transform safety from a cost center into a value center. The key to achieving this transformation lies in leveraging the automation in pharmacovigilance to free teams to focus on the strategic initiatives that can ensure better patient outcomes, versus spending their time in repetitive tasks and workflows.
Beena Wood
The life sciences AI market is expected to reach around $6.7 billion by 2030. By building AI and ML into safety case processing, life sciences organizations can realize better outcomes, such as quicker processes and improved data quality. This frees PV teams to focus on areas where they can add genuine value, like signal detection, PV analytics, and benefit-risk assessment.
About the Author
Beena Wood is the VP of product management, LifeSphere Safety and Medical Affairs, at ArisGlobal. She has been in the information technology space building products for over 26 years, with experience across multiple domains. She has led engineering as well as product management teams. Wood comes from Oracle, where she was in the pharmacovigilance space for over 15 years, leading their Argus Safety Product Management teams, and most recently from Anju Software, leading their Medical Affairs space, and loves building solutions to keep patients safe.
Filed Under: Industry 4.0, machine learning and AI, Regulatory affairs
