The life sciences (LS) industry has recently seen remarkable changes in safety assessment, cost, and clinical effectiveness of new products, supplemented by advancements in technology.
LS organizations always monitor the effectiveness and safety profile risk of a drug from the conception stage to ensure that the benefits always outweigh the associated risks.
Adverse events are reported throughout the drug lifecycle by physicians, patients, caregivers, and other sources. The LS industries collect, process, and evaluate these adverse events on a global scale and then aggregate and analyze the reports to identify potential safety signals and trends.
Safety signals, once detected, are evaluated, and risk management actions are taken if required. This includes changes to drug labeling and communicating with physicians and health authorities.
However, of late, traditional data capture mechanisms are being replaced by innovative & intelligent electronic platforms due to the latter's efficiency, cost-effectiveness, and reliable data capture record, both in clinical trials as well as commercial program setting.
Key Levers in Guiding Business Outcomes
Today, Real-World Evidence (RWE), Real-World Data (RWD), and patient & customer voice also play a crucial role in influencing business decisions and outcomes. Hence, LS organizations are increasingly implementing various systems and programs to improve the performance of their products.
The Role of Artificial Intelligence in Handling Safety Data
With the ever-evolving global landscape of patient and customer interactions across diverse digital data sources, there has been a proportional increase in the volume of safety data and information in the LS industry.
And so, there is a need for enhanced technology solutions and business processes to handle the same.
This is where the role of artificial intelligence (AI) comes into play. With its cognitive technology, AI has the potential to provide scalable solutions for handling the ever-increasing safety case volumes and facilitate informed pharmacovigilance decision-making. AI capabilities have already demonstrated significant success in capturing safety cases from diverse data sources. Hence, safety cases and customer experiences are now being reported through alternate channels, too.
Some of the new alternate channels include:
- Voice-based interaction - It has been noted that in routine and emergency circumstances, customers prefer talking to typing.
- Social media/other global/external modes of communication - Over the past few years, more customers prefer to publish their experiences on social media.
- Mobile applications - The use of mobile applications to report product safety alerts and product experiences has grown significantly in the last decade. Most regulatory authorities already accept mobile applications as a valuable source of information for safety surveillance activities.
Voice-based interaction platforms are a secure method and allow customers to express their satisfaction and concerns. They provide better reach to physicians and healthcare professionals and enable quick salesforce training since the platform performs most of the voice interpretation and transcription.
For the management of voice-based interactions, LS companies typically work with solutions such as Interactive Voice Response System (IVRS) and voice-bots. However, while IVRS is a promising technology, its adoption by LS industries has challenges, such as:
1. Voice-based interaction systems cannot operate as a stand-alone entity and almost always require to be incorporated as part of a bigger digital ecosystem.
2. IVRS needs to be both reactive and proactive to patient and customer expectations, as well as predictive to understand when to hand off the case to a live trained agent.
3. IVRS lacks accents and colloquialisms and thus is exposed to the risk of mispronounced product names.
Key Tenets of Cognitive Powered Voice-Based Interaction Platform
An ideal cognitive-powered voice-based interaction platform should have the following features.
1. It should be scalable, flexible, and easily integrated with any other existing platform.
2. It should be able to ingest and process source agnostic data to extract information and make cognitive decisions.
3. It should allow a smooth transition to an AI-based automation case intake from traditional intake methods, for instance, from a webpage to modern interaction methods such as mobile applications.
4. It should leverage an AI-based voice interactive system with voice-based IVRS.
5. It should accelerate the automation of the voice-to-text process and case intake using natural language processing and machine learning approach:
a. Automation of speech to text with three essential features:
i. Provide accurate transcription
ii. Annotate parts of conversation based upon user voices (agent and customer)
iii. Conduct sentiment analysis of the voice
Conclusion
Today, all stakeholders in the LS industry consider the ‘patient voice’ important since it provides feedback on the effectiveness of a new product in real life. However, one must not forget that the adoption of patient voice, direct patient report outcomes, and customer experience as key metrics in the evaluation of product safety must also be complemented by strategically designed safety vigilance activities and innovative solutions.
Only an intelligent solution that can perform all of the above will help LS organizations move into a more proactive, intelligent, cost-effective, and predictive model that is based on actionable evidence safety and effective data.
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