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With the advent of AI/ML models in the pharmaceutical industry, its respective stakeholders are navigating new ways to gain insights, which is eventually beneficial to the end users and other complementary stakeholders like -patients, caregivers, NPPAs, HCPs, pharmacists, etc.
As the industry is embracing Pharma 5.0 trends with more focus on advanced digital techniques such as AI/ML, IoT, NLP, Blockchain, and Big Data to expedite the overall drug discovery, clinical trials, and manufacturing, there happens to be more questions coming in the way regarding the ethical part of it. They include- AI models’ accountability and transparency in areas of rare diseases and many more.
In this blog, we will discuss both the significance as well as the challenges, regarding the inclusion of AI in Pharma Insights and other prospects.
Pharmaceutical insights for brand teams: Prompter and more affordable with advanced technologies in action
AI/ML models coupled with NLP techniques have proven to minimize the drug discovery and final commercialization timeline during Pharma 4.0. In no time these tools can gather biological systems and diseases to identify drug candidates with a high probability of success in optimizing clinical trials. Simultaneously, these tools are also assisting brand teams in finding new sources of data and business insights that eventually minimize the overall launch-to-market timeframes as compared to traditional methodologies (that took at least 5 years).
Survey Software Technology
SaaS platforms for pharmaceutical insights have unleashed transformative alternatives over the past decade, molding them into a more digitized, connected, and automotive way of executing operations. There happens to be a category of software that caters to different needs-
- Pharmaceutical ERP software- proven to offer a seamless method to keep track of the inventory, active pharmaceutical ingredients (APIs), and packaging.
- Pharmaceutical manufacturing software- with the respective process analytical technology (PAT), you will get the insight and notification you need to act upon if your attributes should drift from their design space and hamper the integrity and efficacy of your drug product.
- CRM software- Works like any other industry-agnostic CRM software, pharma CRMs are used to fulfill- (a) Customer database and contact details (b) Marketing/sales campaign automation(c) Sales methodologies coupled with interaction tracking and tracing
- Pharmaceutical supply chain software- While ERP and CRM software are holding the forte from inventory management to offering CMO databases, the dedicated supply chain software would be responsible for (a) supplier orientation and assessment (b) Traceability and chronology of distributed products (c) Any temperature and contingent supervising systems required for your product as it’s moved.
In recent times it is seen that brand teams are at an advantage as predictive maintenance pharma software tools are boosting the equipment effectiveness, automating calibration, and cutting downtime. On the other hand, regulatory and quality content can be crafted in the blink of an eye by an AI writer, integrating product data in real-time to provide users with an audit-ready document stack in no time.
Synthetic Data Surge
It has already invaded the industry and enterprises are using this data to test new drugs without seeking a plethora of volunteers. Synthetic data is helping brand teams predict how well patients will follow their treatment plans and navigate effective ways to launch new medications in the market.
Market research is also being disrupted and has started to feel the wrath of synthetic data. Now it is up to the industry itself to explore the power of leveraging synthetic personas to gather a better understanding of patient preferences, unmet needs, and market receptiveness- subjecting to potentially robust models for therapeutic adherence.
First-party customer access
First-party data is becoming more crucial and valuable to the pharma industry as brand teams are seeking to understand and create bonding with consumers more effectively. Conversational AI complements these kinds of data and thus helps in making real-time decision-making based on those insights. Once the customer data platform (CDP) is ready (filled with HCPs’ whereabouts) brand teams get ready with omnichannel campaigns for better market penetration.
Behavioral Science Techniques
Behavioral science is revolutionizing brand strategy and tactics. Almost all stakeholders like physicians, nurses, and payers (with disease monitoring behaviors), all have contributing factor that affects patients. Gathering these insights at one platform with refined tactics of crafting interventions to enable positive behavior change happens to be a promising way of enhancing successful outcomes.
Brand teams are therefore seeking stakeholders whose role is to understand patient behavior, helping them to make the correct health decisions. This, in turn, assists them in future commercial success.
Challenges in using AI/ML tools for Pharmaceutical Insights
Now that we are done with the boons of advanced technologies like AI/ML, let us venture into the key ethical challenges below-
- Data Privacy and Security- AI models need huge sets of patient data to run their predictive analysis and that makes the pharma sector more volatile. The enterprises are always on top of their toe due to the wide spectrum of data protection regulations of their respective countries. For example- GDPR in Europe and HIPAA in the U.S.
- Amalgamation with running systems and workflows- Enterprises with legacy systems often struggle with cutting-edge AI tools. It needs thoughtful planning and customization to avoid havoc. Hence, it asks for necessary upgrades to ensure that both systems work with the same bandwidth as existing technologies.
- Ethical thought process towards AI decision-making- Algorithms that influence patient treatment or drug development should be free from biases. However, human interventions during data gathering at the initial stage have concerns regarding biases, which results in a non-fair outcome. Setting up ethical guidelines and human oversight of mechanisms can lead to the ethical usage of AI tools.
- It’s a no-brainer that the pharma industry is overwhelmed with regulatory norms. This steers that the implementation of AI in drug development and clinical trials should follow the same. Navigating these regulations requires a lot of investment, ensuring that AI models meet all necessary standards.
- The inclusion of AI in pharmaceutical insights calls for skilled personnel like data scientists, AI specialists, etc. which increases the manpower costs for an enterprise. Proper investing in training and retaining talent methodically, helps organizations to curb financial losses in the long run.
From a broader perspective, all the relevant stakeholders are witnessing how AI and ML models are transforming the pharmaceutical industry’s drug discovery, personalized medicine manufacturing, marketing insights, and techniques through enhanced efficiency and data analysis. But on the other side, challenges like data privacy, biases, ethical concerns, and regulatory issues need addressing at the ground level. As AI technology steers through the entire pharma ecosystem, there could be more improvements and alterations that should be compiled with a robust focus on ethical AI and rationalization.