IDC identified digital transformation as the top trend in the life sciences and healthcare industry this year, so it’s no surprise that there is now increased interest from this industry in automation use cases where the addition of AI and ML with RPA could add value across the entire ecosystem. The aim is to create a scalable digital workforce that has the capacity to execute processes that don’t require human intervention and deliver a return on investment in less than 12 months.
Providing greater visibility of data in real-time is also being used by pharmaceutical companies and medical device manufacturers, for example, to eliminate potential compliance concerns by reducing fraud and error rates and to increase accuracy, safety and security. This is especially the case in the life sciences industry.
Intelligent automation is being harnessed to fast-track drug discovery, vaccine development, and clinical trials, by automating processes relating to documentation and regulatory monitoring. Removing bottlenecks is proving to be the key to addressing some of the challenges posed by the pandemic, especially with regard to providing test kits and Fast Track analysis.
AI use cases in healthcare for Covid-19 and beyond
The ability to standardize data, use larger data sets, remove biases, and train algorithms more efficiently to identify, for example, which compounds may be more effective or worth moving through the drug discovery process quicker, is providing results quicker and almost making it possible to do the work in advance. This in itself suggests that assessment, outcomes, the possibility of approval and efficacy could be done in the drug discovery stage, alongside clinical development, regulatory and document processing, potentially leading to virtual clinical trials.
Introducing more automation in laboratories will also enable data to be linked back into manufacturing, and other data lakes, to provide greater visibility of trends, faster and delivering scale manufacturing, and more agile supply chains which are major requirements, especially at this time.
Production demand forecasting is a core use case for example – predicting where there may be a surge of demand based on externalities such as a rise in the flu or increase in Covid-19, or a potential change in the population, may increase demand. Similarly, being able to monitor and track quality issues of pharmacovigilance and complaint handling – seeing trends regarding regulatory submissions or complaints as they come in, monitoring trends sooner, updating field teams so they can proactively manage issues (regarding samples and shipments for example) within days rather than weeks – can contribute to increased sales.
Fortunately, intelligent automation is enabling the life sciences and healthcare industry to manage and integrate legacy systems and achieve the benefits of digital transformation without updating software, developing APIs, or building a new system, within weeks, rather than months or in some cases years.