While AI is already helping improve process efficiency in the life sciences industry, it is likely that, within the next three to five years, it will prove its value in R&D and across other areas of the value chain.
The COVID-19 pandemic has increased the focus on the use of AI across the life sciences organization, from R&D to manufacturing, supply chain, and commercial functions. During the pandemic, company leaders and managers realized they could run many aspects of their businesses remotely with digital solutions. This experience has changed mindsets, and leaders seem more likely to lean into a future powered by digital investments, data, and AI.
At present, the life sciences industry has only begun to scratch the surface of AI’s potential, primarily using it to automate existing processes. By melding AI with rigorous medical and scientific knowledge, companies can do even more to leverage this technology to transform processes and achieve a competitive edge. AI has the potential to identify and validate genetic targets for drug development, design novel compounds, expedite drug development, make supply chains smarter and more responsive, and help launch and market products.
To explore the use of AI by the life sciences industry, Deloitte surveyed global leaders of biopharma and medtech companies about their AI investments, outcomes, and challenges. The research revealed that:
- More than 60% of life sciences companies spent over $20 million on AI initiatives in 2019, and more than half expect investments in AI to increase in 2020.
- Top outcomes life sciences companies are attempting to achieve with AI include enhancing existing products (28%), creating new products and services (27%), and making processes more efficient (22%). Most (43%) reported having used AI successfully to make processes more efficient.
- Top challenges affecting AI initiatives include difficulty in identifying business cases with the highest value (30%), data challenges (28%), and integration of AI into the organization (28%).
AI is already demonstrating its value in making processes more efficient and that value is likely to increase over time. The next three to five years are likely to prove AI’s usefulness in transforming biopharma R&D, especially in drug discovery. During this period, companies could consider piloting additional AI projects and perhaps adopting AI for other processes. As this development unfolds, it will become essential to create frameworks and protocols to manage AI-related risk and to audit AI systems for bias and transparency in order to meet compliance and regulatory requirements.
Applying AI to Risk Management
Companies are increasingly applying AI to help identify, monitor, and address risks to support compliance and protect against cyber threats.
Traditionally, compliance functions have taken a risk-based approach to set priorities and then monitor activities with the highest potential for compliance risk. As regulatory pressures mount to proactively monitor risk, compliance staff cannot always keep up with the vast amount of information compliance programs are expected to track. Applying AI solutions can help to proactively identify anomalies, prioritize compliance risks, and improve the efficiency of compliance staff and activities.
Consider a case involving a biopharma company that deployed a natural language processing (NLP) tool for initial email screening to identify potential risks. The initiative reduced the number of emails compliance officers needed to review from 2,500 to 20, freeing them to investigate those 20 emails to identify business risks and perform employee retraining.
Many biopharma companies are increasingly applying AI to handle the growing volume of data related to automating the processing of adverse events. Using a cognitive case-processing algorithm, a large biopharma company automated the processing of adverse event data from patients, health care professionals, and regulatory agencies.
The company applied machine learning and natural language processing (NLP) to automate routine cases and route exceptional cases to experts for targeted adjudication. It then learned how to address similar cases to improve efficiencies. Applying the solution improved the quality and consistency of safety information and freed resources to focus on developing a deeper understanding of the safety profile of the company’s products. The company reported it reduced its case-processing time by 50% to 60% per case, which has resulted in 65% to 75% savings per year in case-processing costs.
Leaders also may want to use AI to improve their contract management process. Biopharma companies routinely interact with health care practitioners through contractual arrangements such as paying a standard fee for participation in an event or for provision of services. These contracts are subject to regulatory and legislative scrutiny. Companies could increasingly use software bots, NLP, and machine learning to monitor contracts for compliance with internal norms and external regulations. Deloitte experience indicates that current systems can accurately and confidently make “approve/reject” decisions for more than 70% of contracts.
AI solutions are also being used as part of cyber strategy. Modern cyberattacks can circumvent traditional, rule-based security controls by learning detection rules. Smart cyber technologies will be increasingly needed to protect the growing volume of data biopharma companies own and can access. AI-based approaches can improve threat intelligence and prediction and enable faster attack detection and response, augmenting efforts by cybersecurity experts.
As AI moves from a “nice to have” to a “must have,” companies and their leaders can consider building a vision and strategy to leverage AI, then put in place the building blocks needed to scale its use. These include an effective IT infrastructure, talent and skill sets to complement AI, and plans to create ecosystems and alliances to access or build AI capabilities.
—by Aditya Kudumala, principal, Deloitte Consulting LLP, and Deloitte’s Life Sciences technology practice leader; Dan Ressler, principal, Risk & Financial Advisory, Deloitte & Touche LLP, and Life Sciences and Healthcare leader for Global Risk Advisory; and Wendell Miranda, senior analyst with the Deloitte Center for Health Solutions, Deloitte Services LP
Read the full survey report, “Scaling up AI across the life sciences value chain.”