Medical device manufacturers that make rehabilitation and lifesaving products like orthopedic joint replacements and cardiac monitors are under growing pressure to enhance operational resilience and develop products that give patients the highest quality of life. Companies have seen revenue growth slow substantially, increasing only 3.5% in 2022 compared to 16% in 2021 — due partly to limited innovation. To address this, device manufacturers are seeking novel ways to reduce costs and increase revenue.
Generative AI offers both immediate solutions and immense future potential. It provides a new, powerful opportunity for device manufacturers to get more out of one of their most valuable assets — their data — to drive innovation and address the growing costs associated with supply chains and product quality.
During product development and delivery, device manufacturers produce and collect vast amounts of structured and unstructured data, including customer feedback, research and development practices, product manufacturing, and information from clinical trials. This data, and external data such as peer reviewed literature, are essential to optimizing product development. Put simply, data fuels innovation, making generative AI an invaluable technology for device makers, all of whom are competing in a landscape where healthcare demands are increasing across the board.
Using C3 Generative AI, medical device manufacturers can:
- Increase innovation
- Increase product quality
- Avoid recalls
- Forecast product demand
- Optimize inventory levels
Generative AI in Healthcare: Helping R&D Engineers Innovate Faster and Prevent Failures
Clinical standards and patient expectations for high-quality devices, such as demand for remote patient monitoring devices, drive medical device manufacturers to innovate while maintaining safety and regulatory standards.
To innovate effectively, research and development engineers rely on an endless trove of documents: published scientific literature, internal standard operating procedure (SOP) documents, details of past experiments, and product feedback. These are used to model, design, document, and publish product development research. A cardiac device R&D engineer, for example, must search across multiple disparate sources including PubMed, internal CAD designs, sister product failure reports, and material property databases. With C3 Generative AI, a cardiac device R&D engineer can search and interact with internal and external data directly to rapidly get accurate answers to such questions as:
- What are the failure modes for heart valve devices?
- What are the latest meta studies on left ventricular assist devices (LVAD)?
- What are the existing CAD designs for a thoracic aortic graft?
McKinsey estimates that generative AI can increase worker productivity up to 60%. Further, being able to derive quick, dependable insights from all your data can improve design effectiveness. The benefits of applying C3 Generative AI are reducing R&D costs and reducing failure events, preventing product recalls.
Generative AI in Healthcare: Supply Chain Managers Plan for Demand and Optimize Inventory Levels
Medical device makers are adjusting to post-pandemic healthcare operations. Specialized medical device need has grown, while elective procedures have been postponed or cancelled, creating significant fluctuations in product demand with a growing patient backlog.
For example, demand for interbody fusion devices used in back surgery can decrease when inpatient bed availability decreases because such procedures can require overnight stays. Forecasting and stocking are further complicated by factors such as long lead times and competition.
Using internal data such as sales orders and customer information, as well as external data such as epidemiology studies and hospital reports, are essential to solving this challenge. Accurate forecasts and stock levels ensure product availability so patient needs are met, without overstocking product and incurring unnecessary costs.
With C3 Generative AI for Supply Chain, supply chain managers can search and interact with internal and external data directly to ask questions such as:
- What are my inventory levels for all orthopedic knee joint products?
- How is demand for permanently implanted cardiac devices changing in the mid-Atlantic?
- How many temporary implanted devices have had stock outs in the past three months?
Generative AI can enable supply chain managers and others to efficiently search, source, and understand key processes and changing business operations in their supply chain. The outcome is a streamlined supply chain with fewer stock outs, higher service levels, reduced inventory costs, and more accurate demand forecasts.
