One of the most important problems in healthcare is the need for more drugs. A lack of life-saving drugs and commonly used treatments can make it difficult for doctors to care for their patients, stall treatments, and force medical professionals to make difficult choices.
Managing supplies the old way, like with static databases or calling shops by hand, only sometimes works well for these issues.
Data science is the key to finding an answer. Data-driven tools that identify gaps before they happen are revolutionizing pharmaceutical supply lines. In pharmaceutical manufacturing, these tools enhance efficiency by predicting potential disruptions, ensuring a steady production flow, and maintaining the timely delivery of critical medications.
With the help of predictive analytics, machine learning, and real-time tracking, hospitals, distributors, and manufacturers can avoid supply shortages and keep patients safe.
The Growing Problem of Pharmaceutical Shortages
Drug shortages are not new, but they’ve become more severe in recent years. According to the American Society of Health-System Pharmacists (ASHP), there were 323 reported shortages in early 2024, an all-time high. These shortages affect everything from critical cancer treatments to prescriptions, leaving pharmacies scrambling to find alternatives.
The causes are varied – supply chain disruptions, manufacturing delays, and sudden demand spikes all play a role.
For instance, during the COVID-19 pandemic, there was a huge demand for some medicines, which made it hard for suppliers to keep up. Even in everyday situations, manufacturers can struggle to keep up with the uncertain demand and production capacity.
When hospitals and pharmacies need more stock, patients must wait longer for treatment or use less effective options. This situation highlights the importance of being strategic and planning to prevent shortages.
Using Data Science to Predict Shortages
Data science is becoming a game-changer in tackling pharmaceutical shortages. By analyzing a large amount of data, such as manufacturing plans and buying patterns, predictive models can determine when and where shortages are most likely to occur.
For example, consider data mining for shipping. French researchers used computer records from hospitals and drug distributors to find problems with the supply chain.
They could predict shortages weeks in advance by examining supply patterns and finding outliers. Because of this research, healthcare providers can change what they have on hand and get new supplies before something goes wrong.
Take the case of Wegovy in short supply, where rising demand was higher than production capacity. These situations show how important it is to get early signs through data analytics. If they had tools like prediction models, manufacturers could have seen when demand increased and changed their production or supply plans to meet market needs.
How Predictive Analytics Helps Manage Inventory
Accurate forecasting involves more than finding gaps; it also involves improved inventory management. Predictive analytics helps stores and hospitals ensure they have the right stock.
One thing that big data tools can do is examine how demand changes over a year. This helps companies prepare to make more when needed, reduce waste, and ensure that important medicines are always available.
Predictive models can find patterns in drug use, such as the number of people in different places, how drugs change over time, or how much they use each year. This helps ensure that plans are correct.
This approach significantly improves patient care. When companies know they will always have enough, they can focus on providing the best solutions without worrying about issues. Reduced financial losses for hospitals due to high-priced, last-minute purchases and better patient health outcomes are positive developments.
Transforming Patient Care Through Proactive Planning
Anticipating and managing drug shortages well has benefits that spread throughout the healthcare system. Patients get care without interruptions, healthcare workers keep patients’ trust, and hospitals don’t have to make expensive purchases at the last minute.
Organizing patient treatments with accurate supply predictions is a big plus. If a patient’s preferred medication isn’t available, doctors can easily switch them to another choice that won’t affect their care. This method fixes problems right away and improves the healthcare system.
The Role of Collaboration and Data Sharing
Data science has great potential, but for it to succeed, everyone in the pharmaceutical supply chain must work together. Hospitals, distributors, and manufacturers must be willing to share data and insights to create a comprehensive picture of supply and demand.
Recent regulations requiring the serialization of pharmaceutical products are a step in the right direction. Stakeholders can identify bottlenecks and respond more effectively by tracking every stage of a drug’s journey – from production to delivery.