This approach uses machine learning and supply chain data to predict and mitigate pharmaceutical shortages, enhancing patient care.
Currently, the pharmaceutical industry is being affected by severe upheavals that may lead to a shortage of life-essential drugs across health systems everywhere. Because pharmaceutical supply chains are so detailed, starting with buying materials and ending with delivering products, any interruption at any stage can cause difficulties throughout the network and for patient care. Most efforts to manage shortages of pharmaceuticals are reactive and take place once shortages appear, rather than stopping or minimizing them in advance. Mixing cross-supply-chain data with information from patients can support new ways to predict risks and create complete strategies to handle issues on both sides of the healthcare system. It discusses how data fusion can dramatically improve how pharmaceutical shortages are managed by giving real-time access to supply chain operations and integrating medical records to guide proper intervention strategies. With this framework, it is possible for pharmaceutical companies, distributors, healthcare practitioners and regulatory authorities to cooperate using shared information to identify and tackle possible shortages before those shortages harm patients. Also, the use of artificial intelligence and machine learning with traditional supply chain systems makes it easier to create intelligent models that can see ahead and suggest proper division of products. Careful attention needs to be given to rules on data safety, how systems can share data and ways to bring stakeholders together to allow effective working across different organizations in pharmaceuticals. It leads to a positive shift from responding to crises to preparing ahead for future risks, all of which helps the health system stay steady and improves patient access to necessary medicines around the world.
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Oluwatofunmi et al. (2023) studied this question.
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