If not, you may be missing an important step in designing your AI/ML algorithms and to ensure successful project outcomes within the healthcare supply chain.
AI/ML are affecting many areas of healthcare supply chain management, including the use of Business KPI’s.
Healthcare supply chain management includes the flow of medicines, medical devices, PPE, and healthcare services from manufacturer to patient and helps ensure that healthcare providers and patients get the medications and treatments where and when they need them.
By improving healthcare supply chain management, hospitals, healthcare providers, pharmaceutical companies, and other stakeholders in the healthcare sector can work together to increase access to timely care, and to improve patient wellbeing and health outcomes.
KPI’s provide the measurement points in AI/ML supply chain projects by defining desired outcomes when using the models.
KPI’s can also connect complex and siloed data sets, common throughout healthcare, that AI/ML models decipher and interpret.
Coupling performance indicators with AI/ML can laser-focus algorithms towards specific supply chain applications such as demand forecasting and inventory management.
This has been particularly evident throughout the pandemic and more recently with the earthquake in Turkey-Syria. Running AI/ML models using a set of KPI’s to identify the ‘highest performers’ in the supply chain, could streamline access to critically important medical supplies and medications.
Of course, the healthcare industry is dynamic and constantly evolving, through technology, medical breakthroughs, the payor system (value based care), and new data sources.
This means that KPI’s and AI/ML models need to be dynamic as well, based not only on these external factors, but also on key insights and learnings from past models that feed and inform future models.
When thinking about your healthcare supply chain, whether local, national or global, do you have KPI’s that inform your AI/ML models?
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