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With perpetual resource constraints, providers are constantly put in the position where they need to make trade offs on resource allocations. At the most basic level, the objective providers have when it comes to managing their supply chain is to fulfill the medical resource needs of their clinicians while limiting on-hand inventory of non-essential items to secure levels in the case of emergencies. With the spread of COVID-19, it has never been more difficult for providers to accurately adjust their estimated resource needs. Routine estimates that were in place before COVID-19 have now proven to be unreliable in an environment where patient behaviors have abruptly changed. Providers require urgent solutions to accurately forecast their resource needs to ensure that they are able to fulfill evolving patient requirements while minimizing excess inventory at a time where resource prioritization is of the highest priority.
While COVID-19 was a surprise to the world, healthcare procurement managers now have the opportunity to adjust their methods for forecasting resource needs based on the newly acquired data they’ve collected post-outbreak. AI is able to learn your historical data to forecast total patient volumes in the near future, which acts as a benchmark for short-term resource demands. The forecasts can be done in various windows, depending the lead time required for these resources. Based on your historical patient demand, resource needs, geography, and economy, AI uncovers patterns within multi-dimensional data to create forecasts more accurate than linear estimates or simple averages. In an environment that is constantly changing, AI also offers your procurement managers with the top statistical reasons behind each forecast to help them create rapid but thoughtful decisions on whether they should augment the predicted forecasts.
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Healthcare companies are using machine learning and AI to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from readmission risk and occupancy rates to marketing, in order to make data-driven decisions that lead to increased profitability.