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An instrumented hospital, with real-time locating and sensing/monitoring, allows more automated and efficient allocation and "routing" of scarce resources (doctors, nurses, equipment), based on the ever-changing needs and circumstances.
Using big data to monitor supply risk provides an example that might be relevant to monitoring patient risk. Firms have been able to better predict in advance when suppliers or their supply chain are at risk by monitoring and correlating a wider array of risk-correlated data, such as the supplier's performance, quality issues, lawsuits against suppliers, late payments to the supplier's suppliers, reduced shipment volumes, and so forth (in addition to monitoring the supplier's financial statements). By taking in a wide range of structured and unstructured data, and creating algorithms that look for and learn about combinations, patterns and predictive correlations, firms can get much earlier warning of potential supply issues, giving them the time to address them. Perhaps there are similar opportunities to look beyond the traditional patient risk indicators, to diagnose problems earlier within patient populations, identify at-risk individuals, and intervene sooner and more effectively.
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Keywords: RFID, supply chain management IT, healthcare supply chain, big data, analytics in healthcare, healthcare supply base, healthcare supplier network, asset monitoring, patient monitoring
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