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Health systems around the world are under tremendous fiscal pressures. Health system inflation continues to outpace GDP growth in most countries. Health system inflation has been resistant to policy measures, to traditional interventions such as productivity enhancing technologies and to optimization of performance metrics such as length of stay (LOS) and wait times. Organizations that are solving the issue are using specific information that individualizes costs per patient, rather than using average costs per case, which is misleading in most important, high cost, situations. In this paper, we propose an architecture for a health information system that not only individualizes costs, but also leverages the learning health system model to drive down costs, while increasing value for patients and the health care system.
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