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• W2973103744 abstract "The field of predictive analytics in medicine is constantly expanding, in turn changing the paradigm in which providers and patients experience health care. Machine learning and neural networks are being utilized in diagnosis, individualizing care, and assisting in medical decision making in many fields, including orthopaedics. This technology's utility and accuracy are largely limited by providers’ ability to communicate complex predictions to patients and the input which generates the predictive model including patient reported outcomes (PROs). PROs, classically, are utilized in comparative and cost effectiveness research, but increasingly are being incorporated in patient-provider interactions. However, several limitations in PROs exist including a diverse number of legacy PROs, differing agendas amongst health care systems, providers, and patients, as well as providers’ ability to translate these scores into meaningful information for patients. The Patient Reported Outcome Measurement Information System (PROMIS) is a tool developed by the National Institutes of Health (NIH) to combat some of these limitations. PROMIS is health domain rather than disease specific and therefore can be applied across many fields of medicine or subspecialties within a field. Shared decision making is a framework for patients and physicians to incorporate predictive analytics, PROs, and patient values to make complex medical decisions. Decision aides are utilized to reinforce information provided by physicians to patients. The overarching goal is to maximize patient education, manage expectations and deliver high value health care. As health care systems, providers, and patients generate more data points, the strength of predictive analytic models will improve. These advances will change the way providers deliver health care and present information to patients." @default.
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• W2973103744 date "2019-06-01" @default.
• W2973103744 modified "2023-09-23" @default.
• W2973103744 title "Patient Reported Outcomes Measures and the Evolving Role of Predictive Analytics in Spine Care" @default.
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• W2973103744 doi "https://doi.org/10.1016/j.oto.2019.100712" @default.
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