O'Brien Megan K, Hohl Kristen, Lieber Richard L, Jayaraman Arun
Shirley Ryan AbilityLab, Chicago, IL, USA.
Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA.
Digit Biomark. 2024 Aug 26;8(1):149-158. doi: 10.1159/000540492. eCollection 2024 Jan-Dec.
Wearable sensors have been heralded as revolutionary tools for healthcare. However, while data are easily acquired from sensors, users still grapple with questions about how sensors can meaningfully inform everyday clinical practice and research.
We propose a simple, comprehensive framework for utilizing sensor data in healthcare. The framework includes three key processes that are applied together or separately to (1) automate traditional clinical measures, (2) illuminate novel correlates of disease and impairment, and (3) predict current and future outcomes. We demonstrate applications of the Automate-Illuminate-Predict framework using examples from rehabilitation medicine.
Automate-Illuminate-Predict provides a universal approach to extract clinically meaningful data from wearable sensors. This framework can be applied across the care continuum to enhance patient care and inform personalized medicine through accessible, noninvasive technology.
可穿戴传感器被誉为医疗保健领域的革命性工具。然而，尽管数据能轻松从传感器获取，但用户仍在纠结传感器如何才能切实为日常临床实践和研究提供有意义的信息。
我们提出了一个在医疗保健中利用传感器数据的简单、全面的框架。该框架包括三个关键过程，这些过程可一起应用或单独应用于：（1）使传统临床测量自动化，（2）揭示疾病和损伤的新关联因素，以及（3）预测当前和未来的结果。我们使用康复医学的例子展示了“自动化 - 揭示 - 预测”框架的应用。
“自动化 - 揭示 - 预测”提供了一种从可穿戴传感器中提取具有临床意义的数据的通用方法。该框架可应用于整个护理过程，通过便捷、无创的技术来改善患者护理并为个性化医疗提供信息。
