Research challenges in measuring data for population health to enable predictive modeling for improving healthcare

B. Schatz, C. Marsh, D. Gustafson, K. Patrick, J. Krishnan, Santosh Kumar, N. Contractor
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引用次数: 5

Abstract

At the core of the healthcare crisis is a fundamental lack of actionable data, needed to stratify individuals within populations, to predict which persons have which outcomes. A new health system with better health management will require better health measurement, to improve cost and quality. It is now possible to use new technologies to provide the rich datasets necessary for adequate health measurement, which enables new information systems for new health systems. This report is a summary of a workshop on Measuring Data for Population Health, sponsored by the NSF SmartHealth program with assistance from the NIH mHealth initiative, held on January 12--13, 2012 in Washington DC. There were 42 attendees, including invited researchers from academia, government and industry, plus program officers from NSF and NIH. The workshop had background talks by leaders in health systems and information systems, followed by breakout discussions on future challenges and opportunities in measuring and managing population health. This report describes the observations on what problems of health systems should be addressed and what solutions of information systems should be developed. The recommendations cover how new information technologies can enable new health systems, with support from future initiatives of federal programs. The workshop and its report identify research challenges that utilize new computing and information technologies to enable better measurement and management for practical healthcare. The measurement technologies focus on deeper monitoring of broader populations. The management technologies focus on utilizing new personal health records to provide personalized treatment guidelines, specialized for each population cohort. This would enable predictive modeling for health systems to support viable healthcare at acceptable cost and quality. A workshop website contains background and discussion notes: https://wiki.engr.illinois.edu/display/hiworkshop/NSF+Workshop+Population+Health
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研究在测量人口健康数据方面的挑战,以实现改善医疗保健的预测建模
医疗保健危机的核心是根本缺乏可操作的数据,这些数据是对人群中的个体进行分层、预测哪些人有哪些结果所必需的。一个具有更好卫生管理的新卫生系统将需要更好的卫生测量,以改善成本和质量。现在有可能利用新技术提供充分的卫生测量所需的丰富数据集,从而使新的信息系统能够用于新的卫生系统。本报告是2012年1月12日至13日在华盛顿特区举行的人口健康测量数据研讨会的总结,该研讨会由美国国家科学基金会智能健康计划主办,并得到美国国立卫生研究院移动健康倡议的协助。共有42名与会者,包括来自学术界、政府和工业界的受邀研究人员,以及来自美国国家科学基金会和美国国立卫生研究院的项目官员。讲习班由卫生系统和信息系统的领导人进行了背景介绍,随后就衡量和管理人口健康方面的未来挑战和机遇进行了分组讨论。本报告描述了对卫生系统应解决哪些问题以及应开发哪些信息系统解决方案的看法。这些建议涵盖了新的信息技术如何在联邦计划未来举措的支持下使新的卫生系统成为可能。该研讨会及其报告确定了利用新的计算和信息技术为实际医疗保健提供更好的测量和管理的研究挑战。测量技术侧重于对更广泛的人群进行更深入的监测。管理技术侧重于利用新的个人健康记录来提供个性化的治疗指南,专门针对每个人群队列。这将使卫生系统能够以可接受的成本和质量进行预测建模,以支持可行的医疗保健。研讨会网站包含背景和讨论笔记:https://wiki.engr.illinois.edu/display/hiworkshop/NSF+Workshop+Population+Health
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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