基于大数据制定合理政策：迈向循证听力健康政策
Big Data for Sound Policies: Toward Evidence-Informed Hearing Health Policies.
作者信息
Gutenberg Johanna, Katrakazas Panagiotis, Trenkova Lyubov, Murdin Louisa, Brdaric Dario, Koloutsou Nina, Ploumidou Katherine, Pontoppidan Niels Henrik, Laplante-Lévesque Ariane
机构信息
Eriksholm Research Centre, Oticon A/S, Denmark.
Biomedical Engineering Laboratory, National Technical University of Athens, Greece.
出版信息
Am J Audiol. 2018 Nov 19;27(3S):493-502. doi: 10.1044/2018_AJA-IMIA3-18-0003.
PURPOSE
The scarcity of health care resources calls for their rational allocation, including within hearing health care. Policies define the course of action to reach specific goals such as optimal hearing health. The process of policy making can be divided into 4 steps: (a) problem identification and issue recognition, (b) policy formulation, (c) policy implementation, and (d) policy evaluation. Data and evidence, especially Big Data, can inform each of the steps of this process. Big Data can inform the macrolevel (policies that determine the general goals and actions), mesolevel (specific services and guidelines in organizations), and microlevel (clinical care) of hearing health care services. The research project EVOTION applies Big Data collection and analysis to form an evidence base for future hearing health care policies.
METHOD
The EVOTION research project collects heterogeneous data both from retrospective and prospective cohorts (clinical validation) of people with hearing impairment. Retrospective data from clinical repositories in the United Kingdom and Denmark will be combined. As part of a clinical validation, over 1,000 people with hearing impairment will receive smart EVOTION hearing aids and a mobile phone application from clinics located in the United Kingdom and Greece. These clients will also complete a battery of assessments, and a subsample will also receive a smartwatch including biosensors. Big Data analytics will identify associations between client characteristics, context, and hearing aid outcomes.
RESULTS
The evidence EVOTION will generate is relevant especially for the first 2 steps of the policy-making process, namely, problem identification and issue recognition, as well as policy formulation. EVOTION will inform microlevel, mesolevel, and macrolevel of hearing health care services through evidence-informed policies, clinical guidelines, and clinical care.
CONCLUSION
In the future, Big Data can inform all steps of the hearing health policy-making process and all levels of hearing health care services.
目的
医疗保健资源的稀缺性要求对其进行合理分配，包括听力保健领域。政策规定了实现特定目标（如最佳听力健康）的行动方针。政策制定过程可分为4个步骤：(a) 问题识别与议题确认；(b) 政策制定；(c) 政策实施；(d) 政策评估。数据和证据，尤其是大数据，可为这一过程的每个步骤提供信息。大数据可为听力保健服务的宏观层面（确定总体目标和行动的政策）、中观层面（组织中的具体服务和指南）和微观层面（临床护理）提供信息。EVOTION研究项目应用大数据收集和分析，为未来的听力保健政策形成证据基础。
方法
EVOTION研究项目从听力障碍患者的回顾性和前瞻性队列（临床验证）中收集异质性数据。将合并来自英国和丹麦临床资料库的回顾性数据。作为临床验证的一部分，超过1000名听力障碍患者将从英国和希腊的诊所获得智能EVOTION助听器和手机应用程序。这些客户还将完成一系列评估，并且一个子样本还将获得包括生物传感器的智能手表。大数据分析将识别客户特征、背景与助听器效果之间的关联。
结果
EVOTION将产生的证据尤其与政策制定过程的前两个步骤相关，即问题识别与议题确认以及政策制定。EVOTION将通过基于证据的政策、临床指南和临床护理，为听力保健服务的微观层面、中观层面和宏观层面提供信息。
结论
未来，大数据可为听力健康政策制定过程的所有步骤以及听力保健服务的各个层面提供信息。
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