Pham Quynh, Shaw James, Morita Plinio P, Seto Emily, Stinson Jennifer N, Cafazzo Joseph A
Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada.
J Med Internet Res. 2019 Nov 11;21(11):e14849. doi: 10.2196/14849.
The widespread adoption of digital health interventions for chronic disease self-management has catalyzed a paradigm shift in the selection of methodologies used to evidence them. Recently, the application of digital health research analytics has emerged as an efficient approach to evaluate these data-rich interventions. However, there is a growing mismatch between the promising evidence base emerging from analytics mediated trials and the complexity of introducing these novel research methods into evaluative practice.
This study aimed to generate transferable insights into the process of implementing research analytics to evaluate digital health interventions. We sought to answer the following two research questions: (1) how should the service of research analytics be designed to optimize digital health evidence generation? and (2) what are the challenges and opportunities to scale, spread, and sustain this service in evaluative practice?
We conducted a qualitative multilevel embedded single case study of implementing research analytics in evaluative practice that comprised a review of the policy and regulatory climate in Ontario (macro level), a field study of introducing a digital health analytics platform into evaluative practice (meso level), and interviews with digital health innovators on their perceptions of analytics and evaluation (microlevel).
The practice of research analytics is an efficient and effective means of supporting digital health evidence generation. The introduction of a research analytics platform to evaluate effective engagement with digital health interventions into a busy research lab was ultimately accepted by research staff, became routinized in their evaluative practice, and optimized their existing mechanisms of log data analysis and interpretation. The capacity for research analytics to optimize digital health evaluations is highest when there is (1) a collaborative working relationship between research client and analytics service provider, (2) a data-driven research agenda, (3) a robust data infrastructure with clear documentation of analytic tags, (4) in-house software development expertise, and (5) a collective tolerance for methodological change.
Scientific methods and practices that can facilitate the agile trials needed to iterate and improve digital health interventions warrant continued implementation. The service of research analytics may help to accelerate the pace of digital health evidence generation and build a data-rich research infrastructure that enables continuous learning and evaluation.
数字健康干预措施在慢性病自我管理中的广泛应用，促使用于证明其效果的方法选择发生了范式转变。最近，数字健康研究分析的应用已成为评估这些数据丰富的干预措施的一种有效方法。然而，分析介导试验中出现的有前景的证据基础与将这些新研究方法引入评估实践的复杂性之间的差距日益增大。
本研究旨在深入了解实施研究分析以评估数字健康干预措施的过程，从而得出可推广的见解。我们试图回答以下两个研究问题：（1）应如何设计研究分析服务以优化数字健康证据的生成？（2）在评估实践中扩大、推广和维持该服务面临哪些挑战和机遇？
我们对在评估实践中实施研究分析进行了定性多层次嵌入式单案例研究，包括对安大略省政策和监管环境的审查（宏观层面）、将数字健康分析平台引入评估实践的实地研究（中观层面），以及就数字健康创新者对分析和评估的看法进行的访谈（微观层面）。
研究分析实践是支持数字健康证据生成的一种有效手段。将一个用于评估与数字健康干预措施有效互动的研究分析平台引入一个繁忙的研究实验室，最终得到了研究人员的认可，在他们的评估实践中成为常规操作，并优化了他们现有的日志数据分析和解释机制。当具备以下条件时，研究分析优化数字健康评估的能力最强：（1）研究客户与分析服务提供商之间的协作工作关系；（2）数据驱动的研究议程；（3）强大的数据基础设施，且分析标签有清晰记录；（4）内部软件开发专业知识；（5）对方法变革的集体容忍度。
有助于进行迭代和改进数字健康干预措施所需的敏捷试验的科学方法和实践值得持续实施。研究分析服务可能有助于加快数字健康证据的生成速度，并建立一个数据丰富的研究基础设施，以实现持续学习和评估。
