Crowdsourcing Treatments for Low Back Pain

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems Pub Date : 2018-04-21 DOI:10.1145/3173574.3173850

S. Hosio, J. Karppinen, E. Takala, J. Takatalo, Jorge Gonçalves, N. V. Berkel, S. Konomi, V. Kostakos

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引用次数: 13

Abstract

Low back pain (LBP) is a globally common condition with no silver bullet solutions. Further, the lack of therapeutic consensus causes challenges in choosing suitable solutions to try. In this work, we crowdsourced knowledge bases on LBP treatments. The knowledge bases were used to rank and offer best-matching LBP treatments to end users. We collected two knowledge bases: one from clinical professionals and one from non-professionals. Our quantitative analysis revealed that non-professional end users perceived the best treatments by both groups as equally good. However, the worst treatments by non-professionals were clearly seen as inferior to the lowest ranking treatments by professionals. Certain treatments by professionals were also perceived significantly differently by non-professionals and professionals themselves. Professionals found our system handy for self-reflection and for educating new patients, while non-professionals appreciated the reliable decision support that also respected the non-professional opinion.

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众包治疗腰痛

腰痛(LBP)是一种全球常见的疾病，没有银弹解决方案。此外，缺乏治疗共识导致在选择合适的解决方案进行尝试的挑战。在这项工作中，我们众包了关于LBP治疗的知识库。知识库用于对最终用户进行排序并提供最匹配的LBP治疗。我们收集了两个知识库:一个来自临床专业人员，一个来自非专业人员。我们的定量分析显示，非专业最终用户认为两组的最佳治疗方法同样好。然而，非专业人员提供的最差治疗显然不如专业人员提供的最差治疗。非专业人员和专业人员本身对专业人员的某些治疗也有显著不同的看法。专业人士发现我们的系统便于自我反思和教育新患者，而非专业人士则欣赏可靠的决策支持，同时也尊重非专业人士的意见。

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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Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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