Using human-centered design to understand market feasibility and user needs for an early-stage clinical gut microbiome AI-based analysis platform

“微生物是肉眼看不见的，但它们运行着我们的世界。肠道微生物群落的平衡对人类健康至关重要，而紊乱往往与疾病有关。宏基因组学方法通过直接测序和分析样品中存在的所有物种来评估微生物群落，对于这些方法成为临床环境中的一般实践，临床医生能够消化产生的大量数据并将其转化为可操作的见解至关重要。通过了解重要的宏基因组输出，临床医生可以通过药物治疗，益生菌以及饮食和生活方式的改变来实现将肠道微生物种群恢复到正常水平的方法。Lifebit创建了一个功能齐全且准确的后端管道，允许用户输入原始宏基因组测序数据，以确定1）存在哪些微生物，2）它们的水平和3）它们的生物学功能。我们的总体目标是将这一管道推向市场，然而，我们需要进一步了解潜在用户的特定需求。由于宏基因组学分析涉及大量在临床背景下有用的数据，我们需要了解如何使临床医生在没有广泛的计算培训的情况下操作和分析数据。因此，满足他们需求的直观易懂的UI是产品成功的关键组成部分。这项可行性研究将使我们能够对设计做出明智的选择，使这条管道对正在使用或需要使用宏基因组学分析的临床医生有用，以促进患者的健康。具体来说，我们这项研究的主要目标是：* 了解管道的市场，* 了解用户在临床宏基因组学背景下的需求，* 确定定制设计和前端功能如何满足用户需求。为了实现这些关键目标，我们将使用以人为本的设计（HCD）流程和方法。主要关注领域是HCD，用于设计临床AI驱动的宏基因组学软件。宏基因组学和机器学习在临床微生物分析中的结合尚未被探索，这使得我们的努力具有创新性。此外，这类软件的设计模式尚未建立，因此UI和数据可视化的设计将包括新的方法。最后，使用HCD来验证假设对于生命科学软件的开发并不常见，这使得项目本身在这种设计背景下具有创新性。"

"Microorganisms are invisible to the human eye, but they run our world. A well-balanced community of gut microorganisms is critical in the context of human health, and disturbances are often associated with disease. Metagenomic approaches allow the assessment of microbial communities by directly sequencing and analysing all species present in a sample.For these approaches to become a general practice in a clinical setting, it is critical for clinicians to be able to digest the large amount of data generated and translate it into actionable insights. By understanding important metagenomic outputs, clinicians can implement ways to bring the gut microbiotic population back to normal levels through drug treatments, probiotics, and changes in diet and lifestyle.Lifebit has created a fully functional and accurate backend pipeline which allows users to input their raw metagenomic sequencing data in order to determine 1) which microorganisms are present, 2) their levels and 3) their biological function. Our overall goal is to bring this pipeline to market, however, we need to further understand the context-specific needs of potential users.Since metagenomics analyses involve huge amounts of data that is useful in the clinical context, we need to understand how to enable clinicians without extensive computational training to manipulate and analyse the data. Therefore, an intuitive and understandable UI that meets their needs is a crucial component for the success of the product.This feasibility study will allow us to make informed choices about the design needed to make this pipeline useful to clinicians that are either using, or need to use, metagenomics analysis to advance their patients' health. Specifically, our key objectives for this study are to:* Understand the market for the pipeline,* Understand what users need in the context of clinical metagenomics, and* Identify how tailored design and front-end features could address user needs.To accomplish these key objectives, we will use human-centered design (HCD) processes and methods. The main area of focus is HCD for the design of clinical AI-powered metagenomics software. The combination of metagenomics and machine learning for clinical microbial analysis has not been explored, rendering our efforts innovative. Furthermore, design patterns for this type of software are not yet established, so the design for UI and data visualisations will include novel approaches. Finally, using HCD to validate assumptions is not common for the development of life science software, making the project itself innovative in this design context."