PDF(Pubmed)
Abstract：
BACKGROUND: Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation.
OBJECTIVE: This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England.
METHODS: This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence\'s Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from \"definitely exclude\" to \"definitely include,\" and suggest edits. The document will be iterated between rounds based on participants\' feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote.
RESULTS: Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024.
CONCLUSIONS: The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas.
UNASSIGNED: DERR1-10.2196/50568.
OBJECTIVE: This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England.
METHODS: This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence\'s Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from \"definitely exclude\" to \"definitely include,\" and suggest edits. The document will be iterated between rounds based on participants\' feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote.
RESULTS: Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024.
CONCLUSIONS: The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas.
UNASSIGNED: DERR1-10.2196/50568.
摘要：
背景：糖尿病眼筛查（DES）代表了应用机器学习（ML）技术的重要机会，这可能会改善临床和服务结果。然而,将ML成功集成到DES需要仔细的产品开发，评估，和执行。目标产品概况（TPP）总结了成功实施所需的要求，以便这些要求可以指导产品开发和评估。
目的：本研究旨在为英格兰的DES中使用的ML自动视网膜成像分析软件（ML-ARIAS）系统生产TPP。
方法：这项工作将包括3个阶段。第一阶段将确定TPP要解决的特征。候选特征列表将从以下来源生成：诊断测试TPP的系统评价概述;数字健康TPP的系统评价;和国家健康与护理卓越研究所的数字健康技术证据标准框架。由DES主要利益相关者的代表组成的研究咨询小组（SAG）将完善和验证特征列表。这包括糖尿病患者;医疗保健专业人员;医疗保健经理和领导者;以及监管机构和政策制定者。在第二阶段，将在与这些利益相关者团体的参与者进行一系列半结构化访谈后，起草这些特征的规范。从这些访谈中收集的数据将使用候选特征列表作为框架进行分析，之后将起草规范以创建TPP草案。经SAG批准后,在第三阶段，草案将进入一项基于互联网的德尔福共识研究，参与者从先前确定的小组中寻求，以及ML-ARIAS开发人员，以确保可行性。参与者将被邀请以“绝对排除”到“绝对包括”的等级对特征和规格对进行评分，\"并建议进行编辑。文档将根据参与者的反馈在各轮之间进行迭代。在文件草案的最终内容在面对面的协商一致会议上达成一致之前，将征求ML-ARIAS开发人员对文件草案的反馈。在这次会议上,来自先前确定的利益相关者团体的代表(减去ML-ARIAS开发人员，为避免偏见）将提供Delphi结果和用户组的反馈，并要求通过投票就最终内容达成一致。
结果：第一阶段于2023年11月完成。第二阶段正在进行中，预计将于2024年3月完成。第三阶段预计将于2024年7月完成。
结论：在英格兰开发用于DES的ML-ARIASTPP的多利益相关者开发将帮助开发人员生产满足患者需求的工具，卫生保健提供者,和他们的员工。TPP开发过程还将提供方法和模板，以在其他疾病领域产生类似的文件。
■DERR1-10.2196/50568。
目的：本研究旨在为英格兰的DES中使用的ML自动视网膜成像分析软件（ML-ARIAS）系统生产TPP。
方法：这项工作将包括3个阶段。第一阶段将确定TPP要解决的特征。候选特征列表将从以下来源生成：诊断测试TPP的系统评价概述;数字健康TPP的系统评价;和国家健康与护理卓越研究所的数字健康技术证据标准框架。由DES主要利益相关者的代表组成的研究咨询小组（SAG）将完善和验证特征列表。这包括糖尿病患者;医疗保健专业人员;医疗保健经理和领导者;以及监管机构和政策制定者。在第二阶段，将在与这些利益相关者团体的参与者进行一系列半结构化访谈后，起草这些特征的规范。从这些访谈中收集的数据将使用候选特征列表作为框架进行分析，之后将起草规范以创建TPP草案。经SAG批准后,在第三阶段，草案将进入一项基于互联网的德尔福共识研究，参与者从先前确定的小组中寻求，以及ML-ARIAS开发人员，以确保可行性。参与者将被邀请以“绝对排除”到“绝对包括”的等级对特征和规格对进行评分，\"并建议进行编辑。文档将根据参与者的反馈在各轮之间进行迭代。在文件草案的最终内容在面对面的协商一致会议上达成一致之前，将征求ML-ARIAS开发人员对文件草案的反馈。在这次会议上,来自先前确定的利益相关者团体的代表(减去ML-ARIAS开发人员，为避免偏见）将提供Delphi结果和用户组的反馈，并要求通过投票就最终内容达成一致。
结果：第一阶段于2023年11月完成。第二阶段正在进行中，预计将于2024年3月完成。第三阶段预计将于2024年7月完成。
结论：在英格兰开发用于DES的ML-ARIASTPP的多利益相关者开发将帮助开发人员生产满足患者需求的工具，卫生保健提供者,和他们的员工。TPP开发过程还将提供方法和模板，以在其他疾病领域产生类似的文件。
■DERR1-10.2196/50568。
