Marketplace Readiness of a Novel Transdiagnostic Assessment for Clinical Research and Practice

摘要 在SBIR第1阶段提案中，我们的目标是开发和自动化工作流程， 跨诊断心理健康评估工具，以提高心理健康筛查的准确性。的 目前使用的大多数仪器都是根据DSM标准设计的，该标准受到了批评 因为它被理论上定义，忽略了患者之间症状的异质性。此外，委员会认为， 个别工具在评估同一疾病的症状时是不完整和异质的。因此，在本发明中， 个体被置于预定义的类别中，这些类别并不反映他们的总体症状经历和生活 因素，诊断可以根据工具的选择而有所不同。这使得很难和耗时， 了解个体患者以确定治疗轨迹，有效招募患者参加研究试验， 并了解基于完整症状表型的结果。为了应对这些挑战， 我们的合作伙伴非营利研究组织Sapien开发了一种新的评估工具cMHQ 实验室，并提供了一个全面的患者症状概况，映射在10个基于DSM的心理健康 障碍，以及六个功能领域的维度得分，从而在 目前的诊断环境和更多的研究导向RDoC框架。拟议研究 专注于将cMHQ及其成果从实验室转移到市场，以实现更深入的临床研究 提供更深入的见解，并提供更知情的治疗和转诊方案。在目标1中，我们将建立cMHQ的输入 通过开发响应式前端评估应用程序和编码进行评估和诊断分析 分析脚本，以生成数据评分指标和诊断标准。在目标2中，我们将开发多个数据- 经过可用性和可接受性测试的cMHQ输出格式，包括自动生成的 cMHQ临床医生报告，对患者的心理健康状况进行清晰全面的分析 以及一个API，使表格数据输出能够集成到 研究系统。作为这一目标的一部分，我们还将把所有元素集成到一个可扩展的架构中， 测试并确保数据流符合HIPAA。如果这个项目成功，第二阶段的项目将 有必要在临床研究和实践领域进行试点测试，以证明其益处， 研究和治疗的结果。

ABSTRACT In this SBIR Phase 1 proposal, we aim to develop and automate the workflow for a comprehensive transdiagnostic mental health assessment tool for increased precision in the screening of mental health. The majority of instruments presently in use have been designed based on DSM criteria, which has been critiqued for being theoretically defined and ignoring the heterogeneity of symptoms across patients. Furthermore, individual tools are incomplete and heterogeneous in their assessment of symptoms for the same disorder. Thus, individuals are placed into pre-defined categories that do not reflect their overall symptom experience and life factors, and diagnosis can vary based on the choice of tool. This makes it difficult and time consuming to understand individual patients to determine treatment trajectories, effectively recruit patients into research trials, and understand outcomes based on complete symptom phenotypes. In order to address these challenges, a novel assessment tool, the cMHQ, has been developed by our partner non-profit research organization, Sapien Labs, and provides a comprehensive patient symptom profile that maps across 10 DSM-based mental health disorders, as well as dimensional scores across six functional areas, thereby forming a bridge between the current diagnostic environment and the more research oriented RDoC framework. The proposed research focuses on transitioning the cMHQ and its outcomes from lab to marketplace to enable deeper clinical research insights and provide more informed treatment and referral regimens. In aim 1, we will build the cMHQ’s input assessment and diagnostic analysis by developing a responsive front-end assessment application and coding analysis scripts to generate data scoring metrics and diagnostic criteria. In aim 2, we will develop multiple data- output formats for the cMHQ that are tested for usability and acceptability, including an automatically generated cMHQ clinician report that provides a clear and comprehensive analysis of the patient’s mental health profile across multiple dimensions and disorders, as well as an API to enable tabular data outputs for integration into research systems. As part of this aim, we will also integrate all of the elements into a scalable architecture for testing and ensure HIPAA-compliance of the data-flow. If this project is successful, a further Phase 2 project will be warranted to pilot test its application in clinical research and practice domains to demonstrate the benefits to outcomes in both research and treatment.