SBIR Phase I: Harnessing Natural Language Processing for Scalable Text-based Behavioral Health Care

SBIR 第一阶段：利用自然语言处理实现可扩展的基于文本的行为医疗保健

• 批准号: 1913999
• 负责人: Satya Prateek Bommaraju
• 金额: $ 22.48万
• 依托单位: BEACON TECH INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1913999&HistoricalAwards=false
• 关键词: SBIR; Phase; Harnessing; Natural; Language

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to address a systematic behavioral health and substance use provider shortage, cost-effectively improve patient retention in treatment, and proactively treat chronic behavioral health patients. Left untreated, these conditions cost over $1 trillion annually and result in countless early deaths. Peer support is an effective tool to engage patients unwilling or unable to access clinical care, particularly in marginalized populations. However, scaling peer support is challenging, with current online support forums rife with trolling and abuse. Our Natural Language Processing (NLP) tools can extrapolate the emotional sentiment of text messages, automatically flagging clinically relevant or critical content. This allows clinicians to easily moderate groups by focusing their time on the patients most in need, while peers generate the touchpoints necessary for day-to-day engagement. This Small Business Innovation Research (SBIR) Phase I project will greatly enhance the ability of clinicians to track the mental health of patients within a support group. Currently, a challenge in managing peer groups is identifying the health of a group as a whole - some groups can be far more constructive than others. Given the volume of messages generated in an online support group, together with expected caseloads for care manager and peer support specialists, who may be managing dozens of groups, this is an impossible task without the aid of technology. To achieve this goal we focus on three main areas: 1) improving the performance of our existing NLP algorithms by developing novel techniques to identify and track multiple conversations that might be co-occurring in the group, 2) developing a method of tracking the overall health and stability of a group by analyzing interactions among peers and 3) design new interfaces that effectively display all of the insights generated by the algorithms. These NLP tools will power a platform to give patients more access to support. Providers will have access to a novel high-fidelity data source to better triage outreach and personalize care.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该小企业创新研究 (SBIR) 第一阶段项目的更广泛影响/商业潜力是解决系统性行为健康和物质使用提供者短缺问题，经济有效地提高患者治疗保留率，并积极治疗慢性行为健康患者。如果不加以治疗，这些疾病每年花费超过 1 万亿美元，并导致无数人过早死亡。同伴支持是吸引不愿意或无法获得临床护理的患者的有效工具，特别是在边缘化人群中。 然而，扩大同伴支持具有挑战性，当前的在线支持论坛充斥着恶意攻击和滥用行为。我们的自然语言处理 (NLP) 工具可以推断短信的情感情绪，自动标记临床相关或关键内容。这使得临床医生可以通过将时间集中在最需要的患者身上来轻松地调节小组，而同行则可以产生日常参与所需的接触点。这个小型企业创新研究 (SBIR) 第一阶段项目将极大地提高临床医生跟踪支持小组内患者心理健康状况的能力。目前，管理同侪群体的一个挑战是确定整个群体的健康状况——一些群体可能比其他群体更具建设性。考虑到在线支持小组中生成的消息量，以及护理经理和同伴支持专家（可能管理着数十个小组）的预期案件量，如果没有技术的帮助，这是一项不可能完成的任务。为了实现这一目标，我们重点关注三个主要领域：1）通过开发新技术来识别和跟踪组中可能同时发生的多个对话，从而提高现有 NLP 算法的性能，2）开发一种跟踪整体的方法通过分析同行之间的互动来评估团队的健康和稳定性；3）设计新的界面，有效地显示算法生成的所有见解。这些 NLP 工具将为平台提供支持，为患者提供更多支持。提供者将能够访问新颖的高保真数据源，以更好地进行分类推广和个性化护理。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。