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

小型企业创新研究(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药物使用提供者短缺的问题，以经济高效的方式提高患者在治疗中的留存率，并主动治疗慢性行为健康患者。如果不加以治疗，这些疾病每年的成本超过1万亿美元，并导致无数人过早死亡。同伴支持是一种有效的工具，可以让不愿意或无法获得临床护理的患者参与进来，特别是在边缘人群中。然而，扩展同行支持是具有挑战性的，因为目前的在线支持论坛充斥着恶意攻击和辱骂。我们的自然语言处理(NLP)工具可以推断文本消息的情绪，自动标记与临床相关或关键的内容。这使得临床医生可以通过将他们的时间集中在最需要帮助的患者身上来轻松地调节群体，而同行则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增强临床医生在支持小组内跟踪患者心理健康的能力。目前，管理同龄人群体的一个挑战是识别一个群体作为一个整体的健康状况--一些群体可能比其他群体更具建设性。鉴于在线支持小组产生的信息量，加上护理经理和同伴支持专家预计的工作量，他们可能管理着数十个小组，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，我们专注于三个主要领域：1)通过开发新的技术来识别和跟踪可能在群中共存的多个对话，来提高现有NLP算法的性能；2)开发一种方法，通过分析同行之间的交互来跟踪群的整体健康和稳定性；以及3)设计新的界面，有效地显示算法生成的所有见解。这些NLP工具将为一个平台提供动力，让患者更多地获得支持。提供者将可以访问新的高保真数据源，以更好地分类扩展和个性化护理。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.