Using NLP to Target Patient Led and Scalable Primary Care Interactions

全科医生(GP)士气低落、全科医生队伍萎缩、无情的需求和不断增加的工作量使许多人对全科医生陷入危机敲响了警钟。在线咨询(OC)系统是解决方案的一部分。它们帮助全科医生更有效地管理患者请求和工作量，同时允许患者无需等待电话即可联系他们的全科医生。使用自由文本界面的系统允许患者用自己的语言描述他们的需求，从而改善了患者的理解和体验。然而，它们需要临床人员手动对请求进行分类和编码，在英格兰引入了大约2.4亿GB/年的时间和资源负担[3]。这意味着临床工作人员将时间花在管理上，而这些时间本可以更好地用于其他地方，特别是在由于教育或语言障碍而要求不够详细的情况下。此外，OC捕获当前的患者症状，这意味着全科医生必须参考患者的纵向临床病史来了解上下文，这在已经很短的咨询中可能很耗时。自然语言处理(NLP)建立在人工智能的基础上，以实现文本理解和摘要。NLP的进步使训练计算机模型能够处理文本的速度和数量远远超出人类的能力。在这个项目中，我们将利用我们在NLP方面的内部学术专家，通过开发一个名为ASPIRE的工具来显著改进自由文本OC，该工具可以自动对患者的自由文本请求进行分类和编码，并向GP提供临床总结和建议。重要的是，该工具将是患者主导的和个性化的。这意味着ASPIRE将利用患者的自由文本请求和他们的个人健康记录(PHR)中的数据；这是最全面的健康记录，包括病史(GP记录)数据和患者报告的环境数据(例如非处方药、过敏、自我报告的健康结果)。一般做法将受益于提高的准确性和减轻的临床编码负担。全球定位系统将受益于支持对相关数据进行自动编码和标记，从而作出更明智的决定。患者将受益于更个性化的护理--在他们的PHR.\[1\]\_General\_Practice\_In\_England\_[https://journals.lww.com/ambulatorycaremanagement/Abstract/2022/04000/General\_Practice\_in\_England\_\_The\_Current\_Crisis，范围内做出的决定.7.aspx][0]\[2\]\_Access\_to\_and\_delivery\_of\_general\_practice\_services\_https://www.health.org.uk/publications/access-to-and-delivery-of-general-practice-services\[3\]\_Calculation\_based\_on\_~30seconds\_GP\_coding\_time\_required\_per\_consultation，本文中的\_at\_an\_equivalent\_cost\_of\_£80/hr.~367\_million\_GP\_appointments\_in\_2021\_equates\_to\_~£240million.\[4\]\_clinical“编码”描述了在电子健康记录中使用结构化的临床词汇，以确保护理信息被清楚地记录、一致和全面。[0]：https://journals.lww.com/ambulatorycaremanagement/Abstract/2022/04000/General_Practice_in_England__The_Current_Crisis，.7.aspx

Collapsing morale among general practitioners (GPs), a shrinking GP workforce, relentless demands, and increasing workload have caused many to sound alarm on a general practice in "crisis" \[1\].Online Consultation (OC) systems are part of the solution. They help GPs to manage patient requests and workload more effectively while allowing patients to contact their GP without waiting on the phone. Systems that use a free-text interface improve patient uptake\[2\] and experience by allowing patients to describe their needs in their own words.They, however, require clinical staff to manually sort and code requests introducing a time and resource burden of around £240Million/year in England\[3\]. This means clinical staff spend time on administration that could be better spent elsewhere, especially if requests are insufficiently descriptive due to education or language barriers. In addition, OC captures current patient symptoms meaning the GP must refer to the patient's longitudinal clinical history, for context, which can be time consuming in an already short consultation.Natural language processing (NLP) builds on artificial intelligence to enable text understanding and summarisation. Advances in NLP allow training computer models that process text at a speed and volume way beyond the capability of humans.In this project we will utilise our in-house academic expert in NLP to significantly improve free-text OC by developing a tool named ASPIRE, that can automatically categorise and code\[4\] free-text requests from patients and provide a clinical summary and recommendation to GPs.Importantly, the tool will be patient-led and personalised. Meaning ASPIRE will utilise data from both the patient's free-text request and their personal health record (PHR); this is the most comprehensive health record, including both medical history (GP record) data and patient-reported environmental data (e.g. over-the-counter medication, allergies, self-reported health outcomes).General practice will benefit from improved accuracy and reduced burden of clinical coding. GPs will benefit from support to make better informed decisions from the automatic coding and flagging of relevant data. Patients will benefit from more personalised care-decisions made within the context of their PHR.\[1\]\_General\_Practice\_In\_England\_[https://journals.lww.com/ambulatorycaremanagement/Abstract/2022/04000/General\_Practice\_in\_England\_\_The\_Current\_Crisis,.7.aspx][0]\[2\]\_ Access\_to\_and\_delivery\_of\_general\_practice\_services\_https://www.health.org.uk/publications/access-to-and-delivery-of-general-practice-services\[3\]\_Calculation\_based\_on\_~30seconds\_GP\_coding\_time\_required\_per\_consultation,\_at\_an\_equivalent\_cost\_of\_£80/hr.~367\_million\_GP\_appointments\_in\_2021\_equates\_to\_~£240million.\[4\]\_clinical 'coding' in this context describes the use of structured clinical vocabulary for use in an electronic health record to ensure care information is clearly recorded, consistent, and comprehensive.[0]: https://journals.lww.com/ambulatorycaremanagement/Abstract/2022/04000/General_Practice_in_England__The_Current_Crisis,.7.aspx