Integrating hospital outpatient letters into the healthcare data space

分析作为临床护理的一部分收集并存储在电子健康记录中的健康数据的重要性是众所周知的。这导致了关于疾病的发生和发展、治疗的有效性和安全性以及卫生服务提供的重要研究。当前的新冠肺炎大流行表明，公共卫生需要有效地利用在护理点收集的数据，以快速了解新出现疾病的模式、风险因素和结果。这项工作的大部分来自初级保健电子健康记录，其中全科医生(GP)输入和使用结构化的、编码的保健数据。然而，医院的情况却截然不同。在英国，四分之一的人患有一种或多种长期疾病，如心血管疾病、慢性呼吸系统疾病、2型糖尿病、关节炎和癌症，这些疾病占NHS预算的70%。关于长期疾病(LTC)管理的专门意见是通过医院门诊提供的。然而，来自门诊诊所的数据和洞察力几乎完全缺失。令人惊讶的是，医院门诊没有记录诊断的全国性系统。相反，有关关键临床事件的信息记录在门诊信件中，主要用于与患者和全科医生沟通。信件的书写方式及其敏感内容意味着它们不能用于更大规模的“二次使用”，即支持临床实践、研究或服务改进。例如，针对当前大流行的屏蔽依赖于医院临床团队手动查看患者信件，根据关于诊断和药物的自由文本信息来识别需要屏蔽的人，并具有明确的时间限制和对患者防护不足和过度保护的风险。自然语言处理(NLP)和文本挖掘开发了自动从自由文本文档中提取相关信息的计算机算法。该项目将在学术界、二级保健和产业界之间建立合作伙伴关系，以开发一个基于标准的信息管理框架，以安全地解锁门诊信件中存储的信息，将其与其他健康数据联系起来，并通过两个案例研究展示其影响和好处。我们将开发新的方法，从信件中提取关键临床事件，并将其详细信息(如所用药物、症状持续时间)以电脑形式表示，以便用户轻松访问。在这样做的过程中，我们将使用NHS采用的标准，以便门诊信件可以链接到其他医院的数据库，而不是存在于自己的孤岛中。对可能出现在门诊数据中的敏感数据的保护是一个主要问题，因此我们将制定关于谁可以以及如何访问这些数据的明确规则，特别是考虑到第三方(例如行业)可能需要访问这些数据来开发他们的工具。这些规则将在患者代表、临床医生和专家之间的密切合作下制定，以确保决策的保障、公众信任和透明度。我们将通过与我们的临床和商业合作伙伴进行的两个案例研究来展示建议方法的潜在影响。我们的第一个案例研究将展示拟议的模型如何帮助及时、高效、动态和透明地识别患者，以便在大流行期间进行屏蔽或进行疫苗优先接种。在第二个案例研究中，我们将说明如何使用相同的信息来解决我们在健康和护理方面的知识中的重要差距，例如，包括疾病流行率和药物使用模式。所有的产出都将以一种可以超越单一临床地点和单一专业的方式进行开发。

The importance of analysing health data collected as part of clinical care and stored in electronic health records is well-established. This has led to vital research about the occurrence and progression of disease, treatment effectiveness and safety, and health service delivery. The current Covid-19 pandemic has demonstrated the public health need to efficiently use data collected at the point of care to rapidly understand patterns, risk factors and outcomes of emerging diseases. Much of this work comes from primary care electronic health records, where general practitioners (GPs) enter and use structured, coded healthcare data. The picture in hospitals, however, is very different. One in four people in the UK live with one or more long-term conditions like cardiovascular diseases, chronic respiratory diseases, type 2 diabetes, arthritis and cancer, which account for 70% of the NHS budget. Specialised opinion about management of long-term conditions (LTCs) is provided through hospital outpatient care. Data and insight from outpatient clinics, however, is almost entirely absent. There is, surprisingly, no national system for recording diagnoses in hospital outpatient clinics. Information about key clinical events is instead recorded in outpatient letters, which are primarily used to communicate with patients and GPs. The ways in which letters are written and their sensitive content mean that they are not available for larger-scale "secondary use", i.e. to support clinical practice, research or service improvement. For example, shielding for the current pandemic relied on hospital clinical teams going through patient letters manually to identify those who needed shielding based on free-text information about diagnoses and medications, with clear time constraints and risks to under- and over-shield patients. Natural language processing (NLP) and text mining develop computer algorithms to automatically extract relevant information from free-text documents. This project will establish a partnership between academia, secondary care and industry to develop a standards-based information management framework to safely unlock information stored in outpatient letters, link it with other health data and demonstrate its impact and benefits through two case studies. We will develop new methods to extract key clinical events from letters and represent their details (e.g. medication used, duration of symptoms) in a computerised form so that it can be easily accessed. In doing so, we will use the NHS-adopted standards so that the outpatient letters can be linked to other hospital databases and do not live in their own silo. The protection of sensitive data that potentially appear in outpatient data is a prime concern, so we will develop clear rules on who and how can access such data, in particular considering that third parties (e.g. industry) may need to access that data for developing their tools. These rules will be developed in a close collaboration between patient representatives, clinicians and specialists to ensure safeguards, public trust and transparency of decision making. We will demonstrate the potential impact of the proposed methods through two case studies with our clinical and business partners. Our first case study will demonstrate how the proposed models can assist in timely, efficient, dynamic and transparent identification of patients for shielding in a pandemic, or for vaccination prioritisation. In the second case study, we will illustrate how the same information can be used address important gaps in our knowledge about health and care, including, for example, disease prevalence and drug utilisation patterns. All outputs will be developed in a way that can be scaled beyond the single clinical site and single speciality.