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已得到充分证实。这导致了对疾病的发生和进展、治疗有效性和安全性以及卫生服务提供的重要研究。当前的新冠肺炎疫情表明，公共卫生需要有效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。这些工作大部分来自初级保健电子健康记录，全科医生（GP）输入并使用结构化的编码医疗数据。然而，医院的情况却大不相同。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，慢性呼吸道疾病，2型糖尿病，关节炎和癌症，占NHS预算的70%。通过医院门诊护理提供关于长期疾病（LTC）管理的专业意见。然而，来自门诊诊所的数据和见解几乎完全缺失。令人惊讶的是，没有一个国家系统记录医院门诊的诊断。关于关键临床事件的信息被记录在门诊信件中，这些信件主要用于与患者和全科医生沟通。信函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，针对当前流行病的屏蔽依赖于医院临床团队手动查看患者信件，以根据有关诊断和药物的自由文本信息来确定需要屏蔽的人，并明确了时间限制以及屏蔽不足和过度患者的风险。自然语言处理（NLP）和文本挖掘开发了计算机算法，可以从自由文本文档中自动提取相关信息。该项目将在学术界、二级保健和工业界之间建立伙伴关系，开发一个基于标准的信息管理框架，以安全地解锁存储在门诊信件中的信息，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益处。我们将开发新方法，从信件中提取关键临床事件，并以计算机化形式表示其详细信息（例如使用的药物，症状持续时间），以便可以轻松访问。在此过程中，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数据库，而不是生活在自己的筒仓中。保护可能出现在门诊数据中的敏感数据是一个主要问题，因此我们将制定明确的规则，规定谁可以访问这些数据以及如何访问这些数据，特别是考虑到第三方（例如行业）可能需要访问这些数据以开发其工具。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，以确保保障措施，公众信任和决策的透明度。我们将通过与我们的临床和业务合作伙伴的两个案例研究来证明所提出的方法的潜在影响。我们的第一个案例研究将展示所提出的模型如何帮助及时、高效、动态和透明地识别患者，以便在大流行中进行屏蔽或优先接种疫苗。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对健康和护理知识的重要差距，包括疾病流行和药物使用模式。所有输出将以可扩展到单个临床研究中心和单个专业之外的方式开发。

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.