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Integrating hospital outpatient letters into the healthcare data space

将医院门诊信件整合到医疗保健数据空间中

基本信息

批准号：
EP/V047949/1
负责人：
Goran Nenadic
金额：
$ 97.8万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV047949%2F1
关键词：
Integrating hospital outpatient letters into

项目摘要

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.

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公认的。这导致了关于疾病发生和进展、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Covid-19大流行表明，公共卫生需要有效利用在护理点收集的数据，以快速了解新发疾病的模式、风险因素和结果。大部分工作来自初级保健电子健康记录，全科医生（gp）在其中输入并使用结构化的编码医疗保健数据。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%。通过医院门诊提供关于长期疾病（LTCs）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，在医院门诊没有一个全国性的诊断记录系统。关键临床事件的信息被记录在门诊信件中，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，对当前大流行的屏蔽依赖于医院临床团队根据有关诊断和药物的自由文本信息，手动浏览患者信件，以确定需要屏蔽的人，并且有明确的时间限制以及对屏蔽不足和过度的患者的风险。自然语言处理（NLP）和文本挖掘开发了从自由文本文档中自动提取相关信息的计算机算法。该项目将在学术界、二级保健和工业界之间建立伙伴关系，以制定一个基于标准的信息管理框架，安全地解锁储存在门诊信函中的信息，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效益。我们将开发新的方法，从信件中提取关键的临床事件，并以计算机形式表示其细节（例如，使用的药物，症状持续时间），以便于访问。在这样做的过程中，我们将使用nhs采用的标准，使门诊信件可以与其他医院的数据库连接，而不是生活在自己的孤岛中。对门诊数据中可能出现的敏感数据的保护是一个主要问题，因此我们将制定明确的规则，规定谁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，以确保保障措施、公众信任和决策的透明度。我们将通过与临床和商业合作伙伴的两个案例研究来展示拟议方法的潜在影响。我们的第一个案例研究将展示拟议的模型如何有助于及时、有效、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，我们将说明如何利用同样的信息解决我们在卫生和保健知识方面的重要差距，例如，包括疾病流行和药物利用模式。所有产出将以一种可扩展的方式开发，超越单一临床地点和单一专业。