An Innovative Multidisciplinary Patient-centric Early Detection Care Model

创新的多学科以患者为中心的早期检测护理模式

• 批准号: EP/F058640/1
• 负责人: Glyn Elwyn
• 金额: $ 47.66万
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2008
• 资助国家: 英国
• 起止时间: 2008 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FF058640%2F1
• 关键词: Innovative; Multidisciplinary; Patient; centric; Early

We are of the view that in the next 30 years healthcare practitioners will be better able to measure the condition of a patient in order to predict outcomes. This means that earlier detection and intervention in disease treatment will be possible - a dream held by clinicians today. This Grand Challenge exploration study aims to move us closer to this dream by setting an agenda for research to deliver this vision. In undertaking the study we will utilise the skills and knowledge of a team drawn from diverse backgrounds - four clinical domains (chronic obstructive pulmonary disease (COPD), heart failure, diabetes, cancer), a general practitioner, and information engineers with a range of skills. It is the diversity of this team that gives us confidence that we can successfully undertake the first stage of this Grand Challenge.First, we consider the 'windows of opportunity'. The NHS, like other healthcare providers internationally, has espoused the use of multidisciplinary care pathways that bridge the primary and secondary care interfaces (i.e., GPs and hospitals). Although these are laudable aims, the reality in many settings is that patients face uncoordinated efforts and experience fragmentation of services more often than not, especially for complex chronic conditions that require multiple inputs and frequent adjustments in medication. Care for such patients is a mix of self and carer intervention at home, inputs from general practitioners and where there is deterioration or a crisis, admission to hospital. Rising levels of emergency and unplanned admissions is one of the major problems facing healthcare systems and is the cause of difficulties in achieving proposed elective care (number of operations) and increasing levels of hospital-acquired morbidity (e.g., infections).There are a number of conditions where it may be possible to detect deterioration at a sufficiently early stage in order to intervene and potentially prevent admission. However, the detection of such early levels of deterioration is often difficult for several reasons. Firstly, deteriorations may not at first give rise to symptoms that are evident to the patient and they may well be unaware of problems that may soon beset them. Secondly, patients may not be sufficiently aware of symptoms that indicate early deterioration or they may resist alerting others to the change in their condition. Often they hope the symptoms will resolve but are unaware that they are on a 'slippery slope' to serious problems. Thirdly, clinicians in primary care, even when alerted to a set of symptoms, may not have information about the patient's baseline state, and thus lack a benchmark against which to appraise new clinical information. Clinicians, especially when visiting a patient at home, typically have to make decisions on a very limited dataset, gained by clinical examination alone. They are bereft of a range of data that is now potentially available, given advances in diagnostic technologies. The potential gains of being able to view and interpret accurate physiological data, combine and compare to previously stored data (i.e., in EPR), either collected by the patient, by primary or secondary care clinicians, are evident. Similarly, the ability to compare such data with a set of longitudinally collected data from the same patient, thereby gaining a benchmark against which to determine thresholds for therapeutic interventions, would also be a major advance. The ability to share this data with specialist colleagues would enable decisions about the need for admission or for the implementation of therapies in the patient's home to be far more informed than is currently the case.

我们认为，在未来的30年里，医疗从业者将能够更好地衡量病人的状况，以预测结果。这意味着疾病治疗的早期检测和干预将成为可能——这是当今临床医生的梦想。这项大挑战探索研究旨在通过制定研究议程来实现这一愿景，从而使我们更接近这一梦想。在开展这项研究时，我们将利用来自不同背景的团队的技能和知识——四个临床领域（慢性阻塞性肺疾病（COPD）、心力衰竭、糖尿病、癌症）、一名全科医生和具有一系列技能的信息工程师。正是这个团队的多样性给了我们信心，我们可以成功地完成这个大挑战的第一阶段。首先，我们考虑“机会之窗”。与国际上其他医疗保健提供者一样，NHS支持使用多学科护理途径，以连接初级和二级护理接口（即全科医生和医院）。尽管这些目标值得称赞，但在许多情况下，现实情况是，患者面临的努力不协调，服务往往是碎片化的，特别是对于需要多种投入和频繁调整药物的复杂慢性疾病。对这类患者的护理包括自我干预和家庭护理干预，以及全科医生的投入，如果病情恶化或出现危机，则住院治疗。急诊和计划外入院人数的增加是医疗保健系统面临的主要问题之一，也是实现拟议的选择性护理（手术数量）和医院获得性发病率（例如感染）增加的困难的原因。在一些情况下，可能有可能在足够早的阶段检测到恶化，以便进行干预并可能防止入院。然而，由于几个原因，这种早期恶化程度的检测往往是困难的。首先，病情的恶化在开始时可能不会引起对病人来说很明显的症状，他们很可能没有意识到可能很快就会困扰他们的问题。其次，患者可能没有充分意识到表明病情早期恶化的症状，或者他们可能不愿向他人通报自己病情的变化。他们通常希望症状会消失，但没有意识到他们正处于滑向严重问题的“滑坡”上。第三，初级保健的临床医生，即使在对一组症状发出警报时，也可能没有关于患者基线状态的信息，因此缺乏评估新临床信息的基准。临床医生，特别是在家探视病人时，通常必须根据非常有限的数据集做出决定，这些数据集仅通过临床检查获得。由于诊断技术的进步，他们失去了现在可能获得的一系列数据。能够查看和解释准确的生理数据，结合和比较以前存储的数据（即EPR）的潜在收益是显而易见的，无论是由患者收集的，还是由初级或二级护理临床医生收集的数据。同样，能够将这些数据与同一患者的一组纵向收集的数据进行比较，从而获得一个基准，以确定治疗干预的阈值，也将是一项重大进步。与专家同事分享这些数据的能力，将使有关是否需要入院或在患者家中实施治疗的决定比目前的情况更加知情。