CONSULT: Collaborative Mobile Decision Support for Managing Multiple Morbidities

为患有长期疾病的人提供医疗保健是一个日益严峻的挑战，对于越来越多患有多种疾病的英国人口来说，这一挑战尤为严峻。研究表明，让患者参与到自己的疾病管理中来对健康有长期的好处。无线传感器技术的进步意味着，患者在家中监测广泛的健康和健康数据是可行的，包括血压、心脏功能和血糖水平，而不需要医务人员的直接监督。智能手机技术的出现，在全国范围内广泛出现，使公众能够将最先进的智能决策支持系统投入到公众手中，这是令人兴奋的可能性。然而，这种传感器数据目前与患者的上下文(由电子健康记录提供)和治疗计划(基于当前的最佳证据指南)和由患者的全科医生定制的连接都是断开的。在多种疾病的情况下，没有明确的战略将多个指南合并为一个连贯的整体。此外，个性化治疗计划是僵化的，不能动态适应患者情况的变化。最后，患者病情和做出的决定的记录不是以标准化的方式常规获取的，从而阻止了从关于治疗有效性的反馈中学习。为了解决这些问题，咨询公司将把无线“健康”传感器与在移动设备上运行的智能软件结合起来，以支持患者的决策，从而积极地让患者参与管理他们的医疗保健。我们的软件将使用计算论证来帮助患者遵循治疗指南，并将了解特定于个人的细节，在医学合理的范围内个性化治疗建议。至关重要的是，该软件将检测治疗指南中经常出现的冲突，这些冲突出现在多种疾病的管理中。该软件将提供关于遵循哪些治疗选项、何时患者可以解决冲突以及何时解决需要临床医生干预的建议。因此，该软件将帮助患者处理他们的病情的日常维护，同时确保在适当的时候咨询医疗专业人员。这将使患者能够控制自己的病情，同时得到传统和新方式的充分支持。通过例行公事地获取建议的数据来源、采取的行动以及由此导致的患者进展，该软件将为多发病情况下治疗的有效性和潜在指导方针提供有价值的见解。这项技术将在一项概念验证研究中进行多个维度的评估，包括中风患者、他们的护理人员和医疗专业人员，同时利用伦敦国王学院在中风研究方面的世界领先地位及其现有的患者群体，特别是那些与伦敦南部中风登记计划相关的患者。帮助患者管理自己的护理将减少对医疗专业人员的要求，同时收获自我管理的健康益处。整合来自监测设备的实时信息将有可能区分需要医疗专业人员关注的情况和不需要关注的情况，从而减少患者和医生需要安排的额外预约数量。使用实时信息还可以检测疾病过程中的变化，从而采取先发制人的行动，从而减少患有长期疾病的人可能需要住院的时间。总体而言，我们的方法不仅将提供更高效的护理，还将使护理更好地适应每个人的需求。

The provision of healthcare to people with long-term conditions is a growing challenge, which is particularly acute for the growing proportion of the UK population that suffers from multiple morbidities.Research has established that involving patients in the management of their own disease has long-term health benefits. Advances in wireless sensor technology means that it is practical for patients to monitor a wide range of health and wellness data at home, including blood pressure, heart function and glucose levels, without direct supervision by medical personnel. The advent of smart phone technologies, appearing widely throughout the nation's population, enables the exciting possibility of putting state-of-the-art intelligent decision-support systems into the hands of the general public.However, such sensor data is currently disconnected both from the patient context, provided by the Electronic Health Record, and from the treatment plan, based on current best-evidence guidelines and customised by the patient's GP. In cases of multi-morbidities, there is no clear strategy for combining multiple guidelines into a coherent whole. Furthermore, personalised treatment plans are rigid and do not dynamically adapt to changes in a patient's circumstances. Finally, the record of patient condition and decisions made is not routinely captured in a standardised way, preventing learning from feedback about treatment effectiveness. To address these problems, CONSULT will combine wireless "wellness" sensors with intelligent software running on mobile devices, to support patient decision making, and thus actively engage patients in managing their healthcare. Our software will use computational argumentation to help patients follow treatment guidelines and will learn details specific to individuals, personalising treatment advice within medically sound limits. Critically, the software will detect conflicts in treatment guidelines that frequently arise in the management of multiple morbidities. The software will provide advice regarding which treatment options to follow, when the conflicts can be resolved by the patient and when a resolution requires an intervention from a clinician. The software will thus help patients handle routine maintenance of their conditions, while ensuring that medical professionals are consulted when appropriate. This will enable patients to take charge of their own conditions, while being fully supported in both traditional and new ways. By routinely capturing the data provenance of the recommendations made, actions taken and the resulting patient progress, the software will provide valuable insights into the effectiveness of treatments and underlying guidelines in multi-morbidity scenarios.The technology will be evaluated across multiple dimensions in a proof-of-concept study, engaging stroke patients, their carers and medical professionals, while capitalising on King's College London's world-leading position in stroke research and its established patient groups, particularly those connected to the South London Stroke Register programme.Helping patients to govern their own care will reduce the demands made on medical professionals, while reaping the health benefits of self-management. Integrating live information from monitoring devices will make it possible to distinguish between situations that need attention from medical professionals, and those that do not, reducing the number of extra appointments that patients and doctors need to schedule. Using live information will also make it possible to detect changes in the course of a disease, allowing pre-emptive actions to be taken, and thus reducing the amount of time that someone suffering from a long-term condition may have to spend in hospital. Overall, our approach will not only provide more efficient care, but also allow care to be better tailored to the needs of each individual.