CONSULT: Collaborative Mobile Decision Support for Managing Multiple Morbidities

向患有长期疾病的人提供医疗保健是一项日益严峻的挑战，对于患有多种疾病的英国人口比例日益增长来说，这一挑战尤其严峻。研究已经证实，让病人参与到自己疾病的管理中来，对健康有长期的好处。无线传感器技术的进步意味着患者可以在家中监测各种健康和保健数据，包括血压、心脏功能和血糖水平，而无需医务人员的直接监督。智能电话技术的出现，在全国人口中广泛出现，使最先进的智能决策支持系统进入公众手中成为令人兴奋的可能性。然而，这些传感器数据目前既与电子健康记录提供的患者背景脱节，也与基于当前最佳证据指南和患者全科医生定制的治疗计划脱节。在多种疾病的情况下，没有明确的策略将多种指南结合成一个连贯的整体。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，患者病情和决策的记录没有以标准化的方式进行常规记录，从而阻碍了从有关治疗效果的反馈中学习。为了解决这些问题，CONSULT将无线“健康”传感器与运行在移动设备上的智能软件相结合，以支持患者的决策，从而使患者积极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，并将学习具体的个人细节，在医学合理的范围内提供个性化的治疗建议。至关重要的是，该软件将检测出在多种疾病管理中经常出现的治疗指南中的冲突。该软件将提供有关哪些治疗方案可以遵循的建议，何时冲突可以由患者解决，何时解决需要临床医生的干预。因此，该软件将帮助患者处理日常维护他们的条件，同时确保在适当的时候咨询医疗专业人员。这将使患者能够对自己的病情负责，同时得到传统和新方式的充分支持。通过常规获取所提出的建议、所采取的行动和由此产生的患者进展的数据来源，该软件将为治疗的有效性和多发病情况的基本指南提供有价值的见解。这项技术将在概念验证研究中进行多维度评估，让中风患者、他们的护理人员和医疗专业人员参与其中，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已建立的患者群体，特别是与南伦敦中风登记项目相关的患者群体。帮助患者管理自己的护理将减少对医疗专业人员的需求，同时获得自我管理的健康益处。整合来自监控设备的实时信息将使区分需要医疗专业人员关注和不需要关注的情况成为可能，从而减少患者和医生需要安排的额外预约次数。使用实时信息还可以发现疾病过程中的变化，从而可以采取先发制人的行动，从而减少患有长期疾病的人可能不得不在医院度过的时间。总的来说，我们的方法不仅可以提供更有效的护理，还可以根据每个人的需要更好地进行护理。

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.