Nagaratnam Kiruba, Harston George, Flossmann Enrico, Canavan Clara, Geraldes Rui Carmelo, Edwards Chani
Royal Berkshire NHS Foundation Trust, Reading, UK.
Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Future Healthc J. 2020 Jun;7(2):169-173. doi: 10.7861/fhj.2020-0034.
Acute stroke care demands real-time, specialist-led treatment decisions, including thrombolysis and referral for mechanical thrombectomy. Pathways designed to deliver time-critical interventions for stroke patients are under intense pressure due to the impact of COVID-19 pandemic. In response to this unprecedented burden on acute care services, stroke clinicians are having to reconfigure existing clinical pathways both within and between hospitals. Incorporating artificial intelligence and digital communication support into clinical pathways offers an opportunity to mitigate the disruption to acute stroke care. In this case study we describe how Royal Berkshire Hospital, working collaboratively with Brainomix, a UK-based artificial intelligence software company, adopted technological innovation and integrated it into the hyperacute stroke pathway. A case is presented to demonstrate how this innovation can support patient care and deliver successful patient outcomes. We believe this model can be adopted in other hospitals and networks to deliver safe and efficient hyperacute stroke care.
急性中风护理需要实时、由专家主导的治疗决策，包括溶栓治疗和转介进行机械取栓术。由于新冠疫情的影响，旨在为中风患者提供时间紧迫干预措施的路径面临巨大压力。为应对急性护理服务前所未有的负担，中风临床医生不得不重新配置医院内部和医院之间现有的临床路径。将人工智能和数字通信支持纳入临床路径，为减轻对急性中风护理的干扰提供了一个机会。在本案例研究中，我们描述了皇家伯克郡医院如何与英国人工智能软件公司Brainomix合作，采用技术创新并将其整合到超急性中风路径中。本文呈现了一个案例，以展示这种创新如何支持患者护理并实现成功的患者治疗结果。我们相信，该模式可在其他医院和医疗网络中采用，以提供安全、高效的超急性中风护理。
