Real-time symptom monitoring using ePROs to prevent adverse events during care transitions

摘要 护理过渡期间的不良事件(AE)从19%到28%不等，可能导致再次住院，代表 对病人安全的持续威胁。早期识别患者报告的症状并将其上报给住院患者 而门诊医生是至关重要的，特别是对于患有多种慢性病(MCC)的患者。临床上 集成的数字健康应用程序有可能更准确地预测出院后AEs并改善 为患者、他们的护理人员和护理团队进行沟通。这样的工具可以提供个性化的风险 通过系统收集相关患者报告的结果(PRO)并利用 标准化的应用程序编程接口(API)，将其与电子健康记录(EHR)相结合 数据。虽然患者报告的结果(PRO)越来越多地用于门诊环境，但它们在真正的- 在从医院过渡期间对症状进行时间监控和上报是一种新颖且有潜力的做法 变革性-既使患者能够更好地了解他们出院后的个性化风险 Aes，以及在出院时改善监测。我们提出的干预措施是基于 基于证据的框架，用于护理过渡，以及数字健康工具的扩展和传播。通知我们的 干预：我们建议开发和验证住院患者出院后不良事件的预测模型。 MCC患者使用相关专业问卷和电子健康记录(EHR)衍生变量。我们 然后将合并、调整、扩展和完善我们以前开发的EHR集成医院和 以门诊为重点的数字健康基础设施，支持MCC患者进行实时症状监测 在转出医院时使用专业人员。我们的干预使用可互操作的数据交换 标准和API可与现有供应商患者门户产品无缝集成，从而解决 关键差距和支持完整的连续护理。我们的多学科团队使用以下原则 以用户为中心的设计和敏捷的软件开发，以快速识别、设计、开发、改进和 落实患者和临床医生的要求。我们的团队将严格评估这次干预措施 这是一项大规模随机对照试验，我们将我们的实时症状监测干预与 MCC患者出院的常规护理。最后，我们将进行稳健的混合 方法评估，以产生新的知识和最佳实践，以传播、实施和 在具有不同电子病历供应商的类似机构中使用这种可互操作的干预措施。

ABSTRACT Adverse events (AE) during care transitions range from 19-28% and may lead to readmissions, representing an ongoing threat to patient safety. Early identification and escalation of patient-reported symptoms to inpatient and ambulatory clinicians is critical, especially for patients with multiple chronic conditions (MCC). Clinically integrated digital health apps have the potential to more accurately predict post-discharge AEs and improve communication for patients, their caregivers, and the care team. Such tools can provide individualized risk assessments of AEs by systematically collecting relevant patient-reported outcomes (PROs) and leveraging standardized application programming interfaces (API) to combine them with electronic health record (EHR) data. While patient-reported outcomes (PROs) are increasingly used in ambulatory settings, their use for real- time symptom monitoring and escalation during transitions from the hospital is novel and potentially transformative–by both empowering patients to better understand their individualized risks of post-discharge AEs, and improving monitoring while transitioning out of the hospital. Our proposed intervention is grounded in evidence-based frameworks for care transitions, and scaling and spread of digital health tools. To inform our intervention, we propose developing and validating a predictive model of post-discharge AEs for hospitalized MCC patients using relevant PRO questionnaires and electronic health record (EHR) derived variables. We will then combine, adapt, extend, and refine our previously developed EHR-integrated hospital and ambulatory-focused digital health infrastructure to support MCC patients in real-time symptom monitoring using PROs when transitioning out of the hospital. Our intervention uses interoperable, data exchange standards and APIs to seamlessly integrate with existing vendor patient portal offerings, thereby addressing critical gaps and supporting the complete continuum of care. Our multidisciplinary team uses principles of user-centered design and agile software development to rapidly identify, design, develop, refine, and implement requirements from patients and clinicians. Our team will rigorously evaluate this intervention in a large-scale randomized controlled trial in which we compare our real-time symptom monitoring intervention to usual care for patients with MCCs transitioning out of the hospital. Finally, we will conduct a robust mixed methods evaluation to generate new knowledge and best practices for disseminating, implementing, and using this interoperable intervention at similar institutions with different EHR vendors.