Martin Carmel M, Sturmberg Joachim P, Stockman Keith, Hinkley Narelle, Campbell Donald
Monash Health Community, Clayton, VIC, Australia.
University of Newcastle, Callaghan, NSW, Australia.
Front Public Health. 2019 Jan 28;6:376. doi: 10.3389/fpubh.2018.00376. eCollection 2018.
Potentially preventable hospitalizations (PPH) are minimized when adults (usually with multiple morbidities ± frailty) benefit from alternatives to emergency hospital use. A complex systems and anticipatory journey approach to PPH, the Patient Journey Record System (PaJR) is proposed. PaJR is a web-based service supporting ≥weekly telephone calls by trained lay Care Guides (CG) to individuals at risk of PPH. The Victorian HealthLinks Chronic Care algorithm provides case finding from hospital big data. Prediction algorithms on call data helps optimize emergency hospital use through adaptive and anticipatory care. MonashWatch deployment incorporating PaJR is conducted by Monash Health in its Dandenong urban catchment area, Victoria, Australia. A Complex Adaptive Systems (CAS) framework underpins PaJR, and recognizes unique individual journeys, their dependence on historical and biopsychosocial influences, and difficult to predict tipping points. Rosen's modeling relationship and anticipation theory additionally informed the CAS framework with data sense-making and care delivery. PaJR uses perceptions of current and future health (interoception) through ongoing conversations to anticipate possible tipping points. This allows for possible timely intervention in trajectories in the biopsychosocial dimensions of patients as "particulars" in their unique trajectories. Monash Watch is actively monitoring 272 of 376 intervention patients, with 195 controls over 22 months (ongoing). Trajectories of poor health (SRH) and anticipation of worse/uncertain health (AH), and CG concerns statistically shifted at a tipping point, 3 days before admission in the subset who experienced ≥1 acute admission. The -3 day point was generally consistent across age and gender. Three randomly selected case studies demonstrate the processes of anticipatory and reactive care. PaJR-supported services achieved higher than pre-set targets-consistent reduction in acute bed days (20-25%) vs. target 10% and high levels of patient satisfaction. Anticipatory care is an emerging trajectory data analytic approach that uses human sense-making as its core metric demonstrates improvements in processes and outcomes. Multiple sources can provide big data to inform trajectory care, however simple tailored data collections may prove effective if they embrace human interoception and anticipation. Admission risk may be addressed with a simple data collections including SRH, AH, and CG perceptions, where practical. Anticipatory care, as operationalized through PaJR approaches applied in MonashWatch, demonstrates processes and outcomes that successfully ameliorate PPH.
当成人（通常患有多种疾病±身体虚弱）受益于急诊医院使用的替代方案时，潜在可预防的住院（PPH）可降至最低。本文提出了一种针对PPH的复杂系统和预期旅程方法——患者旅程记录系统（PaJR）。PaJR是一项基于网络的服务，支持经过培训的非专业护理指导（CG）每周至少一次给有PPH风险的个人打电话。维多利亚州健康链接慢性病护理算法可从医院大数据中找出病例。通话数据上的预测算法有助于通过适应性和预期性护理优化急诊医院的使用。莫纳什健康中心在澳大利亚维多利亚州丹德农市区实施了纳入PaJR的莫纳什观察项目。一个复杂适应系统（CAS）框架是PaJR的基础，它认识到独特的个人旅程、它们对历史和生物心理社会影响的依赖性以及难以预测的临界点。罗森的建模关系和预期理论通过数据理解和护理提供为CAS框架提供了额外信息。PaJR通过持续对话利用对当前和未来健康的感知（内感受）来预测可能的临界点。这使得能够在患者生物心理社会维度的轨迹中作为其独特轨迹中的“细节”进行可能的及时干预。莫纳什观察项目正在对376名干预患者中的272名进行积极监测，同时在22个月内对195名对照患者进行监测（仍在进行中）。健康状况不佳（SRH）和对更差/不确定健康状况的预期（AH）以及CG的担忧在临界点上有统计学上的变化，即在经历过≥1次急性入院的亚组中入院前3天。-3天这个时间点在年龄和性别上总体一致。三个随机选择的案例研究展示了预期性和反应性护理的过程。PaJR支持的服务实现了高于预设目标——急性病床日持续减少（20 - 25%），而目标是10%，并且患者满意度很高。预期性护理是一种新兴的轨迹数据分析方法，它以人类理解作为核心指标，显示出在过程和结果方面的改善。多个来源可以提供大数据来为轨迹护理提供信息，然而，如果简单的定制数据收集包含人类内感受和预期，可能会证明是有效的。在可行的情况下，通过包括SRH、AH和CG感知在内的简单数据收集可以解决入院风险问题。通过莫纳什观察项目中应用的PaJR方法实施的预期性护理，展示了成功改善PPH的过程和结果。
