Resilience, health perceptions, (QOL), stressors, and hospital admissions-Observations from the real world of clinical care of unstable health journeys in Monash Watch (MW), Victoria, Australia.
Resilience, health perceptions, (QOL), stressors, and hospital admissions-Observations from the real world of clinical care of unstable health journeys in Monash Watch (MW), Victoria, Australia.
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DOI：
10.1111/jep.13031
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发表时间：
2018-12
影响因子：
2.4
通讯作者：
Campbell D
中科院分区：
文献类型：
--
作者：
Martin C;Hinkley N;Stockman K;Campbell D
Monash Watch (MW) 旨在通过 Health Links Chronic Care (HLCC) 算法使用个人、诊断和服务数据来减少高于风险“阈值”的人群中潜在可预防的住院治疗。 MW 使用患者旅程记录系统 (PaJR) 通过拨出电话进行定期患者监测。 PaJR 警报旨在充当压力源、复原力和健康认知的自我报告晴雨表，每次呼叫的警报越多，表明风险越大。目的：基于急性入院静态和动态指标的理论模型，描述 PaJR 警报（通过外拨电话自我报告）的预测因素和急性入院的预测因素。参与者：预计 MW 服务部门每年入院 3 人以上且持续时间超过 40 天的 HLCC 队列； n = 244。基线测量——临床衰弱指数 (CFI)；康纳·戴维斯复原力 (CD-RISC)：SF-12v2 健康调查分数心理 (MSC) 和身体 (PSC) 以及 ICECAP-O。动态措施：PaJR 警报/调用 10 869 MW 记录。来自维多利亚州承认病例数据库的急性（非手术）入院。分析：使用 XLSTAT 的逻辑回归、相关性和时间序列同质性指标。除 SF-12_MCS 外，基线指标均显着相关。 SF12-MSC、SF12-PSC 和 ICECAP-O 最佳预测 PaJR 警报/呼叫（ROC：0.84）。 CFI 最好地预测急性入院（ROC：0.66），添加 CD-RISC、SF-12_MCS、SF-12_PCS 和 ICECAP-O 以及双向交互改进模型（ROC：0.70）。 PaJR 警报在急性入院前 ≤10 天较高，并且与入院显着相关。四个案例研究中的 PaJR 警报模式表明，动态变化预示着风险。总体而言，所有基线指标都是支持理论模型的解释性指标。 PaJR 警报和紧急入院的时间反映了不断变化的压力源、复原力和健康认知，这不是根据基线指标预测的，而是为服务干预提供了触发因素。代表压力源、复原力和健康认知的静态和动态指标都有可能以临床上有用的方式为入院风险阈值模型提供信息。
Monash Watch (MW) aims to reduce potentially preventable hospitalisations in a cohort above a risk “threshold” identified by Health Links Chronic Care (HLCC) algorithms using personal, diagnostic, and service data. MW conducted regular patient monitoring through outbound phone calls using the Patient Journey Record System (PaJR). PaJR alerts are intended to act as a self‐reported barometer of stressors, resilience, and health perceptions with more alerts per call indicating greater risk. Aims: To describe predictors of PaJR alerts (self‐reported from outbound phone calls) and predictors of acute admissions based upon a Theoretical Model for Static and Dynamic Indicators of Acute Admissions. Participants: HLCC cohort with predicted 3+ admissions/year in MW service arm for >40 days; n = 244. Baseline measures—Clinical Frailty Index (CFI); Connor Davis Resilience (CD‐RISC): SF‐12v2 Health Survey scores Mental (MSC) and Physical (PSC) and ICECAP‐O. Dynamic measures: PaJR alerts/call in 10 869 MW records. Acute (non‐surgical) admissions from Victorian Admitted Episode database. Analysis: Logistic regression, correlations, and timeseries homogeneity metrics using XLSTAT. Baseline indicators were significantly correlated except SF‐12_MCS. SF12‐MSC, SF12‐PSC and ICECAP‐O best predicted PaJR alerts/call (ROC: 0.84). CFI best predicted acute admissions (ROC: 0.66), adding CD‐RISC, SF‐12_MCS, SF‐12_PCS and ICECAP‐O with two‐way interactions improved model (ROC: 0.70). PaJR alerts were higher ≤10 days preceding acute admissions and significantly correlated with admissions. Patterns in PaJR alerts in four case studies demonstrated dynamic variations signifying risk. Overall, all baseline indicators were explanatory supporting the theoretical model. Timing of PaJR alerts and acute admissions reflecting changing stressors, resilience, and health perceptions were not predicted from baseline indicators but provided a trigger for service interventions. Both static and dynamic indicators representing stressors, resilience, and health perceptions have the potential to inform threshold models of admission risk in ways that could be clinically useful.
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影响因子：
3.3
作者：
Sperandei S
通讯作者：
Sperandei S
影响因子：
7.4
作者：
Connor, KM;Davidson, JRT
通讯作者：
Davidson, JRT
DOI：
10.1073/pnas.1312114110
发表时间：
2014-01-07
影响因子：
11.1
作者：
van de Leemput, Ingrid A.;Wichers, Marieke;Scheffer, Marten
通讯作者：
Scheffer, Marten
影响因子：
2.4
作者：
Martin, Carmel Mary
通讯作者：
Martin, Carmel Mary
DOI：
10.1111/j.1440-1614.2006.01886.x
发表时间：
2006-09-01
影响因子：
4.6
作者：
Windsor, Timothy D.;Rodgers, Bryan;Jorm, Anthony F.
通讯作者：
Jorm, Anthony F.