Premium
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
Author(s) -
Martin Carmel,
Hinkley Narelle,
Stockman Keith,
Campbell Donald
Publication year - 2018
Publication title -
journal of evaluation in clinical practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.737
H-Index - 73
eISSN - 1365-2753
pISSN - 1356-1294
DOI - 10.1111/jep.13031
Subject(s) - medicine , logistic regression , cohort , stressor , mental health , receiver operating characteristic , emergency department , acute care , phone , emergency medicine , health care , medical emergency , nursing , psychiatry , linguistics , philosophy , economics , economic growth
Abstract Rationale, aims, and objectives 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 . Methods 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. Findings 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. Conclusion 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.