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

Martin, Carmel M.; Hinkley, Narelle; Stockman, Keith; Campbell, Donald

doi:10.1111/jep.13031

Cited by 13 publications

(14 citation statements)

References 25 publications

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“…Interestingly, the subgroup of very unstable journeys (perhaps an artefact) in a general practice setting resonates with the patterns of unstable journeys in MonashWatch (and the ASI groups identified in Cohn's paper). In the MW paper, individuals identified as have potentially preventable hospitalizations by disease diagnoses and frequent utilization.…”

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confidence: 53%

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Resilience and health (care): A dynamic adaptive perspective

Martin

2018

Evaluation Clinical Practice

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confidence: 53%

“…Four empirical papers demonstrate both real world observational material with very different approaches. Survey and database material form the basis for the material which is used in different ways.…”

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confidence: 99%

Resilience and health (care): A dynamic adaptive perspective

Martin

2018

Evaluation Clinical Practice

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“…Nevertheless, anticipation and resilience emerge as observable patterns of CAS living systems, that are associated with survival in response to biopsychosocial challenges. In a previous publication, we proposed a pragmatic Theoretical Model for Static and Dynamic Indicators of Acute Admissions based upon an iterative and empirical analysis of data (3). This paper aims to provide a deeper explanation and analysis.…”

Section: Aimsmentioning

confidence: 99%

“…A Theoretical Model for Static and Dynamic Indicators of Acute Admissions (TSDIAA) (3) was derived iteratively and empirically during the initial 6/12 of PaJR MonashWatch service deployment using additional measures over and above the HLCC original service and disease predictors of repeat admissions. TSDIAA additional baseline measures included Clinical Frailty Index (CFI) (10); Connor Davis Resilience (CD-RISC) (11): SF-12v2 Health Survey scores Mental (MSC) and Physical (PSC) and ICEpop CAPability measure for Older people (ICECAP-O).…”

Section: Emerging Theoretical Frameworkmentioning

confidence: 99%

“…Big data analytics or other case finding methods can identify cohorts who are at risk of readmissions using a set of threshold parameters. However, understanding the complex systems of individual journeys in such cohorts in a timely manner requires an understanding of personal health and illness dynamics (3). Medicine is a human science that deals with the vagaries and uncertainties entailed in the multi-layered nature of “physical embodiment” within a multi-layered system encompassing ones' physical and sociocultural environment.…”

Section: Introductionmentioning

confidence: 99%

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Anticipatory Care in Potentially Preventable Hospitalizations: Making Data Sense of Complex Health Journeys

Martin

Sturmberg

Stockman

et al. 2019

Front. Public Health

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Purpose: 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.Application: 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.Theory: 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.Evaluation: 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.Discussion: 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.Conclusion: Anticipatory care, as operationalized through PaJR approaches applied in MonashWatch, demonstrates processes and outcomes that successfully ameliorate PPH.

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