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

Feasibility of patient-reported outcome research in acute geriatric medicine: an approach to the 'post-hospital syndrome'

BACKGROUND

A patient's self-reported health-related quality of life (HRQoL) can be quantified by a patient-reported outcome measure (PROM). A patient's HRQoL can provide another avenue to understand the 'post-hospital syndrome', a period after hospital discharge that a patient remains vulnerable to subsequent re-admission. The purpose of the study was to establish the feasibility of collecting HRQoL of older inpatients treated for acute illnesses on medical ward. Feasibility of the PROM would be qualitatively judged upon completion time, response rate and sensitivity to change in HRQoL over time.

METHODS

A prospective observational cohort of consecutively admitted patients to a step-down medical ward over 1 year. The COOP/WONCA chart was the PROM. Patients were interviewed by the author face-to-face within 48 hours of admission and then 2 weeks after discharge by telephone.

RESULTS

From the 300 patients admitted, 182 were excluded. Of the remaining 118, median age was 78 years (interquartile range, IQR, 64-86 years), and 71 (60.2%) were female. Proxies were used for 26 (22%) patients. Ninety-two (78%) completed follow-up. The participants were contacted at a median of 14 days (IQR, 13-16) after discharge. Exploratory analyses found that the COOP/WONCA had test-retest responsiveness, that is detected change in HRQoL over time.

CONCLUSION

The completion time of 3 minutes, high response rate (78%) and test-retest responsiveness are evidence that collecting PROs from acutely unwell elderly patients using the COOP/WONCA is feasible. PRO research could become fundamental to the understanding of the 'post-hospital syndrome'.

Potentially preventable hospitalizations-The 'pre-hospital syndrome': Retrospective observations from the MonashWatch self-reported health journey study in Victoria, Australia

RATIONALE, AIMS, AND OBJECTIVES

HealthLinks: Chronic Care is a state-wide public hospital initiative designed to improve care for cohorts at-risk of potentially preventable hospitalizations at no extra cost. MonashWatch (MW) is an hospital outreach service designed to optimize admissions in an at-risk cohort. Telehealth operators make regular phone calls (≥weekly) using the Patient Journey Record System (PaJR). PaJR generates flags based on patient self-report, alerting to a risk of admission or emergency department attendance. 'Total flags' of global health represent concerns about self-reported general health, medication, and wellness. 'Red flags' represent significant disease/symptoms concerns, likely to lead to hospitalization.

METHODS

A time series analysis of PaJR phone calls to MW patients with ≥1 acute non-surgical admissions in a 20-day time window (10 days pre-admission and 10 days post-discharge) between 23 December 2016 and 11 October 2017. Pettitt's hypothesis-testing homogeneity measure was deployed to analyse Victorian Admitted Episode/Emergency Minimum Datasets and PaJR data.

FINDINGS

A MW cohort of 103 patients (mean age 74 ± 15 years; with 59% males) had 263 admissions was identified. Bed days ranged from <1 to 37.3 (mean 5.8 ± 5.8; median 4.1). The MW cohort had 7.6 calls on average in the 20-day pre- and post-hospital period. Most patients reported significantly increased flags 'pre-hospital' admission: medication issues increased on day 7.0 to 8.5; total flags day 3, worse general health days 2.5 to 1.8; and red flags of disease symptoms increased on day 1. These flags persisted following discharge.

DISCUSSION/CONCLUSION

This study identified a 'pre-hospital syndrome' similar to a post-hospital phase aka the well-documented 'post-hospital syndrome'. There is evidence of a 10-day 'pre-hospital' window for interventions to possibly prevent or shorten an acute admission in this MW cohort. Further validation in a larger diverse sample is needed.

Psychological contributors to the frail phenotype: The association between resilience and frailty in patients with cirrhosis

We examined whether a key psychological trait-resilience, defined as one's ability to recover quickly from difficulties-contributes to the frail phenotype in patients with cirrhosis. Included were 300 adult patients with cirrhosis who underwent outpatient physical frailty testing using the Liver Frailty Index and resilience testing using the Connor-Davidson Resilience Scale (CD-RISC). The Liver Frailty Index was categorized as robust, prefrail-robust, prefrail-frail, and frail; CD-RISC was categorized using population norms as: least, less, more, and most resilient. Linear regression was used to assess factors associated with frailty (by the Liver Frailty Index per 0.1 unit change). Among the most resilient, only 10% were frail; among the least resilient, 29% were frail. In univariable analysis, resilience was strongly associated with the Liver Frailty Index (coef = -0.13 per point increase; 95% confidence interval [CI], -0.20 to -0.60; P < .001) and remained significantly associated with frailty in multivariable adjustment (coef = -0.13, 95% CI -0.19 to -0.07; P < .001). Low resilience is strongly associated with the frail phenotype in patients with cirrhosis. Given that resilience is modifiable, our data suggest that effective interventions to mitigate frailty should include strategies to build resilience in patients with low baseline resilience.

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

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.

Resilience and health (care): A dynamic adaptive perspective

This special forum on resilience explores particular worldviews of resilience-clinical, psychosocial, sociological, complexity science, organizational, and political economy through eight papers. This forum aims to open up the wealth of understandings and implications in health care by taking a transdisciplinary overview.

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

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.