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.

Rationale, aims, and objectivesMonash 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.MethodsParticipants: 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.FindingsBaseline 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.ConclusionBoth 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.

Background MonashWatch is a telehealth public hospital outreach pilot service as a component of the Government of Victoria’s statewide redesign initiative called HealthLinks: Chronic Care. Rather than only paying for hospitalizations, projected funding is released earlier to hospitals to allow them to reduce hospitalization costs. MonashWatch introduced a web-based app, Patient Journey Record System, to assess the risk of the journeys of a cohort of patients identified as frequent admitters. Telecare guides call patients using the Patient Journey Record System to flag potential deterioration. Health coaches (nursing and allied health staff) triage risk and adapt care for individuals. Objective The aim was a pragmatic controlled evaluation of the impact of MonashWatch on the primary outcome of bed days for acute nonsurgical admissions in the intention-to-treat group versus the usual care group. The secondary outcome was hospital admission rates. The net promoter score was used to gauge satisfaction. Methods Patients were recruited into an intention-to-treat group, which included active telehealth and declined/lost/died groups, versus a systematically sampled (4:1) usual care group. A rolling sample of 250-300 active telehealth patients was maintained from December 23, 2016 to June 23, 2019. The outcome—mean bed days in intervention versus control—was adjusted using analysis of covariance for age, gender, admission type, and effective days active in MonashWatch. Time-series analysis tested for trends in change patterns. Results MonashWatch recruited 1373 suitable patients who were allocated into the groups: usual care (n=293) and intention-to-treat (n=1080; active telehealth: 471/1080, 43.6%; declined: 485, 44.9%; lost to follow-up: 178 /1080, 10.7%; died: 8/1080, 0.7%). Admission frequency of intention-to-treat compared to that of the usual care group did not significantly improve (P=.05), with a small number of very frequent admitters in the intention-to-treat group. Age, MonashWatch effective days active, and treatment group independently predicted bed days. The analysis of covariance demonstrated a reduction in bed days of 1.14 (P<.001) in the intention-to-treat group compared with that in the usual care group, with 1236 bed days estimated savings. Both groups demonstrated regression-to-the-mean. The downward trend in improved bed days was significantly greater (P<.001) in the intention-to-treat group (Sen slope –406) than in the usual care group (Sen slope –104). The net promoter score was 95% in the active telehealth group compared with typical hospital scores of 77%. Conclusions Clinically and statistically meaningful reductions in acute hospital bed days in the intention-to-treat group when compared to that of the usual care group were demonstrated (P<.001), although admission frequency was unchanged with more short stay admissions in the intention-to-treat group. Nonrandomized control selection was a limitation. Nonetheless, MonashWatch was successful in the context of the HealthLinks: Chronic Care capitation initiative and is expanding.

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.

Rationale aims and objectives Potentially preventable hospitalizations (PPH) are a challenge. What happens before hospital admission? Are there crucial tipping points before admissions in at-risk cohorts' trajectories? HealthLinksChronicCare (HLCC) hospital risk-prediction algorithms using admission, diagnosis, and lifestyle data identifies at-patients. MW monitors HLCC patients with outbound phone calls using telehealth -the Patient Journey Record System with alerts representing a real-time anticipated risk of PPH. Health Coaches triage and intervene to optimize GP, hospital and community service utilization to reduce the risk of PPH. Aims To describe a time series of telehealth phone calls related to an acute admission (? 10 days) to investigate tipping points in self-reported biopsychosocial environmental concerns (total alerts) and or condition symptoms of concern (red alerts). Methods MW participants had an acute (non-surgical) admission and >44 calls between 23/12/16 -11/10/17. The Patient Journey Record System (PaJR) and Victorian Admitted Episode Data/ Emergency Minimum Dataset provided longitudinal data. Descriptive time series analysis employed Pettitt's homogeneity test to detect 'tipping points' using XLSTAT package. Findings One hundred three patients aged 74 ± 15.4 years, with 59% male and 61% female, provided 764 call records around admission(s) and 22,715 records over 10 months. Total alerts and red alerts were higher in the 10 days before and after admission. Total alerts significantly increased (tipped) at day 3 before hospitalisation persisting until 10 days. Red alerts increased (tipped) 1 day before admission and remained high following discharge. Discussion and Conclusion Self-report in phone calls describe a pre-hospital phase of 'post-hospital syndrome' (PHS), which began at least 10 days before admission and persisted after discharge. Wide-ranging health, psychosocial, and environmental concerns preceded a tipping point into acute symptoms. Telehealth monitoring of biopsychosocial, as well as disease, concerns require further investigation.