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.

Capitated Telehealth Coaching Hospital Readmission Service in Australia: Pragmatic Controlled Evaluation.

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.

Mixed methods quality evaluation of structured interprofessional medical ward rounds.

BACKGROUND: Standardised structure and content of interdisciplinary medical ward rounds can promote patient safety and patient-centred teamwork. AIMS: To evaluate interdisciplinary ward rounds using a Structured Interdisciplinary Bedside Rounding (SIBR) intervention. METHODS: The study involved multi-method, repeat measures with a non-equivalent control group. Non-participant observations were collected at: 1 and 6 months, on an intervention ward that introduced SIBR; and a control ward that continued traditional rounds. Focus group and survey data were also collected on the intervention ward. Participants were clinicians (medicine, nursing and allied health) working in two general medicine wards at a tertiary hospital in Melbourne, Australia. RESULTS: Seventy-eight patient rounds were observed at Time 1, and 239 at Time 2. The SIBR ward rounds had all clinician disciplines represented, whereas allied health and nurses were present for less than 20% of non-SIBR rounds. Interdisciplinary participation and frequency of desired clinician behaviours increased, and variability in duration of time per patient decreased, on the SIBR rounds. Longitudinal data reflected decreased performance of some SIBR behaviours in the intervention ward, while some desired behaviours increased in the control ward. Qualitative survey and focus group data indicated many positive views, but fit with broader ward routines impacted SIBR implementation. CONCLUSIONS: The overall recommendation by staff was that SIBR should continue. The results provide insights into the adoption of SIBR behaviours and illustrated diffusion of behaviours across wards. Highlighting successes and identifying barriers can help meet challenges for ongoing improvement.

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.

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.

Computer simulation of improvements in hospital length of stay for rehabilitation patients.

OBJECTIVE: To estimate the potential improvement in acute and rehabilitation hospital length of stay for rehabilitation patients from hypothetical scenarios that address barriers to patient flow. DESIGN: Data about the duration of key processes for patients (n = 360) admitted to acute hospitals and subsequently transferred to inpatient rehabilitation in 2 wards in Melbourne, Australia were used to develop a computer simulation model. SUBJECTS: Simulated patients. METHODS: A computer model of length of stay was developed, validation checks performed and alternate care pathways simulated. RESULTS: Almost all scenarios resulted in significant changes in the length of stay compared with baseline. The effect size for the changes was typically small to medium. The duration of the rehabilitation discharge barriers showed significant changes in all hypothetical scenarios. The effect size was smaller when changes were made to a single barrier, but larger when multiple barriers were changed simultaneously. CONCLUSION: Health system modelling can provide information regarding potential improvements in length of stay from addressing barriers to patient flow affecting rehabilitation patients. This can inform reforms to models of care and assist with cost benefit analyses.

From theory to practice of electronic handover.

Clinical handover is an essential process occurring at many levels of inpatient care. Multiple studies within a hospital setting have identified that a breakdown in the handover process can lead to poor patient outcomes and serious adverse events. The use of electronic handover tools is an intervention identified to decrease errors in clinical care arising from poor handover practice. An electronic handover tool was implemented in a general medical unit in a metropolitan tertiary hospital setting. The program was written by a Medical Professional who also used the tool. The program was evaluated with a pre- and post-intervention survey within the medical, allied health and nursing staff members of the multidisciplinary teams. The use of the Electronic Handover program resulted in improved satisfaction of the handover process within the medical, nursing and allied health professions. This trial demonstrates that an electronic handover program can be successfully integrated into normal medical work practice, resulting in positive outcomes for a multidisciplinary staff team. Further work is required to determine whether patient outcomes are improved as a result.