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\begin{document}

\title{Potentially Preventable Hospitalizations: MonashWatch telehealth
self-reported journeys before and after acute admission}



\author[1]{Carmel Martin }%
\author[2]{Narelle Hinkley}%
\author[3]{Donald Campbell}%
\author[2]{Keith Stockman}%
\affil[1]{Monash Health}%
\affil[2]{Affiliation not available}%
\affil[3]{Monash University Faculty of Medicine Nursing and Health Sciences}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\selectlanguage{english}
\begin{abstract}
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 \textgreater{}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 \selectlanguage{ngerman}± 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.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\section*{}

{\label{potentially-preventable-hospitalizations-monashwatch-telehealth-self-reported-journeys-before-and-after-acute-admission}}

Introduction

Potentially preventable hospitalizations (PPH) are the subject of
considerable concern for stakeholders -- patients and families, hospital
and community services and funders. They are of particular interest when
they are 'band-aid' solutions to managing community crises in older
adults with uncertain health improvements.

Post-hospital syndrome (PHS is \emph{'a transient period of generalized
susceptibility to disease as well as an elevated risk for adverse
events, including hospital readmission and death'} \textsuperscript{1}.
There have been a range of theories -- from biomedical to
hospitalization-induced allostatic overload \textsuperscript{2.} Much
PPH effort has focused on post-hospital transitions of care. An
international literature review indicates that transitional care
interventions can successfully support older patients with complex
conditions \textsuperscript{3}. Nevertheless, an irreversible loss of
systemic resilience in the longer term and fast(er) decline follows most
hospital admissions in this category \textsuperscript{4}. The importance
of intervention before hospital admission would seem a logical approach;
however, few articles report findings on this topic. Resilience theories
provide a comprehensive conceptual framework \textsuperscript{5} with
practical application to reflect biological, psychosocial, and
environmental influences on admissions, which open up diverse
opportunities for early intervention in collective and individual
journeys \textsuperscript{6}. The Patient Journey Record (PaJR)system is
a telehealth system that addresses the risk of PPH \textsuperscript{7}.
The PaJR system was initially developed and validated with an Irish
primary care cohort.

Background

The Victorian Department Health and Human Services' (DHHS) state-based
public hospitals' database HealthLinks Chronic Care (HLCC) employs data
analytics to predict a cohort at risk of 3 repeat hospitalisations in
the subsequent 12 months \textsuperscript{8}. The HLCC algorithm
identifies an eligible cohort of patients with parameters including
acute non-surgical admissions in previous 6 months; emergency department
visits in previous 3 months; age; residence status, smoking; and chronic
conditions including gastrointestinal disorders, renal disease, asthma,
chronic obstructive pulmonary disease (COPD), rheumatoid arthritis,
diabetes, pancreatic conditions, cirrhosis/alcoholic hepatitis, with the
exclusion of conditions such as serious mental illness. The DHHS
provides a 'HLCC eligible cohort' list to hospital groups with periodic
updates.

Monash Health, the largest hospital group in Victoria, Australia,
implemented an HLCC program called MonashWatch (MW), incorporating the
PaJR telehealth system 23/12/2016 \textsuperscript{9}. The MW service
started in lower socio-economic, and an ethnically diverse area of
Melbourne proximate to Dandenong Hospital. This paper reports on an
internal formative evaluation in the first 10 months of the active group
of the pragmatic MW clinical trial.

The Service

{\label{the-service}}

The MW service monitored the participating HLCC cohort through outbound
phone calls using (PaJR) (See Figure 1). The PaJR system provides
analytics of semi-structured call data. Self-reported observations of
daily health and living data are processed to generate alerts to assist
in proactively managing HLCC patients. Alerts (total alerts and red
alerts) are indicators of stressors, resilience, and health perceptions
with more alerts per call, indicating vulnerability to worse health and
or hospital admission \textsuperscript{10}.

\begin{itemize}
\tightlist
\item
  \emph{Total alerts} reflect generic issues including perceptions of
  self-rated health (SRH), illness and coping, and concerns about health
  care, medications, social and environmental issues.
\item
  \emph{Red alerts} are those that require prompt clinical assessment
  including typical symptoms that are likely to lead to hospitalisation
  including chest pain, severe pain of any nature, breathlessness, fever
  and infections, falls, crises in mental health crisis and or recent
  attendance at the ED.
\end{itemize}

MW Clinician assessments and proactive interventions, triggered by
alerts, aim to address root causes of worsening health and potential
readmissions \textsuperscript{11}.

Aims

This paper aims to describe patterns of total alerts (self-reported
biopsychosocial concerns) and red alerts (disease symptoms of concern)
and self-rated health patient trajectories 10 days before and after
admission in the intervention cohort of MonashWatch.

Methods

A descriptive time series analysis aims to identify significant shifts
(potential tipping points) in patient journeys derived from phone call
records from PaJR database and admission data from the Victorian
Admitted Episode Data/Victorian Emergency Minimum Dataset.

Descriptive homogeneity tests on a time series aimed to determine if a
series is homogeneous over time, or if there is a time at which a change
occurs (potential tipping point). For all tests, XLSTAT provides
p-values using Monte Carlo resampling. Pettitt's test was selected as a
descriptive tool for detecting changes and suitable for all continuous
distributions. All tests are two-sided, and alpha was set at
0.05\textsuperscript{12}.

Monash Health Research Ethics Committee (HREC) provided ethics approval
for the MW pilot service and its internal evaluation by the MonashWatch
team.

Findings

The study characterized 103 patients' emergency non-surgical admissions
with 768 calls before and after an acute non-surgical admission in the
23/12/16 - 11/10/17 period in the context of their total 22,715 calls in
that period with an average of 5.52 calls per participant per month. The
age range was 65 years with a mean 71 \selectlanguage{ngerman}± 15.4 and median 74 years. Gender
distribution was male 59\% female 61\%. Each individual admission time
series had a median of 7.63 total calls before and after admission.
Calls were distributed evenly in relation to admission day = 0 (See
Figure 2). Calls were not intentionally made during admissions, although
a few did occur.

Admissions demonstrated a wide range of length of stay (LOS) from
\textgreater{}1 to 37.3 bed days with median 4.1 and mean 5.8 ± 5.8
days. Admission rankings of the top 6 conditions were in descending
order (with complexity): chest pain (minor); COPD (minor); chest pain
(major); abdominal pain and mesenteric adenitis (minor); other digestive
system disorders (major); and respiratory infections and inflammations
(major). See Figure 3 for most common DRG admission code for the group.

Alerts in the 10 days before and after an acute admission were compared
to general rates of alerts for these patients in the same 23/12/16 -
11/10/17 period. Total alerts 10 days before and after an admission were
3.1 per call vs 1.6 average total alert rates. Red alerts in the
admission ± 10 days period were 0.8 per call vs 0.6 average. Chi-square
test indicated a significant difference in both red alerts and total
alerts in general versus 10 days before and after an admission. alpha =
0.05.

The time series of alerts and other measures within the 10 days before
and after an acute admission were analysed using Pettitt's test of
homogeneity. This demonstrated that there was a significant shift
towards higher levels of total and red alerts before admission. See
Figure 4

Figure 4 Tipping Points in alerts identified by homogeneity metrics in
103 admission trajectories.

Features of alerts

Total alerts time series demonstrated a statistically significant shift
before the day of admission, day -3. Red alerts time series demonstrated
a statistically significant shift 1 day before admission (day -1) based
on 768 calls and 103 admissions. Self-rated health today (SRH) was
reported fair to good on average from day -10 before admission and
demonstrated a statistically significant improvement after discharge on
day +4. Pain was reported throughout all the 20 days at a rate of 20\%
of calls. Feeling depressed some or most of the time was reported in
20\% of calls with improvement on day +5. Medication change and more GP
visits were reported on average in 75\% of calls in the 10 days before
and after the admission, with a peak or tip of 82\% calls at 2 days
before an admission using Pettitt's measure of homogeneity.

Total alerts patterns were associated with increased sleep disturbances,
not eating and drinking or going to the toilet as usual and not going
out as usual; and environmental concerns related to housing, transport,
weather and finances. Calls reported that participants had more concerns
about people who cared for them or were close to them in the 10 days
before admission. Rating 'anticipated health over the next few days,'
100 of the 736 (14\%) calls indicated possible poorer future health,
which was significantly higher than at other times.

Most frequent symptom complexes reported in the red alerts before and
after admission were: fever shivering or infection symptoms related to
bladder, bowels, skin, etc. in 73 calls; significant pain 40 calls;
'weakness/unsteadiness/falls/collapse' 31 calls; Breathlessness 26
calls; significant 'coughing/wheezing/increase coloured phlegm' 17
calls; depression, mental or behavioural problems 14; 'swelling of
ankles/legs; weight gain 1-2 kg in 1-2 days' 3 calls; and 'chest pain,
sweating heart, angina type pain' in 2 calls.

Discussion

The high levels of alerts post-discharge support the counterintuitive
notion of a post-hospital syndrome that started before admission and may
persist to day 10 at least. This finding is in accord with the
post-hospital syndrome and PPH literature.

A prodromal phase of an acute non-surgical admission -- 'pre-hospital
syndrome' is self-reported in this study with more alerts, health
disturbances and medication changes before an admission compared to an
average level over several months. The pre-hospital syndrome was
associated with a general pattern that had more total alerts and red
alerts than average for the cohort with these patterns persisting after
discharge. Tipping points were identified in general alerts 3 days and
red alerts 1 day before admission. However, while these tipping points
were statistically significant, some individuals had no alerts and
others had different variations before admission.

Poorer health, feeling depressed, worse experiences of daily living
functions, increased GP visits and medication changes, all appeared to
increase before the emergence of disease or condition symptoms. Also
increased concerns about caregivers, and concerns about issues in the
physical environment reportedly increased before admission. These
patterns persisted in the 10 days post-discharge.

Do these findings represent disturbances in individuals across
biological, health care, psychosocial and environmental domains; with an
emergent instability potentially tipping to 'clinical disease'? It is
difficult to disentangle the impact of the internal systemic stress
triggered by an emerging acute illness from external stressors related
to medical treatment, health system, psychosocial and environmental
issues. In a vulnerable group, hospital care may not balance out these
systemic and external stresses \textsuperscript{2} and provide a band
aid to complex individual and community-based dynamics. Moreover, such
patients may be even more susceptible to adverse outcomes related to the
stress of hospitalization. If this is a common pattern, addressing PHS
would require reduction of modifiable stressors encountered by patients
before hospitalisation.

There is a need to identify mechanisms to promote physical and
psychosocial environmental resilience in order to address interconnected
network processes \emph{within an individual in their internal and
external milieu} \textsuperscript{13}.

Recognising PPH as a \emph{complex system and an adaptive systems
response} to dynamic networks is called for. MonashWatch Integrative
care delivery approaches such as broad-based monitoring of self-reported
health and coaching, aim to more adequately address individual dynamics
as well as disease management. Identifying the manifestation of an
emerging deterioration with potential tipping point(s) is key to
offering anticipatory and reactive care.

Tipping points -- or the prediction of potential tipping points in
resilience in different domains form is an important component of
complex adaptive care \textsuperscript{14}. However, the prediction is a
very short-term phenomenon in such interconnected non-linear
systems\textsuperscript{15}. Ongoing wide-ranging self-reported
narratives can provide opportunities for addressing PPH, particularly
before an acute admission. Research indicates that each admission in
these older complex patients is likely to result in less resilience, the
ability to bounce back to a pre-prodromal state and ongoing
decline\textsuperscript{5}. On the other hand, many such
hospitalizations may signify an unavoidable decline that hospital
admission cannot avert, and post-hospital transitional care has a
significant role in ameliorating the decline.

Telehealth approaches with a high-risk cohort can utilise a broad-based
biopsychosocial approach to address the prehospital phase and
post-admission phase of PPH. Whether biometric monitoring is needed for
improved clinical care is an unanswered question. However, reported
concerns and a tipping point in wide-ranging health, psychosocial and
environmental areas can track interconnected multifaceted individual
journeys. Research into potential tipping points and phases for
increased intervention and support is needed.

Conclusion

This study of self-reported pre - post acute hospital admission
trajectories in a cohort of high-risk individuals describes a
pre-hospital phase (with intense medication/drug/alcohol changes) and
high levels of poor health, symptoms and pain around acute admissions.

This prodromal phase pre-empted admissions and persisted on discharge
with only some measures - self-rated health and feeling depressed
improving 4 - 5 days after discharge.

Self-reported concerns and a tipping point in wide ranging health,
psychosocial and environmental issues preceded a tipping point in acute
disease symptoms signifying an interconnected multifaceted prodrome and
possible phase for intervention and support. It would seem that
telehealth approaches interconnected with GP and social care involvement
would might be a way forward and entails further investigation and
research into trajectories before as well as after hospitalisation.

\subsection*{References}

{\label{references}}

1. Krumholz, H.M., \emph{Post-hospital syndrome--an acquired, transient
condition of generalized risk.} N Engl J Med, 2013.\textbf{368} (2): p.
100-2.

2. Goldwater, D., et al., \emph{Is Posthospital Syndrome a Result of
Hospitalization-Induced Allostatic Overload?.} J Hosp Med,
2018.\textbf{May 30, 2018.} (Online Only.).

3. Le Berre, M., et al., \emph{Impact of Transitional Care Services for
Chronically Ill Older Patients: A Systematic Evidence Review.} J Am
Geriatr Soc, 2017. \textbf{65} (7): p. 1597-1608.

4. Ferrucci L, et al., \emph{Frailty as a Nexus Between the Biology of
Aging, Environmental Conditions and Clinical Geriatrics.} Public Health
Reviews, 2010(32): p. 475-88.

5. Ferrucci, L., F. Giallauria, and D. Schlessinger, \emph{Mapping the
road to resilience: novel math for the study of frailty.} Mechanisms of
ageing and development, 2008. \textbf{129} (11): p. 677-679.

6. Martin, C.M., \emph{What matters in ''multimorbidity''? Arguably
resilience and personal health experience are central to quality of life
and optimizing survival.} J Eval Clin Pract, 2016.

7. Martin, C.M., et al., \emph{Implementation of complex adaptive
chronic care: the Patient Journey Record system (PaJR).} Journal of
Evaluation in Clinical Practice, 2012. \textbf{18} (6): p. 1226-1234.

8. Ferrier, D., et al. \emph{HealthLinks: Incentivising better value
chronic care in Victoria.} International Journal of Integrated Care.,
2017. \textbf{12} , A129 DOI:\emph{http://doi.org/10.5334/ijic.3241}.

9. \emph{MonashWatch: Keeping People Healthy At Home.
http://monashhealth.org/page/monashwatch} . 2016 {[}cited 2018 19th
August{]}; Available
from:\emph{http://monashhealth.org/page/monashwatch}.

10. Martin, C. Hinkley., N. Stockman, K. Campbell, D., \emph{Resilience,
Health Perceptions, Stressors and Hospital Admissions -- observations
from the real world of clinical care of unstable health journeys in
Monash Watch (MW), Victoria, Australia.} Journal of Evaluation in
Clinical Practice, 2018. \textbf{in press} (Resilience Special Edition).

11. Martin, C.M., et al., \emph{Supporting Complex Dynamic Health
Journeys Using Conversation to Avert Hospital Readmissions from the
Community: An Ecological Perspective Incorporating Interoception} ,
in\emph{Putting Systems and Complexity Science into Practice} . 2018,
Springer: Champ.

12. \emph{XLSTAT: Homogeneity tests for time series} . {[}cited 2018
19th August{]}; Available
from:\emph{https://www.xlstat.com/en/solutions/features/homogeneity-tests-for-time-series}.

13. Sturmberg, J.P., et al., \emph{'Multimorbidity' as the manifestation
of network disturbances.} J Eval Clin Pract, 2017. \textbf{23} (1): p.
199-208.

14. Martin, C.M., \emph{Resilience and health (care): A dynamic adaptive
perspective.} J Eval Clin Pract, 2018. \textbf{24} (6): p. 1319-1322.

15. Martin, C.M., \emph{Self-rated health: patterns in the journeys of
patients with multi-morbidity and frailty.} J Eval Clin Pract,
2014.\textbf{20} (6): p. 1010-6.

Acknowledgement

This paper acknowledges the innovative funding model (HLCC) developed by
the Victorian Department of Health and Human Services, Victoria,
Australia. It acknowledges the stellar work of the MonashWatch clinical
team -- the Telecare Guides and the Health Coaches who have made the
model work to date. It also acknowledges the work of Kevin Smith and
John-Paul Smith of PHC Research Pty Ltd who implemented and supported
the PaJR Application.

Conflict of Interest

Carmel Martin is a co-developer of the PaJR software and health services
research adviser to PHC Research Pty Ltd which owns the PaJR software.

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