Implementing evidence-based interventions to prevent readmissions in the real world
Implementing evidence-based interventions to prevent readmissions in the real world
Nate L. Ewigman 1 2
Lea Vella 2
Jessica A. Eng jessica.eng@va
0 Division of Geriatrics, University of California San Francisco , San Francisco, CA , USA
1 Department of Psychiatry, University of California San Francisco , San Francisco, CA , USA
2 San Francisco VA Healthcare System , San Francisco, CA , USA
S often cause hospital readmissions. Developing specialuboptimal transitions from the hospital to the community ized teams and standardized processes during transitions are increasingly seen as potential approaches to reducing acute care utilization and improving clinical outcomes.1 Some interventions have shown promising results in randomized controlled trials, although systematic reviews have reported mixed results.2 While the literature on implementing such interventions is nascent, there are a few principles to which successful programs adhere.
Identification based on triangulation of data. The two
common methods of identifying patients for intervention are
based either on (
) clinician judgment or (
data. These two methods often result in substantially different
cohorts. For example, administrative data usually cannot take
into consideration social support and health literacy, both of
which are key to patients successfully following discharge
instructions including medication regimens and attending
follow up appointments. While administrative data can
accurately predict acute care utilization, it cannot capture the critical,
yet elusive, concept of modifiability. Clinical providers have
knowledge of a patient’s readiness to change and insight into
their psychosocial context. They tend to identify patients for
intervention who are older and have problems with mental
health, substance use, medical decision-making, and care
coordination. However, provider-led identification tends to only
modestly predict those at risk of rehospitalization.3 Ideally,
patients are identified through use of a triangulated approach
in which clinical and administrative approaches are melded to
account for the inherent weaknesses in each method.1
Intervention intensity based on risk stratification. Once
identified, patients should be stratified by risk to allow for
right-sizing of intervention. While home visits can be an
effective tool to address medication adherence and low health
literacy issues that often play a role in poor transitions, they are
time-intensive and expensive. Telephone-based interventions
are less time-intensive and less costly but might not
completely assess and address the needs of complex patients.
Stratifying based on potential needs can guide a stepped-care,
population-based interventional approach1 that provides the
optimal intervention dose for the most benefit.
Use of evidence-based processes. There are many
evidencebased models to use or draw from although the literature is
limited about these models’ implementation outside research
trials.2, 4, 5 Thoughtful consideration of the type of outcomes
most important to the patient and healthcare system can guide
the choice of specific evidence-based interventions. Certain
healthcare systems might want to focus on particular
performance areas, such as congestive heart failure readmissions,
while others might want to broaden the focus to supporting
primary care in quality of care for older adults, and the choice
of model and specific intervention processes should be aligned
with the expected outcomes.
Adaptation of intervention to healthcare system. Intervention
feasibility will be affected by many factors. Without
adaptation, interventions usually come to settings with a
poor fit, resisted by individuals who will be affected, and
requiring an active process to engage individuals in order to
accomplish implementation.6,7 The size of a hospital’s
catchment area will determine the feasibility of home visits.
Relationship strength with referring primary care practices
will determine their interest in interacting with teams
designed to ease the transition from inpatient back to these
practices. In addition, when considering implementation of
telephone-based or home-based interventions, hospitals
should consider the current resources invested in these areas,
such as disease-specific telehealth programs and home-based
primary care, and how the new intervention will enhance
rather than duplicate current services.
Use of quadruple aim. While different stakeholders may want
to focus on particular outcomes (e.g., cost), intervention goals
should consider the Quadruple Aim: costs, patient satisfaction
and experience, quality and outcomes, and provider
burnout.8,9 While having such variety of goals makes
evaluation of outcomes difficult, in our experience, this
multi-factorial approach is both the logical and ethical
approach towards delivering effective care.
In this issue of JGIM, Hoyer et al. present a prospective
observational study that represents their health system’s
commitment to reducing readmissions at two Maryland hospitals
through risk-stratifying all patients hospitalized for a nearly
three-year period. This study signifies a major commitment to
reducing risk of readmission among all patients. We believe
this study is an example of an intervention that meets several
of the principles we have articulated.
Their selection strategy triangulates multidisciplinary
team assessment and the evidence-based Early Screen for
Discharge Planning (ESDP) score. A greater percentage of
patients with high ESDP scores were referred to the more
intensive intervention (22% of those referred to the lower
intensity Patient Access Line [PAL] intervention vs. 35%
of those referred to the higher intensity Transition Guide
[TG] intervention), suggesting that the clinical and team
assessment had an impact on risk stratification
decisionmaking. Consistent with literature showing additive
predictive value of more data points,9 it is likely that the
clinical and administrative data inform one another to
appropriately risk stratify. They then stratified patients
into low- and high-risk categories, based on the team or
ESDP assessment. This allowed for a stepped care
approach to right-size a lower and higher intensity
intervention: those considered higher need were offered a nurse
TG, and those who were low-risk were assigned the PAL
intervention, a post-discharge phone call from a trained
nurse. Additionally, the intervention design also
incorporated a population-based strategy by providing
intervention for all patients. This approach recognizes the
universal vulnerability experienced by patients being discharged
from the hospital.10 Lastly, this intervention was designed
to improve cost through reducing readmissions but also
aimed to improve the experiences and health outcomes of
patients at a vulnerable point in their lives. In our
experience, recurrent readmissions can be a source of perceived
failure and ineffectiveness, hopelessness, and burnout for
providers and staff across the healthcare system, making
this intervention important for the providers of care as
well as making it consistent with the quadruple aim.
Hoyer et al.11 showed that both the higher and lower
intensity interventions reduced readmissions. Compared to those
who received each intervention, those who did not receive
them had greater odds of being readmitted (1.83 for TG, 1.27
for PAL). These results are impressive given the general
difficulty reducing overall 30-day readmissions, rather than
focusing disease-specific cohorts. However, the results are
tempered by the methodological concerns common to many
studies looking at studying interventions in real world
circumstances without the benefit of randomization. The use of a
comparison group is preferable to not having one; however,
the choice of individuals who did not receive the intervention
(presumably due to refusal or inability to contact) as the
comparison makes it likely that intervention patients were
the Blower hanging fruit,^ i.e., those more likely to benefit,
particularly in the TG group. Another methodological concern
involves the mixture of low- and high-risk individuals in the
PAL arm. Some high-risk participants were first referred to the
TG group but declined, and were then offered the PAL
intervention. This contamination between groups makes it difficult
to describe the PAL group as a low-risk cohort but also makes
it more impressive that the PAL intervention significantly
reduced readmissions compared to those who did not receive
it. Lastly, the authors present data suggesting that those who
did not receive interventions were the Bhardest to reach^ and at
highest risk for readmission. Adjusting for sociodemographic
characteristics would have been helpful in determining the
effectiveness of the intervention.
Overall, we congratulate Hoyer et al. on their work and
institutional commitment to reducing unnecessary and costly
readmissions. This intervention demonstrates the use of
triangulated identification of complex patients who were
riskstratified to evidence-based interventions aimed at improving
the health and experience of patients. It reduced cost to the
health system and burden on providers. While there are likely
not universal interventions that can be applied regardless of
local and regional system and patient variation, Hoyer et al.
have demonstrated the significant impact of high quality
evidence-based interventions that are thoughtfully adapted to
the local context.
Compliance with Ethical Standards:
Conflict of Interest: The authors have no conflicts of interest to
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