Assistance

Griffiths et al. BMC Fam Pract (2021) 22:176
https://doi.org/10.1186/s12875-021-01524-7

RESEARCH

“She knows me best”: a qualitative study
of patient and caregiver views on the role
of the primary care physician follow-up
post-hospital discharge in individuals admitted
with chronic obstructive pulmonary disease
or congestive heart failure
Sarah Griffiths1, Gaibrie Stephen1, Tara Kiran1,2,3,4 and Karen Okrainec4,5,6,7*

Abstract

Background: Patients with chronic obstructive pulmonary disease (COPD) and congestive heart failure (CHF) are at
high-risk of readmission after hospital discharge. There is conflicting evidence however on whether timely follow-up
with a primary care provider reduces that risk. The objective of this study is to understand the perspectives of patients
with COPD and CHF, and their caregivers, on the role of primary care provider follow-up after hospital discharge.

Methods: A qualitative study design with semi-structured interviews was conducted among patients or their fam-
ily caregivers admitted with COPD or CHF who were enrolled in a randomized controlled study at three acute care
hospitals in Ontario, Canada. Participants were interviewed between December 2017 to January 2019, the majority
discharged from hospital at least 30 days prior to their interview. Interviews were analyzed independently by three
authors using a deductive directed content analysis, with the fourth author cross-comparing themes.

Results: Interviews with 16 participants (eight patients and eight caregivers) revealed four main themes. First,
participants valued visiting their primary care provider after discharge to build upon their longitudinal relationship.
Second, primary care providers played a key role in coordinating care. Third, there were mixed views on the ideal time
for follow-up, with many participants expressing a desire to delay follow-up to stabilize following their acute hospitali-
zation. Fourth, the link between the post-discharge visit and preventing hospital readmissions was unclear to partici-
pants, who often self-triaged based on their symptoms when deciding on the need for emergency care.

Conclusions: Patients and caregivers valued in-person follow-up with their primary care provider following dis-
charge from hospital because of the trust established through pre-existing longitudinal relationships. Our results
suggest policy makers should focus on improving rates of primary care provider attachment and systems supporting
informational continuity.

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Open Access

*Correspondence: [email protected]
7 Toronto Western Hospital, 399 Bathurst Street, 8EW-408, Toronto, ON
M5T 2S8, Canada
Full list of author information is available at the end of the article

Page 2 of 9Griffiths et al. BMC Fam Pract (2021) 22:176

Background
The transition to home after a hospital admission rep-
resents a vulnerable period for patients [1]. High quality
transitional care is designed to support patients and their
caregivers grappling with changes to medical therapies
and functional status. Suboptimal transitions may lead
to clinical deterioration and greater health care utiliza-
tion [2]. In 2018, approximately 1 in 11 patients were
readmitted to hospital within a month of their discharge,
representing an estimated $1.8 billion in costs [3]. Esti-
mates regarding which readmissions may be avoidable
varies widely from 5–79% [4]. Interventions to reduce the
rate of readmissions however have yielded mixed results
[5–8].

Patients with congestive heart failure (CHF) have the
highest 30-day readmission rates in Canada, and patients
with chronic obstructive pulmonary disease (COPD)
have the highest volume of readmissions [9]. Accord-
ingly, patients with CHF and COPD are often the focus of
interventions in the post-discharge period [8]. Although
there is some evidence to suggest early physician follow-
up may reduce the risk of 30-day readmission for those
with CHF [10–12], a systematic review by Health Qual-
ity Ontario in 2017 found no difference in readmission
rates with either a seven day or 30-day follow-up post-
discharge for patients with CHF or COPD [13].

Despite the mixed evidence, quality improvement plans
in Canada [14, 15] and financial incentives in the United
States [16] have encouraged organizations to measure
and improve the timeliness of primary care follow-up
after hospital discharge. Timely follow-up theoretically
enables close follow-up to support patient recovery at
home, for example, through adjustment of medications
based on symptoms and/or coordination of home, com-
munity, and specialist services to meet patient needs. Pri-
mary care physicians provide care over a lifetime taking a
whole person approach and thereby help support patients
with multimorbidity balancing competing medical and
non-medical priorities. Studies suggest specific benefits
of continuity with a familiar provider including lowered
risk of death and readmission in the six-month period
post-hospital discharge, independent of the timing of the
initial visit [17]. One factor potentially associated with
attendance at a post-discharge primary care visit is hav-
ing a provider already known to the patient [18].

Although there is significant focus on improving the
timeliness of primary care follow-up after discharge,
there is little known about how patients and caregivers

view the importance and role of the follow-up visit.
Moreover, recent studies show that only a third of
patients are seeing their primary care provider following
discharge [19], and some research suggests that health-
care providers may have different views of the impor-
tance of the follow-up visit from patients [20–22]. To
address this gap, this qualitative study was undertaken
to examine the perceived importance and role of follow-
up with the primary care provider post-discharge for
patients admitted with COPD or CHF.

Methods
This article was written in accordance with the standards
for reporting qualitative research (SRQR) [23].

Context and setting
This study was conducted in Ontario, Canada. Perma-
nent residents of Ontario have access to fully insured
physician and hospital services under provincial health
insurance. Gaps in coverage for some could include
medications, and limited public funding for home and
community care. In 2017, 89.8% of Ontario residents had
a primary care provider [24]. All the primary care pro-
viders of the study participants are family physicians,
although other professionals, such as nurse practitioners,
pharmacists, or physician specialists may see patient’s in
their follow-up visit post-discharge.

Study design
We conducted a qualitative study using semi-structured
interviews to guide discussion.

Participants
Participants were recruited from a randomized con-
trolled trial (RCT) study population (not yet published).
This study randomized individuals to a modified written
discharge instruction tool compared to usual discharge
instructions alone [25]. Individuals who received the
instruction tool received a template written in non-med-
ical language which outlined medication changes, follow-
up plans, potential symptoms to watch out for, along
with individualized plans should they occur. Study par-
ticipants were patients ≥ 18  years or over, or their fam-
ily caregivers, admitted with select diagnoses who were
discharged from one of three acute care or rehabilitation
hospitals in two cities in Ontario (Toronto and Thun-
der Bay). Caregivers’ perspectives were also included to
acknowledge the large role caregivers often undertook in

Keywords: Chronic obstructive pulmonary disease, Congestive heart failure, Post-discharge, Follow-up, Primary care
physician

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patient’s care at home in the post-discharge period, espe-
cially considering that many patients were elderly, many
did not speak English as a first language, and their car-
egivers were often arranging and co-attending medical
appointments.

A separate research team first approached all par-
ticipants in the RCT about a follow-up interview. Our
unpublished study protocol included different qualita-
tive arms which sought to explore factors that influence
outcomes measured in our RCT, such as follow-up with
the primary care provider. Eligible participants for this
qualitative study were comprised of those with either
CHF or COPD, or their caregivers, who had not partici-
pated in other qualitative arms. Thirty-one participants
were subsequently identified as eligible for this study;
sixteen agreed to participate, two declined, and thirteen
could not be reached for interview. A duration of at least
one-week post-discharge was specified to allow time for
participants to see their primary care provider and reflect
on their experience. All participants had a primary care
provider, although this was not a specified inclusion cri-
terion. Participants who spoke a language other than
English or French or with cognitive impairment were eli-
gible if a family member also consented. All consecutive
participants enrolled in the RCT who were interested in
the qualitative interview were contacted for consent for
this study. Enrolment stopped once thematic saturation
had been met.

Data collection
A semi-structured open-ended interview guide was
developed by the authors as informed by previous studies
[18] and gaps in the literature without participant input
(Additional file  1): 1) perceived importance of primary
care provider and other health care provider follow-up
and timeliness/access; 2) role of the primary care pro-
vider in preventing readmission; 3) content explored
during the visit with their primary care provider. Open-
ended questions regarding each of these general domains
as well as questions regarding the general transition to
home after discharge and participant priorities regard-
ing their follow-up were included. One-on-one telephone
interviews were conducted by two research team mem-
bers (SG, GS) from December 2017 to January 2019
and were audiotaped and transcribed for analysis. The
research team members consisted of two residents in
family medicine (SG, GS), a primary care physician and
scientist with expertise in primary care reform on qual-
ity of care (TG) and an internal medicine physician and
scientist with expertise in care transitions (KO). Research
team members (SG, GS, TK, KO) had no role in partici-
pants’ clinical care. Demographic questionnaire data was
collected by a separate research team led by the RCT

principal investigator (KO) and was included to further
understand the interview respondents’ demographics.
Self-reported limited health literacy was assessed for
example based on a response of “somewhat confident”, “a
little confident” and “not at all confident” to the question
“How confident are you in filling out medical forms by
yourself?” [26].

Data analysis
Baseline and discharge characteristics between individu-
als who were consented for the interview and those that
did not were compared using chi-square analyses. Quali-
tative data was analysed using directed content analysis
[27]. The transcripts were analyzed independently by
three authors (SG, GS, KO), who reviewed transcripts
line by line, manually, and coded them utilizing both a
deductive and inductive approach, with a final author
(TK) cross-comparing emergent themes. Similar initial
codes were then grouped together to identify themes.
At each step, disagreements were discussed during cod-
ing meetings until consensus was reached. The aim of
the study being quite narrow, the study team identified
saturation of themes early at 13 interviews. At this stage,
the interview was modified with the input of all authors
to remove or modify questions that had repeatedly pro-
duced close-ended answers. To ensure that saturation
was achieved, three additional interviews were con-
ducted, which did not contribute new findings. At this
point all authors unanimously felt the main themes were
saturated.

Results
A total of 16 interviews (eight patients, eight caregiv-
ers) were conducted a mean of 57 days (SD 65 days) after
discharge. Most participants were female (63%), aged
65 years or older (88%) and had an admission diagnosis
of CHF (75%). In addition, 50% reported limited health
literacy. Almost all participants (15/16) reported having
had a follow-up with their primary care provider fol-
lowing discharge. Those who consented to an interview
were more likely to have a physical disability (75% versus
27%, P = 0.01), lower level of education (44% versus 7%,
P = 0.04) and high level of caregiver involvement (89%
versus 38%, P = 0.05).

Our qualitative analysis identified four main themes: 1)
the importance of a continuous longitudinal relationship
with their primary care provider; 2) the role of the pri-
mary care provider in coordination of care; 3) preference
for individualized timeline for follow-up; 4) participants
self-triaging of symptoms when deciding when to seek
emergency care (Table 1).

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Continuous longitudinal relationship
Participants strongly voiced the value their primary care
provider provided in their care, often in the context of
having a long-standing relationship with that provider.
The post-discharge visit was often discussed as a way to
re-connect and share the participants’ narrative from
their hospital experience. The desire to share their story
persisted even if participants were aware of hospital
records being transferred to their primary care provider,
with one participant remarking that they felt the dis-
charge summary did not fully explain their experience.
Others often framed the importance of sharing their hos-
pitalization experience in the context of the primary care

provider being the provider who knew them best; when
asked about the importance of their follow-up, one par-
ticipant (1118, male CHF patient aged 74 years) stated:

“Just recognize that I have something like a 25-year
history with him.”

A caregiver (5062, son of female CHF patient aged
86  years) highlighted the importance of checking with
the primary care provider to ensure changes in hospital
made sense for the patient:

“The family doctor knows more of her history and
her medications, he can look if there has been any
changes and he can look overall to see how that may

Table 1 Selected exemplar quotes for themes and subthemes

Themes and subthemes Participant quotes

Theme 1: The importance of a continuous longitudinal relationship with their primary care provider

Sharing of experience [Asked why they believed it was important to follow-up with their primary care
provider] “Well I wanted to let him know about the procedure and what hap-
pened.” (1146, female CHF patient aged 73 years)

Personalized approach and relationship “I think that the family doctor knows the patient better [than the specialist]
because they’ve followed them for so long and they know all of their history
and they know more about it.” (5064, daughter of female COPD patient aged
79 years)

Caring and reassurance “She’s cheerful and gives me hope. She’s also pretty concerned also.” (2114, male
CHF patient aged 58 years)

Reinforcement and education [Describing the follow-up visit with the primary care provider] “He saw the
report and everything because he gets a report every time she goes in there,
so he knew about the different medications that she was on and what tests
were done while she was in hospital, he had results of all of that, so, we kind of
reviewed all that, what was good and what wasn’t and, uh, you know, who was
coming in since she’s been home, we went over all of that so it was good and
informative.” (5064, daughter of female COPD patient aged 79 years)

Theme 2: The role of the primary care provider in coordination of care

Information transfer “All of these specialists will contact with the family doc and that’s what I like
about it. They’re all on the same page.” (2120, daughter of female CHF patient
aged 80 years)

Siloed care “Your GP is handicapped because he doesn’t have access to your system. I
thought we were trying to consolidate and harmonize these information
systems. It would help the GP do a much more effective job than depend on
the patient to bring in a write up like “oops, I forgot my requisition.” The reliance
on the patient and the family doctor is over the top and it doesn’t make sense.”
(1124, female CHF patient aged 60 years)

Theme 3: Desire for individualized timeline for follow-up “I don’t see why it needs to be sooner than that [two weeks] unless there’s a
problem.” (1138, female CHF patient aged 66 years)

“Yeah, they said a week or two and I thought within a week the doctor might
not see anything different because it’s just a week and so I thought we could do
two weeks. I liked that, it’s nice the way they did that.” (2120, daughter of female
CHF patient aged 80 years)

Theme 4: Participants self-triaging of symptoms when deciding when
to seek emergency care

“Well, I see how he is. I get that feeling that he needs to go to the hospital. I get
a feeling of it, how he feels or how he looks. And usually, he doesn’t like going to
the hospital, I have to force him.” (2049, son of male COPD patient aged 87 years)

[Asked if the primary care provider could have prevented hospital admission]
“No, nothing he could’ve done on God’s green earth.” (1146, female CHF patient
aged 73 years)

“Whatever they’re doing my mom’s doing well to the standards of her heart,
diabetes and this and that. They’re doing really good and she hasn’t been back
[to hospital].” (2120, daughter of female CHF patient aged 80 years)

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affect her in the long term versus the short term.”

One participant declined a follow-up with a covering
primary care provider stating:

“Yeah the secretary tried to arrange me to see
another doctor within the clinic but I declined
because I was concerned about my history and eve-
rything. I was more prone to seeing her [their pri-
mary care provider]” (2114, male CHF patient aged
58 years).

Participants also reported a desire to be assessed by a
practitioner who was able to provide a sense of caring
and reassurance. Many participants reported a positive
relationship with their primary care provider and high-
lighted the longstanding relationship as a unique role
their primary care provider had in their care. In regards
to their primary care provider and their role in the post-
discharge period, one participant (1146, female CHF
patient aged 73 years) highlighted:

“He is a wonderful, wonderful person. Kind, caring,
goes out of his way, I’ve been with him for many, a
long time.”

Due to their primary care provider’s awareness of their
individual health literacy, one participant remarked on
the ability of their primary care provider to:

“Take the diagnosis and bring it down to my level”
(1118, male CHF patient aged 74 years).

Other participants remarked on the ability of the pri-
mary care provider to re-iterate important pieces of
information from the admission that they knew would
have relevance to the individual.

Care coordination
In a system of increasing complexity, participants often
raised the importance of medical information transfer
between multiple providers. Participants valued review-
ing the discharge summary with their primary care pro-
vider, and often remarked that this was a key feature in
their visit. When this information transfer between hos-
pital and primary care provider was not occurring, par-
ticipants would often take it upon themselves to be the
conduit; bringing in their own records and sharing them
with the primary care provider. Participant 1146 (female
CHF patient aged 73 years) illustrates this point:

“So, I have a rheumatologist, and a respirologist and
these two work together with the cardiologist, hand
in hand, to figure out how they’re going to help me.
And my family doctor follows up and makes sure
everybody gets the report of everything. I carry them
with me, you know, one report the (hospital) gave me

so everybody is on the same page hopefully.”

The transfer of information between various providers
was also discussed as an important feature of the post-
discharge visit, with the primary care provider often
acting as the main coordinator. Many participants had
specialist and other health care providers such as rapid
response nurses, nurse practitioners, telehealth services,
and physiotherapists involved in their care. When com-
munication between these providers was well established,
participants felt satisfied with their interdisciplinary care.
In participants who reported a negative experience with
their primary care provider, a key feature was often a lack
of communication between various providers. This could
be in the context of a perceived missed diagnosis due to
lack of collaboration between providers, or the perceived
reliance on the patient or their family caregiver to coor-
dinate their own information transfer. One participant
(2113, daughter of male CHF patient aged 85  years)
expressed their concern that communication between
the various care providers wasn’t occurring:

“Um, so if he could have his family doctor able to
connect with each other, which, I don’t think that’s
my dad’s case. You know, if they could all connect
together, the heart the kidney specialist, they could
always be in one place so if anything happens, they
could, you know, maybe talk or discuss a medication
or something.”

Many participants spoke of the pressure they believed
their primary care provider faced to remain up to date
on their care while grappling with a heavy patient load,
paperwork, and short visits.

Self‑tailored timeline for follow‑up
Participants were asked their thoughts on the appropri-
ate timeline for follow-up for themselves post-discharge,
with many expressing appointments were chosen based
on convenience or a desire to trial an adjustment period,
rather than a fixed timeline. Participants would often
speak of this trial as a way to gather information for their
primary care provider about their condition, whether this
be blood pressure values, or a sense of how their recovery
will progress at home. One participant (1118 male CHF
patient aged 74 years) describes their rationale for wait-
ing over a week before making their appointment:

“The reason there was a delay was in part to, uh,
take some blood pressure readings and find out what
the reaction was to the reduced dose of blood pres-
sure and the increase in the furosemide. In other
words, I wanted to be able to go to him with a pic-
ture of, just, how I was reacting so he had something
else to put his hands around.”

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Participants also felt that the appropriate time for fol-
low-up was dependent on the particular patient context,
with no one size fits all approach. One participant stated:

“Well, people are in the hospital for various reasons
so, I guess, whether it’s seven days or three days or
ten days depends really on the circumstances. I was
comfortable with the ten-day period” (1118, male
CHF patient aged 74 years).

Participants were asked if they would prefer a phone
call from their primary care provider over an in-person
visit, with the majority feeling that it was important to
see them in person. While for many this was a function
of needing to do physical exam maneuvers such as blood
pressure and weight checks, one caregiver remarked he
worried his mother would minimize symptoms over the
phone, a sentiment that was echoed by other participants.

Role of self‑triaging symptoms
Many participants felt they were comfortable gauging the
severity of their symptoms and had a good understand-
ing of what required emergent care versus ambulatory
care with their primary care provider. A caregiver (2120,
daughter of female CHF patient aged 80 years) describes
the process they undergo to determine if they need to
bring their loved one to the emergency department:

“I would hear her breathing and say ‘ok how her
breathing, is it bad enough to go to the hospital?’ I
would listen to the way she talks. Does she stutter a
bit? I would know the difference just from that and
see the difference between this is a doctor thing or an
emergency thing by looking at her and talking to her,
seeing her movement.”

Some did feel a visit with the primary care provider
could be helpful in the context of triaging and described
their visit as a:

“First pass to ensure this wasn’t something I was
making up or that this was in my head, that it was
serious” (1118, male CHF patient aged 74 years).

A few participants also mentioned that in  situations
where their primary care provider is accessible, they do
reach out to them initially as they have had experiences
of having their primary care provider be able to stream-
line them into services. Participants echoed the general
sentiment that prior training they had received on ‘rea-
sons to go to the emergency department’ from healthcare
providers either in hospital or with the primary care pro-
vider helped in this process of self-triaging and deciding
when to go to an emergency department. Participants
did not see the post-discharge visit as an independent

factor in preventing readmissions. Many believed hos-
pital admission was inevitable based on the severity of
their health conditions, while others cited their overall
care from their primary care provider as preventing them
from requiring admission.

Discussion
Our study provides the novel perspectives and experi-
ences of patients and caregivers on the role of a follow-up
visit with their primary care provider after an admission
for CHF or COPD. Our findings suggest that the post-dis-
charge visit with the primary care provider is important
to the majority of participants with the fundamentally
important feature being continuity and coordination
of care. While many participants agreed that reviewing
medications, vitals, and setting up further investigations
were necessary components of their visit, the majority
spoke of re-establishing their relationship with their pri-
mary care provider as the primary motivator for booking
a follow-up. Participants strongly favoured in-person fol-
low-up but there was no consensus on the optimal timing
for follow-up.

Our findings are in keeping with the literature describ-
ing patients’ desire to have access to a known, trusted
primary care provider [28]. Indeed, a continuous longi-
tudinal relationship with a single primary care provider
may lead to increased patient satisfaction, lower rates of
preventable hospitalization [29–33] and higher rates of
guideline-based care [34]. Many of our participants had
home visiting non-physician health professionals, yet
still had a desire to reconnect with their primary care
provider for reassurance and ongoing care coordination.
Future research should seek to understand how the avail-
ably of other health professionals changes the perceived
necessity of a timely primary care provider follow-up
[35]. Several participants in our study spoke of the role
their physician plays in educating them of their new con-
dition or reviewing their new medications, two activi-
ties have been found to improve medication adherence
in patients who report a good relationship with their
primary care provider [36]. As well, participants unani-
mously preferred an in-person visit with their primary
care provider over a phone call, a finding with important
implications given the growing use of virtual care during
COVID-19.

Many participants believed that good primary care
could prevent hospital admission however many also
accepted that admissions were likely inevitable due to
the severity of their health conditions. Healthcare pro-
viders have been found to be more likely to link prevent-
able readmissions with difficulty obtaining follow-up care
in the community, while premature discharge as well as
problems related to housing and social supports are more

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commonly cited by patients and their caregivers [20].
Our participants did not see a clear link between their
post-discharge follow-up specifically and re-admission
risk, viewing this appointment as only one aspect of their
overall continuous care. Participants’ ability to self-triage
symptoms in deciding when to return to hospital may be
related to the reported satisfaction and understanding
with discharge instructions provided by their primary
care provider regarding symptoms to watch for.

While many quality improvement initiatives are
focused on improving rates of follow-up within a spe-
cific timeline of 7–14 days of discharge [37], participants
in our study expressed a desire to have the timing of the
visit customized to their needs and to allow an adjust-
ment period at home. Many specifically chose to follow-
up more than a week following discharge. Krumholz [37]
describes a “post-hospital syndrome” of decreased psy-
chological reserve and impaired physiology during this
critical period which may explain, in part, why rates of
follow-up within seven days of hospital discharge have
been reported to be as low as 32% in Ontario [36] and
50% in the United States [38]. Challenges with timely
access due to appointment availability, while a contribu-
tor to low rates of timely follow-up in other studies, was
not one which was represented in our study. Further,
participants in our study nearly unanimously attended
and valued their appointment which limits the generaliz-
ability of our findings to populations without a primary
care provider, those who report more limited access, and
those who declined a post-discharge follow-up.

This study has some inherent limitations. The gener-
alizability of our findings may be limited by our partici-
pant sample, who were discharged from tertiary care,
often academic centers, and who all had primary care
providers. Individuals in the study may have had a higher
degree of caregiver involvement than many Ontarians
being discharged from hospital and our small sample size
does not allow us to make a distinction between themes
identified by patients versus their caregivers. A more in-
depth analysis such as phenomenology may have identi-
fied additional and more nuanced themes. In Ontario,
COPD and CHF are both quality-based diagnoses linked
to government funding, with post-discharge pathways
and additional resources such as telehealth and rapid
response nursing. As well, our participants were selected
as part of an ongoing RCT where some of them may have
received a written discharge instruction tool, the poten-
tial impact of this tool still being unknown [24]. The role
these interventions may have played in the post-discharge
transition is unclear. The post-discharge landscape would
presumably be much different in smaller, less resourced
settings. All of the study authors are clinicians who work
as either hospitalists or primary care providers. While

these represent different roles, the investment they share
in improving the post-discharge process, either through
improving pre-discharge communication, or in their role
as primary care provider for those recently discharged
may have influenced the conclusions drawn.

The implications for patient-centered policy from this
study are many. In contrast to Ontario’s provincial qual-
ity metrics, the timeliness of the primary care provider
follow-up was not a concern for our participants. Instead,
our participants spoke for the need of integrated health
systems to aid their primary care provider in their role
as care coordinator. Most often, they spoke of the value
of continuity with their primary care provider, highlight-
ing the need for ongoing effort in supporting patient’s
access to providers for whom they can have a continuous
relationship with. These relationships must begin during
periods of relative wellness to build the knowledge and
trust that participants valued in times of stress.

Conclusion
Our study provides new and valuable insight into the
patient and caregiver perspective on the post-discharge
visit with their primary care provider for those living
with, or caring for those with COPD or CHF. Our find-
ings highlight the importance of the post-discharge visit
with the primary care provider to maintain continuity
of care and coordinate care between various providers.
Patients and caregivers preferred a customized approach
to timeliness of follow-up with some explicitly prefer-
ring longer intervals; most however valued an in-per-
son visit. Overall, participants did not see the link with
their post-discharge visit and hospital readmission, but
valued reconnecting with a provider they had a long-
standing relationship with. This study has implications
for policy makers hoping to design reforms and quality
improvement targets focused on patient-centered care
that considers the patient perspective on post-discharge
follow-up.

Abbreviations
COPD: Chronic obstructive pulmonary disease; CHF: Congestive heart failure;
SRQR: Standards for reporting qualitative research; RCT : Randomized con-
trolled trial.

Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s12875- 021- 01524-7.

Additional file 1. Semi-structured interview guide

Acknowledgements
The authors would like to acknowledge our participants, the research assis-
tants on the PODS Study team who consented participants for qualitative

Page 8 of 9Griffiths et al. BMC Fam Pract (2021) 22:176

interviews, and Amy Troup, the PODS research coordinator, for providing
quantitative data and editing for the manuscript.

Authors’ contributions
SG and GS conducted interviews and drafted the manuscript. All authors (SG,
GS, TK, KO) were involved in the design, analysis and interpretation of the
study. All authors (SG, GS, TK, KO) read and approved the final manuscript and
revisions.

Funding
Karen Okrainec receives funding from an Early Research Award and for this
project from an AFP AMO Innovation Fund, both from the Government of
Ontario. This funder did not have any involvement in the design, conduct or
analyses of this project.
Tara Kiran is the Fidani Chair in Improvement and Innovation at the University
of Toronto. She is supported as a Clinician Scientist by the Department of
Family and Community Medicine at St Michael’s Hospital and the University of
Toronto. At the time of writing, Dr. Kiran was also supported by Health Quality
Ontario and the Canadian Institutes of Health Research as an Embedded Clini-
cian Researcher.
Sarah Griffiths and Gaibrie Steven have no sources of funding to report.

Availability of data and materials
The datasets generated and/or analysed during the current study are not
publicly available due the ongoing nature of the randomized controlled trial
but are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate
This study was approved by the institutional review boards at University
Health Network and Thunder Bay Regional Health Sciences Centre. The experi-
ment protocol for involving humans was in accordance to guidelines of the
Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans
(TCPS 2) [39]. Informed consent was obtained by all participants in this study.

Consent for publication
Not applicable.

Competing interests
The authors declare that they have no competing interests.

Author details
1 Department of Family and Community Medicine, St. Michael’s Hospital,
Toronto, ON, Canada. 2 Department of Family and Community Medicine, Uni-
versity of Toronto, Toronto, ON, Canada. 3 MAP Centre for Urban Health Solu-
tions, St. Michael’s Hospital, Toronto, ON, Canada. 4 Institute of Health Policy,
Management and Evaluation, University of Toronto, Toronto, ON, Canada.
5 Toronto General Hospital Research Institute, University Health Network,
Toronto, ON, Canada. 6 Department of Medicine, University Health Network,
Toronto, ON, Canada. 7 Toronto Western Hospital, 399 Bathurst Street, 8EW-408,
Toronto, ON M5T 2S8, Canada.

Received: 19 February 2021 Accepted: 18 August 2021

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Contents lists available at ScienceDirect

Psychiatry Research

journal homepage: www.elsevier.com/locate/psychres

Timely follow-up visits after psychiatric hospitalization and readmission in
schizophrenia and bipolar disorder in Japan

Yasuyuki Okumuraa,b,⁎, Naoya Sugiyamac, Toshie Nodad

a Research Department, Institute for Health Economics and Policy, Association for Health Economics Research and Social Insurance and Welfare, Tokyo, Japan
bDepartment of Psychiatry and Behavioral Science, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
cNumazu Chuo Hospital, Fukkokai Foundation, Shizuoka, Japan
d Atami Chuo Clinic, Fukkokai Foundation, Shizuoka, Japan

A R T I C L E I N F O

Keywords:
Bipolar disorder
Schizophrenia
Transition care
Quality of health care

A B S T R A C T

The study objective was to investigate the association between timely follow-up visits after psychiatric hospi-
talization and the risk of readmission in patients with schizophrenia or bipolar disorder. A retrospective cohort
study was conducted using a nationwide claims database in Japan. Between April 2014 and March 2015, all
psychiatric hospitalization data were obtained and patients with a principal diagnosis of schizophrenia or bi-
polar disorder were followed up from 180 days before admission to 210 days after discharge. The primary
outcome of this study was psychiatric readmission during the 180-day period (between 31 and 210 days) after
the index discharge. A total of 48,579 eligible patients were identified. After psychiatric hospitalization, 15% of
patients received no follow-up visits to a psychiatrist within 30 days. Patients who received follow-up visits had
lower readmission rates during the subsequent 180 days (21.7% vs. 37.5%; adjusted risk ratio, 0.54 [95%
confidence interval, 0.52–0.57]) than those who did not. Timely follow-up visits after discharge could be helpful
for reducing the readmission risk in patients.

1. Introduction

Timely follow-up visits after psychiatric hospitalization are con-
sidered an important component in the clinical process for promoting
further recovery and preventing relapse (Hermann et al., 2004). How-
ever, it remains unclear whether timely follow-up visits after psychia-
tric hospitalization are associated with a reduced risk of readmission
(Beadles et al., 2015; Kurdyak et al., 2018; Lin and Lee, 2008; Marcus
et al., 2017).

A cohort study of 24,934 Medicaid patients, aged 22–64 years, re-
ported no association between follow-up visits within 30-days after
discharge and readmission within the subsequent 6-months in a de-
pression cohort and a small association in a schizophrenia cohort
(Beadles et al., 2015). A recent cohort study of 71,776 commercially
and Medicaid insured patients, aged 18–64 years, showed that receipt
of a follow-up visit within 30 days after discharge was associated with
slightly lower odds of readmission within the subsequent 90 days in
schizophrenia (odds ratio [OR], 0.88) and in bipolar (OR, 0.91) cohorts
(Marcus et al., 2017). A recent cohort study of 19,132 patients with
schizophrenia in Canada also found small associations (hazard ratio,

0.83–0.88) between follow-up visits within 30 days after discharge and
readmissions within the subsequent 180 days (Kurdyak et al., 2018).
However, a cohort study of 15,607 patients with schizophrenia in
Taiwan found strong associations (OR, 0.33) between follow-up visits
within 60 days after discharge and readmission within the subsequent
120 days after discharge (Lin and Lee, 2008).

Thus, the strength of the association between timely follow-up visits
after discharge and subsequent readmission may vary by diagnosis and
country. In the present study, we aimed to investigate the association
between timely follow-up visits after psychiatric hospitalization and the
risk of readmission in patients with schizophrenia and bipolar disorder
in Japan.

2. Methods

2.1. Design

A retrospective cohort study was conducted using the National
Database of Health Insurance Claims and Specific Health Checkups of
Japan (NDB). The NDB includes almost all claims in Japan (Ministry of

https://doi.org/10.1016/j.psychres.2018.10.020
Received 7 March 2018; Received in revised form 8 October 2018; Accepted 8 October 2018

⁎ Corresponding author at: Department of Psychiatry and Behavioral Science, Tokyo Metropolitan Institute of Medical Science, 2-1-6 Kamikitazawa, Setagaya-ku,
Tokyo 156-8506, Japan.

E-mail address: [email protected] (Y. Okumura).

Psychiatry Research 270 (2018) 490–495

Available online 09 October 2018
0165-1781/ © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/BY-NC-ND/4.0/).

T

Health, Labour and Welfare, 2016; Okumura et al., 2017), with the
exception of claims solely covered by public funds. The NDB includes
information on patient identification number, sex, and age group, along
with medical practice codes, administration dates, and diagnostic
codes.

The institutional review board at the Institute for Health Economics
and Policy reviewed and approved the study protocol. Acquisition of
informed consent was waived because of the anonymous nature of the
data.

2.2. Setting

Japan has had a universal healthcare system since 1961. Japan had
330,694 psychiatric beds in 1599 hospitals in 2014 (Ministry of
Health, Labour and Welfare and National Center of Neurology and
Psychiatry, 2016). Hospitals with psychiatric beds are mainly private
hospitals rather than public ones. There is no obligation for the hos-
pitals to follow-up patients after discharge. In general, the universal
health insurance system pays for 70% of the outpatient treatment costs,
the System of Medical Payment for Services and Supports for Persons
with Disabilities pays for approximate 20%, and patients are re-
sponsible for the remaining amount (approximately 10%).

2.3. Patient selection

We identified all patients aged<65 years who were admitted to
any psychiatric unit between April 2014 and March 2015. The psy-
chiatric units included in the present study are presented in Table S1.
To increase traceability, we used patient identification numbers, called
“ID0” (Kubo et al., 2018). Initial admissions to psychiatric units during
the period were identified as index admissions. Planned admissions for
electroconvulsive therapy with a hospital stay of ≤ 3 days were ex-
cluded. Patients with a principal diagnosis of schizophrenia and related
psychoses (International Classification of Diseases, Tenth Edition [ICD-
10]: F20–F29) or bipolar affective disorder (F30–F31) were included
using the algorithm defined by the Ministry of Health, Labour and
Welfare (Ministry of Health, Labour and Welfare, 2015). Patients with a
secondary diagnosis of dementia (F00–F03, F05.1, and G30–G31) or
intellectual disability (F70–F79) in addition to the principal diagnosis
of schizophrenia or bipolar disorder were excluded. Patients hospita-
lized for longer than 180 days were excluded as including these patients
would have meant that some patients would not have the required
follow-up period of 210 days. Patients discharged to a non-psychiatric
unit or deceased were excluded. Patients who enrolled in the database
at least 180 days before the index admission and 210 days after the
index discharge were included. Patients admitted to any type of hos-
pital unit within 30 days after the index discharge were excluded be-
cause of the time window for the exposure status. All patients were
followed up from 180 days before the index admission to 210 days after
the index discharge.

2.4. Exposure

The exposure of interest was a timely follow-up visit to a psychia-
trist. A timely follow-up visit was defined as an outpatient visit to a
psychiatrist within 30 days after the index discharge (the medical
practice codes for psychiatric visits are listed in Table S2). Our defini-
tion of follow-up visit included passive outpatient visits as well as
home-visit services by psychiatrists. In addition, the definition included
psychiatric consultation for at least 5 min delivered in an individual-
based format rather than a group-based format.

2.5. Outcomes

The primary outcome of this study was psychiatric readmission
during the 180-day period (between 31 and 210 days) after the index

discharge. The secondary outcome was psychiatric readmission during
the 90-day period (between 31 and 120 days) after the index discharge.
Planned readmissions for electroconvulsive therapy were excluded
from the definition of psychiatric readmission.

2.6. Other variables

As potential covariates, we extracted information on patient de-
mographic characteristics (sex and age), characteristics during the 180
days prior to the index admission (Charlson index (Sundararajan et al.,
2007), diagnosis of substance use disorders [ICD-10 codes: F10–F19],
number of psychiatric visits, history of psychiatric admission, and

Table 1
Sample characteristics of the entire cohort.

No follow-up
visit (N=7246)

Follow-up visit
(N=41,333)

Characteristics n % n % Standardized
difference, %

Age, years
0–19 202 2.8 1145 2.8 0.0
20–34 1609 22.2 9877 23.9 −4.0
35–49 2727 37.6 17,293 41.8 −8.6
50–64 2708 37.4 13,018 31.5 12.4

Sex, female 3625 50.0 24,088 58.3 −16.7
Number of psychiatric visits within 180 days before index admission

0 2377 32.8 4810 11.6 52.8
1–3 1693 23.4 5686 13.8 24.9
4–6 1311 18.1 8907 21.5 −8.5
7–12 1377 19.0 15,074 36.5 −39.9
13 or greater 488 6.7 6856 16.6 −31.2

History of psychiatric
admission within
180 days before
index admission

1160 16.0 4957 12.0 11.5

History of intensive care
unit admission
within 180 days
before index
admission

182 2.5 979 2.4 0.6

Charlson index within 180 days before admission
0 4933 68.1 28,663 69.3 −2.6
1 1501 20.7 8448 20.4 0.7

2 468 6.5 2605 6.3 0.8
3 or greater 344 4.7 1617 3.9 3.9

Diagnosis of substance
use disorders within
180 days before
admission

336 4.6 1838 4.4 1.0

Type of hospital at admission
General hospital 823 11.4 5345 12.9 −4.6
Non-general
hospital

6423 88.6 35,988 87.1 4.6

Type of unit at admission
Acute care unit 3941 54.4 25,580 61.9 −15.2
Non-acute care unit 3305 45.6 15,753 38.1 15.2

Type of admission
Involuntary 3136 43.3 18,579 45.0 −3.4
Voluntary 4110 56.7 22,754 55.0 3.4

Principal diagnosis (ICD-
10 codes)
Schizophrenia (F2) 6050 83.5 32,097 77.7 14.7
Bipolar affective
disorder (F30–F31)

1196 16.5 9236 22.3 −14.7

Use of ECT during index
admission

172 2.4 1030 2.5 −0.6

Length of hospital stay
1st (1–21 days) 1614 22.3 7993 19.3 7.4
2nd (22–40 days) 1300 17.9 8418 20.4 −6.4
3rd (41–64 days) 1227 16.9 8581 20.8 −10.0
4th (65–89 days) 1287 17.8 8002 19.4 −4.1
5th (90–180 days) 1818 25.1 8339 20.2 11.7

Abbreviations: ECT, electroconvulsive therapy; ICD, international classification
of diseases.

Y. Okumura et al. Psychiatry Research 270 (2018) 490–495

491

history of intensive care unit [ICU] admission), and characteristics of
index admissions (type of hospital, unit, and admission; principal di-
agnosis, use of electroconvulsive therapy, and length of stay). These
covariates were selected based on evidence derived from previous
studies (Beadles et al., 2015; Kurdyak et al., 2018; Lin and Lee, 2008;
Marcus et al., 2017).

The type of hospital was classified as either general or non-general;
general hospitals have ≥ 100 beds with at least the following five
specialties: internal medicine, surgery, obstetrics and gynecology, oto-
laryngology, and ophthalmology. The type of unit was classified as ei-
ther acute care or non-acute care; acute care units were defined as the
eight types of psychiatric units listed in Table S1. The type of admission
was classified as either voluntary or involuntary.

2.7. Statistical analyses

First, we assessed covariate balance using standardized differences,
in which an absolute value greater than 10% indicates an important
imbalance in the prevalence of a covariate between the groups
(Austin, 2011). Second, we fitted a Poisson regression model and
compared risks between the groups. All potential covariates were si-
multaneously entered into the models. Risk ratios (RR) and their 95%
confidence intervals (CI) were derived from the model. Third, we
conducted a subgroup analysis to examine whether the association
varied across the levels of all covariates. We assessed the statistical
significance of interaction terms with a significance level of 0.05.
Fourth, we conducted a sensitivity analysis using a propensity matching
method. We estimated propensity scores by using a logistic regression

Fig. 1. Follow-up visits and psychiatric readmission during the 180-day period (between 31 and 210 days) after the index discharge.
*p< .05; CI, confidence interval; ECT, electroconvulsive therapy; ICU, intensive care unit; RR, risk ratio; LOS, length of stay.

Y. Okumura et al. Psychiatry Research 270 (2018) 490–495

492

model with the following algorithm: nearest neighbor matching
method, matching ratio of 1:1, caliper width of 0.2 of the standard
deviation of the logit of the propensity score, and no replacement
(Austin, 2011). Within the propensity score matched cohort, we used
the method of Agresti and Min to compare readmission rates
(Agresti and Min, 2004). Fifth, we conducted sensitivity analyses based
on another time frame in which a timely follow-up visit was defined as
an outpatient visit to a psychiatrist within 60 days after the index dis-
charge, and outcomes were defined as psychiatric readmissions during
the 180-day period (between 61 and 240 days) and the 90-day period
(between 61 and 150 days) after the index discharge. Statistical ana-
lyses were performed using R version 3.4.1 (R Foundation for Statistical
Computing, Vienna, Austria) with the MatchIt package.

3. Results

3.1. Study population

The study cohort included 48,579 patients (Fig. S1). Among these,
7246 patients (14.9%) had no follow-up visit within 30 days after
discharge. Of the patients receiving timely follow-up visits, only 325
(0.7%) received home-visit services by psychiatrists. A between-groups
comparison of the sample characteristics showed major imbalances in
the number of prior psychiatric visits within 180 days before admission
(Table 1).

Fig. 2. Follow-up visits and psychiatric readmission during the 90-day period (between 31 and 120 days) after the index discharge
*p< .05; CI, confidence interval; ECT, electroconvulsive therapy; ICU, intensive care unit; RR, risk ratio; LOS, length of stay.

Y. Okumura et al. Psychiatry Research 270 (2018) 490–495

493

3.2. Main analyses

Patients who received timely follow-up visits had lower readmission
rates during the 180-day (21.7% vs. 37.5%; adjusted RR, 0.54 [95% CI,
0.52–0.57]) and 90-day (12.3% vs. 29.0%; adjusted RR, 0.40 [95% CI,
0.38–0.42]) periods than patients who did not receive follow-up visits
(Figs. 1 and 2). Subgroup analysis showed that the strength of the as-
sociations was modified by several factors, although the direction of the
associations did not vary by subgroup. For example, the strength of the
association was weaker among patients with a history of psychiatric
admissions (adjusted RR, 0.70) than among those without (adjusted RR,
0.50) (Fig. 1). The strength of the association was similar among pa-
tients with bipolar disorder and those with schizophrenia (Fig. 1). Si-
milar findings were observed in the secondary outcome results (Fig. 2).

3.3. Sensitivity analyses

We identified a propensity score-matched cohort of 14,454 patients
from the entire cohort (Table 2; Table S3). The covariate balance was
considerably improved (Table 2). Follow-up visits were associated with
reduced readmission rates during the 180-day (21.2% vs. 37.4%; RR,
0.57 [95% CI, 0.54–0.60]) and 90-day (12.7% vs. 28.9%; RR, 0.44
[95% CI, 0.41–0.47]) periods (Table S4). The strength of the association
between follow-up visits and readmission was weaker when follow-up
visits were defined as ≤ 60 days rather than ≤ 30 days (Tables S4 and
S5).

4. Discussion

Our study found a moderate association between timely follow-up
visits after psychiatric hospitalization and risk of readmission in pa-
tients with schizophrenia or bipolar disorder using the nationwide
claims database. The strength of this association appears to be much
higher in Japan than in the United States and Canada (Kurdyak et al.,
2018; Marcus et al., 2017), although the risk of readmission in patients
receiving follow-up visits was similar in Japan (180-day readmission
rate: 22%; 90-day readmission rate: 12%) and in the United States (90-
day readmission rate: 10%–13%) and Canada (180-day readmission
rate: 22%). Follow-up rates after psychiatric hospitalization and base-
line risk of readmission (i.e., risk of readmission in patients receiving no
follow-up visits) might mutually influence the strength of the associa-
tion between timely follow-up visits and readmission. Patients with no
follow-up visits who are living in a country with high follow-up rates
are likely to have a higher risk of readmission than those living in a
country with low follow-up rates.

We found that the follow-up rate within 30 days after psychiatric
hospitalization was 85%, which was much higher than that reported in
previous studies. For example, Marcus et al. reported that the 30-day
follow-up rate was 64%–65% in a schizophrenia cohort and 62%–73%
in a bipolar cohort of commercially and Medicaid insured adults
(Marcus et al., 2017). Fontanella et al. also found that the 30-day
follow-up rate was 70% in a mood disorder cohort of Medicaid enrolled
youth (Fontanella et al., 2016). Kurdyak et al. reported that the 30-day
follow-up rate for psychiatrists or primary care physicians was 65% in a
schizophrenia cohort of Canadian adults (Kurdyak et al., 2018).

The baseline risk of readmission within the subsequent 180 days
was higher in our study (38%) than that reported by Kurdyak et al.
(26%) (Kurdyak et al., 2018). Similarly, the baseline risk of readmission
within the subsequent 90 days was also much higher in our study (29%)
than that reported by Marcus et al. (13% in schizophrenia and 11% in
bipolar cohorts) (Marcus et al., 2017).

Our study has several limitations. First, we could not measure im-
portant potential covariates, such as history of suicide attempts, fi-
nancial status, marital status, education level, patient satisfaction with
treatment, insight into illness, and type of discharge (e.g., discharge on/
against medical advice) (Donisi et al., 2016). For example, patients who
have better insight into their illness are more likely to receive follow-up
visits and to have better adherence to medications. As a result, those
patients might have lower risk of readmission. Future studies are
needed to confirm the balance pertaining to these unmeasured covari-
ates between the groups. Second, our data did not include claims solely
covered by public funds, comprising approximately 19% of psychiatric
discharges (Niimura et al., 2017). Third, our data could not exclude
healthy-user bias because we focused only on patients who were dis-
charged to the community within 180 days after admission and those
who enrolled in the database at least 210 days after discharge. Fourth,
we could not conduct subgroup analyses by diagnostic subtype (i.e.,
manic, depressed, or mixed episode) due to concerns about coding ac-
curacy. Fifth, we focused on limited aspects of the exposures and out-
comes. It would be valuable to further investigate the effectiveness of
timely follow-up visits on the risk for readmission in the short term

Table 2
Sample characteristics of the propensity score-matched cohort.

No follow-up
visit (N=7227)

Follow-up visit
(N=7227)

Characteristics n % n % Standardized
difference, %

Age, years
0–19 202 2.8 237 3.3 −2.9
20–34 1609 22.3 1661 23.0 −1.7
35–49 2727 37.7 2662 36.8 1.9
50–64 2689 37.2 2667 36.9 0.6

Sex, female 3625 50.2 3659 50.6 −0.8
Number of psychiatric visits within 180 days before index admission

0 2358 32.6 2404 33.3 −1.5
1–3 1693 23.4 1593 22.0 3.3
4–6 1311 18.1 1209 16.7 3.7
7–12 1377 19.1 1454 20.1 −2.5
13 or greater 488 6.8 567 7.8 −3.8

History of psychiatric
admission within 180
days before index
admission

1141 15.8 1121 15.5 0.8

History of intensive care
unit admission within
180 days before index
admission

177 2.4 190 2.6 −1.3

Charlson index within 180 days before admission
0 4932 68.2 4931 68.2 0.0
1 1493 20.7 1492 20.6 0.2
2 461 6.4 464 6.4 0.0
3 or greater 341 4.7 340 4.7 0.0

Diagnosis of substance
use disorders within
180 days before
admission

335 4.6 347 4.8 −0.9

Type of hospital at admission
General hospital 822 11.4 813 11.2 0.6
Non-general hospital 6405 88.6 6414 88.8 −0.6

Type of unit at admission
Acute care unit 3941 54.5 3918 54.2 0.6

Non-acute care unit 3286 45.5 3309 45.8 −0.6
Type of admission

Involuntary 3136 43.4 3187 44.1 −1.4
Voluntary 4091 56.6 4040 55.9 1.4

Principal diagnosis (ICD-10 codes)
Schizophrenia (F2) 6031 83.5 5994 82.9 1.6
Bipolar affective
disorder (F30–F31)

1196 16.5 1233 17.1 −1.6

Use of ECT during index
admission

169 2.3 188 2.6 −1.9

Length of hospital stay
1st (1–21 days) 1613 22.3 1611 22.3 0.0
2nd (22–40 days) 1300 18.0 1292 17.9 0.3
3rd (41–64 days) 1227 17.0 1206 16.7 0.8
4th (65–89 days) 1287 17.8 1271 17.6 0.5
5th (90–180 days) 1800 24.9 1847 25.6 −1.6

Abbreviations: ECT, electroconvulsive therapy; ICD, international classification
of diseases.

Y. Okumura et al. Psychiatry Research 270 (2018) 490–495

494

(e.g., within 30 days after discharge) as well as in the long term (e.g.,
360 days after discharge). In addition, studies are needed to determine
the comparative effectiveness of type of follow-up visits with con-
sideration of the costs and benefits. Sixth, the generalizability of our
findings is uncertain because healthcare system structure differs among
countries and it may influence the follow-up and readmission rates.

Nevertheless, our findings suggest that 15% of discharged patients
with schizophrenia or bipolar disorder do not receive timely follow-up
visits to a psychiatrist. These patients are at higher risk of psychiatric
readmission. Therefore, timely follow-up visits after discharge could be
helpful for reducing the readmission risk in patients.

Conflict of interest

For the past 3 years, YO has received personal fees from Merck &
Co., Inc.; Janssen Pharmaceuticals, Inc.; the Medical Technology
Association; Cando Inc.; and the Japan Medical Data Center. He has
also received research grants from the Japan Agency for Medical
Research and Development; the Ministry of Health, Labour and
Welfare; the Japan Society for the Promotion of Science; the Institute
for Health Economics and Policy; and Mental Health and Morita
Therapy. Outside the submitted work, NS has received grants from the
Ministry of Health, Labour and Welfare, along with personal fees and
non-financial support from Otsuka Pharmaceutical, Janssen
Pharmaceutical K.K., Eli Lilly Japan K.K., Pfizer Inc., Meiji Seika
Pharma Co., Ltd., MSD K.K, and Daiichi-Sankyo Company, Ltd. TN has
no conflicts of interest.

Acknowledgments

We would like to thank Editage (www.editage.jp) for English lan-
guage editing. This work was funded by a grant from the Japan Society
for the Promotion of Science (number: 18K09991).

Supplementary materials

Supplementary material associated with this article can be found, in
the online version, at doi:10.1016/j.psychres.2018.10.020.

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Review

Posthospitalization Follow-Up of Patients With Heart Failure Using
eHealth Solutions: Restricted Systematic Review

Ingvild Margreta Morken1,2*, PhD; Marianne Storm3,4*, Prof Dr; Jon Arne Søreide5,6*, Prof Dr; Kristin Hjorthaug

Urstad1,7*, Prof Dr; Bjørg Karlsen3*, Prof Dr; Anne Marie Lunde Husebø2,3*, Prof Dr
1Department of Quality and Health Technologies, University of Stavanger, Stavanger, Norway
2Research Group for Nursing and Health Sciences, Stavanger University Hospital, Stavanger, Norway
3Department of Public Health, University of Stavanger, Stavanger, Norway
4Faculty of Health Sciences and Social Care, Molde University College, Molde, Norway
5Department of Gastrointestinal Surgery, Stavanger University Hospital, Stavanger, Norway
6Department of Clinical Medicine, University of Bergen, Bergen, Norway
7Faculty of Health Studies, VID Specialized University, Oslo, Norway
*all authors contributed equally

Corresponding Author:
Anne Marie Lunde Husebø, Prof Dr
Department of Public Health
University of Stavanger
Prof Olav Hanssens vei 10
Stavanger, 4021
Norway
Phone: 47 99262805
Email: [email protected]

Abstract

Background: Heart failure (HF) is a clinical syndrome with high incidence rates, a substantial symptom and treatment burden,
and a significant risk of readmission within 30 days after hospitalization. The COVID-19 pandemic has revealed the significance
of using eHealth interventions to follow up on the care needs of patients with HF to support self-care, increase quality of life
(QoL), and reduce readmission rates during the transition between hospital and home.

Objective: The aims of this review are to summarize research on the content and delivery modes of HF posthospitalization
eHealth interventions, explore patient adherence to the interventions, and examine the effects on the patient outcomes of self-care,
QoL, and readmissions.

Methods: A restricted systematic review study design was used. Literature searches and reviews followed the (PRISMA-S)
Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search extension checklist, and the CINAHL,
MEDLINE, Embase, and Cochrane Library databases were searched for studies published between 2015 and 2020. The review
process involved 3 groups of researchers working in pairs. The Mixed Methods Appraisal Tool was used to assess the included
studies’ methodological quality. A thematic analysis method was used to analyze data extracted from the studies.

Results: A total of 18 studies were examined in this review. The studies were published between 2015 and 2019, with 56%
(10/18) of them published in the United States. Of the 18 studies, 16 (89%) were randomized controlled trials, and 14 (78%)
recruited patients upon hospital discharge to eHealth interventions lasting from 14 days to 12 months. The studies involved
structured telephone calls, interactive voice response, and telemonitoring and included elements of patient education, counseling,
social and emotional support, and self-monitoring of symptoms and vital signs. Of the 18 studies, 11 (61%) provided information
on patient adherence, and the adherence levels were 72%-99%. When used for posthospitalization follow-up of patients with HF,
eHealth interventions can positively affect QoL, whereas its impact is less evident for self-care and readmissions.

Conclusions: This review suggests that patients with HF should receive prompt follow-up after hospitalization and eHealth
interventions have the potential to improve these patients’ QoL. Patient adherence in eHealth follow-up trials shows promise for
successful future interventions and adherence research. Further studies are warranted to examine the effects of eHealth interventions
on self-care and readmissions among patients with HF.

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(J Med Internet Res 2022;24(2):e32946) doi: 10.2196/32946

KEYWORDS

adherence; eHealth; heart failure; posthospitalization follow-up; patient outcome; review

Introduction

Background
Heart failure (HF) affects an estimated 64 million people
worldwide [1]. It poses a burden on the health care system in
general and on primary care specifically because the total
number of patients with HF is increasing, reflecting the chronic
course of the disease as well as population growth and aging
[2,3]. Symptomatic HF is a complex clinical syndrome with a
symptom burden of dyspnea and fatigue [4] and can be
troublesome for patients and their families because of frequent
hospitalizations and symptom and treatment burden negatively
affecting their quality of life (QoL) [5-7]. QoL is understood
as a multidimensional and subjective concept that includes
physical, functional, emotional, and social well-being [8].
Effective self-care behavior is essential for patients with HF
[9,10]. Self-care in the context of HF is an overarching concept
based on three key concepts: (1) self-care maintenance (eg,
compliance with medication regimens and following diet and
physical activity recommendations), (2) self-care monitoring
(eg, regular weighing), and (3) self-care management (eg,
changing diuretic dose in response to symptoms) [10]. Upon
discharge from the hospital, many patients transition from care
provided by health professionals in a safe hospital setting to
individual self-care at home [11]. This period, when patients
transition between hospital and home, is a vulnerable and
stressful time for patients with HF and many struggle to perform
recommended self-care and navigate the health care system,
particularly when posthospitalization care is poorly executed
as a result of inadequate coordination of resources or follow-up
[5,7]. Of any diagnosis, HF is associated with the highest 30-day
all-cause readmission rate (approximately 20%), whereas nearly
35% of the patients with HF are readmitted within 90 days [2,6].
During this phase, the lack of resources for following up or poor
medical education leaves this population vulnerable to
deterioration and rehospitalization [12]. Posthospitalization HF
disease management programs include education,
self-management, weight monitoring, sodium restriction or
dietary advice, exercise recommendations, and medication
review [13]. In addition to social and psychological support with
a high degree of care coordination, as well as the higher intensity
of follow-up, these components may be important for better
self-care behavior, increased QoL, and reduced readmission
rates [4,13,14]. The impact of the COVID-19 pandemic has
raised the requirement for, and importance of, eHealth solutions
as a tool for health care professionals to perform such follow-up
of patients with HF [15]. Insight into ensuring a more seamless
eHealth care service from inpatient to outpatient care for patients
with HF is necessary if they are to achieve adequate self-care
support and feel safe [15,16]. eHealth care service is defined as
“health services and information delivered or enhanced through
the internet and related technologies” [17] and holds the
potential to increase the efficiency and quality of health care
services [18]. In this review, eHealth comprises digital solutions

to deliver health care services, including patient education;
telemonitoring of weight, blood pressure, and heart rhythm; and
social and emotional support. Previous research suggests that
the use of posthospitalization eHealth interventions to follow
up on patients may promote self-care for people with long-term
illness [18]. Several recent reviews have summarized the
findings from eHealth follow-up interventions for patients with
HF and provided information about the efficiency of such
interventions. Auener et al [19] investigated the effects of
telemonitoring programs on different aspects of health care use
from 16 randomized controlled trials (RCTs) and 13
nonrandomized studies. All studies included weight as a
parameter, whereas only 4 included electrocardiography
measures as a physiological parameter. The results revealed
that telemonitoring has the potential to reduce hospitalization
rates. However, the number of non–emergency department visits
increased in most of the studies [18]. Ding et al [20] extracted
18 telemonitoring strategies from 26 RCTs involving patients
with HF. Some strategies were commonly used, such as call
center support and daily weight monitoring, whereas others,
including nurse support, interventions for depression and
anxiety, and exercise interventions, were seldom used.
Telemonitoring strategies involving medication support and
mobile health (mHealth) interventions were associated with
improvements in all-cause mortality or hospitalization outcomes
[20]. A systematic review conducted in 2017 identified 39
relevant RCTs of telemedicine, largely based on assessments
of symptoms, weight, heart rate and rhythm, and blood pressure,
and found that telemonitoring was associated with reductions
in all-cause mortality of 20% and HF hospitalization of 37%
[21]. In contrast, nurse-based telephone-supported care seemed
to provide little benefit, and only a reduction in the rate of
HF-related admission was noted compared with the control
group. However, a combination of home-based teletransmission
and nurse-based telephone reinforcement may be encouraged
[21]. Although these reviews generally support the effectiveness
of eHealth interventions for patients with HF, the outcomes
mainly focus on readmission and health care use, and only one
of them focuses specifically on the hospital-to-home transition
phase [21]. Moreover, they mostly lack information about
self-management, QoL, and participants’ adherence to the
eHealth interventions. Adherence to self-management and
medication regimens is crucial during the transition from
hospital discharge to home to prevent hospital readmission and
achieve improved health outcomes and QoL [22-24]. Therefore,
the success of an intervention aiming to support patients’chronic
disease management depends on patient adherence to the
intervention components [25]. Intervention adherence refers to
the degree to which the behavior of trial participants corresponds
to the intervention assigned to them [26]. Adherence varies
according to the patient’s health status, treatment regimens,
access to support, and psychological factors such as motivation
and beliefs. The long-term success of interventions depends on
patients assuming responsibility for their own health and can

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be achieved with the aid of coordinated measures such as patient
education and regular follow-up contacts [26]. An accurate
assessment of intervention adherence is warranted to verify
whether changes in health outcomes are due to a particular
intervention [26].

There is a knowledge gap concerning the synthesis of recent
posthospitalization eHealth follow-up interventions for patients
with HF focusing on outcomes of self-care, QoL, and adherence
to the interventions. Therefore, this restricted review will
investigate eHealth interventions that may better prepare patients
for the period after hospital discharge, strengthen their self-care
and QoL, reduce readmissions, and help them to recover well.
Furthermore, the review will address the issue of adherence and
discuss how it may affect intervention outcomes. Therefore, the
aim is to summarize the most recent information about the
content and delivery mode of HF posthospitalization eHealth
interventions, explore patient adherence to the interventions,
and systematically investigate the effects on patient outcomes
of self-care, QoL, and readmissions.

Research Questions
The a priori research questions were designed according to the
FINER framework, which states that a review research question
should be feasible, interesting, novel, ethical, and relevant [27].

Our research questions were as follows:

• What are the content and delivery modes of
posthospitalization eHealth interventions for patients with
HF?

• What is the reported adherence to posthospitalization
eHealth interventions in HF?

• Which effects can be expected from posthospitalization
eHealth interventions on self-care, QoL, and readmissions
of patients who have received treatment for HF?

Methods

Reporting Standards
This study used a framework proposed for restricted systematic
reviews [27]. The restricted systematic review framework is

proposed to be applicable when conducting a rapid review
because it consists of core steps that are minimum requirements
for systematic reviews, thereby accommodating factors such as
a short time frame and limited resources [28]. Such factors are
important to consider when conducting a literature search and
review as part of developing complex interventions [29]. The
framework comprises six core steps: (1) literature search, (2)
study selection, (3) data extraction, (4) critical assessment of
the included studies, (5) data synthesis, and (6) publication [28].

Step 1: Literature Search and Search Terms
The literature search was performed as part of a more extensive
review study on eHealth interventions in noncommunicable
diseases. This paper reports the results from HF populations. A
research librarian performed comprehensive literature searches
in the CINAHL, MEDLINE, Embase, and Cochrane Library
databases. To ensure that our results reflect current conditions
and avoid repeating previous review efforts, this rapid review
was limited to data published between 2015 and 2020 in English
or a Scandinavian language. Searches were performed in the
publication title or abstract. Appropriate search terms, including
relevant Medical Subject Headings, were closely matched with
the Population, Intervention, Control, and Outcome elements
(see next section). Documentation of the search strategy and
search terms is presented in Multimedia Appendix 1. The search
strategy also included manually hand searching the reference
lists of the included studies and relevant background material.
The searches were performed on March 20, 2020.

A Priori Eligibility Criteria
Key components of the synthesis are encapsulated by the
Population, Intervention, Control, and Outcome framework
[30].

• Population: patients initially treated for HF
• Intervention: posthospitalization eHealth follow-up services
• Control: standard care and nondigital follow-up services
• Outcomes: self-management and self-care, QoL, and

readmissions

The inclusion and exclusion criteria are displayed in Textbox
1.

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Textbox 1. Inclusion and exclusion criteria.

Inclusion criteria

• Empirical intervention studies

• Populations of adult patients with heart failure

• eHealth interventions from hospital to home

• Patient outcomes of self-care, quality of life, and readmissions

• Experimental and quasi-experimental randomized and nonrandomized controlled trials

• Pre–post design with a comparison group

• Peer-reviewed studies

• Published in English

Exclusion criteria

• Review studies, study protocols, book chapters, and conference contributions

• Children and adolescent patients

• Older adults (aged >80 years)

• Community health care services context

• >3 months since hospital discharge

• Insufficient detail provided to estimate study outcome

• Mixed patient samples

• Noncomparator study designs

Step 2: Study Selection
After removing duplicates using EndNote (Clarivate), a member
of the research team (AMLH) carried out an initial broad review
of all included titles and abstracts, using the a priori inclusion
and exclusion criteria. Next, the abstracts verified for potential
inclusion were reviewed for full-text extraction by all authors,

divided into 3 review teams. Full-text articles were extracted
for 9.8% (69/701) of the abstracts. Finally, team members
resolved conflicting opinions by assessing reasons for exclusion
and deciding whether to include the study. The results of the
data search and selection process are displayed in a PRISMA
(Preferred Reporting Items for Systematic Reviews and
Meta-Analyses) flowchart (Figure 1) [31].

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Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow chart of the study selection process.

Step 3: Data Extraction
An Excel spreadsheet (Microsoft Corp) was created to ensure
consistent data extraction, including data fields of publication
identifiers, study design, study context and participants, eHealth
intervention or program, and outcomes. The review teams used
the spreadsheet to extract relevant data from the included
articles. Any inconsistency within the group was resolved
through assessment by a reviewer from one of the other groups.

Step 4: Critical Assessment of Included Studies
To minimize bias, an assessment of internal validity of the
included studies, risk of bias (eg, over- or underestimation of
intervention effect), and potential conflicts of interest were
examined using the Mixed Methods Appraisal Tool (MMAT)
[32]. The MMAT, which aims to appraise the methodological
quality of included studies in systematic reviews, consists of a
checklist of qualitative, quantitative, and mixed methods studies
[32]. For this review, checklists for randomized and
nonrandomized research designs were used. Each checklist is
initiated with 2 screening questions to allow for further
assessment, and each list contains 5 assessment criteria to be
answered with Yes, No, or Can’t tell. A total score of 7
constitutes a Yes response to the screening and assessment

criteria [32]. The developers recommend that the MMAT be
used to describe only the study quality and to avoid excluding
studies based on total scores [32].

To assess data quality, each review team member independently
rated the studies, followed by a discussion to achieve consensus.
For 10% (2/18) of the included studies, the quality scoring was
verified through independent scoring by 2 reviewers (IMM and
AMLH). The quality of included studies was above moderate
(ie, of the 7 criteria, 6 [86%] were answered with Yes; Textbox
1).

Step 5: Data Synthesis
The findings on service content and delivery mode, adherence,
and the effects of posthospitalization follow-up eHealth
interventions were systematically analyzed by using thematic
analysis as well as searching for patterns, themes, and categories
across studies, which were then narratively summarized as
suggested by Whittemore and Knafl [33]. Because of the
heterogeneity of the study designs, participants, and outcome
measures, meta-analysis was not recommended. Thus, the effects
on patient outcomes were reviewed and reported narratively.

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Step 6: Publication
The results from this restrictive systematic review will be
published, including all appendices and added data. In addition,
the study’s findings will be disseminated in relevant clinical
settings and websites.

Results

Overview
The literature search process is outlined in Figure 1. The search
yielded a total of 1149 references (ie, records screened for 2
patient populations); after the removal of 318 (27.68%)
duplicates, 831 (72.32%) titles and abstracts were assessed for
inclusion. Of the 831 titles and abstracts, 701 (84.4%) titles
pertaining to eHealth interventions for patients with HF were
screened for eligibility using the web tool [34]. Of the 69 studies
evaluated for eligibility in full text, 16 (23%) met all inclusion
criteria and were included. Screening the reference lists of the
included studies yielded another study and screening the
reference lists of relevant background material identified a
further study. Finally, this review included 18 studies.

Study Characteristics
Detailed characteristics of the included studies are displayed in
Table 1. All studies were published between 2015 and 2019.
Of the 18 studies, 10 (56%) were performed in the United States
[35-44]. Although RCT was the predominant study design, 11%
(2/18) of the studies applied a quasi-experimental method
[37,43]. Among the 18 studies, enrollment of patients with HF
to the posthospitalization eHealth service varied from
recruitment at the hospital before hospital discharge to
recruitment within 3 months of recent hospitalization (Textbox
1). In 56% (10/18) of the studies, all patients were recruited
upon hospital discharge to an intervention with a duration of
14 days to 12 months [35,37,38,40-42,44-47]. In 22% (4/18)
of the studies, patients were recruited after recent (within 30
days) hospitalization to an intervention with a 3- to 12-month
duration [39,43,48,49]. In another study, patients with HF were
enrolled during hospitalization or within 3 months of discharge
for an HF exacerbation, and the intervention lasted for 3 months
[36]. In 17% (3/18) of the studies, patients were recruited at
hospital discharge or at the HF outpatient clinic to an
intervention with a duration of 3-9 months [50-52].

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Table 1. Characteristics of included eHealth intervention studies involving patients with heart failure (HF; N=18).

MMATa

scores out
of 7, n (%)

DurationContent, focus, and mode of instructionSample sizeDesignStudy (country)

Cc, n
(%)

Ib, n
(%)

Total sample
(N)

2 (29)30 daysTelemonitoring (HeartMapp); daily
measures of weight, heart rate, blood

9 (50)9 (50)18RCTdAthilingam et al [35]
(United States)

pressure, and HF symptoms. HF educa-
tion: 10 modules, home visit after 2-3
days by a nurse. A phone call to all par-
ticipants. Nurses checked the dashboard
daily to monitor participants’ progress.

6 (86)6 monthsTelemonitoring and telephone support.
Daily measures of weight, heart rate, and

97
(54.5)

81
(45.5)

178RCTComin-Colet et al
[45] (Spain)

blood pressure. HF nurses reviewed
alarms and alerts from the system every
day.

4 (57)6 monthsTelephone support; education and coun-
seling on diet, medications, self-monitor-

64
(47.8)

70
(52.2)

134RCTDunbar [36] (United
States)

ing, symptoms, and physical activity;
self-monitored blood glucose level and
weight; self-care with follow-up home
visits and telephone counseling.

7 (100)3 monthsTelemonitoring and telephone support;
daily measures of weight, heart rate, and

21 (50)21 (50)42Quasi-experimentalEvangelista et al
[37] (United States)

blood pressure. Telemonitoring provided
alerts and feedback in the case of worri-
some responses to questions or if vital
signs were outside of preset limits. The
research nurse communicated with the
patient through teleconferencing and
collaborated with the patient’s primary
care provider to facilitate a plan of ac-
tion. Telephone support as usual to the
control group.

6 (86)6 monthsTelemonitoring; daily measurements of
weight, heart rate, and blood pressure

80 (50)80 (50)160RCTFrederix et al [46]
(Belgium)

were forwarded to a central computer. If
the recordings were outside of predefined
alert limits, both the general practitioner
and HF clinic were alerted by email. At
that moment, per protocol, the general
practitioner (or cardiologist) was asked
to visit or contact the patient and adapt
the treatment if they felt that it was nec-
essary. The HF nurse contacted the pa-
tient by telephone 1-3 days after the alert
to verify whether the intervention had
been effective.

7 (100)30 daysTelemonitoring; electronic measurement
of adherence to loop diuretics. A licensed

20 (50)20 (50)40RCTGallagher et al [38]
(United States)

clinical social worker reviewed adher-
ence data daily during the first 7 days
after discharge and weekly after that and
then contacted participants who were
nonadherent for ≥2 days per week.

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MMATa

scores out
of 7, n (%)

DurationContent, focus, and mode of instructionSample sizeDesignStudy (country)

Cc, n
(%)

Ib, n
(%)

Total sample
(N)

7 (100)3 monthsTelemonitoring and telephone support;
participants were instructed to self-
monitor and verbally report their blood
pressure, heart rate, and oxygen satura-
tion levels at the start of each rehabilita-
tion session. The intervention group re-
ceived electronic education sessions.

29
(54.7)

24
(45.3)

53RCTHwang et al [48]
(Australia)

6 (86)6 monthsTelephone calls are used for technical
support by interactive voice response;
symptoms and daily weight; patients
were instructed to call a toll-free number
daily for 6 months, respond to a series
of automated questions regarding their
symptoms, and enter their daily weight.
They were also provided with education-
al materials.

765
(50.3)

756
(49.7)

1521RCTJayaram et al [39]
(United States)

6 (86)15 monthsTelemonitoring and telephone support,
measurement of weight, heart rate, and
blood pressure daily. Physicians could
provide telephone guidance, change
medications, and order hospital readmis-
sion if required. Full-time nurses moni-
tored acquired data on a secure website.
Telephone support from a physician as
usual.

91 (50)90 (50)181RCTKotooka et al [47]
(Japan)

6 (86)9 monthsTelemonitoring and telephone support;
daily measurement of weight, heart rate,
and blood pressure. HF nurses automati-
cally received notifications by mobile
phone and email and then discussed
symptoms and treatment with patients
within 2 hours.

93
(52.8)

83
(47.2)

176RCTKraai et al [50]
(Netherlands)

5 (71)3 monthsTelephone support; nurse-led symptom
monitoring, education on signs and
symptoms of worsening HF, HF-specific
diet, and fluid restriction. When seeking
help, patients were advised to use a diary
to document body weight, blood pres-
sure, heart rate, and edema on a daily
basis. If necessary, after discharge from
the hospital, patients received 4 tele-
phone calls within 3 months about
changes in HF-related symptoms and
treatment.

52
(47.3)

58
(52.7)

110RCTKöberich et al [51]
(Germany)

6 (86)9 monthsTelemonitoring and telephone support;
daily measurement of body weight,
blood pressure, and heart rate. HF nurses
automatically received notifications by
mobile phone and email and, within 2
hours, discussed the symptoms and
treatment with the patients. An HF nurse
provided education on HF.

60
(50.8)

58
(49.2)

118RCTLycholip et al [49]
(Netherlands)

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MMATa

scores out
of 7, n (%)

DurationContent, focus, and mode of instructionSample sizeDesignStudy (country)

Cc, n
(%)

Ib, n
(%)

Total sample
(N)

6 (86)3 monthsTelephone support MIe: a tailored inter-
vention at discharge to improve self-care,
involving a home visit and follow-up
calls. A nurse used the MI approach to
identify client-directed self-care goals.
Participants received written educational
material.

26
(38.8)

41
(61.2)

67RCTMasterson- Creber
et al [40] (United
States)

5 (71)6 monthsTelemonitoring and telephone support;
weight, heart rate, and blood pressure
were measured daily. A total of 9 tele-
phone health coaching calls over 6
months, generally from the same call
center nurse.

722
(50.3)

715
(49.7)

1437RCTOng et al [41] (Unit-
ed States)

6 (86)6 monthsTelemonitoring and telephone support;
measurement of blood pressure, oxygen
saturation, weight, and heart rate daily;
a geriatrician evaluated the data received
every day. Participants received educa-
tion on medical treatment and lifestyle
counseling by telephone.

46
(47.9)

50
(52/1)

96RCTPedone et al [52]
(Italy)

7 (100)2 monthsInteractive voice response and telephone
support; symptoms and body weight
measured daily; E-Coach intervention:
an intervention with condition-specific
customization and in-hospital and post-
discharge support by a care transition
nurse, interactive voice response, postdis-
charge calls, and care transition nurse
follow-up.

258
(50.5)

253
(49.5)

511RCTRitchie et al [42]
(United States)

6 (86)12 monthsTelemonitoring and telephone support;
measurement of heart rate and blood
pressure daily. Data were monitored on
weekdays by a telehealth nurse who ana-
lyzed the data for abnormalities and lack
of response; if clinical data caused con-
cern for declining health status, a phone
call was initiated to the patient. All pa-
tients also received a monthly follow-up
call.

870
(81.5)

197
(18.5)

1067Cohort–controlSrivastava et al [43]
(United States)

6 (86)6 monthsTelephone support: the patient-activated
care at home intervention contained a
variety of formats (eg, verbal, written,
and visual) with 12 weeks of post dis-
charge education sessions delivered by
telephone. Besides self-management
workbooks, each subject was provided
with a self-management toolkit, includ-
ing a calendar for weight and daily salt-
intake logging, a step-on weight scale
with large and bright readings, and an
electronic pill organizer reminder alarm.

51
(48.6)

54
(51.4)

105RCTYoung et al [44]
(United States)

aMMAT: Mixed Methods Appraisal Tool.
bI: intervention.
cC: control.
dRCT: randomized controlled trial.
eMI: motivational interview.

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Themes Derived From Data Analysis
In the following section, the data analysis results are presented,
thereby answering the research questions concerning
intervention content and delivery mode, intervention adherence,
and the effects of eHealth on patient outcomes.

Delivery Mode and Content of Posthospitalization
eHealth Follow-Up Interventions
In all, 2 different modes of delivering an eHealth service were
identified (Table 1). The specific technologies identified
included (1) structured telephone calls and (2) telemonitoring
or telemonitoring in combination with telephone support.

Structured Telephone Call
Of the 18 studies in our review, 6 (33%) included structured
telephone calls to deliver the intervention to patients with HF
[36,39,40,42,44,51]. Of these 6 studies, in 2 (33%), interactive
voice response devices were used to examine the patients’
symptoms and vital sign registrations [39,42]. In these studies,
patients were instructed to call a toll-free number daily for 6
months, respond to a series of automated questions about their
symptoms, and enter their daily weight. Responses that met
prespecified criteria triggered a variance within the system.
Conflicts were then flagged for immediate attention by on-site
clinicians [39].

Nurses performed all telephone calls. Four dominant categories
of content and use of the telephone-supported HF interventions
were identified as follows: (1) keeping logs: encouraging
patients to keep logs for monitoring symptoms, blood pressure,
and weight; (2) goal-setting skills: teaching patients goal-setting
skills to manage their condition or behavior changes; (3)
problem-solving skills: teaching patients problem-solving skills
to manage their condition; and (4) advice about when to seek
help in case of worsening HF. In addition, education and
counseling were combined with follow-up home visits in 17%
(1/6) of the studies [36], whereas in another study, customized
HF education was provided on the patient’s response to
questions on symptoms and self-management [40]. Each
intervention session lasted 15-50 minutes. In the trial conducted
by Ritchie et al [42], support calls were provided to patients
only when required, whereas in 67% (4/6) of the studies, 4-10
calls were delivered for 2-4 months [36,40,44,51].

Telemonitoring
Of the 18 included studies, 12 (67%) included a telemonitoring
program. In 75% (9/12) of these studies, weight, heart rate, and

blood pressure were measured daily [35,37,41,45-47,49,50,52].
Athilingam et al [35] also included a medication tracker in their
HeartMapp app and physiological exercises to reset the
autonomic nervous system and improve functional capacity.
Pedone et al [52] included oxygen saturation in addition to
measuring blood pressure and heart rate daily. These studies
also used assessments of symptoms related to HF and action
plans for clinical decisions based on out-of-limit alerts from the
data monitoring. In 75% (9/12) of the studies, nurses specialized
in HF care and telemedicine, or care transition performed the
daily data monitoring [35,37,41,43,45-47,49,50]. Of the 12
telemonitoring studies, 2 (17%) provided patients with
automated feedback triggered by out-of-limit alerts [35,39]. In
cases where these alerts indicated possible mild to moderate
decompensation, nurses could promote diuretic dose adjustments
following specific protocols [45] and alerts could be routed to
clinicians (eg, physicians and cardiologists) who evaluated the
data and contacted patients if necessary. For cases in which
out-of-limit alerts indicated severe decompensation, patients
were advised to call the emergency number or go to the nearest
hospital emergency department [35,37,41,43,45-48,50]. In 17%
(2/12) of the studies, clinicians (physicians and geriatricians)
conducted data monitoring and management simultaneously
[39,52]. Of the 12 studies, 1 (8%) was a telerehabilitation
investigation in which participants were guided to self-monitor
and verbally report their blood pressure, heart rate, and oxygen
saturation levels at the start of each rehabilitation session [48].
Finally, 75% (9/12) of the telemonitoring studies provided the
participants with telephone support to either follow up on alerts
generated from the patient’s registrations of symptoms and vital
signs [43,45,49,50], technical support [48], and follow-up of
control group or as usual care [37,47] or to provide patient
education [41,52].

Adherence to Posthospitalization Follow-Up eHealth
Interventions in HF
Of the 18 included studies, 11 (61%) reported patients’
adherence to the intervention [35,38,41,42,44,45,47-50,52]. In
91% (10/11) of these studies, adherence was reported as a
secondary study outcome, whereas in 9% (1/11) of the studies,
adherence was included as a primary outcome [38]. Overall,
adherence levels were reported at a rate of 72%-99%. Among
the studies using telephone support as a delivery mode, 33%
(2/6) included measures of adherence with adherence levels of
86% [42] and 84% (T1) and 86% (T2) [44]. Details regarding
reported adherence are provided in Table 2.

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Table 2. Reporting intervention program adherence in the included studies (N=18).

Adherence resultsDefinition and assessment of adherenceAdherence
reported

Study

Adherence was low, with only 72% of the participants
completing the 30-day follow-up.

Duration for which the participants accessed interven-
tion features.

YesAthilingam et al [35]

Adherence was very high, with missed biometric daily
transmissions less than 1% of the expected number of
daily transmissions.

Daily automated telemonitoring of biometrics and
symptoms using the intervention platform.

YesComin-Colet et al [45]

——aNoDunbar et al [36]

——NoEvangelista et al [37]

——NoFrederix et al [46]

Median correct dosing adherence was 81%, and 33%
of the participants were classified as adherent. Reasons
for nonadherence were identified as follows: ran out
of pills, out of usual routine, side effects, and did not
know the correct dose.

Adherence to loop diuretics in the 30 days after dis-
charge. Nonadherence=adherence <88%. Adherence
was calculated as the percentage of days on which the
correct number of doses was taken as prescribed, irre-
spective of dose timing.

YesGallagher et al [38]

Of the 51 participants who attended the rehabilitation
programs, 49 (96%) were categorized as adherent or
partly adherent. None of the intervention participants
were nonadherent.

Attendance rates were categorized into adherent
(>80%), partly adherent (20%-80%), and nonadherent
(<20%), based on the proportion of sessions attended
by each participant.

YesHwang et al [48]

——NoJayaram et al [39]

——NoKöberich et al [51]

The mean rates of adherence at 1, 6, and 12 months
after randomization were 96%, 90%, and 91%, respec-
tively.

Adherence was measured as the number of days that
each patient measured their body weight and blood
pressure in a month.

YesKotooka et al [47]

The median adherence rate was 95% (range 87%-99%
for the total study period).

Adherence of patients to telemonitoring was assessed
by daily weighing and measuring of blood pressure.

YesKraai et al [50]

The median adherence rate was 95% (range 87%-99%
for the total study period).

Adherence of patients to telemonitoring was assessed
by daily weighing and measuring of blood pressure.

YesLycholip et al [49]b

——NoMasterson-Creber et
al [40]

Overall, 83% (591/715) of the intervention participants
used telemonitoring equipment.

Telemonitoring adherence: percentage of total days
during 30 and 180 days; telephone coaching adherence:
percentage of total days during 30 and 180 days.

YesOng et al [41]

On average, 62% of the scheduled measurements were
completed (weight once a day, blood pressure and
heart rate twice a day, and peripheral oxygen saturation
thrice a day); adherence was best for pulse oximeter
(70%) and worst for the scale (56%); 64% of the par-
ticipants completed at least half of the scheduled
measurements.

Percentage of the total amount of expected symptom
measurements.

YesPedone et al [52]

Of the patients with HF, 144 (86%) received a total
intervention dose.

Total (100%) adherence was defined as answering all
interactive voice response system calls. Optimal adher-
ence: daily response to the interactive voice response
during the first 7 days. Answering a call was defined
as a patient completing the questions of the call.

YesRitchie et al [42]

——NoSrivastava et al [43]

Participants in the intervention group who received
the patient-activated care at home intervention had
significantly higher self-reported adherence to self-
management behaviors; 84% at 3 months and 86% at
6 months reported not missing any doses in the previ-
ous week, compared with 68% at both time points in
the control group.

Frequencies of self-reported self-management behav-
iors of daily weighing, following a low-sodium diet,
taking prescribed medications, exercising, and attend-
ing follow-up appointments.

YesYoung et al [44]

aData not available.
bSame study population and intervention as in the study by Kraai [50].

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Effects From Follow-Up Interventions on Patient
Outcomes

Overview
Of the 18 included studies, only 1 (6%) investigated all 3 patient
outcomes of interest to this review (ie, QoL, readmissions, and

self-care behavior) [45]. Included in 61% (11/18) of the studies,
QoL was the most frequently analyzed patient outcome,
followed by readmissions in 56% (10/18) of the studies.
Self-care was explored in 44% (8/18) of the included studies.
Details concerning the effects of eHealth interventions are
provided in Table 3.

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Table 3. Effects of intervention programs on patient outcomes of quality of life (QoL), self-care, and readmissions (N=18).

OutcomePostbaseline measuresBaselineSample size n (%),

Ia/Cb
Study

T2d (days), P valueT1c (days), P value

N/AeHospital discharge9/9 (50/50)Athilingam et al [35] • Self-care maintenance• .93 (30)
• .01 (30) • Self-care management
• .03 (30) • Self-care confidence
• .18 (30) • QoL

N/AHospital discharge81/97 (46/54)Comin-Colet et al [45] • Self-care• .06 (180)
• .001 (180) • QoL
• .01 (180)

• Readmissions

Hospital discharge
or within 3 months
after discharge

54/54 (50/50)Dunbar et al [36] • QoL• .002 (180)• <.001 (90)

N/AHospital discharge21/21 (50/50)Evangelista et al [37] • QoL overall• <.001 (90)
• <.001 (90) • QoL emotional subscale

• Self-care maintenance• <.001 (90)
• Self-care management• <.001 (90)
• Self-care confidence• <.001 (90)

N/AHospital discharge80/80 (50/50)Frederix et al [46] • Readmissions• .04 (180)

N/AHospital discharge20/20 (50/50)Gallagher et al [38] • Self-care (medication adherence)• .41 (30)
• .72 (30) • Readmissions

Recent discharge24/26 (48/52)Hwang et al [48] • QoL• .03 (720)• .03 (360)

Recent discharge756/765
(49.7/50.3)

Jayaram et al [39] • QoL• .04 (180)• .32 (90)

N/AHospital discharge90/91- (50/50)Kotooka et al [47] • QoL• .94 (352)
• .42 (352) • HFf readmissions

N/AHospital discharge
or outpatient clinic

94/83 (53/47)Kraai et al [50] • QoL• .62 (270)
• •.87 (270) HF readmissions

N/AHospital discharge
or outpatient clinic

58/52 (53/47)Köberich et al [51] • QoL• .20 (90)
• •<.001 (90) Self-care

N/ARecent discharge58/60 (49/51)Lycholip et al [49] • Self-care• .77 (90)

N/AHospital discharge41/26 (61/39)Masterson-Creber et
al [40]

• QoL• .36 (90)
• •.03 (90) Self-care maintenance

• Self-care confidence• .31 (90)

Hospital discharge715/722
(49.8/50.2)

Ong et al [41] • QoL• .02 (180)• .25 (30)
• ••.63 (30) Readmissions.54 (180)

N/AHospital discharge
or outpatient clinic

50/46 (52/48)Pedone et al [52] • Readmissions• .04 (180)

N/AHospital discharge245/233
(51.3/48.7)

Ritchie et al [42] • Readmissions• .18 (30)

N/ARecent discharge197/870
(18.5/81.5)

Srivastava et al [43] • Readmissions• .07 (352)

Hospital discharge54/51 (51/49)Young et al [44] • Readmissions• .09 (180)• .09 (90)
• <.001 (90) • Self-care adherence• <.001 (180)

aI: intervention.
bC: control.

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cT1: first postbaseline data collection.
dT2: second postbaseline data collection.
eN/A: not applicable.
fHF: heart failure.

Impact on QoL
QoL was included as a patient outcome in 61% (11/18) of the
studies among patients with HF [35-37,39-41,45,47,48,50,51].
Of these 11 studies, 4 (36%) found that an eHealth intervention
significantly improved patients’ overall QoL [36,37,45,48]. Of
these 4 studies, 3 (75%) contained telemonitoring combined
with telephone support and with an intervention duration of 3-6
months [35,43,45], whereas the study by Dunbar et al [36]
provided only telephone support lasting for 6 months. In both
the studies by Jayaram et al [39] and Ong et al [41], the QoL
was nonsignificant at the first postbaseline data collection. In
contrast, QoL was significantly improved in both studies 6
months after beginning the intervention [39,41]. Of the 3 studies
recruiting participants later than at discharge (ie, within 30 days
after discharge), 2 (67%) reported significant effects from an
eHealth intervention on QoL [39,48].

Self-care Behavior
Self-care was investigated in 39% (7/18) of the studies
[35,37,38,44,45,49,51]. Athilingam et al [35] and Evangelista
et al [37] both reported on self-care measured by the Self-Care
of Heart Failure Index, which included the subscales self-care
maintenance, self-care management, and self-care confidence.
Self-care management was found to be significantly increased
by eHealth interventions in both studies; in addition, Evangelista
et al [37] found that self-management maintenance also seemed
to be significantly improved. The subscale self-care confidence
was enhanced to a significant degree by an eHealth intervention
in 14% (1/7) of the studies [35]. Comin-Colet et al [45] used
the Self-care Behavior Scale to study self-care behavior in
patients with HF who were remotely followed by the Home
Tele-HealthCare platform, with the authors detecting a
marginally significant difference between the intervention and
control groups. Köberich et al [51] and Lycholip et al [49]
measured self-care behavior by using the European Heart Failure
Self-care Behavior Scale. Of the 7 studies, 1 (14%) found
significant improvements from the eHealth interventions on
self-care behavior [51], whereas Lycholip et al [49] determined
that such interventions did not influence self-care behavior; this
study recruited patients within 14 days after discharge [49].
Finally, in the study by Gallagher et al [38], self-care was
defined as medication adherence, with no significant effect from
the eHealth intervention being noted. The studies showing
significant effects on self-care behavior delivered the
interventions for 30 days to 6 months. Of the 7 studies, only 1
(14%) did not include digital monitoring of symptoms and vital
signs [46].

Readmissions
Of the 18 studies, 10 (56%) included readmissions as a patient
outcome [38,41-47,50,52]. A significant reduction in
readmissions associated with eHealth interventions was detected
in 30% (3/10) of these studies [45,46,52]. All the studies
combined telemonitoring and telephone support as the

intervention delivery mode, and the intervention lasted for 6
months. Comin-Colet et al [45] found a significant reduction
in readmissions in the HF intervention group compared with
controls. The study by Frederix et al [46] identified a significant
reduction (P=.04) in days lost to HF-related readmissions among
patients in the intervention group but not for all-cause
readmissions (P=.26). Pedone et al [52] revealed a significantly
(P=.04) higher risk of readmissions (42%) at 180 days in the
control group compared with 21% for patients with HF who
were given remote follow-up. None of the studies that recruited
patients later than discharge achieved significant effects on
readmissions [43,44,47].

Discussion

Summary of Evidence and Comparison With Prior
Work
In this restricted systematic review, we have evaluated and
synthesized the findings from 18 posthospitalization follow-up
eHealth interventions targeting QoL outcomes, self-care, and
readmissions of patients with HF. To summarize, patients with
HF were enrolled in the interventions upon or after hospital
discharge. Interventions were delivered mainly by telephone or
email and focused on patient education and counseling, social
and emotional support, and self-monitoring of vital signs and
symptoms. Posthospitalization eHealth follow-up for patients
with HF holds potential for improving their QoL, whereas a
positive impact on self-care and readmissions is less evident.

Some of the included studies used more traditional tools to
follow up with patients, such as the telephone. Because of its
familiarity and ease of use, the telephone may be appropriate
to contact patients remotely. Individuals at risk of low eHealth
literacy, such as older or less educated patients, may benefit
from using a more traditional eHealth tool such as the telephone
[53]. However, when comparing the effects on patient outcomes
from studies using eHealth solutions other than the telephone
as the delivery mode, telephone interventions do not stand out
as more or less appropriate. We found that telephone
interventions as a delivery mode effectively improved patients’
self-care behaviors, but the effects on QoL and readmissions
were less promising. This finding suggests that self-care
follow-up is likely to be more important than the specific mode
of follow-up.

Most studies in this review included features that required
patients to monitor their vital signs and report health behaviors
and symptoms. Giving patients with HF a more active role in
their healing processes through posthospitalization eHealth
interventions may promote their experience as true partners in
shared decision-making, improve their well-being, and result
in better adherence to treatment [54]. However, the value of
eHealth interventions as part of health care for patients who are
chronically ill may vary. Runz-Jørgensen et al [55] found that
patients with multimorbidity and more significant illness and

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treatment burden perceived eHealth interventions as more
favorable than those with less complex disease and treatment.
This result may be explained by considering the burdens of HF
[55]. Patients with HF are vulnerable because they require
regular and ongoing disease monitoring and management to
reduce the risk of deterioration, and many fragile patients with
HF have limited access to the health care system. The
COVID-19 pandemic has forced health care systems to
re-evaluate reimbursement for eHealth solutions to promote
more widespread adoption of HF care [15,56,57].

We believe that for patients with HF to perceive the eHealth
follow-up service as appropriate and be willing to use it, the
timing of the introduction of the service is a crucial factor. In
our review, patients were primarily enrolled in the eHealth
interventions upon hospital discharge, ensuring patient support
immediately after release. However, of the 18 included studies,
5 (28%) recruited patients during the first 4 weeks after
discharge, demonstrating that eHealth interventions significantly
increased QoL but had little impact on readmissions and
self-care. Nevertheless, the findings of the effects from eHealth
follow-up on patient outcomes suggest that patients with a severe
heart condition benefit from prompt posthospitalization
follow-up. It may be essential to provide patients with HF with
self-care support at discharge to avoid 30-day readmission.

Remote monitoring as a feature of eHealth interventions may
include parameters for detecting symptom and illness
deterioration, successfully reducing readmissions among patients
with HF [19-21]. However, in our study, the effects on
readmissions from remote monitoring were inconclusive. For
monitoring to be successful, aspects of measurement reliability
and frequency, patient interface and adherence, and prompt
interpretation by health professionals need to be considered
[58]. Most of the included studies involving remote monitoring
also provided contact with health care professionals, mainly
nurses, who regularly stayed in touch with patients by either
telephone calls or email. Koivunen and Soranto [59] identified
communication and patient–nurse relationships as essential
factors of telehealth in nursing practice. Patient–nurse
interactions enable nurses to inquire about and assess patients’
self-care needs and symptoms, express empathy, and increase
patients’ sense of security [58]. Another vital aspect of the
patient–nurse interaction in the included eHealth interventions
was whether the technology was acceptable to patients. Lack
of required engagement among patients may be attributed to
the nature of the technology [60], and patient adherence to the
system is crucial for an intervention’s success. Ding et al [61]
found high adherence to the intervention component of weight
monitoring (6 out of 7 days) in their recent telemonitoring RCT
of patients with HF (published after our literature research).
The intervention resulted in a significant improvement in
self-management related to health maintenance, medication
adherence, and diet [61]. We found that intervention adherence
in most of the remote monitoring studies with patient–nurse
interaction was 81%-99%. Comin-Colet et al [62] found that
despite low expectations among patients before entering a
telemedicine HF care intervention, adherence and satisfaction
levels were high during the intervention, likely because of the
HF care teams’ proactive engagement with patients [62].

Clinicians who practice patient-centered communication
adopting the patient’s perspective may contribute to increased
adherence levels in patients with HF, particularly during care
transitions such as discharge from hospital to home, which to
many patients can be confusing and demanding related to
follow-up on treatment regimens [63]. The World Health
Organization states that the quality of the treatment relationship
is an essential determinant of adherence [26].

eHealth interventions have excellent potential to reinforce
patient education on self-care [53]. Most of the reviewed studies
in this review provided patients with education or counseling
delivered by nurse specialists before the trial or during the trial,
covering disease- and treatment-specific topics, psychosocial
issues, and health behavior change. These studies seem to
support improved self-care from eHealth interventions that
include an educational aspect. Although the educational focus
of many eHealth and mHealth interventions is illness
management [64], a more holistic approach to self-care
education not limited to only disease management is suggested.
According to Lewis et al [65], addressing the holistic needs of
patients with comorbidities using eHealth technology supports
more patient-centered health care. Interestingly, of the 18
included studies, only 1 (6%) assessed changes in patients’
knowledge at the completion of the intervention period. This
study found that an HF education program involving iterative
teaching tools expanded patients’ HF knowledge [35]. This
finding is in line with a review by Bashi et al [64], in which
only 2 of the 15 mHealth interventions included an evaluation
of patient knowledge as a study outcome. On the contrary, a
recent Cochrane review of mHealth-delivered educational
interventions for patients with HF found no evidence of a
difference in HF knowledge or other patient-reported
outcomes [66]. However, validated tools of patient knowledge
can be an efficient measure of intervention success, and an
assessment of patient knowledge as part of eHealth protocols
is recommended [66].

Limitations
Several limitations should be mentioned. First, heterogeneity
in the included studies made meta-analysis impossible, and a
qualitative thematic analysis was applied. Such an analysis is
prone to interpretation bias [33]. Second, the included eHealth
interventions pertain to the transition phase between hospital
and home, thus limiting generalizability to all stages of
follow-up of patients with HF. Third, although most of the
included studies indicated good methodological quality, most
of them did not apply a blinded randomization process, and
50% (9/18) of the studies did not report adherence to the
intervention.

Conclusions
This review identified 18 studies of posthospitalization
follow-up interventions in patients with HF. Most of the
included studies enrolled patients in eHealth interventions upon
hospital discharge to ensure support in the critical post–hospital
discharge period. The most common mode for
posthospitalization follow-up was telemonitoring with telephone
support. Patients received education or counseling about their
disease, psychosocial issues, and health behavior changes. Most

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studies also required the patients to monitor vital signs and
report their health behaviors and symptoms.

The findings of the effects of interventions on patient outcomes
such as QoL, self-care, and readmissions propose that patients
with HF should receive prompt follow-up after hospital
discharge. eHealth interventions, including patient education,
support, and self-monitoring, have the potential to improve
QoL, but it is less clear how eHealth interventions affect
self-care behavior and readmissions in populations of patients
with HF.

Aspects of measurement reliability and frequency, user interface
and adherence, and prompt interpretation by health professionals

need to be considered to ensure successful monitoring in eHealth
interventions. These findings are important to inform future
intervention studies to support patients with HF after discharge
from the hospital. eHealth interventions have the potential to
improve well-being, adherence to treatment, and patients’
experiences of being engaged partners in shared
decision-making.

Systematic reviews of the literature are recommended during
the planning and development of complex interventions [29].
The findings from this review will be used to inform the
development of a post–hospital discharge follow-up service
addressing the burden of treatment and self-management among
patients with HF.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Documentation of the literature search.
[PDF File (Adobe PDF File), 103 KB-Multimedia Appendix 1]

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A systematic review and meta-analysis of the outcomes of structured telephone support or telemonitoring as the primary
component of chronic heart failure management in 8323 patients: abridged Cochrane review. Eur J Heart Fail 2011
Sep;13(9):1028-1040 [FREE Full text] [doi: 10.1093/eurjhf/hfr039] [Medline: 21733889]

61. Ding H, Jayasena R, Chen SH, Maiorana A, Dowling A, Layland J, et al. The effects of telemonitoring on patient compliance
with self-management recommendations and outcomes of the innovative telemonitoring enhanced care program for chronic
heart failure: randomized controlled trial. J Med Internet Res 2020 Jul 08;22(7):e17559 [FREE Full text] [doi: 10.2196/17559]
[Medline: 32673222]

62. Comín-Colet J, Enjuanes C, Lupón J, Cainzos-Achirica M, Badosa N, Verdú JM. Transitions of care between acute and
chronic heart failure: critical steps in the design of a multidisciplinary care model for the prevention of rehospitalization.
Rev Esp Cardiol (Engl Ed) 2016 Oct;69(10):951-961. [doi: 10.1016/j.rec.2016.05.001] [Medline: 27282437]

63. Piña IL, Di Palo KE, Brown MT, Choudhry NK, Cvengros J, Whalen D, et al. Medication adherence: importance, issues
and policy: a policy statement from the American Heart Association. Prog Cardiovasc Dis 2021;64:111-120. [doi:
10.1016/j.pcad.2020.08.003] [Medline: 32800791]

64. Bashi N, Fatehi F, Fallah M, Walters D, Karunanithi M. Self-management education through mHealth: review of strategies
and structures. JMIR Mhealth Uhealth 2018 Oct 19;6(10):e10771 [FREE Full text] [doi: 10.2196/10771] [Medline: 30341042]

65. Lewis J, Ray P, Liaw S. Recent worldwide developments in eHealth and mHealth to more effectively manage cancer and
other chronic diseases – a systematic review. Yearb Med Inform 2016 Nov 10(1):93-108 [FREE Full text] [doi:
10.15265/IY-2016-020] [Medline: 27830236]

66. Allida S, Du H, Xu X, Prichard R, Chang S, Hickman LD, et al. mHealth education interventions in heart failure. Cochrane
Database Syst Rev 2020 Jul 02;7:CD011845 [FREE Full text] [doi: 10.1002/14651858.CD011845.pub2] [Medline: 32613635]

Abbreviations
HF: heart failure
mHealth: mobile health
MMAT: Mixed Methods Appraisal Tool
QoL: quality of life
RCT: randomized controlled trial

Edited by R Kukafka; submitted 16.08.21; peer-reviewed by S Ye, M Johansson; comments to author 15.09.21; revised version received
09.11.21; accepted 03.12.21; published 15.02.22

Please cite as:
Morken IM, Storm M, Søreide JA, Urstad KH, Karlsen B, Husebø AML
Posthospitalization Follow-Up of Patients With Heart Failure Using eHealth Solutions: Restricted Systematic Review
J Med Internet Res 2022;24(2):e32946
URL: https://www.jmir.org/2022/2/e32946
doi: 10.2196/32946
PMID:

©Ingvild Margreta Morken, Marianne Storm, Jon Arne Søreide, Kristin Hjorthaug Urstad, Bjørg Karlsen, Anne Marie Lunde
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soap and water, wipe the electrode area with
a washcloth or gauze to roughen a small area
of the skin when appropriate. Do not use
alcohol for skin preparation; it dries out the
skin. [level B]

b. Consider daily ECG electrode changes. [level E]
c. Do not use Spo2 finger clip sensor on the ear.

[level C]
d. Place Spo2 probe on warm extremities.

[level C]

2. Assess alarm parameter settings and customize
according to individual patient’s condition and
age to reduce clinically insignificant alarms. Check
alarm settings at the start of every shift, with any

AACN Practice Alert

Scope and Impact of the Problem
Alarm fatigue is a patient safety risk, occurring when

clinicians are exposed to excessive numbers of alarms,
particularly false and clinically insignificant alarms. This
overexposure results in sensory overload and desensiti-
zation to alarms. Consequently, response to alarms may
be delayed or alarms may be missed altogether. Patients’
deaths have been attributed to alarm fatigue when a seri-
ous clinical event was missed because the alarm was not
heard or was assumed to be false.1 In recent studies,2,3
from 89% to 99% of electrocardiographic (ECG) monitor
alarms were found to be false or clinically insignificant.
To date, clinical strategies to reduce alarms and alarm
fatigue have been focused on ECG and oxygen saturation
(Spo2) alarms. However, evidence for these strategies is
limited. Interventions presented here are primarily sup-
ported by expert opinion and/or have demonstrated
success in quality improvement projects. To reduce false
and clinically insignificant alarms and alarm fatigue,
clinical units should assess their alarm burden and select
interventions that address unit-specific needs.

Expected Nursing Practice
Bedside Care Providers

1. Use technology correctly and according to manu-
facturer’s recommendations to minimize false and
technical alarms:

a. Provide proper skin preparation for ECG
electrodes. Wash the electrode area with

©2018 American Association of Critical-Care Nurses doi: https://doi.org/10.4037/ccn2018468

Managing Alarms in Acute Care Across the Life
Span: Electrocardiography and Pulse Oximetry

AACN Levels of Evidence
Level A Meta-analysis of quantitative studies or metasyn-

thesis of qualita tive studies with results that consis tently
support a specific action, intervention, or treatment
(including systematic review of randomized controlled
trials)

Level B Well-designed, controlled studies with results that
consistently support a specific action, intervention, or
treatment

Level C Evidence from qualitative, systematic reviews of
qualitative, descriptive, or correlational studies, or
randomized controlled trials with inconsistent results

Level D Peer-reviewed professional organizational
standards with clinical studies to support recommen-
dations

Level E Multiple case reports, theory-based evidence from
expert opinions, or peer-reviewed professional organiza-
tional standards without clinical studies to support
recommendations

Level M Manufacturer’s recommendations only

e16 CriticalCareNurse Vol 38, No. 2, APRIL 2018 www.ccnonline.org

change in patient condition and with any change
in caregiver. Customize alarms according to unit
or hospital policy. [level E]

Nursing Leaders
1. Establish interprofessional teams to gather alarm-

related data and address issues related to alarms,
such as the development of policies and proce-
dures. Consider developing a culture of suspend-
ing alarms when staff are at the bedside performing
patient care that may produce false alarms. Stan-
dardize monitoring practices across clinical envi-
ronments. Develop policies and procedures for
nurses to customize bedside monitor alarms. [level E]

2. Ensure that the unit’s default alarm settings are
appropriate for the patient population. Collaborate
with an interprofessional team, including biomedi-
cal engineering, to determine the appropriate default
alarm settings for the unit’s patient population.
Adjustments may include changes to alarm param-
eter limits, on/off status, delay status, and priority
level of alarm. [level E]

3. Provide initial and ongoing education for end users
of devices with alarms. Provide education on moni-
toring systems and alarms, as well as operational
effectiveness, to new nurses and all staff members
on a periodic basis. Budget for ongoing education
when purchasing monitoring systems. [level E]

4. Consider use of an alarm notification system (eg, mid-
dleware, monitor watchers/technicians). [level E]

5. Monitor only those patients with clinical indications
for monitoring. Collaborate with an interprofessional
team to determine which patients in a population
or care unit should be monitored and what param-
eters to use. When appropriate, use the American
Heart Association’s Update to Practice Standards
for Electrocardiographic Monitoring in Hospital
Settings.4 [level C]

Supporting Evidence
Bedside Care Providers

1. Use technology correctly and according to manu-
facturer’s recommendations.

a. Provide proper skin preparation for ECG
electrodes. Research,5 quality improvement
reports,6-8 and expert opinion9-11 support
proper skin preparation to decrease the

number of false and technical alarms. Proper
skin preparation before ECG electrodes are
placed decreases skin impedance and signal
noise, thereby enhancing conductivity.11
Washing the electrode area with soap and
water, wiping with a washcloth or gauze, or
when appropriate using the sandpaper on
the electrode to roughen the skin (which
helps remove part of the stratum corneum
[outer layer of the epidermis] to reduce
impedance) is the recommended skin
preparation.4,5,10 Excessive hair at the
electrode site should be clipped.10 Proper
skin preparation has been included in quality
improvement projects on alarm management
as one component of bundled interventions
that demonstrated reductions in alarms of
44% to 89%.6-8

b. Consider daily ECG electrode changes.
Expert opinion10 and results of quality
improvement projects6-8,12,13 suggest that
changing ECG electrodes daily may decrease
the number of false and technical alarms.
In 3 quality improvement projects, daily
electrode changes with proper skin prepara-
tion resulted in a 19% to 46% reduction in
alarms.6,8,12 In another quality improve-
ment project, daily electrode changes were
included in a bundled intervention, and
alarms were reduced overall by 89%.7 A
pediatric quality improvement project that
included daily electrode changes in an alarm
management bundle resulted in 55% reduc-
tion in alarms.13 The effect of daily elec-
trode changes as an intervention to reduce
alarms has not been confirmed through
research. Daily electrode changes may not
be appropriate in patients with fragile skin
such as older adults or premature infants.
Soaking electrodes with water during the
patient’s bath may reduce pain during
electrode removal.13

c. Do not use Spo2 finger clip sensor on the
ear. In a study of 30 adult patients under-
going pulmonary function tests, Haynes14

www.ccnonline.org CriticalCareNurse Vol 38, No. 2, APRIL 2018 e17

e18 CriticalCareNurse Vol 38, No. 2, APRIL 2018 www.ccnonline.org

demonstrated that a pulse oximeter finger
clip placed on an ear did not provide
clinically reliable Spo2 readings when
compared with arterial blood gas analysis.

d. Place Spo2 probe on warm extremities.
Temperature was found to have an impact
on the degree of pulse oximetry error in the
operating room.15

2. Assess alarm parameter settings and customize
according to the individual patient’s condition.
Customizing alarm settings on the bedside moni-
tor to reflect a patient’s condition-specific factors
and age may reduce clinically insignificant alarms.
For example, turning off the atrial fibrillation
alarm for a patient with known atrial fibrillation
that will not be treated would eliminate alarms
that are not actionable for that patient. Education
on alarm customization has been provided as part
of several quality improvement projects that have
demonstrated reductions in total alarms.7,16-18
Nurses should check alarm settings to ensure that
the settings are appropriate for the patient’s condi-
tion at the start of each shift, with any change in
the patient’s condition, and with any change in
caregiver, and nurses should customize alarm
settings in accordance with unit or hospital policy.

Nursing Leaders
1. Establish interprofessional teams to gather alarm-

related data and address improvement opportuni-
ties related to alarms. Using an interprofessional
team approach with stakeholders from the clinical,
technical, and information technology communi-
ties to gather alarm data and develop policies and
response algorithms helps to reduce alarms.13,17,19-22
The interprofessional team should include staff
nurses. Gathering alarm data will assist in deter-
mining the alarms that are most problematic for
the specific unit (eg, false alarms, clinically insignif-
icant alarms, technical alarms, duplicate alarms).
The interprofessional team can establish policies
to provide direction on which patients to monitor
and on appropriate alarm parameters to optimize
alarm systems and reduce alarms. For example,
the policy should include appropriate suspension
of alarms during patient care, which can decrease

the number of audible alarms by 20%.23 Incorpo-
rating this practice into nursing standards of care
and unit orientation fosters a culture of appropri-
ate alarm use, leading to safer environments for
patients.21,22

2. Ensure default alarm settings are appropriate for
the patient population. Changing the unit’s alarm
default settings has decreased alarm rates,17,24
most likely by reducing the number of clinically
insignificant alarms. In a medical-surgical unit
with telemetry monitoring, changing the alarm
default for high heart rate from 120 to 130 beats
per minute resulted in a 50% decrease in the num-
ber of alarms.25 In a small pilot randomized trial,26
researchers investigated changes in default alarm
settings as a method for reducing alarms. Several
quality improvement projects have included changes
to unit default settings as part of bundled interven-
tions that resulted in reduction in the overall num-
ber of alarms. These changes included changing
the priority level of an alarm parameter, such as
changing the alarm for ventricular tachycardia for
>2 beats from high to low priority/nonaudible,16,26
eliminating duplicate alarms,7,12,18 changing alarm
parameter default settings from on to off (eg, alarms
for premature ventricular contractions),7,27-29 and
widening alarm parameter limits (eg, increasing
high heart rate limit and decreasing low heart rate
limit).16,18,30 Widening alarm parameter limits was
also supported by a recent systematic review.24
In a simulation study, increasing Spo2 alarm delays
from 5 to 15 seconds decreased alarms by 70%,
and decreasing the alarm limits from 90% to 88%
decreased alarms by 45%.22 By combining these
2 approaches, alarms were reduced by 85%. In a
pediatric quality improvement project, the Spo2
alarm delay was increased from 5 to 10 seconds
and the high respiratory rate limit was increased,
which resulted in an additional 25% reduction in
alarms on the unit.13

However, changing default alarm parameter set-
tings must be undertaken with caution because of
the potential patient safety risk if actionable alarms
are inadvertently eliminated by default alarm set-
tings that are too wide or are inappropriate for the
patient population.24,31 An interprofessional team

www.ccnonline.org CriticalCareNurse Vol 38, No. 2, APRIL 2018 e19

should determine the appropriate default alarm
settings for the unit’s patient population. In
addition to changing the unit’s default alarm
settings, consider development of alarm limit
profiles for specialty patient groups (eg, based
on age or diagnosis).

3. Provide initial and ongoing education on devices
with alarms. Education increases understanding
of how monitoring systems and their alarms should
be managed.17,32 Quality improvement projects to
reduce alarms have included education of nursing
staff.18,28 One project demonstrated that after
receiving education and retraining, nurses indi-
vidualized alarm settings at the outset, instead of
adjusting settings in response to continual activa-
tion of an alarm.18 Education must be robust,
given the complexity of monitoring systems.28
The cost for educating end users of technology
should be included in budgets.

4. Consider use of an alarm notification system. Alarm
notification systems (eg, middleware, monitor
watchers/technicians) are an additional safety
measure to help nurses manage alarms. Notifying
nurses of alarms via pagers or phones may be
useful on units where alarm audibility is difficult
because of the unit’s layout.33 Escalation rules and
delays can be programmed into some systems to
route an alarm to another caregiver if no response
is received and to decrease the number of alarms
to which the nurse is exposed.16,33 Reduction of
alarms was demonstrated in a quality improve-
ment project using a paging system with an alarm
escalation strategy and programmed delay times.33
Successful implementation requires unit-specific
decisions about the type of device used, which
alarms are forwarded to the device, and what rules
are in place for delays, acknowledgment, and esca-
lation.34 One study has demonstrated the potential
for monitor watchers to reduce nurses’ exposure to
alarms by intercepting false and clinically insignifi-
cant alarms.35 Insufficient evidence exists to support
the use of monitor watchers to improve patients’
outcomes,36 although other potential benefits have
been suggested, such as reducing nurses’ time man-
aging technical issues.4,37 One study demonstrated

faster communication between monitor watchers
and nurses using a 2-way communication badge,
compared with a 1-way pager system.38 Imple-
menting alarm notification systems requires cau-
tion to ensure that alarm fatigue is not exacerbated
by increasing the number of notifications to which
nurses are exposed.

5. Monitor only those patients with clinical indica-
tions for monitoring. Expert opinion and research
recommend monitoring only those patients with
clinical indications for monitoring and for only as
long as necessary, which can significantly decrease
the number of clinically insignificant alarms.39-41
An interprofessional team should determine which
patients in a population or care unit should be mon-
itored and for what parameters. In 2017, the Amer-
ican Heart Association published an update of their
2004 standards for ECG monitoring in hospitalized
patients, specifying who should be monitored and
for how long.4

Implementation/Organizational Support
for Practice

Bedside Care Providers
Provide proper skin preparation for and placement

of ECG electrodes.
Use proper Spo2 probe and placement.
Check alarm settings at the start of each shift, with

any change in the patient’s condition, and with any
change in caregiver.

Customize alarm parameter settings for individual
patients in accordance with unit or hospital policy.

Nurse Leaders
Organize an interprofessional alarm management

team.
Develop unit-specific default parameters and alarm

management policies.
Provide ongoing education on monitoring systems

and alarm management for unit staff.
Develop policies/procedures for monitoring only

those patients with clinical indications for monitoring.

Need More Information or Help?
1. Contact a clinical practice specialist for additional

information: Go to www.aacn.org, click Clinical

e20 CriticalCareNurse Vol 38, No. 2, APRIL 2018 www.ccnonline.org

Resources, and scroll down to select AACN Practice
Resource Network.

2. AAMI Foundation alarm resources: http://www
.aami.org/thefoundation/content.aspx?
ItemNumber=1730

3. ECRI Institute alarm resources: https://www.ecri
.org/resource-center/Pages/Alarms.aspx

4. National Association of Clinical Nurse Specialists
Alarm Fatigue Toolkit: http://nacns.org/professional
-resources/toolkits-and-reports/alarm-fatigue
-toolkit/

5. The Joint Commission National Patient Safety Goal
on clinical alarm safety (NPSG.06.01.01): https://
www.jointcommission.org/assets/1/6/NPSG
_Chapter_HAP_Jan2017.pdf

Original Authors: Stacy Jepsen, MS, APRN, ACNS-BC, CCRN, and
Susan Sendelbach, RN, PhD, CCNS, FAHA

Contributing Authors: Halley Ruppel, RN, MSN, CCRN, Marjorie
Funk, RN, PhD, FAHA, FAAN, and Sharon Wahl, MSN, APRN-CCNS, CCRN

Approved by the Clinical Resources Task Force, August 2017.

Financial Disclosures
None reported.

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