JMIR FORMATIVE RESEARCH

Po et alOriginal Paper

Efficacy of Remote Health Monitoring in Reducing Hospital
Readmissions Among High-Risk Postdischarge Patients:
Prospective Cohort Study
Hui-Wen Po1, MSc, RN; Ying-Chien Chu1, RN; Hui-Chen Tsai1, RN; Chen-Liang Lin2, PhD; Chung-Yu Chen3, MD,
PhD; Matthew Huei-Ming Ma4, MD, PhD
1

Department of Nursing, National Taiwan University Hospital Yunlin Branch, Douliou, Taiwan

2

Biomedical Technology and Device Research Labs, Industrial Technology Research Institute, Hsinchu, Taiwan

3

Department of Internal Medicine, National Taiwan University Hospital Yunlin Branch, Douliou, Yunlin, Taiwan

4Department of Emergency Medicine, National Taiwan University Hospital Yunlin Branch, Douliou, Taiwan

Corresponding Author:
Chung-Yu Chen, MD, PhD
Department of Internal Medicine
National Taiwan University Hospital Yunlin Branch
No.579. Sec 2,
Yunlin Rd
Douliou, Yunlin, 640
TaiwanPhone: 886 055323911
Email: c8101147@ms16.hinet.net

Abstract
Background: Patients with respiratory or cardiovascular diseases often experience higher rates of hospital readmission due tocompromised heart-lung function and significant clinical symptoms. Effective measures such as discharge planning, casemanagement, home telemonitoring follow-up, and patient education can significantly mitigate hospital readmissions.
Objective: This study aimed to determine the efficacy of home telemonitoring follow-up in reducing hospital readmissions,emergency department (ED) visits, and total hospital days for high-risk postdischarge patients.
Methods: This prospective cohort study was conducted between July and October 2021. High-risk patients were screened for
eligibility and enrolled in the study. The intervention involved implementing home digital monitoring to track patient healthmetrics after discharge, with the aim of reducing hospital readmissions and ED visits. High-risk patients or their primary caregivers
received education on using communication measurement tools and recording and uploading data. Before discharge, patientswere familiarized with these tools, which they continued to use for 4 weeks after discharge. A project manager monitored thedaily uploaded health data, while a weekly video appointment with the program coordinator monitored the heart and breathingsounds of the patients, tracked health status changes, and gathered relevant data. Care guidance and medical advice were provided
based on symptoms and physiological signals. The primary outcomes of this study were the number of hospital readmissions andED visits within 3 and 6 months after intervention. The secondary outcomes included the total number of hospital days and patientadherence to the home monitoring protocol.
Results: Among 41 eligible patients, 93% (n=38) were male, and 46% (n=19) were aged 41-60 years, while 46% (n=19) wereaged 60 years or older. The study revealed that home digital monitoring significantly reduced hospitalizations, ED visits, andtotal hospital stay days at 3 and 6 months after intervention. At 3 months after intervention, average hospitalizations decreased
from 0.45 (SD 0.09) to 0.19 (SD 0.09; P=.03), and average ED visits decreased from 0.48 (SD 0.09) to 0.06 (SD 0.04; P<.001).
Average hospital days decreased from 6.61 (SD 2.25) to 1.94 (SD 1.15; P=.08). At 6 months after intervention, averagehospitalizations decreased from 0.55 (SD 0.11) to 0.23 (SD 0.09; P=.01), and average ED visits decreased from 0.55 (SD 0.11)
to 0.23 (SD 0.09; P=.02). Average hospital days decreased from 7.48 (SD 2.32) to 6.03 (SD 3.12; P=.73).
Conclusions: By integrating home telemonitoring with regular follow-up, our research demonstrates a viable approach to
reducing hospital readmissions and ED visits, ultimately improving patient outcomes and reducing health care costs. The practicalapplication of telemonitoring in a real-world setting showcases its potential as a scalable solution for chronic disease management.

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Po et al(JMIR Form Res 2024;8:e53455) doi: 10.2196/53455
KEYWORDS
telemonitoring; discharge planning; case manager; hospital readmission; telehealth; remote healthcare; high-risk; post-discharge;
respiratory disease; respiratory diseases; cardiovascular disease; cardiovascular diseases; case management; patient education;
readmission; health status tracking; care guidance; medical advice; male; men; older adult; older adults; elder; elderly; older
person; older people; home monitoring; physiological signal; physiological signals; mobile phone

Introduction
Health is a fundamental human right, and health care services
are crucial for maintaining well-being. By 2030, the World
Health Organization (WHO) aims to achieve universal healthcoverage, ensuring equitable access to comprehensive andquality care throughout the life of a person, regardless offinancial means or socioeconomic status [1,2]. However,reaching this goal requires significant reform of existing healthcare systems and delivery models. Quality health care involves
providing timely and appropriate treatments tailored toindividual needs, while minimizing risks and conserving
resources [3]. Despite these aspirations, individuals in
economically disadvantaged regions face significant barriers toaccessing high-quality medical care. These challenges include
a severe shortage of health care providers, with only 14.3% to44.3% of the required workforce available; inadequate
diagnostic accuracy, ranging from 34.0% to 72.2%; andsuboptimal adherence to clinical protocols, with compliance
rates between 22.0% and 43.8% [1].
The lack of health care resources exacerbates the difficulty ofmaintaining continuous care for patients before and after acuteillnesses or injuries. The WHO has identified two major
challenges for these regions: (1) achieving “patient-centeredhealth care” and (2) ensuring “health care continuity” [4]. These
global challenges are also evident in the rural areas of Taiwan,
a country known for its comprehensive health insurance system.
In these regions, modern and seemingly costly information
communication technology and telemedicine hold significant
potential to improve health care quality. The growing older
adult population, with its complex health needs, has increased
the demand for critical care. Patients previously treated inintensive care units (ICUs) for conditions such as heart failureor respiratory distress are at high risk of relapse or deterioration
after discharge. Preventing postdischarge regression andpromptly identifying complications are crucial for both medical
practice and public health [5].
The transition of ICU patients to general wards often requires
prolonged hospital stays due to concerns about postdischarge
exacerbations. As patients near the end of their hospital stay,
the focus often shifts to ensuring physiological stability. Thisissue is particularly relevant globally in the context of aging
populations, population mobility, and situations where primarycare decision-makers are not close to older adult patients [6].
Currently, the benchmarks, metrics, methodologies, and benefitsof home monitoring or telemonitoring using portable devices,
wearable devices, and the internet of things remain unclear.

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While home monitoring for stable chronic patients may notshow significant advantages due to the low likelihood of adverse
events, the predictive value of various physiological data, bodily
signals, and patient-reported symptoms in the early
postdischarge period is not well understood [7-10].
Building on the intelligent home care system, our study focuseson managing high-risk patients during the postdischarge phase.
This includes those who received ICU treatment, high-risk
discharges, and patients with thoracic and cardiovascularconditions with high readmission rates. The primary goal is tomonitor disease control status, medication adherence, vital signchanges, and self-care ability. We are also conducting acomprehensive prospective analysis of factors contributing torecurrent readmissions among high-risk patients andadministering follow-up questionnaires to identify risk factors
for predicting readmission probabilities. Our overarching aim
is two-fold: (1) to reduce postdischarge readmission rates among
high-risk patients and (2) to address the digital divide challenges
encountered in implementing intelligent home care devices.

Methods
High-Risk Discharged Patient Home Care Network
Based at National Taiwan University Hospital Yunlin Branch,
the telehealth smart discharge care network (iCARE) wasestablished. Network members include attending physicians
and case managers from the National Taiwan University
Hospital Yunlin Branch, relevant patients and their family
members, as well as primary care physicians in Yunlin. Casemanagers are responsible for coordinating the home monitoringprocess for high-risk discharged patients, with a monitoring
period of 2-8 weeks. Primary care physicians visit the patients
before discharge and actively participate throughout themonitoring process. After discharge, patients input and uploaddisease-related symptoms and cardiopulmonary physiologicalparameters through home monitoring devices. Case managers
schedule regular video consultations with patients and theircaregivers to inquire and record information. When necessary,
they can contact the collaborative care team, including the
National Taiwan University Hospital Yunlin Branch and primarycare physicians. In response to changes in the patient’s
condition, members of the collaborative care team, including
primary care physicians or hospital case managers andphysicians, can provide verbal guidance to the patients or theircaregivers. If necessary, the primary care physician can visit
the patient for further assessment and management. The
objective is to ensure that patients can smoothly transition backto the community and prevent readmissions (Figure 1).

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Po et alFigure 1. Efficient home monitoring for high-risk discharged patients.

Case managers oversee 2-8 weeks of home monitoring forhigh-risk discharged patients, collaborating with primary care
physicians. Patients use monitoring devices to input data. Casemanagers schedule video consultations and contact the careteam, including primary care physicians and the hospital. The
team offers guidance and in-person visits when needed to ensure
a smooth community transition and reduce readmissions.
The primary outcomes of this study were the number of hospital
readmissions and ED visits within 3 and 6 months after
intervention. The secondary outcomes included the total numberof hospital days and patient adherence to the home monitoring
protocol.

The Artificial Intelligence of Things–Based Home
Surveillance and Intelligent Case Management
Hospital case management and discharge planning have been
implemented for many years, and the effectiveness of casemanagement and discharge planning can generally be reflected
in terms of hospitalization days and readmission rates, indicating
the quality of health care. Previous research has shown thatdisease-related factors contribute to prolonged hospital stays.
Studies have also found higher readmission rates among patientswith acute coronary syndrome, heart failure, pneumonia, andchronic obstructive pulmonary disease (COPD). However,besides the disease itself, there is limited clinical information
available for analysis to predict or plan discharge preparation
services. In this study, retrospective research was conductedusing the electronic medical record database of National Taiwan
University Hospital Yunlin Branch, including basic information,such as gender, age, diagnosis department, medication status,and primary caregivers. High-risk discharged patients were
defined by criteria including activities of daily living scorebelow 60, presence of tubes, pressure injuries or nonclean
wounds, poor chronic disease control, readmission within 14
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inadequate primary caregiver capability, and living alone. The
goal was to understand the probability of prolonged hospital
stays and readmissions after discharge and to analyze relevantrisk factors.
Through home monitoring and communication devices, an“intelligent case management” system was established.
Symptoms, cardiopulmonary signals, and other relevant
physiological parameters of high-risk patients at discharge were
measured regularly using various physiological monitoring
devices connected to home mobile devices. A home monitoringmodel was established to track disease-related changes andphysiological trends, as well as the progress of care, throughthe management platform. Case managers supervised andcoordinated the monitoring process, keeping important membersof the collaborative care team and family members informed.
If necessary, the collaborative care team provided care guidance
and medical advice based on changes in the condition of thepatient.

Definition and Selection of High-Risk Patients
The target population includes patients diagnosed with acutecoronary syndrome, heart failure, pneumonia, and COPD, or
those who have received treatment in the ICU of the National
Taiwan University Hospital Yunlin Branch and have beentransferred to general acute wards. Before discharge, thesepatients are assessed by ward nurses or discharge planning casemanagers. Patients are classified as high-risk if they meet thefollowing criteria: (1) exceeding the average length of stay forthe specific disease or condition—patients who have been
hospitalized for a duration longer than the average length ofstay typically required for their diagnosed condition; (2)exceeding a specific number of days of hospitalization basedon experience—patients whose hospitalization period surpasses
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a certain threshold determined by clinical experience andhospital protocols; and (3) meeting the service requirements
based on high-risk screening—patients who are identified as
high-risk through a screening process that evaluates their needfor postdischarge services, considering factors such as theseverity of illness, comorbidities, and potential for readmission.
Common indicators for high-risk screening include (1)functional assessment score below 60 on the activities of dailyliving scale, (2) presence of tubes or catheters, (3) wounds orulcers, (4) poor control of chronic conditions, (5) readmission
within 14 days due to the same condition, (6) economic issues,(7) insufficient capabilities of the primary caregiver, and (8)living alone.
The following situations indicate high-risk patients at discharge:(1) patients with cardiovascular or pulmonary diseases
complicated by acute respiratory failure or heart failure requiring
admission to the ICU; (2) patients who need to use
antihypertensive medications, oxygen equipment, or positive
pressure ventilators at home after discharge; (3) patients witha lung function of forced expiratory volume 1% <50% orsignificant symptoms (modified Medical Research Council[mMRC]≥2 or COPD Assessment Test [CAT]≥10); (4) patientswith a left ventricular ejection fraction <40% or significant
symptoms (mMRC≥2); (5) patients with a BMI<16 requiring
active nutritional care intervention.

Po et alSurveying the Correlation of the Digital Divide
Through a Questionnaire
The study was conducted at the National Taiwan University
Hospital Yunlin Branch. Recruitment took place between Julyand October 2021. Eligible high-risk patients were identified
and enrolled before their discharge. The study targeted patientsdiagnosed with acute coronary syndrome, heart failure,pneumonia, and COPD, or those who had received treatmentin the ICU and were transferred to general acute wards. The
recruitment process involved screening patients based on
specific high-risk criteria, including prolonged hospital stays
and poor control of chronic conditions. Primary caregivers wereinvolved in the education and monitoring process, ensuring
comprehensive postdischarge care.
Using a closed-ended questionnaire, factors contributing to thedigital divide, such as age, literacy level, education level, andproficiency in using smart devices, will be investigated to gaina better understanding of the occurrence rate of barriers in device
usage, image recognition, questionnaire completion, and datatransmission during the learning and utilization process(Multimedia Appendix 1). This information will serve as a basis
for improvement, aiming to reduce obstacles encountered byindividuals and enhance their willingness to use digital tools.

Ethical Considerations

For high-risk patients with cardiovascular and pulmonary
diseases receiving home care, the following indicators should
be monitored, and the corresponding monitoring items andparameters should be uploaded to the internet or cloud: (1)patient-reported symptoms, including chest tightness, cough,sputum production, breathing condition, and sleep quality; (2)vital signs, including body temperature, heart rate, respiratory
rate, blood pressure, blood oxygen level, and level ofconsciousness; (3) respiratory and heart sound monitoring,
including assessing for the presence of rales or increased
wheezing; (4) electrocardiogram, peak expiratory flow rate,speech volume, and speech rate; (5) basic physical examination,
including assessing general appearance, range of motion inlimbs, and peripheral circulation; (6) basic care skills, including
feeding, repositioning, percussion on the back, chest drainage
posture, hygiene for bowel and bladder management, andbathing or body cleaning; and (7) verification of other carerecords, including confirming the depth and size of pressure
ulcers or wounds and the healing process, exercise or
rehabilitation activities, dietary content, and intake and output
measurements.

The study was conducted following the ethical principles
outlined in the Declaration of Helsinki. The Institutional Review
Board (IRB) of the National Taiwan University Hospital
approved this study (IRB 202103102RINC). All procedures
performed were in accordance with the ethical standards of theinstitutional and national research committee.

In our study, the historical controls consisted of patients who
met the same eligibility criteria as the intervention group but
were discharged from the hospital before the implementationof the home digital monitoring intervention. These patients wereselected from hospital records spanning a comparable time
period before the intervention phase of the study. By using these
historical controls, we were able to compare the health careoutcomes of patients who received standard postdischarge carewith those who received the enhanced monitoring and support
provided by our intervention.

No images included in the paper or Multimedia Appendix 1
identify individual participants. All images were carefully
reviewed to ensure that no identifiable information was visible.
In cases where images were necessary, consent was obtained
from the individuals involved.

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Informed consent was obtained from all individual participants
included in the study. For secondary analyses of the data, theoriginal informed consent allowed for such analyses without
requiring additional consent. This was confirmed by the IRB.
The data collected in this study were deidentified to protect
participant privacy. The information of the participants wasanonymized, ensuring confidentiality. All data were stored
securely and accessed only by authorized personnel for research
purposes.
Participants were compensated for their time and effort. Eachparticipant received a compensation of NT $500 (approximately
US $18) per follow-up session. This compensation was provided
to cover any incidental expenses incurred due to participationin the study.

Statistical Analysis
Statistical analyses were performed using paired 2-tailed t tests
to compare the preintervention and postintervention metrics.
Average hospitalization rates, ED visits, and total hospital days
were calculated for each patient before and after the intervention
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Po et alperiod. All analyses were performed using SPSS computer
statistical software (version 25, IBM Corp). A P value less than
.05 was considered statistically significant.

Results
Impact of Home Digital Monitoring on Reducing
Hospitalizations and ED Visits in High-Risk
Postdischarge Patients
Between July and October 2021, a cohort of 41 patients
underwent initial eligibility screening. Among these, 93% (n=38)
were male. In terms of age distribution, 46% (n=19) were aged

41-60 years, while an equal percentage of 46% (n=19) wereaged 60 years or older. The findings of this study revealed thatthe implementation of home digital monitoring as anintervention led to a significant reduction in various health caremetrics within 3 and 6 months after intervention.
A comparative analysis between the periods before and after 3
months of intervention demonstrated a decrease in mean
hospitalizations from 0.45 (SD 0.09) to 0.19 (SD 0.09; P=.03),
a remarkable decline in mean ED visits from 0.48 (SD 0.09) to
0.06 (SD 0.04; P<.001), and a decrease in mean hospital days
from 6.61 (SD 2.25) to 1.94 (SD 1.15; P=.08; Table 1).

Table 1. The impact of intervention on health care utilization: a 3- and 6-month analysis.
Impact3 months

6 months

Before, mean(SD)

After,
mean (SD)

Hospitalization (n)0.45 (0.09)

ED visit (n)Hospital stay (days)

Change (%)

P value

Before,mean (SD)

After, mean
(SD)

Change (%)

P value

0.19 (0.09) –57.8.03

0.55 (0.11)

0.23 (0.09)

–58.2

.01

0.48 (0.09)

0.06 (0.04) –87.5<.001

0.55 (0.11)

0.23 (0.09)

–58.2

.02

6.61 (2.25)

1.94 (1.15) –70.7.08

7.48 (2.32)

6.03 (3.12)

–19.4

.73

Furthermore, the impact of the intervention remained evident
after 6 months. The average number of hospitalizations
decreased from 0.55 (SD 0.11) to 0.23 (SD 0.09; P=.01), andthe average ED visits reduced from 0.55 (SD 0.11) to 0.23 (SD0.09; P=.02). However, the mean hospital stay days showed aless pronounced change, shifting from 7.48 (SD 2.32) to 6.03
(SD 3.12; P=.73; Table 1).

Educational Background of the Participants andTechnology Usage
The majority of our participants had educational backgroundsof elementary school or junior high school, accounting for 71%(29/41). In their daily lives, 93% (38/41) of the participants
used smartphones, while only 7% (3/41) frequently used otherelectronic devices in addition to their smartphones. Regarding
self-care, 49% (20/41) of the participants indicated that theywould watch television programs related to their illnesses but
would not actively seek out health-related information. Only
7% (3/41) of individuals actively and regularly read articles
related to health, while the rest would only watch television
programs occasionally.
A total of 85% (35/41) of participants were able to operate thedevices independently. Among those who performed themeasurements, 95% (39/41) reported they could do so smoothly.
However, 5% (2/41) mentioned that operating theelectrocardiogram took more time. The difficulties theyencountered included forgetting the steps, failure of the device
to display a signal after attachment, and the need for additional
skin preparation.

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Based on these insights, we concluded that the middle-aged toolder adult participants, despite their limited educational
backgrounds, were generally willing to try and accept new
emerging care models such as telecare. Their familiarity withsmartphones and exposure to health-related information throughtelevision facilitated this transition. However, the effectivenessof the intervention was influenced by their initial hesitance andthe challenges they faced in actively seeking health information.

Impact of Participant Attitudes on the Intervention
To better understand how participant attitudes toward healthinformation and technology usage impacted the effectivenessof the intervention, we conducted interviews with a subset ofparticipants. These interviews revealed several key themes: (1)usage of the built-in assistance information or software of thesoftware; (2) clarity of assistance information or softwareinterface; (3) was the assistance information helpful to you?
and (4) are you satisfied with the experience of using this carepackage?
Reasons for unsuccessful enrollment and data collection
obstacles are outlined (Table 2). Interviews were conducted tounderstand why some individuals declined remote medical care.
Most hesitant participants lack interest in health information,
have concerns about device usage, worry about equipment
damage, and find postdischarge home monitoring inconvenient
due to work commitments. They resist scheduled video
consultations with doctors, believing it is time-consuming, andrely on regular in-person visits and medication. Attitudes varyfrom negative to positive, with common traits such as older age,lack of assistance at home, and resistance to lifestyle changes.

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Po et alTable 2. Reasons for refusing remote medical care.
Reasons

Participants (N=41), n (%)

Aged 75 years or older with children not coresiding, unfamiliar with 3C technology

12 (29)Older adults, aged 75 years or older, unfamiliar with 3C technology, no interest to learn

10 (25)

Physically well, relying on medication, no discomfort

10 (25)

Employed, confident in self-monitoring, no scheduled video consultation

6 (14)

Physically limited, no device use, no family help

1 (3)Willing children, individual resists continuous monitoring

2 (4)Discussion
This study aimed to determine the efficacy of hometelemonitoring follow-up in reducing hospital readmissions,
ED visits, and total hospital days for high-risk postdischarge
patients. Our findings demonstrate that home digital monitoringsignificantly reduced hospitalizations, ED visits, and total
hospital stay days at both 3 and 6 months after intervention.
The biggest obstacle in implementing discharge management
for high-risk patients is the digital divide between patients andcaregivers, and the degree of this divide varies significantly
between urban areas and resource-limited regions. Most older
adult individuals are unable to operate related digital homedevices, and caregivers also have a lower acceptance of digital
tools. Even if other family members living together have theability to use digital devices, they often have to go out to workand cannot operate or transmit data during the day. Among the
successfully enrolled cases, 46% (19/41) were aged 65 years orolder. These older adult cases require remote home monitoring,allowing physicians to monitor their condition from home. The
biggest factor comes from the assistance of younger family
members who can complete most of the monitoring tasks.
Among them, 12% (5/41) of the cases are aged 80 years or older,
and they have a high willingness to self-monitor with theguidance provided. They can operate monitoring devices on
their own and even record heart and lung sounds through video
appointments with physicians. Therefore, although age may
present a learning barrier, if there is support and encouragementfrom younger family members or a proactive attitude toward
promoting health, remote health care can be a beneficial medical
service for rural residents who lack sufficient medical resources[11,12].
Our study provides preliminary evidence suggesting that home
digital monitoring may reduce hospital readmissions and ED
visits among high-risk postdischarge patients. These findingsalign with previous research demonstrating the benefits oftelehealth interventions in managing chronic conditions andimproving patient outcomes. The study by Crowley et al [13]examined the effects of a comprehensive telehealth intervention
compared with telemonitoring and care coordination in patientswith persistently poor type 2 diabetes control. In this randomized
clinical trial of 200 adults, the hemoglobin A1c level improvedby 1.59% at 12 months among those randomized to receive thecomprehensive telehealth intervention, compared with a 0.98%
improvement for the telemonitoring and care coordination group.
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positive impact of remote monitoring and support on healthcare utilization and patient outcomes is consistent with theirfindings. These studies support the notion that digital healthtools can play a crucial role in managing high-risk postdischarge
patients, although the specific interventions and patient
populations may vary.
The digital divide may also exist on the health care provider
side. The operation of digital platforms and equipment requires
learning and familiarity. People’s awareness of telemedicine
centers and whether they are aware of the existence of suchservices on the health care side has been raised during outpatient
education sessions. Some individuals have inquired about thelong-standing implementation of telemedicine and why they
were unaware of this health care resource. If telemedicine is
implemented during outpatient hours, those in need can consult
the telemedicine center, which would increase awareness oftelemedicine and familiarity with the fixed members providing
telemedicine, resulting in higher acceptance. However, if
telemedicine is implemented in the ED, there may be pressure
to complete tasks within limited timeframes due to the lack of
fixed operators, and any difficulties in user experience could
leave a negative impression [14,15]. In addition, in remote areas,
grassroots health care providers are often required to use various
digital tools at different times due to different programs, whichis also a source of the digital divide.
The crucial value of digital health lies in the connection,
including the connection between people and technology. In
recent years, digital health care has flourished in Taiwan, withthe government investing in and implementing telemedicine inmountainous and remote island areas, as well as promoting
regional collaborative health care models in resource-limited
regions. However, due to different regulatory and funding
agencies, it is common to see various telemedicine services andplatforms that cannot communicate with each other within thesame county or city. The introduction of information andcommunication technologies is meant to connect needs andresources, and if incompatible platforms lead to barriers andresource waste, the original intention of connection is lost. As
warned by WHO, the lack of integration and interoperability inthe fragmented and disjointed digital health services, devices,and platforms pose greater challenges to resource-limited areas
in need of assistance [16].
Therefore, when planning and investing in the infrastructure ofdigital health, the government and relevant agencies should
establish a diverse and interoperable mechanism for telemedicine
platforms in accordance with international information standards
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and security frameworks, within the framework of regulations
and institutions. Digital tools and platforms should serve as
mediums and channels for delivering health care content, rather
than limitations. Building an integrated and interoperable
ecosystem is essential to enhance the quality and accessibilityof health care through digital means [17-19].
Several limitations should be considered when interpreting our
findings. The study’s small sample size (41 participants) limits
the generalizability of the results. In addition, the retrospective
design and reliance on historical controls may introduce bias.
The study also did not account for potential variations inindividual patient adherence to the telemonitoring protocol,
which could affect the outcomes. Furthermore, the digital divide,
including differences in the ability of the patients to use
technology and access to reliable internet, could impact theeffectiveness of telemonitoring.

Conclusion
Our study provides preliminary evidence that hometelemonitoring can significantly reduce hospital readmissions
and ED visits among high-risk postdischarge patients. These

Po et alfindings suggest that telemonitoring is a viable and scalable
solution for improving postdischarge care and reducing healthcare utilization.
The broader implications of our findings are substantial.
Telemonitoring can enhance patient outcomes, especially inunderserved areas, by providing continuous health monitoring
and timely medical interventions. This approach can also
alleviate the burden on health care systems by reducing
unnecessary hospitalizations and emergency visits, ultimately
leading to cost savings. Future research should focus on larger,
prospective studies to confirm these results and explore thepotential of telemonitoring in diverse patient populations.
Addressing the digital divide and enhancing systeminteroperability are crucial steps toward maximizing the benefitsof telehealth interventions.
In conclusion, integrating advanced telemonitoring systems in
postdischarge care for high-risk patients holds promise for
improving health care delivery and patient outcomes. Ourfindings underscore the importance of adopting digital healthtools to achieve better health management and resource
optimization in health care systems.

Acknowledgments
This study was supported in part by the National Taiwan University Hospital, Yunlin Branch and Industrial Technology ResearchInstitute, Taiwan (FY111). We are indebted to Ms Hung-Chueh Peng and Mr Hsing-Che Wang for their support in collecting
clinical data.

Data Availability
All data generated or analyzed during this study are included in this published article and Multimedia Appendix 1.

Authors' Contributions
HWP, YCC, and HCT wrote the main article text and conducted formal analysis. CLL validated the data. CYC conducted formal
analysis, investigation, and writing. MHMM provided conceptualization and supervision. All authors reviewed the manuscript.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Questionnaire of remote smart discharge management for high-risk discharged patients.
[DOCX File , 18 KB-Multimedia Appendix 1]

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Abbreviations
CAT: Chronic Obstructive Pulmonary Disease Assessment Test
COPD: chronic obstructive pulmonary disease
ED: emergency department
ICU: intensive care unit
IRB: Institutional Review Board
mMRC: modified Medical Research Council
WHO: World Health Organization

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Po et alEdited by A Mavragani; submitted 10.10.23; peer-reviewed by S Setia, DJ Rubin; comments to author 15.05.24; revised version
received 16.06.24; accepted 14.07.24; published 13.09.24
Please cite as:
Po H-W, Chu Y-C, Tsai H-C, Lin C-L, Chen C-Y, Ma MH-M
Efficacy of Remote Health Monitoring in Reducing Hospital Readmissions Among High-Risk Postdischarge Patients: Prospective
Cohort StudyJMIR Form Res 2024;8:e53455
URL: https://formative.jmir.org/2024/1/e53455
doi: 10.2196/53455
PMID:©Hui-Wen Po, Ying-Chien Chu, Hui-Chen Tsai, Chen-Liang Lin, Chung-Yu Chen, Matthew Huei-Ming Ma. Originally publishedin JMIR Formative Research (https://formative.jmir.org), 13.09.2024. This is an open-access article distributed under the termsof the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly
cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as thiscopyright and license information must be included.

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