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Original Paper

Impact on Readmission Reduction Among Heart Failure Patients
Using Digital Health Monitoring: Feasibility and Adoptability Study
Christopher Park, BA; Emamuzo Otobo, MPH, MD; Jennifer Ullman, NP; Jason Rogers, BA; Farah Fasihuddin,
MPH; Shashank Garg, MS; Sarthak Kakkar, MS; Marni Goldstein, MPH; Sai Vishudhi Chandrasekhar; Sean Pinney,
MD; Ashish Atreja, MPH, MD
Icahn School of Medicine at Mount Sinai, New York, NY, United States

Corresponding Author:
Ashish Atreja, MPH, MD
Icahn School of Medicine at Mount Sinai
1 Gustave L Levy Pl
New York, NY, 10029
United States
Phone: 1 2122418100
Email: ashish.atreja@mssm.edu

Abstract
Background: Heart failure (HF) is a condition that affects approximately 6.2 million people in the United States and has a
5-year mortality rate of approximately 42%. With the prevalence expected to exceed 8 million cases by 2030, projections estimatethat total annual HF costs will increase to nearly US $70 billion. Recently, the advent of remote monitoring technology has
significantly broadened the scope of the physician’s reach in chronic disease management.
Objective: The goal of our program, named the Heart Health Program, was to examine the feasibility of using digital healthmonitoring in real-world home settings, ascertain patient adoption, and evaluate impact on 30-day readmission rate.
Methods: A digital medicine software platform developed at Mount Sinai Health System, called RxUniverse, was used toprescribe a digital care pathway including the HealthPROMISE digital therapeutic and iHealth mobile apps to patients’ personalsmartphones. Vital sign data, including blood pressure (BP) and weight, were collected through an ambulatory remote monitoring
system that comprised a mobile app and complementary consumer-grade Bluetooth-connected smart devices (BP cuff and digitalscale) that send data to the provider care teams. Care teams were alerted via a Web-based dashboard of abnormal patient BP and
weight change readings, and further action was taken at the clinicians’ discretion. We used statistical analyses to determine risk
factors associated with 30-day all-cause readmission.
Results: Overall, the Heart Health Program included 58 patients admitted to the Mount Sinai Hospital for HF. The 30-day
hospital readmission rate was 10% (6/58), compared with the national readmission rates of approximately 25% and the Mount
Sinai Hospital’s average of approximately 23%. Single marital status (P=.06) and history of percutaneous coronary intervention(P=.08) were associated with readmission. Readmitted patients were also less likely to have been previously prescribedangiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (P=.02). Notably, readmitted patients utilized the
BP and weight monitors less than nonreadmitted patients, and patients aged younger than 70 years used the monitors more
frequently on average than those aged over 70 years, though these trends did not reach statistical significance. The percentage ofthe 58 patients using the monitors at least once dropped from 83% (42/58) in the first week after discharge to 46% (23/58) in the
fourth week.
Conclusions: Given the increasing burden of HF, there is a need for an effective and sustainable remote monitoring system for
HF patients following hospital discharge. We identified clinical and social factors as well as remote monitoring usage trends that
identify targetable patient populations that could benefit most from integration of daily remote monitoring. In addition, we
demonstrated that interventions driven by real-time vital sign data may greatly aid in reducing hospital readmissions and costs
while improving patient outcomes.
(JMIR Med Inform 2019;7(4):e13353) doi: 10.2196/13353

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KEYWORDS
heart failure; blood pressure; body weight; mHealth; remote consultation; patient care management; patient readmission; cell
phone; mobile phone; blood pressure monitors; mobile apps

Introduction
Background
Heart failure (HF) currently affects about 6.2 million people in
the United States and has an approximate 5-year mortality rateof 42% [1,2]. With the incidence rate projected to rise by 46%
to exceed 8 million cases by 2030, HF is an increasing publichealth concern [2]. Furthermore, although admission rates for
HF have declined over the past two decades [3], readmission
rates have not [1,4]; Centers for Medicare and Medicaid Services(CMS) data suggest that about 25% of HF patients arereadmitted within 30 days after initial hospitalization, and 35%of these readmissions are because of HF [5]. About half ofdischarged HF patients are readmitted within 6 months post
discharge. In 2012, the HF cost burden was estimated to be US$30 billion, and projections show that by 2030, total HF costs
will increase 125% from 2012 to nearly US $70 billion, which
averages to US $244 for each US adult [2].
Numerous studies have been aimed at understanding the greatest
risk factors for HF readmission, and results are widely varied[6-13]. The American Heart Association reports that old age,
African-American race, hypertension, diabetes mellitus (DM),and low socioeconomic status were associated with higher
incidence of HF [1,2]. Other reported factors include nonclinicalfactors such as single marital status and Medicare/Medicaid
status, and clinical factors such as renal failure, chronic
obstructive pulmonary disease (COPD), and history of drug use[8,9,11,14-22]. Many studies have sought to build models that
use these factors to predict readmission for HF patients[14,16,17,22-26]. However, these prediction schemes havestruggled to universally reduce readmission rates for HF patients
because of lack of evidence and inconsistencies in determining
relevant factors [1,22,26].
Owing to the advent of remote monitoring technology, recent
attempts to reduce readmission rates have begun to integratemodes by which physicians directly track their patients’ healthstatuses [27,28]. In addition to promoting a steadiercommunication between physician and patient, such protocols
actively encourage patient participation in their own health,which has been shown to facilitate informed decision making
and increase health literacy [29-31]. Studies utilizing remotemonitoring for HF patients have shown varying but promising
results; however, many of the reported solutions were done inresearch settings and required significant staff resources andcosts associated with integration of telemonitoring [28,29,32,33].

Objectives
In our quality improvement (QI) initiative, named the Heart
Health Program, we developed a time-efficient and relatively
cost-effective remote monitoring mobile device platform,
prescribed from electronic health record (EHR)–connected

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platform, which helped to reduce hospital readmissions among
HF patients. In addition to assessing readmission rates, we aimed
to identify and analyze monitor usage and adherence trends inreal-world home settings to inform future interventions to be
conducted on larger scales.

Methods
Participants
The program included 60 patients who were admitted to theMount Sinai Hospital for a diagnosis of acute HF. Under anapproved QI protocol, eligible patients were approached around2 days before discharge, and interested patients were enrolled
in the Heart Health Program, a digital health monitoring
initiative.

Intervention Protocol
Our digital care pathway “solution toolkit” consisted of apps
(HealthPROMISE digital therapeutic and iHealth mobile apps)that were prescribed using RxUniverse Digital Medicine
platform (Rx.Health, Inc). RxUniverse is an EHR-integrated
platform that allows digital prescription of automated carepathways including apps, digital therapeutics, education, andreminders directly to patients. This platform has been previously
shown to have high usability and patient activation rate [34].
Patients were provided with HF education content, SMS or text
reminders, and the ability to track their patient-reported
outcomes (PROs) via preselected symptom checkboxes on
HealthPROMISE digital therapeutic. In addition, each patient
was provided with a complementary, consumer-gradeBluetooth-connected weight scale and blood pressure (BP)monitor that connected to the iHealth app installed on their own
smartphone. Patients were instructed to measure their weightand BP each day. The total cost of equipment for each patientwas US $110, paid for by the institution (Figure 1).
Electronic PRO and Bluetooth scale and BP monitor data weresent to a Web-based dashboard (Figure 2), which was monitored
daily by a patient health coordinator (coauthor, EO) who worked
with the clinical team to assess symptoms and respond asneeded. The clinical team comprised the nurse manager for thepatient floor and a cardiologist. Any critical red-flag values, forexample, a greater than 2-pound weight gain within 24 hours
or greater than 5-pound weight gain within a week automaticallyalerted the physician and prompted the patient to seek medical
attention. The alerted physician could then elect to have thepatient contacted to set up an appointment with the cardiologist.
Physicians were also able to contact patients based on individual
judgment of changes in BP or weight. This pipeline is illustrated
in Figure 3. This program was exempted by the institutional
review board and classified as a QI study by the Department ofMedicine’s QI Board.

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Figure 1. Digital care pathway “solution toolkit” for heart failure patients’ remote monitoring.

Figure 2. Screenshot of the electronic patient-reported outcomes data dashboard.

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Figure 3. Intervention protocol pipeline and roles. EHR: electronic heath record.

Dependent Variables
The primary outcome measured was 30-day all-cause
readmission. In addition, usage of the monitors was defined as
the number of times patients would use the weight and BP
monitors per week following discharge. Usage for each monitor
was calculated separately.

Covariates
All patient data were pulled from EHRs, HealthPROMISE
digital therapeutic, and Bluetooth-connected devices.
Demographic variables included age, sex, race, and marital
status. Clinical and social factors included insurance status;
previous percutaneous coronary intervention (PCI); drug abuse;prescription of angiotensin-converting enzyme inhibitor (ACEI)
or angiotensin II receptor blocker (ARB); prior HF; priorhospitalization within the last 12 or 6 months; and diagnosesof systolic HF, atrial fibrillation, COPD, depression, anxiety,cancer, coronary artery disease, HIV, DM, and anemia. Factors
were selected based on a comprehensive literature search offactors correlated with HF readmission.

Analysis
Readmission rates were compared with the reported Mount
Sinai Hospital’s standard HF all-cause 30-day readmission rate
as well as the national readmission rate. Patient characteristics

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were compared between the readmission and nonreadmission
group using t tests and Fischer exact tests as appropriate. Finally,
BP and weight monitor usage trends were qualitatively andquantitatively analyzed based on weekly usage percentage. All
statistical tests are reported with the absolute P values.

Results
Patient Characteristics
A total of 60 patients were enrolled in the Heart Healthdemonstration program. Two patients dropped out because of
personal reasons. Of the remaining 58 patients in the program,
33% (n=19) were female and 67% (n=39) were male, with a
median age of 62 years (Table 1). Of the 58 patients, 57 wereon Medicare or Medicaid. A majority of patients had been
hospitalized in the prior 12 months before discharge (60%,n=36) and were single (55%, n=32).

Readmission Statistics
Overall, there were 6 hospital readmissions (10%, 6/58) after
30 days, compared with the national 30-day readmission rateof 25%, which denotes a significant decrease (P=.06), andMount Sinai Hospital’s readmission rate of 23%. As shown in
Table 1, readmission was associated with single status (P=.06),
a previous PCI (P=.08), and no prescription of ACEI or ARB(P=.02).

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Table 1. Patient characteristics for 30-day readmitted and nonreadmitted patients.
Characteristics

Readmitted (n=6)

Not readmitted (n=52)

P valueAge (years), mean (SD)63 (9.1)

58.7 (14.1).37

Sex, n (%)

.38

Male

3 (50)

36 (69)Female3 (50)

16 (31)

Race, n (%).46

African-American

3 (50)

11 (21)White

1 (17)

17 (33)Other

2 (33)

23 (44)

Unknown

0 (0)

1 (2)

Marital status, n (%)

a.06

Single5 (83)

27 (52)

Not single

0 (0)24 (46)

Unknown

1 (17)

1 (2)Systolic HFa, n (%)

5 (83)

37 (71)>.99

Depression, n (%)

1 (17)4 (8).43Anxiety, n (%)

1 (17)2 (4).28Cancer, n (%)

2 (33)

8 (15).27

Coronary artery disease, n (%)

3 (50)

17 (33).41

History of drug abuse, n (%)

0 (0)

1 (2)

>.99

HIV, n (%)

1 (17)0 (0).10Atrial fibrillation, n (%)

1 (17)

17 (33).65

Chronic obstructive pulmonary disease, n (%)

0 (0)

4 (8)

>.99

Diabetes mellitus, n (%)

3 (50)

18 (35).66

Anemia, n (%)

4 (67)

16 (31).17

Percutaneous coronary intervention, n (%)

3 (50)

8 (15).08

Prior HF, n (%)

4 (67)

21 (40).39

Prior 12 months hospitalization, n (%)

5 (83)

31 (60).39

Prior 6 months hospitalization, n (%)

3 (50)

14 (27).34

Taking angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker, n (%)

3 (50)48 (92).02

Medicare/aid, n (%)

6 (100)51 (98)>.99

HF: heart failure.

Device Usage
The percentage of the total 58 patients using the monitors atleast once dropped steadily from 83% (n=48) in the first week
after discharge to 46% (n=27) in the fourth week (Figure 4; ie,in the fourth week following discharge, 46% of the original 58
patients, or 27 patients, were still using the BP monitor at leastonce). The percentage of patients using the monitors at least
five times per week dropped from around 50% (n=29) to 30%(n=17) by the fourth week. Patients, on average, used the BP

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monitor more often than the weight scale. Device usage was
not correlated with any patient characteristic or demographic
factor.
Readmitted patients utilized the BP and weight monitors lessfrequently than nonreadmitted patients, and patients aged lessthan 70 years used the monitors more frequently on averagethan those aged more than 70 years. Furthermore, patients weremore likely to use the monitors on weekdays compared withweekends. These trends, however, did not reach statisticalsignificance.

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Figure 4. Absolute percentage of the total 58 patients with at least 1 or at least 5 blood pressure (BP) and weight checks each week following discharge.

Discussion
Overview
Readmission rates for HF patients have not decreased in thelast few decades, and in fact, have risen, likely because of thedecreased mortality and subsequent increased prevalence of HF
patients [1]. Thus, strategies aimed at reducing readmissions
are essential. The goal of this QI initiative was to assess
feasibility (in part measured by readmission reduction andadherence rates) of using affordable digital health monitoringin real-world home settings, ascertain patient adoption trends,
and evaluate impact on 30-day readmission rate as the primary
outcome. We demonstrated that remote monitoring of BP and
weight data via an app-based platform can be affordable, be
adopted by patients irrespective of age, and reduce 30-day
readmissions in real-world setting. We further identified usage
trends that will be informative for future population health-wide
implementations.

Readmission Statistics
Our program’s readmission rate of 10% represented a significant
decrease in 30-day readmission rate compared with both theMount Sinai Hospital’s rate (23%) as well as the national rate(25%). Readmitted patients utilized the BP and weight monitors
less frequently than nonreadmitted patients, and device usage
was not correlated with any patient characteristic or
demographic factor. These results support the publishedliterature on the potential role of technology-aided remote carein HF patient management [27,30-32]. However, many of the
published solutions were conducted in research settings andrequired invasive monitor placement, significant staff resources,
or expensive equipment, which may not be necessary for all HF
patients [31,32,34,35].
We were able to integrate an intervention protocol that reduced
barriers to adherence on both the physician side and patient sideby developing mobile apps that automated many parts of theworkflow: PROs and monitor data were sent directly to a
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dashboard via mobile apps, and the patient health coordinatorwould only need to notify the physician of suspicious readings
or symptoms. In addition, the RxUniverse platform would
automatically notify patients to enter their symptoms on the app
and measure their weight and BP. Thus, we were able to remove
the need for telemonitoring, frequent check-ins for every patient,and the hassle that many patients may face in recording daily
symptom and weight readings manually.
Besides physician monitoring of patient health status,interventions such as ours may also play a greater role infacilitating patient engagement with their own health [36]. For
example, in receiving a weight scale, patients can be educatedthat quick increases in weight may reflect fluid retention becauseof high salt intake, which can exacerbate the HF symptoms. In
this way, patients may be educated on their own condition,
which has been shown to decrease mortality, morbidity, andcosts associated with the disease [36-38].

Device Usage
Decreased patient adherence to discharge protocols is one of
the most prominent obstacles in HF patient management [39,40].
In our program, the vast majority of patients used the monitors
in the week following discharge, and about half of the patients
continued to measure their weight and BP at least once in the
fourth week following discharge. The first week after discharge
is the most vulnerable period for patients, and high engagement
during that time was one of the important factors in reducing
readmission rate. This drop in adherence from the first week
onward may be attributable to numerous context-dependent
factors, such as low motivation once patients know they are outof the “danger zone,” lack of long-term nursing or social support
and/or living alone, and low technological proficiency [41].
Although some of these factors were beyond the scope of ourprogram, these data provide insight into how intervention timing
may be improved. As adherence falls after the first week,perhaps a front-loaded approach to increase patient engagement(via automated SMS text messages, direct interactive voice
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response calls, telemedicine and incentives when needed, etc)
rather than an evenly spaced approach would increase and havea more long-lasting effect.
It is important to note that the number of patients taking BP and
weight readings at least five times per week did not fall as
drastically as the number of patients taking readings at least
once per week. Furthermore, the majority of patients using themonitor at least five times in week 1 after discharge were thesame as those in week 4. In other words, frequent monitor users
are more likely to stay frequent users in the long run. Thus,
patients that exhibit drastic decreases in monitor usage after the
first week may represent a more specific targetable population.
In the past decade, remote monitoring, namely telemonitoring,has become a large field of interest in chronic disease
management, and studies such as the Weight and Activity with
Blood Pressure Monitoring System have described detailed
protocols for such modalities [34]. In addition, studies that have
implemented these monitoring solutions have showed great
promise in reducing mortality and readmission [27]. However,many of the proposed workflows, in addition to thetelemonitoring, require personnel-heavy intervention strategies,
such as frequent phone calls and videoconferencing [27,29].
Many mobile apps have also been developed to help patients
better manage HF, such as PatientConnect and HeartMapp [42].
Our app toolkit is similar to these apps in that it provides
educational materials, care plans, reminders, in-app
communication, and input options for PROs. In addition, ourtoolkit (1) integrates Bluetooth connectivity and automatedupdates of patient BP and weight data on the physician’s
dashboard and (2) implements newly developed digital carepathways that can be delivered to patients without the need formobile app downloads (eg, via SMS text messages). In ourHeart Health Program, we reduced readmission by exclusively
using remotely collected BP and weight readings, significantly
reducing the cost and staff burden compared with other moreresource-heavy interventions [29,32]. Coupled with thedevelopment of cheaper Bluetooth monitors and more effective
risk stratification of patients who need monitors, our approach
may be scaled as larger health systems look to expand digital
health monitoring enterprise-wide to all HF patients. The Heart
Health Program is now being expanded across multiple healthsystems through a nationwide digital transformation networkin partnership with the American College of Cardiology.

Park et al

Limitations
Owing to our program being a demonstration program, oursample size was limited to 60 participants. Future researchshould use similar interventions on larger populations as well
as different population demographics. Our sample size was also
influenced by barriers to enrollment, including onboarding time,competition with other hospital initiatives and research trials,
missed recruiting opportunities because of limited notice ofdischarge, language barriers, and smartphone ownership. Our
program addressed these issues by enrolling HF patients around2 days before expected discharge and adding a patient healthcoordinator to hospital rounds. Future research studies using
device-dependent remote interventions on a larger scale could
establish personnel with specific roles to address these issues
and to maximize enrollment efficiency. Finally, consistent withreal-world QI initiatives, specific physician-patientcommunications were not protocolized, and it was left toindividual physicians to follow their best practices when
patients’ alerts were seen. Additional work could be done tointegrate a mobile app–enabled protocol where physicians mayreport their communications with patients to gain more granular
data.

Conclusions
Despite rigorous ongoing research, HF continues to contributeto increased health care costs and hospitalizations [1]. As
technology advances, there is an increasing opportunity for acomprehensive, affordable, and personalized solution that canleverage existing technology to provide care to patients post
discharge in real-world settings [31,43]. Recently, CMS
approved reimbursement codes for remote monitoring andnonface-to-face chronic care disease management [44]. Diseases
with increasing cost and health burdens such as HF could
particularly benefit from such solutions. In our program, we
demonstrated that interventions driven by real-time vital sign
data may greatly aid in reducing hospital readmissions and costs,
improving patient outcomes. Remote monitoring protocols suchas ours both facilitate caregiver-patient interactions as well as
engage the patient in his or her own health. Furthermore, remotemonitoring programs have the potential to be scalable, especiallywhen combined with digital medicine platforms integrated with
EHRs. Future studies should seek to measure population
health-wide impact on outcomes and revenue as digital healthremote monitoring is expanded enterprise-wide.

Acknowledgments
This initiative is supported in part by National Institutes of Health grants (K23, NIDDK, K23DK097451-01A1: AA) and CTSA(UL1TR000067: H Sampson).

Conflicts of Interest
The RxUniverse software platform and HealthPROMISE digital therapeutic are licensed technologies from Icahn School of
Medicine at Mount Sinai to Rx.Health, Inc (New York, NY). AA, SK, and JR own stock in Rx.Health and have recused themselvesfrom data analysis. SP receives consulting fees from Abbott, CareDx, Medtronic, and Procyrion and has recused himself fromdata analysis.

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Abbreviations
ACEI: angiotensin-converting enzyme inhibitor
ARB: angiotensin receptor blocker
BP: blood pressure
CMS: Centers for Medicare and Medicaid Services
COPD: chronic obstructive pulmonary disease
DM: diabetes mellitus
EHR: electronic health record
HF: heart failure
PCI: percutaneous coronary intervention
PRO: patient-reported outcome
QI: quality improvement

Edited by G Eysenbach; submitted 31.01.19; peer-reviewed by P Athilingam, A Miranda; comments to author 27.04.19; revised version
received 22.06.19; accepted 19.08.19; published 15.11.19
Please cite as:
Park C, Otobo E, Ullman J, Rogers J, Fasihuddin F, Garg S, Kakkar S, Goldstein M, Chandrasekhar SV, Pinney S, Atreja A
Impact on Readmission Reduction Among Heart Failure Patients Using Digital Health Monitoring: Feasibility and Adoptability StudyJMIR Med Inform 2019;7(4):e13353
URL: https://medinform.jmir.org/2019/4/e13353
doi: 10.2196/13353
PMID: 31730039©Christopher Park, Emamuzo Otobo, Jennifer Ullman, Jason Rogers, Farah Fasihuddin, Shashank Garg, Sarthak Kakkar, Marni
Goldstein, Sai Vishudhi Chandrasekhar, Sean Pinney, Ashish Atreja. Originally published in JMIR Medical Informatics(http://medinform.jmir.org), 15.11.2019. This is an open-access article distributed under the terms of 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 Medical Informatics, is properly cited. The complete
bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license
information must be included.

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