Effectiveness of In home Monitoring of CHF Patients

How Does the Use of In-home Monitoring for CHF Impact Re-hospitalization Rates?
Impact on In-home Monitoring for CHF on Re-hospitalization Rates
Congestive heart failure (CHF) is one of the major cardiovascular diseases with high global incidence and prevalence in the United States. While numerous advances in evidence-based medical therapy continues to occur, congestive heart failure remains a major problem as it results in significant burden of mortality, morbidity, and costs. The United States has a prevalence of 5.8 million individuals with the condition and more than 960,000 cases annually. It is estimated that approximately one million hospitalizations linked to congestive heart failure occur annually. Most of these hospitalizations arise from worsened congestion among already diagnosed patients. The United States spends approximately $32.7 billion on congestive heart failure annually. Martirosyan et al. (2017) states that readmission rates for this condition remain high as nearly 20% of patients are readmitted within 30 days and nearly 50% within 6 months. This paper aims to present a quality improvement project toward lessening re-hospitalization rates of patients diagnosed with congestive heart failure.
Problem Discussion
Congestive heart failure (CHF) is a cardiovascular disorder characterized by periods of stability and instability (Martirosyan et al., 2017). Deterioration of CHF is linked to repeated and prolonged hospitalizations. These re-hospitalizations in turn exacerbate the condition’s prognosis while enhancing cardiovascular mortality among patients. Given the significant burden of mortality, morbidity and costs associated with this condition, healthcare providers have undertaken various measures to help in management and treatment. Current approaches are centered on early diagnosis and treatment. However, these approaches are not entirely effective in management of the condition as evident in the high rate of hospital admission and rehospitalization. Moreover, patients experience deterioration of their conditions due to various factors including poor compliance with medical therapy, natural course of the disease, and volume overload. Consequently, there is a pressing need to lessen rehospitalization of patients suffering from this condition in attempts to improve their outcomes and lessening the rising healthcare costs (Conn, Schwarz & Borkholder, 2019).
The problem is then how do we lessen rehospitalization rates of patients with congestive heart failure. Literature has shown that early diagnosis and treatment is not entirely effective in management of the condition and reducing rehospitalization. Recent evidence shows that approaches like in-home monitoring would play a critical role in lessening hospital admission and readmissions for CHF. Therefore, does in-home monitoring for CHF reduce the rates of rehospitalization?
Purpose Statement
The purpose of this quality improvement project is to determine the impact of in-home monitoring for CHF on reduction of rehospitalization.
PICO Question
Identifying the PICO question is the first important step in this evidence-based, quality improvement project. The problem (P) determined in the paper is the effect of home-based monitoring on the rate of re-admission of CHF-diagnosed individuals. The intervention (I) is in-home CHF patient monitoring. Comparison (C) is: control group (non-recipients of in-home tracking). The outcome (O) serves to determine whether or not in-home monitoring reduces the rate of re-admission of CHF-diagnosed persons. Hence, the chosen PICO question is, “Does in-home monitoring (I) of CHF patients (P), reduce re-hospitalization rates (O)?”
Literature Search Process
The process of reviewing existing literature on this topic begins with a literature search using the Chamberlain College of Nursing library. The terms “heart failure” “congestive heart failure” “rehospitalization rates”, “health promotion using mHealth”, and “in-home monitoring” were utilized to commence the literature search process. Through the university’s library, numerous databases were available to generate substantial peer-reviewed articles relating to impact in-home monitoring of CHF-diagnosed patient on rehospitalization rates. Some of the databases that were utilized for this literature search were CINAHL, EBSCOHost, PubMed, Wiley and Science Direct. The search for existing literature on the topic was narrowed down to peer-reviewed articles published within the last five years. Additionally, the literature search was narrowed down to full text only articles published in HTML or PDF format and those published in English. While the search produced numerous articles, six articles were selected to support the PICO question and quality improvement project. During the literature search process, specialty organizations like American Heart Association, Alliance for Heart Failure, American College of Cardiology and American Academy of Cardiovascular Perfusion support in-home monitoring of CHF-diagnosed patients.
A total of six peer-reviewed studies that support the PICO question and quality improvement project were identified. The study by Conn, Schwarz & Borkholder (2019) concluded that in-home monitoring for CHF-diagnosed patients reduces heart failure rehospitalizations. Using a toilet seat-based cardiovascular monitoring system, these researchers found that in-home monitoring has positive impacts on blood pressure, stroke volume, and blood oxygenation accuracy in line with gold standard measures (Conn, Schwarz & Borkholder, 2019). Another study carried out by Park et al. (2019) used remote digital monitoring system in a group of 58 CHF-diagnosed patients. The system reduced the rate of rehospitalization of CHF-diagnosed patients to 10% (6/58) after 30 days in comparison to the national 30-day rehospitalization rate of 25%. As a result, they conclude that remote monitoring system for CHF-diagnosed patients lessens the rate of rehospitalization as these patients significantly benefit from daily remote monitoring.
Idris et al. (2015) conducted a randomized controlled pilot study in which a group of CHF-diagnosed patients were assigned to an intervention group (n=14) and control group (n=14). The intervention group were subjected to home telemedicine after hospital discharge for CHF while the control group received usual care. The study showed statistically significant difference in rehospitalization rates between the two groups. Only one (1) participant in the intervention group was re-hospitalized within 30 days in comparison to seven (7) participants in the control group. Another randomized control study by Ong et al. (2016) utilized telemonitoring and telephone calls for in-home monitoring of CHF-diagnosed patients. The intervention group (n=715) was subjected to telemonitoring after discharge while the control group (n=722) received usual care. The study found a significant difference in the quality of life between the intervention and usual care groups with minimal differences in the rate of rehospitalization.
Similarly, a feasibility study by Kohn et al. (2017) concluded that patient satisfaction and compliance can be realized using a sophisticated home monitoring system. Such a system incorporates alternative communication initiatives that ultimately decrease rehospitalization. A systematic review by Bashi et al. (2017) concluded that a series of in-home monitoring systems such as telemonitoring, home telehealth, mobile phone-based monitoring and videoconferencing reduce rehospitalization and mortality.
Theoretical Framework
Nola J. Pender developed the Health Promotion Model (HPM) in 1982, revised 1996, as a complementary framework for health protection. Pender studied health-promoting behavior in the mid-1970s and utilized her experience in the nursing field to develop this theory. The model incorporates preventive health measures and the role nurses play in helping patients to prevent illness through self-care and other initiatives (Gonzalo, 2019). This model describes multi-dimensional nature of individuals as they interact with the healthcare environment. As a result, this healthcare model focuses on increasing the level of wellbeing of a patient. Health Promotion Model (HPM) focuses on three specific areas i.e. individual attributes and experiences, behavior-specific thoughts and affect, and behavioral outcomes. The model has been utilized in various areas in the healthcare environment to enhance self-care practices among different patient populations. For example, Khodaveisi et al. (2017) utilized Pender’s Health Promotion Model to promote nutritional behavior change of overweight and obese women.
Given the nature of the issue in this quality improvement project, Health Promotion Model would be suitable in promoting suitable health behaviors among CHF-diagnosed patients. Through in-home monitoring, CHF-patients will be encouraged to adopt health-promoting behaviors and lifestyle changes. These behaviors and changes will in turn enhance their outcomes resulting in decreased rates of rehospitalization.
In conclusion, it is evident that congestive heart failure is associated with high global incidence and prevalence in the United States. Early detection and treatment approaches are currently utilized to help enhance management of this condition. However, these approaches are not entirely effective given the high rates of rehospitalization, morbidity, and mortality. Therefore, there is a pressing need for healthcare providers to identify suitable measures to enhance CHF management and lessen rehospitalization. Existing literature shows that in-home monitoring for CHF-diagnosed patients have positive impacts on lessening rehospitalization using the three areas of Pender’s Health Promotion Model.
Appendix A - John Hopkins PICO Development Tool
1. What is the problem?
Despite the broad acceptance of holistic illness management’s value in the case of CHF patients, CHF-linked re-hospitalization rates continue to be high affecting adult and older adult ages 25-70 years.
2. Why is the problem important and relevant? What would happen if it were not addressed?
High incidence, Poor quality of life, poor prognosis, elevated disability risk, high hospitalization, and re-hospitalization rates, and high associated financial expenses render CHF a major public health issue (Martirosyan et al., 2017).
3. What is the current practice?
Patient identification early on during decompensation offers modest initiatives a chance at reducing the need for hospitalization. As patients do not directly communicate with healthcare team members for the majority of their lifetime, they may miss early deterioration. Attempts have been made to fill this gap in communication using “high-touch” initiatives that entail frequent home visits or telephonic conversations for improving outpatient assessment and follow-up. Several such initiatives have succeeded at decreasing hospitalization frequency. However, they have been hindered by their expensive, labor-intensive framework that renders it hard for program scaling beyond a few patients. In the past two to three decades, home monitoring machines have been utilized for more effective outpatient assessment. Efforts such as this have reduced hospitalizations (Kohn, 2017).
4. How was the problem identified? (Check all that apply)
· Safety and risk-management concerns
· Quality concerns (efficiency, effectiveness, timeliness, equity, patient-centeredness)
· Unsatisfactory patient, staff, or organizational outcomes
· Variations in practice within the setting
· Variations in practice compared to the community standard
· The current practice that has not been validated
· Financial concerns
5. What are the PICO components?
P – (Patient, population, or problem) Patients aged between 25 and 70 years
I – (Intervention) In-home CHF-diagnosed patient monitoring
C – (Comparison with other interventions, if foreground question) Physiological information (such as weight, blood pressure rate, pulse), and rates of re-admission to hospital
O – (Outcomes are qualitative or quantitative measures to determine the success of change) Lower rate of re-admission of patients who receive in-home monitoring
6. Initial EBP question ? Background ? Foreground
Appendix A Cont’d
7. List possible search terms, databases to search, and search strategies.
Heart failure, congestive heart failure, in-home monitoring, health promotion using mhealth, re-hospitalization rates
Databases: CINAHL, EBSCOHost, PUBMED, ProQuest, Wiley, Science Direct
8. What evidence must be gathered? (Check all that apply)
· Publications (for instance, CINAHL, PubMed, EBSCOHost, Embase)
· Standards ( professional, regulatory, community)
· Guidelines
· Organizational data (e.g., QI, financial data, local clinical expertise, patient/family preferences)
· Position statements
9. Revised EBP question
(Revisions in the EBP question may not be evident until after the initial evidence review; the revision can be in the background question or a change from the background to a foreground question)
In CHF-diagnosed individuals aged between 25and 70 years, does in-home monitoring implementation lead to reduced re-hospitalization rates?
10. Outcome measurement plan
What will we measure?
(structure, process, outcome measure)
How will we measure it?
(metrics are expressed as rate or percent)
How often will we measure it?
(frequency)
Where will we obtain the data?
Who will collect the data?
To whom will we report the data?
Re-hospitalization rates of CHF-diagnosed individuals aged between 25 and 70 years receiving in-home monitoring as against re-admission rates of non-recipients of in-home monitoring.
It will be measured in the form of the percentage rate of those who receive in-home monitoring
Baseline information ought to be gathered once in six months for evaluating re-hospitalization frequency.
Information is gathered from the EHR of the patient at participating healthcare facilities.
Data will be collected by staff members at the participating facilities.
Data will be reported to the researchers conducting the study.
Appendix B Review of Literature Table
Date: 5-19-2020
EBP Question: Does in-home monitoring of CHF patients reduce re-hospitalization rates?
Article Number
Author and Date
Evidence Type
Sample, Sample Size, Setting
Findings That Help Answer the EBP Question
Observable Measures
Limitations
Evidence Level, Quality
1
Bashi, N., Karunanithi, M., Fatehi, F., Ding, H. & Walters, D.
Qualitative (systematic review)
19 systematic reviews
· N/A
The study identified telemonitoring and home telehealth as effective in-home monitoring tools that lessen rehospitalization rates and mortality
Opinions and conclusions of previously conducted studies
Information was not retrieved from primary studies
Level 5, Moderate Quality (C)
2
Conn, N.J., Schwarz, K.Q., Borkholder, D.A
Qualitative (comparative study)
18 CHF-diagnosed patients
· N/A
Toilet seat-based cardiovascular monitoring system is an in-home monitoring approach with positive impacts on blood pressure, stroke volume and blood oxygenation accuracy
Blood pressure, stroke volume and blood oxygenation
Seat-based system is not widely used in the home setting
Level 5, Moderate Quality (C)
3
Idris, S., Degheim,, G., Ghalayini, W., Larsen, T.R., Nejad, D., David, S.
Quantitative (RCT)
A group of 28 patients with systolic heart failure, left ventricular ejection fraction and New York Heart Association class II/III symptoms
· N/A
Hospital readmission rates significantly decline in the intervention group (1 in 30 days) compared to the control group (7 in 30 days)
Hospital readmission
Small sample size for a randomized controlled trial
Level 1, High Quality (A)
4
Kohn, M.S, Haggard, J., Kreindler, J., Birkeland, K., Kedan, I., Zimmer, R., Khandwalla, R.
Qualitative (feasibility study)
18 patients from the Cedars-Sinai group
· N/A
Home monitoring program can significantly enhance patient compliance and satisfaction
Patient compliance and satisfaction with support from clinical team
1) Small sample size
2) Potential bias in selection process
Level 5, Good Quality (B)
Appendix B Cont’d
5
Ong et al.
Quantitative (RCT)
1437 patients hospitalized for CHF between October 2011 and September 2013
· N/A
In-home monitoring is associated with improved quality of life among CH patients
Hospital readmission
1) Limited generalizability due to study sites
2) Failure to directly integrate intervention with physician practices
Level 1, High Quality (A)
6
Park, C., Otobo, E., Ullman, J., Rogers, J., Fasihuddin, F., Garg, S., Kakkar, S., Goldstein, M., Chandrasekhar, S.V., Pinney, S., Atreja, A.
Quasi-experimental
58 patients admitted at Mount Sinai Hospital
· N/A
Effective and sustainable remote monitoring system is needed for CHF-diagnosed patients upon hospital discharge
Blood pressure and weight loss
Lack of specific physician-patient communications
Level 3, Good Quality (B)
References
Bashi, N., Karunanithi, M., Fatehi, F., Ding, H. & Walters, D. (2017, January). Remote Monitoring of Patients with Heart Failure: An Overview of Systematic Reviews. Journal of Medical Internet Research, 19(1). DOI: 10.2196/jmir.6571
Conn, N.J., Schwarz, K.Q. & Borkholder, D.A. (2019). In-Home Cardiovascular Monitoring System for Heart Failure: Comparative Study. Journal of Medical Internet Research, 7(1). DOI: 10.2196/12419
Gonzalo, A. (2019, August 22). Nola Pender: Health Promotion Model. Retrieved May 18, 2020, from https://nurseslabs.com/nola-pender-health-promotion-model/
Idris, S., Degheim, G., Ghalayini, W., Larsen, T.R., Nejad, D. & David, S. (2015). Home Telemedicine in Heart Failure: A Pilot Study of Integrated Telemonitoring and Virtual Provider Appointments. Reviews in Cardiovascular Medicine, 16(2), 156-162.
Khodaveisi, M., Omidi, A., Farokhi, S. & Soltanian, A.R. (2017, April). The Effect of Pender’s Health Promotion Model in Improving the Nutritional Behavior of Overweight and Obese Women. International Journal of Community-based Nursing and Midwifery, 5(2), 165-174.
Kohn, M. S., Haggard, J., Kreindler, J., Birkeland, K., Kedan, L., Zimmer, R., & Khandwalla, R. (2017). Implementation of a home monitoring system for heart failure patients: A feasibility study. JMIR Res Protoc, 6(3). DOI: 10.2196/resprot.5744
Martirosyan, M., Caliskan, K., Theuns, D., & Szili-Torok, T. (2017). Remote monitoring of heart failure: Benefits for therapeutic decision making. Expert Review of Cardiovascular Therapy, 15(7), 503-515. DOI: 10.1080/14779072.2017.1348229
Ong et al. (2016, March). Effectiveness of Remote Patient Monitoring After Discharge of Hospitalized Patients with Heart Failure. JAMA Internal Medicine, 176(3), 310-318.
Park, C., Otobo, E., Ullman, J., Rogers, J., Fasihuddin, F., Garg, S., Kakkar, S., Goldstein, M., Chandrasekhar, S.V., Pinney, S. & Atreja, A. (2019). Impact of Readmission Reduction Among Heart Failure Patients Using Digital Health Monitoring: Feasibility and Adoptability Study. Journal of Medical Internet Research, 7(4). DOI: 10.2196/13353