In-Home Monitoring for CHF and Re-Hospitalization Rates
This quality improvement project examines the impact of in-home monitoring on re-hospitalization rates among patients diagnosed with congestive heart failure (CHF). With nearly one million annual hospitalizations and a 30-day readmission rate approaching 20%, CHF imposes a significant burden on patients and the healthcare system. Using a structured PICO framework, the paper reviews six peer-reviewed studies to evaluate whether in-home monitoring—including telemonitoring, remote digital systems, and toilet seat-based cardiovascular devices—reduces rehospitalization. Findings are interpreted through Nola J. Pender's Health Promotion Model, which supports the adoption of health-promoting behaviors among CHF patients. Supporting appendices include a Johns Hopkins PICO development tool and a literature review table with evidence levels and quality ratings.
- Introduction: CHF burden, prevalence, and rehospitalization statistics
- Problem Discussion and Purpose: Gaps in current CHF care and project purpose
- PICO Question and Literature Search: PICO framework and database search strategy
- Review of the Evidence: Six studies on in-home monitoring outcomes
- Theoretical Framework: Pender's Health Promotion Model applied to CHF
- Conclusion: Summary of findings and implications for practice
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What makes this paper effective
- Uses a well-structured PICO framework to sharpen a clinical question, making the evidence review focused and actionable.
- Synthesizes six peer-reviewed studies of varying designs (RCTs, systematic review, feasibility study, quasi-experimental) to build a multi-level evidence base.
- Anchors the clinical intervention in Pender's Health Promotion Model, connecting nursing theory to real-world practice improvement.
- Appendices add methodological transparency by documenting the evidence search process and rating each study by level and quality.
Key academic technique demonstrated
The paper demonstrates evidence-based practice (EBP) synthesis: rather than conducting new primary research, the author assembles and critically evaluates existing studies to answer a specific clinical question. Each source is mapped to the PICO structure, and evidence levels (Level 1 through Level 5) are assigned using a standardized quality rating system, showing how EBP projects translate research into practice recommendations.
Structure breakdown
The paper opens with epidemiological context establishing the clinical problem, then narrows to a formal purpose statement and PICO question. A literature search section explains database selection and inclusion criteria before moving into a narrative synthesis of six studies. A theoretical framework section grounds the intervention in Pender's HPM. The conclusion ties findings together. Two appendices—a PICO development tool and a literature review table—provide structured evidence documentation used in formal EBP and quality improvement projects.
Introduction
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 continue to occur, congestive heart failure remains a major problem, resulting in a significant burden of mortality, morbidity, and costs. The United States has a prevalence of 5.8 million individuals living with the condition and records more than 960,000 new cases annually. It is estimated that approximately one million hospitalizations linked to congestive heart failure occur each year, with most arising from worsened congestion among already diagnosed patients. The United States spends approximately $32.7 billion on congestive heart failure annually. Martirosyan et al. (2017) report 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 presents a quality improvement project aimed at lessening re-hospitalization rates among patients diagnosed with congestive heart failure.
Appendix A: Johns Hopkins PICO Development Tool
1. What is the problem?
Despite broad acceptance of the value of holistic illness management for CHF patients, CHF-linked re-hospitalization rates continue to be high, affecting adults and older adults aged 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 costs render CHF a major public health issue (Martirosyan et al., 2017).
3. What is the current practice?
Early identification of patient decompensation offers modest opportunities to reduce the need for hospitalization. Because patients do not communicate directly with healthcare team members for the majority of their lives, early deterioration may go undetected. Attempts have been made to fill this communication gap through "high-touch" initiatives involving frequent home visits or telephone conversations to improve outpatient assessment and follow-up. Several such initiatives have succeeded in decreasing hospitalization frequency; however, they have been hindered by expensive, labor-intensive frameworks that make program scaling difficult. Over the past two to three decades, home monitoring devices have been used for more effective outpatient assessment and have contributed to reduced hospitalizations (Kohn, 2017).
4. How was the problem identified?
- 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 community standards
- Current practice that has not been validated
- Financial concerns
5. What are the PICO components?
- P – Patients aged between 25 and 70 years
- I – In-home CHF-diagnosed patient monitoring
- C – Physiological information (such as weight, blood pressure, pulse rate) and rates of re-admission compared with a control group
- O – Lower rate of re-admission among patients who receive in-home monitoring
6. Initial EBP Question Type: Foreground
7. Possible search terms, databases, and search strategies:
Search terms: heart failure, congestive heart failure, in-home monitoring, health promotion using mHealth, re-hospitalization rates.
Databases: CINAHL, EBSCOHost, PubMed, ProQuest, Wiley, Science Direct.
8. Evidence to be gathered:
- Publications (e.g., CINAHL, PubMed, EBSCOHost, Embase)
- Professional, regulatory, and community standards
- Clinical guidelines
- Organizational data (quality improvement data, financial data, local clinical expertise, patient/family preferences)
- Position statements
9. Revised EBP question:
In CHF-diagnosed individuals aged between 25 and 70 years, does implementation of in-home monitoring lead to reduced re-hospitalization rates?
10. Outcome measurement plan:
- What will we measure? Re-hospitalization rates of CHF-diagnosed individuals aged 25–70 years receiving in-home monitoring, compared to re-admission rates of non-recipients of in-home monitoring.
- How will we measure it? As the percentage rate of re-hospitalization among those who receive in-home monitoring.
- How often will we measure it? Baseline information will be gathered once every six months to evaluate re-hospitalization frequency.
- Where will we obtain the data? From the electronic health records (EHR) of patients at participating healthcare facilities.
- Who will collect the data? Staff members at participating facilities.
- To whom will we report the data? To the researchers conducting the study.
Appendix B: Review of Literature Table
EBP Question: Does in-home monitoring of CHF patients reduce re-hospitalization rates?
Article 1
Author and Date: Bashi, N., Karunanithi, M., Fatehi, F., Ding, H., & Walters, D. (2017)
Evidence Type: Qualitative (systematic review)
Sample: 19 systematic reviews
Findings: Identified telemonitoring and home telehealth as effective in-home monitoring tools that lessen rehospitalization rates and mortality.
Observable Measures: Opinions and conclusions of previously conducted studies.
Limitations: Information was not retrieved from primary studies.
Evidence Level and Quality: Level 5, Moderate Quality (C)
Article 2
Author and Date: Conn, N.J., Schwarz, K.Q., & Borkholder, D.A. (2019)
Evidence Type: Qualitative (comparative study)
Sample: 18 CHF-diagnosed patients
Findings: A toilet seat-based cardiovascular monitoring system demonstrated positive impacts on blood pressure, stroke volume, and blood oxygenation accuracy.
Observable Measures: Blood pressure, stroke volume, and blood oxygenation.
Limitations: The seat-based system is not widely used in the home setting.
Evidence Level and Quality: Level 5, Moderate Quality (C)
Article 3
Author and Date: Idris, S., Degheim, G., Ghalayini, W., Larsen, T.R., Nejad, D., & David, S. (2015)
Evidence Type: Quantitative (RCT)
Sample: 28 patients with systolic heart failure, reduced left ventricular ejection fraction, and New York Heart Association class II/III symptoms
Findings: Hospital readmission rates significantly declined in the intervention group (1 readmission within 30 days) compared to the control group (7 readmissions within 30 days).
Observable Measures: Hospital readmission rates.
Limitations: Small sample size for a randomized controlled trial.
Evidence Level and Quality: Level 1, High Quality (A)
Article 4
Author and Date: Kohn, M.S., Haggard, J., Kreindler, J., Birkeland, K., Kedan, I., Zimmer, R., & Khandwalla, R. (2017)
Evidence Type: Qualitative (feasibility study)
Sample: 18 patients from the Cedars-Sinai group
Findings: A home monitoring program can significantly enhance patient compliance and satisfaction.
Observable Measures: Patient compliance and satisfaction with clinical team support.
Limitations: Small sample size; potential selection bias.
Evidence Level and Quality: Level 5, Good Quality (B)
Article 5
Author and Date: Ong et al. (2016)
Evidence Type: Quantitative (RCT)
Sample: 1,437 patients hospitalized for CHF between October 2011 and September 2013
Findings: In-home monitoring is associated with improved quality of life among CHF patients.
Observable Measures: Hospital readmission rates.
Limitations: Limited generalizability due to restricted study sites; failure to directly integrate the intervention with physician practices.
Evidence Level and Quality: Level 1, High Quality (A)
Article 6
Author and Date: Park, C., Otobo, E., Ullman, J., Rogers, J., Fasihuddin, F., Garg, S., Kakkar, S., Goldstein, M., Chandrasekhar, S.V., Pinney, S., & Atreja, A. (2019)
Evidence Type: Quasi-experimental
Sample: 58 patients admitted at Mount Sinai Hospital
Findings: An effective and sustainable remote monitoring system is needed for CHF-diagnosed patients upon hospital discharge.
Observable Measures: Blood pressure and weight.
Limitations: Lack of specific physician-patient communication protocols.
Evidence Level and Quality: 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 Research Protocols, 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
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