Content area
Background
Hospital readmissions present a substantial burden to patients and healthcare systems in terms of outcomes and financial penalties. The primary purpose of this study was to identify patient and program-specific factors linked to 30-day readmissions in patients with congestive health failure (CHF) enrolled in a hospital-based transitional care program.
Methods
We performed a retrospective analysis of electronic health record (EHR) data and program records from 343 community-dwelling adults (median age 81, 50.4% female, 67.9% White, 21% Black) with congestive heart failure (CHF) who were discharged from a community hospital in Westchester County, NY between January 1, 2023 and December 31, 2023. Our outcome of interest was 30-day hospital readmission. Logistic regression models were used to examine patient and program-specific predictors of 30-day readmission.
Results
Of 343 individuals, 19.8% were readmitted within 30 days. Each point increase in comorbidities was associated with 52% greater odds of readmission (p < 0.001, CI 1.24, 1.85). Those with ambulatory follow-up within 7 days had 63% lower odds of 30-day readmission compared to those without 7-day follow-up (p = 0.002, CI 0.2, 0.69). Those who interacted with the Transitional Care Management (TCM) team had an 84% lower risk of readmission compared to those who were not reached by the team.
Conclusion
Comprehensive transitional care programs have potential to reduce the risk of unnecessary hospital readmissions in patients with CHF. Touch points are key for patients discharged from the hospital following a CHF admission, whether via a transitional care program, ambulatory providers, or (ideally) both regardless of how soon after discharge this occurs.
Clinical trial number
Not applicable
Introduction
The 30-day window following a hospital discharge is a vulnerable period, during which individuals are at increased risk of experiencing adverse events and unnecessary readmissions [1, 2–3]. Congestive heart failure (CHF) is a common cause of hospital readmission and the leading cause of readmission in adults over the age of 65 [4, 5–6]. Approximately 20 to 25% of adults hospitalized with CHF are readmitted during this 30-day benchmark [4, 7, 8]. Readmissions are associated with psychological distress [9], poor outcomes [10], and care fragmentation that compromises quality of care and patient safety [11, 12]. Readmissions are also expensive, resulting in high-cost encounters [4] and incurring penalties on hospital systems [13].
Many readmissions are preventable or avoidable [14, 15, 16, 17, 18–19]. Transitional care involves services that promote care continuity as patients transition from one setting to another [20]. Research on transitional care programs has yielded mixed results, which may in part reflect the considerable variability in program designs and the lack of standardization in their implementation and evaluation [15, 16, 21, 22]. A study of 424,115 Medicare beneficiaries age 66 and over found transitional care services were associated with reduced healthcare spending and utilization, including reduced readmissions [23]. Despite programs of varying complexity [5, 22, 24], the patient and program-level factors driving outcomes in CHF-related care transitions remain obscure and context-dependent. Several biopsychosocial factors may be linked to readmissions, including age, gender, previous readmission, multimorbidity, lack of social support, and race/ethnicity [6, 7–8].
There are currently no gold-standard transitional care interventions. Our prior work showed transitional care team contact and bundled services reduced 30-day readmissions [25, 26], suggesting tailored interventions may be more effective. This study fills these gaps by evaluating a tailored, hospital-based Transitional Care Management (TCM) program in a population of patients with CHF with the purpose of examining program and patient-level factors associated with 30-day readmissions.
Methods
Study design
This was a retrospective, observational study of 343 patients discharged home between January 1, 2023, and December 31, 2023, with a primary diagnosis of CHF. We limited the analysis to this one-year period to minimize confounding, as our transitional care program underwent expansion and evolution before this timeframe, allowing us to examine outcomes during a period of relative program stability. We follow STROBE guidelines to report our findings [27].
Setting
White Plains Hospital is a 292-bed not-for-profit community hospital located in Westchester County, NY. The hospital has outpatient medical facilities across the county, including multi-specialty practices.
Participants
Adults aged 18 and over discharged to home with a final primary diagnosis of CHF were included in this study. Patients who were discharged to institutionalized settings or hospice were excluded. Ethical approval was obtained via the White Plains Hospital Institutional Review Board (IRB). As a retrospective analysis of de-identified data, no consent to participate was required and consent was waived by the IRB.
Variables
Primary outcome: 30-day readmission
The primary outcome of interest was hospital readmission within 30 days of discharge for any reason. Readmissions were tracked if the patient returned to our facility or partner facilities that utilized the same EHR.
Predictors: Transitional care program factors
The TCM program was staffed by APPs (advanced practice providers; e.g., nurse practitioners and physician’s assistants), with nursing, social work, and clinical pharmacist support, and supervised by a medical director and focused on care transitions for patients admitted with CHF, Chronic Obstructive Pulmonary Disease (COPD), and Pneumonia. For the current analyses, we focused on CHF admissions given the discrete and comprehensive services offered to all CHF patients post-discharge (described in detail below).
TCM patient identification
Patients admitted to the hospital with a primary diagnosis of CHF were identified by inpatient care managers. Additional patients discharged home with a primary diagnosis of CHF were identified by TCM RNs (Registered Nurses) who referred the patient to the TCM APPs for evaluation. Finally, TCM APPs reviewed daily coding lists generated by the hospital’s reporting database to capture any additional patients with a primary CHF diagnosis who may have been missed.
Advanced practice provider outreach within 48 h
APPs targeted all CHF outreaches within 48 h of discharge to perform a comprehensive discharge assessment via telephone. The standardized approach for clinicians during the outreach call included a clinical assessment, medication reconciliation, review of the post-discharge plan of care, and assessment of access barriers to medications, appointments, or services. Patients were provided with education on their diagnosis, symptom management, and monitoring parameters. The clinician coordinated care with ambulatory providers, community paramedicine, and HHS (Home Health Services). APPs outreached patients on a weekly and as-needed basis through the 30-day post-discharge period. Patients were also provided with contact information for the team during their initial call, and they were encouraged to initiate contact with the team between weekly calls if needed.
Social work
The transitional care team included two dedicated social workers who supported APPs with addressing social determinants of health needs (e.g., transportation to appointments, home assistance, caregiver support), obtaining durable medical equipment, and resolving delays in HHS.
Community paramedic program
The TCM program was partnered with two local paramedic groups that conducted weekly and as-needed home visits for high-risk patients. Paramedics visited patients in the hospital prior to discharge to introduce the program and obtain their agreement for enrollment. Paramedics served as the eyes and ears of the team in the home, providing medical control and escalation support when issues were identified. They collaborated with APPs to report on patient status and coordinated care when needs arose (e.g., weight gain, worsening dyspnea or edema). Paramedics were also able to administer medications in the home under the supervision of the TCM medical director (e.g., IV furosemide) and obtain blood work for monitoring. Paramedics were additionally tasked with initiating RPM (Remote Monitoring Program) services in the home.
Remote monitoring program
Patients with CHF were eligible for RPM if they were able to stand on a scale unassisted, agreed to monitor their vital signs, and were not enrolled in RPM through their healthcare provider. The RPM program included monitoring of weight, blood pressure, and heart rate. Patients were outreached for abnormal measures (e.g., weight gain of more than 2 pounds overnight), abnormal trends (e.g., progressively escalating weight), or consistently missed measures (e.g., > 3 days). RNs referred patients to the APP as needed for weight and symptom management.
Home health services
Patients were generally referred to HHS by the inpatient team prior to discharge for skilled nursing, physical, occupational, or speech therapy. If needs were identified post-discharge, a referral is initiated by the APP and coordinated by the TCM team social worker.
Access center
The access center is a centralized program aimed at improving scheduling efficiency. The access center team coordinates appointments across both hospital-employed and external community practices. Patients were scheduled to see their ambulatory provider within 7 days of discharge by the inpatient care manager or coordinated by the APP post-discharge via referral to the access center.
Potential confounders
Demographics and social factors
Age was calculated based on the patient’s reported date of birth in the Electronic Health Record (EHR). Gender was self-reported as male or female. Race and ethnicity, marital status, and use of interpreter services were based on self-report. Living arrangements were categorized as living alone or with others and were determined through social work or transitional care team assessments. Health behaviors such as current smoking, alcohol abuse, or substance abuse were determined via EHR documentation.
Comorbidities
A comorbidities score was a composite measure of the sum of 9 medical conditions: dementia, diabetes, stroke, cancer, coronary artery disease (CAD), peripheral vascular disease (PVD), COPD, chronic kidney disease (CKD), and liver disease [28]. Conditions were coded as present or absent based on ICD-10 codes.
Data sources
This analysis included data available through the EHR, and TCM program data documented either in the EHR or program-specific records. The data were compiled manually via chart review (see Supplementary material). To ensure the quality of the extracted data we conducted a pilot period where data were extracted individually by 2 study team members (K.A. and C.P.) and reviewed for comparison. If an individual had multiple admissions within the one-year period, only data from the first admission was included to avoid multiple entries as individual health status and ability to participate in the program may change across multiple admissions.
Statistical methods
Descriptive statistics were utilized to describe the sample. We used Wilcoxon Rank Sum tests to compare continuous variables and Chi-Square tests to compare categorical variables to describe the sample. Data were presented in medians, interquartile range, and percentages. We applied logistic regression models to identify program and patient-specific correlates of 30-day readmissions. Age and number of comorbidities were considered as continuous variables. All other variables were categorical. We utilized a backwards elimination method to reach our final model, which included both programmatic factors and patient-specific factors significantly associated with readmissions. All program factors were included, as our primary interest was to identify program factors associated with 30-day readmissions. We conducted several sensitivity analyses including models excluding one outlier patient aged 23 and 19 patients aged 95 and older (Supplementary material, eTable 4), models excluding individuals with the largest percentile (n = 48) of comorbidities to determine if results were driven by sicker patients (eTable 5), and models excluding 6 individuals who were not contacted by the TCM team prior to readmission to test whether results were driven by survivor bias (eTable 6). To further explore the potential for survivor bias we ran a cox model to examine whether patient or program factors were significantly associated with time to readmission (eTable 8). All statistical tests were two-tailed, and a p < 0.05 was considered statistically significant. Data were inspected graphically and statistically, and model assumptions were found to be adequately met. Stata (StataCorp LLC, College Station, TX) Version 18 was used for all analyses.
Results
Participants
Data from 349 patients with CHF were considered for these analyses (depicted visually in Fig. 1). Six patients were excluded from the analyses for various reasons, leaving 343 observations eligible for this investigation.
[See PDF for image]
Fig. 1
Flow diagram of included participants
Descriptive data
Summary characteristics of participants overall and by readmission status were summarized in Table 1. Of the 343 participants, 50.4% were female with a median age of 81 (IQR 70, 89; range 23–100). Participants were mostly White/Caucasian (67.9%) and non-Hispanic (85.4%), 21% identified as Black/African American, and 11.1% identified as belonging to another race. 40% of participants were unmarried, and 28.6% lived alone. Most participants were non-smokers (90.3%) and did not misuse alcohol (95.9%) or other substances (96.2%). Participants had a median of 6 comorbidities (IQR 5,7). CAD was the most common condition among participants (65.3%), followed by CKD (52.5%). Over 50% of participants were contacted by an APP within 48 h post-discharge (53.6%), and 47.9% had a 7-day ambulatory follow-up appointment. Approximately half of the participants received HHS (48.7%) and 39.7% enrolled in the community paramedicine program. Only 27.2% of participants enrolled in RPM.
Table 1. Summary characteristics of participants and by readmission status
Overall (n = 343) | Readmitted (n = 68) | Not Readmitted (n = 275) | p-value | |
|---|---|---|---|---|
Demographic variables | ||||
Age (years), median (IQR), range 23–100 | 81 (70, 89) | 84.5 (76.5, 89.5) | 79 (69, 89) | 0.02* |
Female, %(n) | 50.4 (173) | 51.5 (35) | 50.2 (138) | 0.85 |
Race, %(n) | ||||
White/Caucasian | 67.9 (233) | 73.5 (50) | 66.6 (183) | 0.45 |
Black/African American | 21.0 (72) | 19.1 (13) | 21.5 (59) | |
Other/declined | 11.1 (38) | 7.4 (5) | 12 (33) | |
Ethnicity, %(n) | ||||
Non-Hispanic | 85.4 (293) | 89.7 (61) | 84.4 (232) | 0.46 |
Hispanic | 11.1 (38) | 8.82 (6) | 11.6 (32) | |
Psychosocial variables | ||||
Married, %(n) | 39.7 (136) | 29.4 (20) | 42.2 (116) | 0.05 |
Lives alone, %(n) | 28.6 (98) | 36.8 (25) | 26.6 (73) | 0.10 |
Health behaviors | ||||
Current smoker | 9.7 (33) | 10.3 (7) | 9.5 (26) | 0.84 |
Alcohol abuse | 4.09 (14) | 1.5 (1) | 4.7 (13) | 0.22 |
Substance abuse | 3.8 (13) | 2.9 (2) | 4.0 (11) | 0.68 |
Comorbidities, %(n) | ||||
Dementia | 15.7 (54) | 22.1 (15) | 14.2 (39) | 0.11 |
Diabetes | 44.6 (153) | 47.1 (32) | 44.0 (121) | 0.65 |
Stroke | 15.2 (52) | 13.1 (36) | 23.5 (16) | 0.04* |
Cancer | 31.8 (109) | 38.2 (26) | 30.2 (83) | 0.20 |
Coronary Artery Disease | 65.3 (224) | 73.5 (50) | 63.3 (174) | 0.11 |
Peripheral Vascular Disease | 24.2 (83) | 26.5 (18) | 23.6 (65) | 0.63 |
COPD** | 29.5 (101) | 44.1 (30) | 25.8 (71) | 0.003* |
Chronic Kidney Disease | 52.5 (180) | 67.7 (46) | 48.7 (134) | 0.005* |
Liver Disease | 8.5 (29) | 13.2 (9) | 7.3 (20) | 0.11 |
Global health score, mean (SD) | 2.88 ± 1.52 | 3.57 ± 1.44 | 2.72 ± 1.49 | < 0.001 |
Services, %(n) | ||||
Home health services | 48.7 (167) | 50 (34) | 48.4 (133) | 0.81 |
Community Paramedicine | 39.7 (136) | 39.7 (27) | 39.6 (109) | 0.99 |
7-day follow-up | 47.9 (162) | 30.9 (21) | 52.2 (141) | 0.002* |
APP contact within 48 h | 53.6 (184) | 57.4 (39) | 52.7 (145) | 0.49 |
RPM | 27.2 (93) | 26.5 (18) | 27.4 (75) | 0.88 |
Contacted by RN or APP | 96.2 (330) | 91.2 (62) | 97.5 (268) | 0.02* |
Not contacted at all | 3.8 (13) | 8.8 (6) | 2.6 (7) | |
Contacted within 48 h | 80.8 (277) | 80.9(55) | 80.7 (222) | |
Contacted ≥ 48 h | 15.5 (53) | 10.3 (7) | 16.7 (46) | |
Days to contact, median (IQR) | 1 (1, 2) | 1 (1, 2) | 1 (1, 2) | 0.33 |
Notes: *indicates statistically significant values; 7-day follow-up = follow-up with a provider within 7 days of discharge; APP = advanced practice provider; RPM = remote patient monitoring of weight, blood pressure, pulse oximetry. **COPD = chronic obstructive pulmonary disease
Outcome data
Of 343 participants, 68 (19.8%) were readmitted within 30 days. Those who were readmitted were significantly older (median age 84.5 compared to 79; p = 0.02), were less likely to have had a history of a stroke (p = 0.03), more likely to have had COPD (p = 0.003) and CKD (p = 0.005), and a greater number of comorbidities overall compared to those who were not readmitted. Those who were readmitted were also less likely to have had ambulatory follow-up within 7 days (30.9% vs. 52.2%, p = 0.002).
Main results
We applied logistic regression models to identify patient and program correlates of risk for 30-day readmissions (Table 2). RPM was not included in the final model due to collinearity. A Hosmer-Lemeshow test indicated good model fit (χ2(312) = 322.64, p = 0.33) with a Mcfadden’s R2 of 0.12 indicating that 12% of the variation in 30-day readmissions was explained by the model. Model performance was further assessed using area under the curve (AUC = 0.72), the Brier score (0.14), and calibration plots (supplementary material, eFigure 1). The AUC indicated modest discrimination, and the Brier score indicated reasonable overall accuracy of predicted probabilities. At a cutoff of 0.2, the model achieved a sensitivity of 66%, specificity of 66%, positive predictive value of 31%, and a negative predictive value of 89%, indicating reasonable ability to identify patterns at higher risk for readmission while maintaining good accuracy among those predicted to be at low risk. Each point increase in comorbidities was associated with a 52% greater risk of 30-day readmission (p = < 0.001, CI 1.24, 1.85). Those with ambulatory follow-up within 7 days had 63% lower odds of 30-day readmission compared to those without 7-day follow-up (p = 0.002, CI 0.20, 0.69). Individuals who interacted with the TCM team had an 84% lower risk of 30-day readmission compared to those who were not reached by the team (p = 0.006, CI 0.04, 0.60). Each point increase in age was associated with a 3% increased risk of readmission. However, this finding was not statistically significant (p = 0.07, CI 1.0, 1.06). Other program factors were not significantly associated with 30-day readmission risk. Sensitivity analyses did not significantly alter findings (see Supplementary material for details).
Table 2. Patient and program correlates of 30-day readmissions (n = 338)
Odds ratio | SE | 95% CI | p-value | |
|---|---|---|---|---|
Age | 1.03 | 0.01 | 1.0, 1.06 | 0.07 |
Multimorbidity | 1.52 | 0.16 | 1.24, 1.85 | < 0.001 |
HHS | 0.59 | 0.20 | 0.31, 1.15 | 0.12 |
APP contact within 48 h | 1.60 | 0.51 | 0.85, 2.98 | 0.14 |
ContactedYN | 0.16 | 0.11 | 0.04, 0.60 | 0.006 |
7-day follow-up | 0.37 | 0.12 | 0.20, 0.69 | 0.002 |
Community Paramedicine | 1.36 | 0.36 | 0.71, 2.59 | 0.47 |
Note. SE = standard error; HHS = home health service; APP = Advanced Practice Provider
Discussion
The current study describes patient- and program-level correlates of 30-day hospital readmission in 343 adults with CHF who were discharged home from an acute care facility. We demonstrated that 7-day ambulatory follow-up, contact with the TCM team, and multimorbidity were significantly associated with 30-day hospital readmission risk. We organize our discussion around implications for transitional care.
Patient-specific factors
Our findings suggest that older adults with multi-morbidity may be at higher risk of 30-day readmissions, and transitional care efforts focused on these patients may be most effective. Multimorbidity is common in patients with CHF and is associated with increased length of stay and unplanned readmission [29]. Consideration of the number and complexity of comorbid conditions is crucial for optimizing care and improving patient outcomes [30]. Over 80% of older adults aged 80 and over have more than one chronic condition [30]. While our finding for readmission risk associated with increasing age was trending towards significance at the 7% level, it is supported by prior work highlighting older adults as a uniquely vulnerable group that may benefit from comprehensive transitional care [3, 7].
Access center and importance of timely cardiology Follow-up
We demonstrated that among our population, those with ambulatory follow-up within 7 days of discharge had significantly lower risk of 30-day readmission. Timely follow-up post-discharge may impact patient outcomes in several ways including timely medication adjustment, up-titration of guideline-directed medical therapy (GDMT) – a cornerstone of CHF management, and opportunity for early recognition and management of decompensation. This approach was supported by prior studies demonstrating that early follow-up and aggressive titration of GDMT were associated with improved outcomes and reduced readmissions [31, 32, 33–34]. In particular, the STRONG-HF trial provides relevant context for these findings [34]. The trial found that close follow-up and protocol-driven medication titration significantly reduced hospital readmissions and mortality [34]. These findings underscore the potential clinical value of our access center’s work in facilitating cardiology appointments. It is possible that patients who successfully attended follow-up visits may represent a different demographic or risk profile than those who did not. Additionally, achieving consistent 7-day follow-up remains a major operational challenge due to the need to coordinate across both hospital-employed and independent community cardiologists, as well as limited availability and full practice panels among cardiology providers. This remains a priority area for ongoing quality improvement within our transitional care strategy.
TCM program impact
We showed that contact with the TCM team was associated with reduced readmission risk. In some cases, the first outreach was conducted by a TCM RN and subsequently handed off to an APP for further evaluation and management. We believe this association reflects the nature of our program’s support, which focused heavily on care coordination, patient education, assistance with social work escalations, facilitating access to follow-up appointments, and clarifying, reviewing, and reconciling medication lists for patients. This added support may have mimicked the benefits of a traditional follow-up appointment, reducing confusion and vulnerability during the post-discharge period. In essence, the more eyes patients have on them and the more support they receive from medical staff after discharge, the less vulnerable they are to preventable harm. Interestingly, the patients who were unreachable by the TCM team were much older than the overall sample and were much less likely to live alone (see Supplementary material), highlighting the potential for inclusion of family members and caregivers in transitional care outreach.
Strengths and limitations
We reported data on a unique cohort of patients enrolled in a comprehensive TCM program. These data provide a rich source of health and psychosocial information. The dataset was limited to adults aged 18 and older who were discharged home from one community-based hospital. Patients discharged to other settings were excluded from the analysis. Additionally, it is possible that patients were readmitted to other facilities that we were unable to track. We did, however, review Centers for Medicare and Medicaid Services (CMS) data for 2023 and noted 84% of CHF discharges were readmitted to our facility, which represents a reasonable estimate of the extent of readmission leakage to non-partner facilities. The same may be true for 7-day ambulatory follow-up appointments that were scheduled outside of our health system, which we were unable to track. Demographic data obtained via the EHR may not reflect transitions that could be important to these analyses (e.g., marital transitions or transitions in living arrangements). Additionally, the data is limited by a lack of information regarding the level of education, which may be an important factor in terms of health literacy and health education – an important focus of the TCM program. Socioeconomic status may also influence access to care, utilization of services, and the ability to self-manage multimorbidity. EHR data presents both opportunities and challenges. While the dataset potentially represents a rich source of health-related data, it is not designed for research and may be error-prone due to the volume of information, number of individuals providing input, and subjectivity inherent in medical care and diagnoses. This is especially true in the context of the current, manually compiled dataset. Despite these shortcomings, we felt that the opportunity to describe this innovative and comprehensive TCM program, which leverages multiple community partners in the care of complex patients, was an important contribution to the literature. We addressed this challenge via careful review of individual records and utilizing a two-person approach for all data. Given the cross-sectional nature of our data, we cannot draw conclusions regarding directionality or causality. Finally, despite efforts to include as many relevant factors as possible in these analyses, we cannot rule out the potential for unmeasured confounding, particularly in a population with variable social and clinical complexity. Our results should thus be interpreted in this context.
Conclusion
Comprehensive TCM programs have the potential to reduce the risk of unnecessary hospital readmissions in patients with CHF. Focusing efforts and resources on the highest risk patients and expanding the reach of transitional care to all eligible patients may have the most significant impact on readmissions. Touch points are key for patients discharged from the hospital following a CHF admission, whether via a transitional care program, ambulatory providers, or (ideally) both regardless of how soon after discharge this occurs and 7-day ambulatory follow-up post discharge is strongly protective.
Acknowledgements
Not applicable.
Author contributions
CP conceived of and conceptualized the study. CP and FJ ran all analyses. ME and FJ were responsible for data requisition and organization; CP and AK were responsible for data collection and curation. CP and AK wrote the first manuscript draft. FJ provided supervision. All authors reviewed the final draft of the manuscript.
Funding
Not applicable.
Data availability
The datasets analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent
This research was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained via the White Plains Hospital Institutional Review Board for this exempted study with waiver of consent. As a retrospective analysis of de-identified data, no consent to participate was required. Individuals provided consent for clinical care at the time of service.
Consent for publication
Not applicable.
Conflict of interest
We have no conflicts of interest to declare.
Competing interests
The authors declare no competing interests.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Krumholz, HM. Post-hospital syndrome–an acquired, transient condition of generalized risk. N Engl J Med; 2013; 368,
2. Kanaan, AO; Donovan, JL; Duchin, NP; Field, TS; Tjia, J; Cutrona, SL et al. Adverse drug events after hospital discharge in older adults: types, severity, and involvement of beers criteria medications. J Am Geriatr Soc; 2013; 61,
3. Glans, M; Kragh Ekstam, A; Jakobsson, U; Bondesson, Å; Midlöv, P. Risk factors for hospital readmission in older adults within 30 days of discharge - a comparative retrospective study. BMC Geriatr; 2020; 20,
4. Weiss AJ, Jiang HJ, overview of clinical conditions with frequent and costly hospital readmissions by payer., 2018. Healthcare Cost and Utilization Project (HCUP) statistical briefs. Rockville (MD): Agency for Healthcare Research and Quality (US); 2006.
5. Whitaker-Brown, CD; Woods, SJ; Cornelius, JB; Southard, E; Gulati, SK. Improving quality of life and decreasing readmissions in heart failure patients in a multidisciplinary transition-to-care clinic. Heart Lung; 2017; 46,
6. Xu, H; Granger, BB; Drake, CD; Peterson, ED; Dupre, ME. Effectiveness of telemedicine visits in reducing 30-Day readmissions among patients with heart failure during the COVID-19 pandemic. J Am Heart Assoc; 2022; 11,
7. Bergethon KE, Ju C, DeVore AD, Hardy NC, Fonarow GC, Yancy CW, et al. Trends in 30-day readmission rates for patients hospitalized with heart failure: findings from the get with the Guidelines-Heart failure registry. Circ Heart Fail. 2016;9(6).
8. Dharmarajan, K; Hsieh, AF; Lin, Z; Bueno, H; Ross, JS; Horwitz, LI et al. Diagnoses and timing of 30-day readmissions after hospitalization for heart failure, acute myocardial infarction, or pneumonia. JAMA; 2013; 309,
9. Alzahrani, N. The effect of hospitalization on patients’ emotional and psychological well-being among adult patients: an integrative review. Appl Nurs Res; 2021; 61, 151488. [DOI: https://dx.doi.org/10.1016/j.apnr.2021.151488] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34544571]
10. Jencks, SF; Williams, MV; Coleman, EA. Rehospitalizations among patients in the medicare fee-for-service program. N Engl J Med; 2009; 360,
11. Bourgeois, FC; Olson, KL; Mandl, KD. Patients treated at multiple acute health care facilities: quantifying information fragmentation. Arch Intern Med; 2010; 170,
12. Feied, CF; Handler, JA; Smith, MS; Gillam, M; Kanhouwa, M; Rothenhaus, T et al. Clinical information systems: instant ubiquitous clinical data for error reduction and improved clinical outcomes. Acad Emerg Med; 2004; 11,
13. Gupta, A; Allen, LA; Bhatt, DL; Cox, M; DeVore, AD; Heidenreich, PA et al. Association of the hospital readmissions reduction program implementation with readmission and mortality outcomes in heart failure. JAMA Cardiol; 2018; 3,
14. Li, J; Young, R; Williams, MV. Optimizing transitions of care to reduce rehospitalizations. Cleve Clin J Med; 2014; 81,
15. Dreyer, T. Care transitions: best practices and evidence-based programs. Home Healthc Nurse; 2014; 32,
16. Earl T, Katapodis N, Schneiderman S. Care Transitions. Making healthcare safer III: a critical analysis of existing and emerging patient safety practices [Internet]. Rockville, MD: Agency for Healthcare Research and Quality.
17. Phelan, D; Smyth, L; Ryder, M; Murphy, N; O’Loughlin, C; Conlon, C et al. Can we reduce preventable heart failure readmissions in patients enrolled in a disease management programme?. Ir J Med Sci; 2009; 178,
18. van Walraven, C; Bennett, C; Jennings, A; Austin, PC; Forster, AJ. Proportion of hospital readmissions deemed avoidable: a systematic review. CMAJ; 2011; 183,
19. Auerbach, AD; Kripalani, S; Vasilevskis, EE; Sehgal, N; Lindenauer, PK; Metlay, JP et al. Preventability and causes of readmissions in a National cohort of general medicine patients. JAMA Intern Med; 2016; 176,
20. Naylor, M; Keating, SA. Transitional care. Am J Nurs; 2008; 108,
21. Albert, NM; Barnason, S; Deswal, A; Hernandez, A; Kociol, R; Lee, E et al. Transitions of care in heart failure: a scientific statement from the American heart association. Circ Heart Fail; 2015; 8,
22. Kripalani, S; Chen, G; Ciampa, P; Theobald, C; Cao, A; McBride, M et al. A transition care coordinator model reduces hospital readmissions and costs. Contemp Clin Trials; 2019; 81, pp. 55-61. [DOI: https://dx.doi.org/10.1016/j.cct.2019.04.014] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31029692][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6559370]
23. Ouayogodé, MH; Hardy, B; Mullahy, J; Smith, MA; Meara, E. Care transition management and patient outcomes in hospitalized medicare beneficiaries. Am J Manag Care; 2024; 30,
24. Tyler, N; Hodkinson, A; Planner, C; Angelakis, I; Keyworth, C; Hall, A et al. Transitional care interventions from hospital to community to reduce health care use and improve patient outcomes: A systematic review and network Meta-Analysis. JAMA Netw Open; 2023; 6,
25. Jafri, FN; Johnson, K; Elsener, M; Latchmansingh, M; Sege, J; Plotke, M et al. A quality Improvement-based approach to implementing a remote Monitoring–Based bundle in transitional care patients for heart failure. Joint Comm J Qual Patient Saf; 2024; 50,
26. Michelle, E; Rachel, CSF; Jonathan, S; Priscilla, H; Farrukh, NJ. Telehealth-based transitional care management programme to improve access to care. BMJ Open Qual; 2023; 12,
27. von Elm, E; Altman, DG; Egger, M; Pocock, SJ; Gøtzsche, PC; Vandenbroucke, JP. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Ann Intern Med; 2007; 147,
28. Charlson, ME; Carrozzino, D; Guidi, J; Patierno, C. Charlson comorbidity index: A critical review of clinimetric properties. Psychother Psychosom; 2022; 91,
29. Carson O, Batty JA, Del Toro T, Hall M. Multimorbidity patterns in adults admitted to hospital with heart failure: an analysis of 175 million hospitalisation episodes. Eur Heart J. 2024;45(1).
30. Forman, DE; Maurer, MS; Boyd, C; Brindis, R; Salive, ME; Horne, FM et al. Multimorbidity in older adults with cardiovascular disease. J Am Coll Cardiol; 2018; 71,
31. Bilicki, DJ; Reeves, MJ. Outpatient Follow-Up visits to reduce 30-Day All-Cause readmissions for heart Failure, COPD, myocardial Infarction, and stroke: A systematic review and Meta-Analysis. Prev Chronic Dis; 2024; 21, E74. [DOI: https://dx.doi.org/10.5888/pcd21.240138] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/39325638][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11451567]
32. Baecker, A; Meyers, M; Koyama, S; Taitano, M; Watson, H; Machado, M et al. Evaluation of a transitional care program after hospitalization for heart failure in an integrated health care system. JAMA Netw Open; 2020; 3,
33. Hernandez, AF; Greiner, MA; Fonarow, GC; Hammill, BG; Heidenreich, PA; Yancy, CW et al. Relationship between early physician follow-up and 30-day readmission among medicare beneficiaries hospitalized for heart failure. JAMA; 2010; 303,
34. Mebazaa, A; Davison, B; Chioncel, O; Cohen-Solal, A; Diaz, R; Filippatos, G et al. Safety, tolerability and efficacy of up-titration of guideline-directed medical therapies for acute heart failure (STRONG-HF): a multinational, open-label, randomised, trial. Lancet; 2022; 400,
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