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Heart Failure (HF) is one of the leading mortality causes in elderly people. The purpose of this study is to assess readmission rates and reasons in elderly patients with HF.

The authors explored medical records of elderly patients with HF (75 years and more) at Chapidze Emergency Cardiology Center (Georgia) from 2015 to 2019. The authors analyzed the structure of the cardiovascular diseases and readmission rates of hospitalized patients with HF (I50, I50.0 I50.1). A multivariate logistic regression model was used to identify factors, associated with readmission for any reason during 6–9 months after the initial hospitalization for HF.

The major complication of cardiovascular diseases in elderly patients is HF (68.6%). Hospitalization rates due to HF in elderly patients have increased in recent years, which is associated with the population aging process. This trend will be most likely continue. Despite significant improvements in HF treatment, readmission rates are still high. HF is the most commonly revealed cause of readmission (48% of all readmissions). About 6–9 months after the primary hospitalization due to HF, readmission for any reason was 60%. Patients had concomitant diseases, including hypertension (43%), myocardial infarction (14%), diabetes (36%) and stroke (8%), affecting the readmission rate.

HF remains an important problem in public health. During HF-associated hospitalizations, both cardiac and non-cardiac conditions should be addressed, which has the potential for health problems and disease progression. Some readmissions may be prevented by the proper selection of medicines and monitoring.

The purpose of this paper is to examine whether patient readmission rates are associated with patient satisfaction and Medicare reimbursement rates in the US hospitals.

The Hospital Compare database was obtained from the Centers for Medicare and Medicaid Services (CMS) in the US. A total of 2,711 acute care hospitals were analyzed for this present study. The data included patient satisfaction surveys, hospital 30-days readmission ratios for heart failure and pneumonia patients and related payments. Exploratory factor analysis was applied in the first stage to operationalize constructs for scale development. Partial least squares (PLS) modeling analysis via Smart-PLS was utilized for testing the hypotheses.

Results indicated that data provided from the Hospital Compare database for the acute care hospitals accurately reflect quality outcomes. Nevertheless, the Medicare Hospital Readmissions Reduction Program (HRRP) did not penalize the hospitals when patients reported lower satisfaction via the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores.

The findings suggest that a high-readmission rate is not associated with lower payment. Such results appear to conflict with the goals of value-based purchasing programs, which seek to penalize hospitals financially for higher readmission rates.

Due to recent Affordable Care Act reforms, prevention of readmissions is a salient issue for hospitals that participate in Medicare, as they are now held accountable for patients who receive post-acute care in facilities over which hospitals have little influence to monitor care. Using resource dependence and transaction cost economics to describe the theoretical advantages of hospital ownership of post-acute care facilities (PACs), we empirically test whether hospitals that own PACs experience reduced readmissions. Our findings indicate partial support for the predicted relationship between PAC ownership and readmission rates. We found that hospital ownership of a skilled-nursing facility (SNF) was related to a lower readmissions rate for some patients, while ownership of other types of PACs did not result in significant findings. Our results offer support for the theoretical advantages of ownership, however, the savings realized by ownership may not merit the ownership investment.

The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.

Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.

Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.

Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.

This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

The measuring of emergency readmission rates to hospital following discharge is one of fifteen health outcomes the United Kingdom government monitors on an annual basis. There is a wide variation between readmission rates, and it is especially important to older people that there is a reduction in unacceptable variations. A closer understanding of reasons for readmission is therefore necessary to inform future developments, identify patients who may be at high risk of readmission and target resources more appropriately. A review of literature from the United Kingdom and international studies may help in determining the reasons for the unplanned readmission of older people. This could then allow for a re‐allocation of resources in the most cost‐effective and cost‐efficient manner. The literature review was conducted via keywords and combination of keyword searches from 1990‐2003 using various electronic databases. There were several themes that emerged from the literature, and these have been described within the paper. Following the review of the literature it emerged that many international studies into the causes of readmission of older people have an inconsistent approach in defining certain terms. However, in the United Kingdom, there appears a more consistent approach. Most studies agree that the majority of readmissions occur as a result of a relapse or complication of an initial illness. However, some American studies associate the readmission of older people with a specific disease, and the antecedent care process. The findings in the literature have identified several gaps that enable recommendations for future research to be made.

Transitional care is increasingly important in reducing readmission rates and length of stay (LOS). Singapore is focusing on transitional care to address the evolving care needs of a multi-morbid ageing population. This study aims to investigate the impact of transitional care programs (TCPs) on acute healthcare utilization.

A retrospective, longitudinal, interventional study was conducted. High-risk patients were enrolled into a transitional care program of local tertiary hospital. Patients received either telephone follow-up (TFU) or home-based intervention (HBI) with TFU. Readmission rates and LOS were assessed for both groups.

There was no statistically significant difference in readmissions or LOS between TFU and HBI. After excluding demised patients, TFU had statistically significant lower LOS than HBI. Both interventions demonstrated statistically significant reductions in readmissions and LOS in pre–post analyses.

TFU may be more effective than HBI in patients with lower clinical severity, despite both interventions showing statistically significant reductions in acute healthcare utilization. Study findings may be used to inform transitional care practices. Future studies should continue to examine the comparative effectiveness of transitional care interventions and the patient populations most likely to benefit.

Previous studies demonstrated promising outcomes for TFU and HBIs, but few have evaluated their comparative effectiveness on acute healthcare utilization and specific patient populations most likely to benefit. This study evaluated interventional effectiveness of both, which might be useful for informing allocation of resources based on clinical complexity and care needs.

Readmission to hospital can be distressing, costly for the National Health Service (NHS) and legally it should be a last resort as it entails restriction of liberty. This study aims to develop an understanding of factors leading to readmission for a cohort of service users under the care of a community forensic mental health team (CFMHT) in England to consider how support could be improved to reduce incidence of future readmissions.

Thematic analysis was used to examine case recording relating to 13 service users who were readmitted to hospital within a specified time period. The same service users were invited to complete questionnaires regarding their views of what contributed to the readmission to cross-reference with themes identified.

The analysis of case notes produced eight sub-themes. These were sorted into three overarching themes, illustrating the challenges that service users face upon discharge from hospital, the sense they make of these challenges and the impact upon relational patterns, in particular with the CFMHT. Researchers highlight that the multiple challenges can increase service users’ sense of being under threat which can lead to distrust of professionals.

Practical suggestions arising from the study were fed back to the subject team and are listed at the end of the paper.

Findings from this study parallel those previously reported. This study contributes an inter-relational aspect of the factors and the central role of meaning-making in the path of recovery and building a life in the community.

This research aims to explore the fine-grained relationships between nurse staffing and hospital operational performance with respect to care quality and operating costs. The authors also investigate the moderation effect of competition in local hospital markets on these relationships.

A six-year panel data is assembled from five separate sources to obtain information of 2,524 USA hospitals. Fixed-effect (FE) models are used to test the proposed hypotheses.

First, nurse staffing is initially associated with improved care quality until nurse staffing reaches a turning point, beyond which nurse staffing is associated with worse care quality. Second, a similar pattern applies to the relationship between nurse staffing and operating costs, although the turning point is at a much lower nurse staffing level. Third, market competition moderates the relationship between nurse staffing and care quality so that the turning point of nurse staffing will be higher when the degree of competition is higher. This shift of turning point is also observed in the relationship between nurse staffing and operating costs.

The study identifies three ranges of nurse staffing in which hospitals will likely experience simultaneous improvements, a tradeoff or simultaneous decline of care quality and operating costs when investing in more nursing capacity. Hospitals should adjust nurse staffing levels to the right directions to achieve better care or reduce operating costs.

Nurses constitute the largest provider group in hospitals and profoundly impact care quality and operating costs among all health care professionals. Optimizing the level of nurse staffing, therefore, can significantly impact the care quality and operating costs of hospitals.

This paper develops a robust hospital readmission prediction framework by combining the feature selection algorithm and machine learning (ML) classifiers. The improved feature selection is proposed by considering the uncertainty in patient's attributes that leads to the output variable.

First, data preprocessing is conducted which includes how raw data is managed. Second, the impactful features are selected through feature selection process. It started with calculating the relational grade of each patient towards readmission using grey relational analysis (GRA) and the grade is used as the target values for feature selection. Then, the influenced features are selected using the Least Absolute Shrinkage and Selection Operator (LASSO) method. This proposed method is termed as Grey-LASSO feature selection. The final task is the readmission prediction using ML classifiers.

The proposed method offered good performances with a minimum feature subset up to 54–65% discarded features. Multi-Layer Perceptron with Grey-LASSO gave the best performance.

The performance of Grey-LASSO is justified in two readmission datasets. Further research is required to examine the generalisability to other datasets.

In designing the feature selection algorithm, the selection on influenced input variables was based on the integration of GRA and LASSO. Specifically, GRA is a part of the grey system theory, which was employed to analyse the relation between systems under uncertain conditions. The LASSO approach was adopted due to its ability for sparse data representation.

Readmissions to the hospital are expensive and can have negative health consequences for patients. Older adults are at greater risk of readmission. Patient perspectives are valuable in identifying areas for improvement in the transition of care. The purpose of this qualitative study is to increase our understanding of patients’ perspectives on the transition of care from hospital to primary care.

This study employs a qualitative methodology to conduct semi-structured interviews with patients who have been discharged from hospitals in the Ireland East Hospital Group region. Remote interviews were conducted with 18 participants from eight general practices. Transcripts were analysed using thematic analysis as described by Braun and Clarke.

The three main themes identified were communication, outpatient supports and patient education. Gaps in communication do occur, but patients are often too external to comment. Patients benefit from a wide variety of outpatient supports including general practice, family, carers, allied health professionals and voluntary organisations. Access and cost are barriers to these supports. Participants were generally positive towards proposed primary care-based interventions such as follow-up appointments with general practitioners (GPs) and education sessions.

This study highlights a number of areas for improvement in the transition of care in current practice including communication between services and access to outpatient care. It also suggests directions for further research, such as explorations of healthcare provider perspectives and pilot studies of readmission reduction interventions.