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Article
Publication date: 14 January 2022

Michelle Louise Gatt, Maria Cassar and Sandra C. Buttigieg

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

Journal of Health Organization and Management, vol. 36 no. 4
Type: Research Article
ISSN: 1477-7266

Keywords

Open Access
Article
Publication date: 9 April 2021

Tengiz Verulava, Revaz Jorbenadze, Ana Lordkipanidze, Ana Gongadze, Michael Tsverava and Manana Donjashvili

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.

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

Journal of Health Research, vol. 36 no. 3
Type: Research Article
ISSN: 0857-4421

Keywords

Article
Publication date: 7 July 2020

Hui-chuan Chen, Tommy Cates, Monty Taylor and Christopher Cates

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.

1145

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

International Journal of Health Care Quality Assurance, vol. 33 no. 4/5
Type: Research Article
ISSN: 0952-6862

Keywords

Article
Publication date: 1 March 2016

Susan Camilleri and Kathleen Colville

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…

Abstract

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.

Details

International Journal of Organization Theory & Behavior, vol. 19 no. 2
Type: Research Article
ISSN: 1093-4537

Article
Publication date: 1 December 2004

Linda Dobrzanska

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…

Abstract

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.

Details

Quality in Ageing and Older Adults, vol. 5 no. 4
Type: Research Article
ISSN: 1471-7794

Keywords

Article
Publication date: 2 November 2021

Yuan Ying Lee, Lay Hwa Tiew, Yee Kian Tay and John Chee Meng Wong

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

Journal of Integrated Care, vol. 29 no. 4
Type: Research Article
ISSN: 1476-9018

Keywords

Article
Publication date: 23 March 2022

Xiaosong (David) Peng, Yuan Ye, Raymond Lei Fan, Xin (David) Ding and Aravind Chandrasekaran

This research aims to explore the fine-grained relationships between nurse staffing and hospital operational performance with respect to care quality and operating costs…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

International Journal of Operations & Production Management, vol. 42 no. 5
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 4 May 2021

Nor Hamizah Miswan, Chee Seng Chan and Chong Guan Ng

This paper develops a robust hospital readmission prediction framework by combining the feature selection algorithm and machine learning (ML) classifiers. The improved…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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

Originality/value

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.

Details

Grey Systems: Theory and Application, vol. 11 no. 4
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 23 August 2018

Murtaza Nasir, Carole South-Winter, Srini Ragothaman and Ali Dag

The purpose of this paper is to formulate a framework to construct a patient-specific risk score and therefore to classify these patients into various risk groups that can…

Abstract

Purpose

The purpose of this paper is to formulate a framework to construct a patient-specific risk score and therefore to classify these patients into various risk groups that can be used as a decision support mechanism by the medical decision makers to augment their decision-making process, allowing them to optimally use the limited resources available.

Design/methodology/approach

A conventional statistical model (logistic regression) and two machine learning-based (i.e. artificial neural networks (ANNs) and support vector machines) data mining models were employed by also using five-fold cross-validation in the classification phase. In order to overcome the data imbalance problem, random undersampling technique was utilized. After constructing the patient-specific risk score, k-means clustering algorithm was employed to group these patients into risk groups.

Findings

Results showed that the ANN model achieved the best results with an area under the curve score of 0.867, while the sensitivity and specificity were 0.715 and 0.892, respectively. Also, the construction of patient-specific risk scores offer useful insights to the medical experts, by helping them find a trade-off between risks, costs and resources.

Originality/value

The study contributes to the existing body of knowledge by constructing a framework that can be utilized to determine the risk level of the targeted patient, by employing data mining-based predictive approach.

Details

Industrial Management & Data Systems, vol. 119 no. 1
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 3 July 2017

Søren Halkjær and Rainer Lueg

The purpose of this paper is to analyze how specialization in hospitals affects operational performance, measured by the length of stay and readmission rate. The authors…

1405

Abstract

Purpose

The purpose of this paper is to analyze how specialization in hospitals affects operational performance, measured by the length of stay and readmission rate. The authors assess a public policy change in the Danish healthcare sector from 2011 which required that some hospital services had to be centralized leading to specialization within the merged departments.

Design/methodology/approach

Taking an institutional theory perspective, the authors conduct a natural experiment. The data include 24,694 observations of urological patient treatments from 2010 to 2012.

Findings

The econometric difference-in-difference analysis finds that the readmission rate decreases by approximately four percentage points in the departments affected by the policy change. Contrary to expectations, the length of stay increases by 0.38 days. The authors complement the natural experiment with a mixed-methods approach that includes proprietary data from the management control system of the hospital, public documentation on the policy change, as well as interviews with key informants. These data suggest that operational deficiency is related to the fact that specialization was externally enforced through the public policy change. The authors illustrate how the hospital staff struggle for legitimacy after this policy change, and how cost savings obstructed the specialized department in achieving its goals.

Originality/value

The authors conclude that the usual economies-of-scales-based logic of (higher)volume-(better)outcome studies cannot easily be transferred to specialization in hospitals, unless one accounts for the institutional reason of the specialization.

Details

International Journal of Operations & Production Management, vol. 37 no. 7
Type: Research Article
ISSN: 0144-3577

Keywords

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