Why Majority of Readmission Risk Assessment Tools Fail in Practice

Tanzer, Matthew W.; Heil, Eric

doi:10.1109/ichi.2013.89

Cited by 5 publications

(4 citation statements)

References 5 publications

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“…In two systematic reviews [ 8 , 12 ] of 99 readmission predictive models reported between 1985 and 2015, 77% of the models were specialized for one patient subpopulation. The condition-specific design limits the adaptability of the models to other patient subpopulations and may overlook patients in some at-risk minority groups if specific models are not available [ 49 , 50 ]. In practice, it can be challenging for a hospital to maintain separate readmission prediction models for different patient subpopulations, and this situation will be further exacerbated if patients have comorbidities [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

“…The condition-specific design limits the adaptability of the models to other patient subpopulations and may overlook patients in some at-risk minority groups if specific models are not available [ 49 , 50 ]. In practice, it can be challenging for a hospital to maintain separate readmission prediction models for different patient subpopulations, and this situation will be further exacerbated if patients have comorbidities [ 50 ]. All-cause models are designed for broad patient populations without limiting diagnoses or procedures.…”

Section: Discussionmentioning

confidence: 99%

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Early Prediction of Unplanned 30-Day Hospital Readmission: Model Development and Retrospective Data Analysis

Zhao¹,

Yoo²,

Naqvi³

2021

JMIR Med Inform

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Background Existing readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians’ impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available. Objective The objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors. Methods We extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors. Results We developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported. Conclusions The performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Early Prediction of Unplanned 30-Day Hospital Readmission: Model Development and Retrospective Data Analysis

Zhao¹,

Yoo²,

Naqvi³

2021

JMIR Med Inform

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“…However, their current performance is not su cient for a wider use. Furthermore, explaining the reasons for a potential 30-day readmission understood by the physician is still a challenge for the ease of medical decision making in the current clinical practice [25].…”

Section: Discussionmentioning

confidence: 99%

Unplanned 30-Day Readmissions: Hospital Data Warehouse Modelling.

Dhalluin¹,

Bouzillé²,

Bannay³

et al. 2021

Preprint

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Background: Unplanned hospital readmissions are a major healthcare and economic burden. This study compared statistical methods and machine learning algorithms for predicting the risk of all-cause 30-day hospital readmission in two French academic hospitals.Methods: The dataset included hospital stays selected from the clinical data warehouses (CDW) of the two hospitals (Rennes and Tours Academic Hospitals) using the criteria of the French national methodology to measure the 30-day readmission rate (i.e. ≥18-year-old patients, geolocation, no iterative stays, and no hospitalization for palliative care). Then, the prediction performance of Logistic Regression, Naive Bayes, Gradient Boosting, Random Forest, and Neural Networks were compared separately for the two hospitals but using the same CDW data pre-processing for all algorithms. The area under the receiver operating characteristic curve (AUC) was calculated for the 30-day readmission prediction performance of each model as well as the time to train the algorithm.Results: In total, 259,092 and 197,815 stays were included from the Rennes and Tours Academic Hospital CDWs, respectively, with readmission rates of 8.8% (Rennes) and 9.5% (Tours). The AUC of the regression models for the two hospitals ranged from 0.61 to 0.64, with computation times exceeding 18 hours. The AUC of the machine learning models ranged from 0.61 to 0.69 with computation times below 13 hours.Conclusions: Better performance and shorter computation times are obtained with machine learning methods. It is still necessary to compare different algorithms to identify the most efficient model.

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“…The choice between conditionspecific and all-cause readmission models has long been under debate. However, condition-specific models have been criticized for the poor generalizability, especially in patients with multiple conditions [8,9]. In addition, readmissions are not always clinically relevant to the index admissions.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Highly Generalizable Risk Factors for Unplanned 30-Day All-Cause Hospital Readmissions

Zhao¹,

Yoo²

2017

J Health Med Inform

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Background: Hospital readmissions are common and expensive. Numerous global efforts have been devoted to predicting readmissions. However, for many reasons including the variations in the studied populations and inconsistent definitions of readmissions, the outcomes of some studies can hardly be generalized to other studies inside or outside the same country or region. Objective:The objective was to identify highly generalizable risk factors for unplanned 30-day all-cause hospital readmissions to guide the selection of baseline predictor variables in different readmission studies.Methods: In July 2017, PubMed was searched to identify articles pertaining to the risk factors for unplanned 30-day all-cause hospital readmissions. To identify potentially eligible risk factors, characteristics of the selected studies were manually extracted. The generalizability of the risk factors was assessed with predefined criteria.Results: 13 articles were eligible for the review. A total of 42 risk factors were identified and 34 of them were found to be highly generalizable. Conclusions:The 34 risk factors are not specific to any populations or places, and the corresponding predictor variables can serve as baseline variables in readmission prediction studies. No major difference has been observed between the risk factors identified inside and outside the United States except that US studies appeared to prefer composite comorbidity measures. All the reviewed studies have used traditional statistical regression-based methods to identify risk factors and more applications of data mining techniques are expected in this field.

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Why Majority of Readmission Risk Assessment Tools Fail in Practice

Cited by 5 publications

References 5 publications

Early Prediction of Unplanned 30-Day Hospital Readmission: Model Development and Retrospective Data Analysis

Early Prediction of Unplanned 30-Day Hospital Readmission: Model Development and Retrospective Data Analysis

Unplanned 30-Day Readmissions: Hospital Data Warehouse Modelling.

A Systematic Review of Highly Generalizable Risk Factors for Unplanned 30-Day All-Cause Hospital Readmissions

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