Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions

Morgan, Daniel J.; Bame, Bill; Zimand, Paul; Dooley, Patrick M.; Thom, Kerri A.; Harris, Anthony D.; Bentzen, Søren M.; Ettinger, W. H.; Garrett-Ray, Stacy; Tracy, J. Kathleen; Liang, Yuanyuan

doi:10.1001/jamanetworkopen.2019.0348

Cited by 83 publications

(59 citation statements)

References 13 publications

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“…Moreover, the current study found only a portion of variables have predictive power for readmission among home care patients (one out four variables in the LACE index, three out of six variables in the HOSPITAL score, respectively). Therefore, some studies suggested introducing more variables or using state-of-the-art methods such as machine learning and deep learning to gain a better predictive power [39][40][41]. However, using the real-world data, we found that both the LACE index and HOSPITAL score models already perform far better than physicians and might be good enough in readmission rate reduction for home care patients.…”

Section: Discussionmentioning

confidence: 79%

Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

Wang

Chen

et al. 2020

IJERPH

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The LACE index and HOSPITAL score models are the two most commonly used prediction models identifying patients at high risk of readmission with limited information for home care patients. This study compares the effectiveness of these two models in predicting 30-day readmission following acute hospitalization of such patients in Taiwan. A cohort of 57 home care patients were enrolled and followed-up for one year. We compared calibration, discrimination (area under the receiver operating curve, AUC), and net reclassification improvement (NRI) to identify patients at risk of 30-day readmission for both models. Moreover, the cost-effectiveness of the models was evaluated using microsimulation analysis. A total of 22 readmissions occurred after 87 acute hospitalizations during the study period (readmission rate = 25.2%). While the LACE score had poor discrimination (AUC = 0.598, 95% confidence interval (CI) = 0.488-0.702), the HOSPITAL score achieved helpful discrimination (AUC = 0.691, 95% CI = 0.582-0.785). Moreover, the HOSPITAL score had improved the risk prediction in 38.3% of the patients, compared with the LACE index (NRI = 0.383, 95% CI = 0.068-0.697, p = 0.017). Both prediction models effectively reduced readmission rates compared to an attending physician's model (readmission rate reduction: LACE, 39.2%; HOSPITAL, 43.4%; physician, 10.1%; p < 0.001). The HOSPITAL score provides a better prediction of readmission and has potential as a risk management tool for home care patients.

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Section: Discussionmentioning

confidence: 79%

Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

Wang

Chen

et al. 2020

IJERPH

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“…[18][19][20][21][22][23][24][25][26] Although many studies have concluded that AI-based models are superior to traditional models for risk stratification, other studies have observed otherwise. 14,[27][28][29][30][31][32][33][34][35][36][37][38][39] While clinical factors have primarily been used to predict readmission, there has also been interest in incorporating sociodemographic factors into models to more accurately account for patients' sociopersonal contexts, which are increasingly recognized to affect health-related outcomes. [10][11][12][13]40 This is critical because health behaviors, social factors, and economic factors are estimated to account for 70% of a person's health.…”

Section: Predictive Analytics For Hospital Readmissionmentioning

confidence: 99%

Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital

et al. 2020

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Background Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. Objective The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. Methods A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. Results Among 2,460 hospitalizations assessed using the tool, 611 were designated by the tool as high risk. Sensitivity and specificity for risk assignment were 65% and 89%, respectively. Over 6 months following implementation, readmission rates decreased from 11.4% during the comparison period to 8.1% (p < 0.001). After accounting for the 0.5% decrease in readmission rates (from 9.3 to 8.8%) at control hospitals, the relative reduction in readmission rate was 25% (p < 0.001). Among patients designated as high risk, the number needed to treat to avoid one readmission was 11. Conclusion We observed a decrease in hospital readmission after implementing artificial intelligence-based clinical decision support. Our experience suggests that use of artificial intelligence to identify patients at the highest risk for readmission can reduce quality gaps when coupled with patient-centered interventions.

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“…Standard methods to predict hospital readmission typically rely on a limited set of clinical data using simple calculations such as a modified LACE score that exams at the length of hospital stay, acuity on admission, comorbidity and emergency department visits. 6 When comparing standard methods to ML, some researchers found that ML scores were better at predicting hospital readmissions. 6 However, others showed mixed results, 7 highlighting the need for further research to clarify discrepancies in predictive models.…”

Section: Introductionmentioning

confidence: 99%

“…6 When comparing standard methods to ML, some researchers found that ML scores were better at predicting hospital readmissions. 6 However, others showed mixed results, 7 highlighting the need for further research to clarify discrepancies in predictive models.…”

Section: Introductionmentioning

confidence: 99%

<p>Prediction of 30-Day Hospital Readmissions for All-Cause Dental Conditions using Machine Learning</p>

Hung¹,

Li²,

Hon³

et al. 2020

RMHP

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Introduction: It is unknown whether patients admitted for all-cause dental conditions (ACDC) are at high risk for hospital readmission, or what are the risk factors for dental hospital readmission. Objective: We examined the prevalence of, and risk factors associated with, 30-day hospital readmission for patients with an all-cause dental admission. We applied artificial intelligence to develop machine learning (ML) algorithms to predict patients at risk of 30-day hospital readmission. Methods: This study used data extracted from the 2013 Nationwide Readmissions Database (NRD). There were a total of 11,341 cases for all-cause index admission for dental patients admitted to the hospitals. Descriptive statistics were used to analyze patient characteristics. This study applied five techniques to build risk prediction models and to identify risk factors. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), and accuracy, sensitivity, specificity and precision. Results: There were 11% of patients admitted for ACDC readmitted within 30 days of hospital discharge. On average, the total charge per patient was $131,004 for those with 30day readmission (n=1254) and $69,750 for those without readmission (n=10,087). Factors significantly associated with 30-day hospital readmission included total charges, number of diagnoses, age, number of chronic conditions, length of hospital stays, number of procedures, Medicare insurance and Medicaid insurance, and severity of illness. Model performance from all methods was similar with the artificial neural network showing the highest AUC of 0.739. Conclusion: Our results demonstrate that readmission after hospitalization with ACDC is fairly common. If one-third of the 30-day readmission cases can be avoided, there is a potential annual saving of over $25 million among the twenty-one states represented in the NRD. The ML algorithms can predict hospital readmission in dental patients and should be further tested to aid the reduction of hospital readmission and enhancement of patientcentered care.

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Assessment of Machine Learning vs Standard Prediction Rules for Predicting Hospital Readmissions

Abstract: This prognostic study compares standard readmission risk assessment scores with a machine learning score, the Baltimore score, for predicting 30-day unplanned hospital readmissions calculated in real time.

Cited by 83 publications

References 13 publications

Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

Assess the Performance and Cost-Effectiveness of LACE and HOSPITAL Re-Admission Prediction Models as a Risk Management Tool for Home Care Patients: An Evaluation Study of a Medical Center Affiliated Home Care Unit in Taiwan

Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital

<p>Prediction of 30-Day Hospital Readmissions for All-Cause Dental Conditions using Machine Learning</p>

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