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Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study

Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study

Readmission is an unintended outcome that occurs in patients discharged from the hospital. In South Korea, the 30-day readmission rate in tertiary general hospitals in 2020 was approximately 30%, increasing yearly along with readmission cost statistics [1]. According to a Center for Medicare and Medicaid Services report in the United States, readmission rates for patients reach 2 million yearly, with readmissions costing $26 billion [2].

Eui Geum Oh, Sunyoung Oh, Seunghyeon Cho, Mir Moon

JMIR Med Inform 2025;13:e56671

Revisits, Readmission, and Mortality From Emergency Department Admissions for Older Adults With Vague Presentations: Longitudinal Observational Study

Revisits, Readmission, and Mortality From Emergency Department Admissions for Older Adults With Vague Presentations: Longitudinal Observational Study

Across all diagnoses, admission carried a significantly greater adjusted risk than a discharge of 30-day readmission (RD=5.8%, 95% CI 5.0 to 6.5). Individual diagnoses yielded adjusted estimates in the same direction as that of the entire sample, but the magnitude was notably larger in a few cases. For patients with weakness, admission carried a greater adjusted risk than a discharge 30-day readmission (RD=61.6%, 95% CI 57.7 to 65.5).

Sebastian Alejandro Alvarez Avendano, Amy Cochran, Valerie Odeh Couvertier, Brian Patterson, Manish Shah, Gabriel Zayas-Caban

JMIR Aging 2025;8:e55929

Predictive Modeling of Hypertension-Related Postpartum Readmission: Retrospective Cohort Analysis

Predictive Modeling of Hypertension-Related Postpartum Readmission: Retrospective Cohort Analysis

All-cause hospital readmission rates are on the rise with risk factors for all-cause postpartum readmission including public insurance, race, presence of comorbid conditions including hypertension and diabetes, and cesarean section [3].

Jinxin Tao, Ramsey G Larson, Yonatan Mintz, Oguzhan Alagoz, Kara K Hoppe

JMIR AI 2024;3:e48588

Efficacy of Remote Health Monitoring in Reducing Hospital Readmissions Among High-Risk Postdischarge Patients: Prospective Cohort Study

Efficacy of Remote Health Monitoring in Reducing Hospital Readmissions Among High-Risk Postdischarge Patients: Prospective Cohort Study

This includes those who received ICU treatment, high-risk discharges, and patients with thoracic and cardiovascular conditions with high readmission rates. The primary goal is to monitor disease control status, medication adherence, vital sign changes, and self-care ability.

Hui-Wen Po, Ying-Chien Chu, Hui-Chen Tsai, Chen-Liang Lin, Chung-Yu Chen, Matthew Huei-Ming Ma

JMIR Form Res 2024;8:e53455

Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study

Identifying Predictors of Heart Failure Readmission in Patients From a Statutory Health Insurance Database: Retrospective Machine Learning Study

To the best of our knowledge, to date, no study exists that applied ML to only outpatient SHI data to predict all-cause readmission in HF. The aims of this study were (1) to evaluate the use of outpatient SHI data to predict 1-year all-cause (primary end point) and HF-specific (secondary end point) readmission after an initial admission for HF and (2) to identify and rank relevant predictors for readmission.

Rebecca T Levinson, Cinara Paul, Andreas D Meid, Jobst-Hendrik Schultz, Beate Wild

JMIR Cardio 2024;8:e54994

Quality Improvement Intervention Using Social Prescribing at Discharge in a University Hospital in France: Quasi-Experimental Study

Quality Improvement Intervention Using Social Prescribing at Discharge in a University Hospital in France: Quasi-Experimental Study

Discharge coordination (DC) has been tested for years, especially in North America and Japan, to reduce the rate of readmission within 30 days, also with mixed results [18-20]. In Europe, concerns over readmission rates are less of a financial concern, but the same lack of coordination issue at discharge remains [21]. Diseases and related treatments are becoming increasingly complex, and multimorbidities represent a challenge in coordination.

Johann Cailhol, Hélène Bihan, Chloé Bourovali-Zade, Annie Boloko, Catherine Duclos

JMIR Form Res 2024;8:e51728

Designing and Implementation of a Digitalized Intersectoral Discharge Management System and Its Effect on Readmissions: Mixed Methods Approach

Designing and Implementation of a Digitalized Intersectoral Discharge Management System and Its Effect on Readmissions: Mixed Methods Approach

In terms of treated cases, the readmission rate was 9.07% (1222/13,477). The rates increased to 17.1% (1542/9016) for patients and 18.85% (1975/10,478) for cases when considering a longer time horizon for the readmission (90 days). Readmission rates were generally higher in the intervention group (80/705, 11.3%) at 30 days and 28.8% (161/560 at 90 days) than in the hospital as a whole and the control group.

Christoph Strumann, Lisa Pfau, Laila Wahle, Raphael Schreiber, Jost Steinhäuser

J Med Internet Res 2024;26:e47133

Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment

Machine Learning Model for Readmission Prediction of Patients With Heart Failure Based on Electronic Health Records: Protocol for a Quasi-Experimental Study for Impact Assessment

While this readmission risk underscores that patients receive life-saving care, it also encompasses implications of health care costs, patient’s stress, and the impact of socioeconomic determinants on care outcomes [2]. The risk of readmission due to the worsening of HF symptoms is heightened by inappropriate treatment strategies, infectious complications, or prematurely executed discharges.

Monika Nair, Lina E Lundgren, Amira Soliman, Petra Dryselius, Ebba Fogelberg, Marcus Petersson, Omar Hamed, Miltiadis Triantafyllou, Jens Nygren

JMIR Res Protoc 2024;13:e52744

Value of Electronic Health Records Measured Using Financial and Clinical Outcomes: Quantitative Study

Value of Electronic Health Records Measured Using Financial and Clinical Outcomes: Quantitative Study

Readmission rates are a part of the value-based purchasing program, and depending on the readmission rate, hospitals are penalized on a yearly basis, hence impacting hospital costs [28]. The readmission rates were measured for 6 conditions or procedures, as patients with these conditions are more likely to be readmitted to the hospital.

Shikha Modi, Sue S Feldman, Eta S Berner, Benjamin Schooley, Allen Johnston

JMIR Med Inform 2024;12:e52524