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Effects of the National Institutes of Health Stroke Scale and Modified Rankin Scale on Predictive Models of 30-Day Nonelective Readmission and Mortality After Ischemic Stroke: Cohort Study

Effects of the National Institutes of Health Stroke Scale and Modified Rankin Scale on Predictive Models of 30-Day Nonelective Readmission and Mortality After Ischemic Stroke: Cohort Study

For our outcomes, we examined readmission within 30 days or mortality within 30 days postdischarge. For readmission and mortality within 30 days, there were no missing data. For those who were readmitted to a non-Kaiser hospital, we identified their readmission through claim data. The study was limited to health plan members only, for whom we had full data on mortality.

Mai N Nguyen-Huynh, Janet Alexander, Zheng Zhu, Melissa Meighan, Gabriel Escobar

JMIR Med Inform 2025;13:e69102

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