Published on in Vol 5, No 2 (2013):

A Public-Private Partnership Develops and Externally Validates a 30-Day Hospital Readmission Risk Prediction Model

A Public-Private Partnership Develops and Externally Validates a 30-Day Hospital Readmission Risk Prediction Model

A Public-Private Partnership Develops and Externally Validates a 30-Day Hospital Readmission Risk Prediction Model

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Journals

  1. Cui Y, Metge C, Ye X, Moffatt M, Oppenheimer L, Forget E. Development and validation of a predictive model for all-cause hospital readmissions in Winnipeg, Canada. Journal of Health Services Research & Policy 2015;20(2):83 View
  2. Krompaß D, Esteban C, Tresp V, Sedlmayr M, Ganslandt T. Exploiting Latent Embeddings of Nominal Clinical Data for Predicting Hospital Readmission. KI - Künstliche Intelligenz 2015;29(2):153 View
  3. Sieck C, Adams W, Burkhart L. Validation of the BOOST Risk Stratification Tool as a Predictor of Unplanned 30-Day Readmission in Elderly Patients. Quality Management in Health Care 2019;28(2):96 View
  4. Tong L, Erdmann C, Daldalian M, Li J, Esposito T. Comparison of predictive modeling approaches for 30-day all-cause non-elective readmission risk. BMC Medical Research Methodology 2016;16(1) View
  5. Wang H, Cui Z, Chen Y, Avidan M, Abdallah A, Kronzer A. Predicting Hospital Readmission via Cost-Sensitive Deep Learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2018;15(6):1968 View
  6. Aguilar K, Campbell R, Fiester A, Simpson R, Hertel C. Bringing Care Home. Nursing Administration Quarterly 2014;38(2):166 View
  7. Arnal L, Pons-Suñer P, Navarro-Cerdán J, Ruiz-Valls P, Caballero Mateos M, Valdivieso Martínez B, Perez-Cortes J, Vellido A. Decision support through risk cost estimation in 30-day hospital unplanned readmission. PLOS ONE 2022;17(7):e0271331 View
  8. Ramakrishnaiah Y, Macesic N, Webb G, Peleg A, Tyagi S. EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes. Journal of Biomedical Informatics 2023;147:104509 View
  9. Hossain M, Khan A, Moni M, Uddin S. Use of Electronic Health Data for Disease Prediction: A Comprehensive Literature Review. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2021;18(2):745 View
  10. Struja T, Baechli C, Koch D, Haubitz S, Eckart A, Kutz A, Kaeslin M, Mueller B, Schuetz P. What Are They Worth? Six 30-Day Readmission Risk Scores for Medical Inpatients Externally Validated in a Swiss Cohort. Journal of General Internal Medicine 2020;35(7):2017 View
  11. Bradshaw S, Buenning B, Chesnut S, Wichman L, Lee B, Olney A. A validation study of the high acuity readmission risk pediatric screen (HARRPS) tool©: Predicting readmission risk within the pediatric population. Journal of Pediatric Nursing 2023;72:e139 View
  12. Rana S, Gupta S, Phung D, Venkatesh S. A predictive framework for modeling healthcare data with evolving clinical interventions. Statistical Analysis and Data Mining: The ASA Data Science Journal 2015;8(3):162 View
  13. Rhee C, Wang R, Song Y, Zhang Z, Kadri S, Septimus E, Fram D, Jin R, Poland R, Hickok J, Sands K, Klompas M. Risk Adjustment for Sepsis Mortality to Facilitate Hospital Comparisons Using Centers for Disease Control and Prevention’s Adult Sepsis Event Criteria and Routine Electronic Clinical Data. Critical Care Explorations 2019;1(10):e0049 View
  14. Grossman Liu L, Rogers J, Reeder R, Walsh C, Kansagara D, Vawdrey D, Salmasian H. Published models that predict hospital readmission: a critical appraisal. BMJ Open 2021;11(8):e044964 View
  15. Zhou H, Della P, Roberts P, Goh L, Dhaliwal S. Utility of models to predict 28-day or 30-day unplanned hospital readmissions: an updated systematic review. BMJ Open 2016;6(6):e011060 View
  16. Rajkomar A, Oren E, Chen K, Dai A, Hajaj N, Hardt M, Liu P, Liu X, Marcus J, Sun M, Sundberg P, Yee H, Zhang K, Zhang Y, Flores G, Duggan G, Irvine J, Le Q, Litsch K, Mossin A, Tansuwan J, Wang D, Wexler J, Wilson J, Ludwig D, Volchenboum S, Chou K, Pearson M, Madabushi S, Shah N, Butte A, Howell M, Cui C, Corrado G, Dean J. Scalable and accurate deep learning with electronic health records. npj Digital Medicine 2018;1(1) View
  17. Noel K, Yagudayev S, Messina C, Schoenfeld E, Hou W, Kelly G. Tele-transitions of care. A 12-month, parallel-group, superiority randomized controlled trial protocol, evaluating the use of telehealth versus standard transitions of care in the prevention of avoidable hospital readmissions. Contemporary Clinical Trials Communications 2018;12:9 View
  18. Wang S, Zhu X. Predictive Modeling of Hospital Readmission: Challenges and Solutions. IEEE/ACM Transactions on Computational Biology and Bioinformatics 2022;19(5):2975 View
  19. Rhee C, Wang R, Zhang Z, Fram D, Kadri S, Klompas M. Epidemiology of Hospital-Onset Versus Community-Onset Sepsis in U.S. Hospitals and Association With Mortality: A Retrospective Analysis Using Electronic Clinical Data. Critical Care Medicine 2019;47(9):1169 View
  20. Wang L, Tong L, Davis D, Arnold T, Esposito T. The application of unsupervised deep learning in predictive models using electronic health records. BMC Medical Research Methodology 2020;20(1) View
  21. Barda A, Ruiz V, Gigliotti T, Tsui F. An argument for reporting data standardization procedures in multi-site predictive modeling: case study on the impact of LOINC standardization on model performance. JAMIA Open 2019;2(1):197 View
  22. McConachie S, Raub J, Trupianio D, Yost R. Development of an iterative validation process for a 30-day hospital readmission prediction index. American Journal of Health-System Pharmacy 2019;76(7):444 View
  23. Tong L, Arnold T, Yang J, Tian X, Erdmann C, Esposito T, Padhukasahasram B. The association between outpatient follow-up visits and all-cause non-elective 30-day readmissions: A retrospective observational cohort study. PLOS ONE 2018;13(7):e0200691 View
  24. Bradshaw S, Buenning B, Powell A, Teasley S, Olney A, Lee B. Retrospective Chart Review: Readmission Prediction Ability of the High Acuity Readmission Risk Pediatric Screen (HARRPS) Tool. Journal of Pediatric Nursing 2020;51:49 View
  25. Gallagher D, Zhao C, Brucker A, Massengill J, Kramer P, Poon E, Goldstein B. Implementation and Continuous Monitoring of an Electronic Health Record Embedded Readmissions Clinical Decision Support Tool. Journal of Personalized Medicine 2020;10(3):103 View
  26. Schmidt C, Hefner J, McAlearney A, Graham L, Johnson K, Moffatt‐Bruce S, Huerta T, Pawlik T, White S. Development and prospective validation of a model estimating risk of readmission in cancer patients. Journal of Surgical Oncology 2018;117(6):1113 View
  27. Ryu B, Yoo S, Kim S, Choi J. Development of Prediction Models for Unplanned Hospital Readmission within 30 Days Based on Common Data Model: A Feasibility Study. Methods of Information in Medicine 2021;60(S 02):e65 View
  28. Stadler J, Donlon K, Siewert J, Franken T, Lewis N. Improving the Efficiency and Ease of Healthcare Analysis Through Use of Data Visualization Dashboards. Big Data 2016;4(2):129 View
  29. Rhee C, Zhang Z, Kadri S, Murphy D, Martin G, Overton E, Seymour C, Angus D, Dantes R, Epstein L, Fram D, Schaaf R, Wang R, Klompas M. Sepsis Surveillance Using Adult Sepsis Events Simplified eSOFA Criteria Versus Sepsis-3 Sequential Organ Failure Assessment Criteria*. Critical Care Medicine 2019;47(3):307 View
  30. Chin D, Wilson M, Trask A, Johnson V, Neaves B, Gojova A, Hogarth M, Bang H, Romano P. Repurposing Clinical Decision Support System Data to Measure Dosing Errors and Clinician-Level Quality of Care. Journal of Medical Systems 2020;44(10) View
  31. Low L, Liu N, Wang S, Thumboo J, Ong M, Lee K, Steyerberg E. Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity. PLOS ONE 2016;11(12):e0167413 View
  32. Rejeb A, Rejeb K, Appolloni A, Zailani S, Iranmanesh M. Navigating the landscape of public–private partnership research: a novel review using latent Dirichlet allocation. International Journal of Public Sector Management 2024 View
  33. Kukde R, Chakraborty A, Shah J. A Systematic Review of Recent Studies on Hospital Readmissions of Patients With Diabetes. Cureus 2024 View

Books/Policy Documents

  1. Sharma P, Kumar T, Tyagi S. International Conference on Innovative Computing and Communications. View
  2. Ferrarello S. The Vulnerability of the Human World. View
  3. Urooj A, Nafis M, Ahmad M. Computer Networks, Big Data and IoT. View
  4. Eigner I, Cooney A. Delivering Superior Health and Wellness Management with IoT and Analytics. View
  5. Ramírez J, Herrera D. Applications of Computational Intelligence. View
  6. McCallie D. Healthcare Information Management Systems. View