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Hospital readmission rates are considered to be an important indicator of quality of care because they may be a consequence of actions of commission or omission made during the initial hospitalization of the patient, or as a consequence of poorly managed transition of the patient back into the community. The negative impact on patient quality of life and huge burden on healthcare system have made reducing hospital readmissions a central goal of healthcare delivery and payment reform efforts. In this study, we will be proposing a framework on how the readmission analysis and other healthcare models could be deployed in real world and a Machine learning based solution which uses patients discharge summaries as a dataset to train and test the machine learning model created. Current systems does not take into consideration one of the very important aspect of solving readmission problem by taking Big data into consideration. This study also takes into consideration Big data aspect of solutions which can be deployed in the field for real world use. We have used HPCC compute platform which provides distributed parallel programming platform to create, run and manage applications which involves large amount of data. We have also proposed some feature engineering and data balancing techniques which have shown to greatly enhance the machine learning model performance. This was achieved by reducing the dimensionality in the data and fixing the imbalance in the dataset. The system presented in this study provides a real world machine learning based predictive modeling for reducing readmissions which could be templatized for other diseases. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

by Chow Wai-hung. / Thesis (M.B.A.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 56-57). / ABSTRACT --- p.ii / TABLE OF CONTENTS --- p.iv / LIST OF FIGURES --- p.vi / LIST OF TABLES --- p.vii / ACKNOWLEDGMENT --- p.viii / Chapter / Chapter I. --- INTRODUCTION --- p.1 / Intensive Care Services --- p.1 / Clinician as an Information Processor --- p.2 / Executive Information System (EIS) for Intensive Care Services --- p.7 / Scope of the Study --- p.7 / The Organization of the Remaining Report --- p.8 / Chapter II. --- LITERATURE REVIEW --- p.9 / Sickness Scoring Systems --- p.9 / Executive Information Systems (EIS) --- p.15 / Information Requirements Determination for EIS --- p.17 / Future Direction of EIS in Intensive Care --- p.20 / Chapter III. --- RESEARCH METHODOLOGY --- p.22 / Survey by Mailed Questionnaire --- p.23 / Personal Interview --- p.24 / Subjects Selection --- p.26 / Analysis --- p.27 / Chapter IV. --- RESULTS AND FINDINGS --- p.28 / Part 1 - Questionnaires --- p.29 / Part 2 - Interviews --- p.31 / Chapter V. --- ANALYSIS AND DISCUSSION --- p.44 / Analysis of Results and Findings --- p.44 / Evaluation on Information Requirements Determination for an EIS --- p.50 / Chapter VI. --- CONCLUSION --- p.52 / Chapter VII. --- FUTURE DIRECTION OF DECISION SUPPORT IN CRITICAL CARE --- p.54 / REFERENCES --- p.56 / INTERVIEWS --- p.59 / APPENDIX --- p.60 / Chapter 1. --- A Sample of Hospital Information System Requirement Survey Questionnaire --- p.61 / Chapter 2. --- Samples of Visual Display --- p.67 / Chapter 3. --- A Sample of Format of a Structured Report --- p.70