Introduction

Predicting patient readmission using electronic health records is a critical area of research in healthcare. With the increasing costs and burden associated with hospital readmissions, it has become imperative for healthcare providers to identify patients at risk of readmission and implement strategies to prevent unnecessary return visits to the hospital. Electronic health records (EHR) contain a wealth of information that can be used to develop predictive models to identify patients at high risk of readmission. This thesis aims to explore the use of EHR data to predict patient readmission and investigate the factors that contribute to hospital readmissions.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

Chapter 2: Literature Review
2.1 Introduction to patient readmission
2.2 Factors contributing to patient readmission
2.3 Previous studies on predicting patient readmission
2.4 Machine learning techniques for predicting patient readmission
2.5 Role of electronic health records in predicting patient readmission
2.6 Challenges in predicting patient readmission using EHR data
2.7 Strategies to reduce hospital readmissions
2.8 The impact of readmission on healthcare costs
2.9 Ethical considerations in predicting patient readmission
2.10 Gaps in the current literature

Chapter 3: Research Methodology
3.1 Introduction
3.2 Data collection and preprocessing
3.3 Feature selection and engineering
3.4 Development of predictive models
3.5 Model evaluation and validation
3.6 Interpretation of results
3.7 Ethical considerations
3.8 Limitations of the study

Chapter 4: Discussion of Findings
4.1 Introduction
4.2 Descriptive analysis of patient readmissions
4.3 Performance evaluation of predictive models
4.4 Factors influencing patient readmission
4.5 Comparison with existing predictive models
4.6 Implications for healthcare practice
4.7 Recommendations for future research
4.8 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of findings
5.2 Contributions to the field
5.3 Practical implications
5.4 Limitations of the study
5.5 Recommendations for future research
5.6 Conclusion

Thesis Overview:

Predicting patient readmission using electronic health records is a crucial area of research in the healthcare industry. Hospital readmissions not only impose a financial burden on healthcare systems but also indicate gaps in patient care and contribute to decreased patient outcomes. This thesis aims to leverage EHR data to develop predictive models that identify patients at high risk of readmission and investigate the factors that contribute to readmissions.

The literature review will provide a comprehensive overview of previous studies on patient readmission, factors contributing to readmissions, machine learning techniques for prediction, the role of EHR data, and strategies to reduce readmissions. The research methodology will detail the data collection, preprocessing, model development, and evaluation techniques used in this study.

The discussion of findings will present a detailed analysis of patient readmissions, performance evaluation of predictive models, factors influencing readmissions, and implications for healthcare practice. The conclusion and summary will provide a concise summary of the findings, contributions to the field, practical implications, limitations of the study, recommendations for future research, and a conclusion to the thesis.

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