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Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges

  • M. Ashok , T. Pandiarajan , G. Kavitha , R. Saravanan and R. Kumar
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Digital Transformation in Healthcare 5.0
This chapter is in the book Digital Transformation in Healthcare 5.0

Abstract

Big data analytics can revolutionize healthcare by using vast amounts of data to improve patient care and healthcare management. However, several challenges must be overcome to fully exploit its benefits. The first challenge is to integrate and harmonize data from different sources, ensuring interoperability of electronic health records, medical devices, wearable sensors, and control systems. Ensuring the quality and reliability of health data is very important for accurate analysis and decision-making, as incomplete or incorrect data can lead to incorrect conclusions. Privacy and security are critical concerns when handling sensitive patient data, requiring strong measures to anonymize data, store it securely, and control access. Scalability and performance issues arise when large data sets and complex algorithms are processed in real time or near real time. Developing accurate predictive models and decision support systems that can use big data to improve patient care is a research challenge. Ethical and legal considerations related to consent, data ownership, transparency, and algorithms must be considered to ensure the ethical use of patient data. Implementing big data analytics solutions in healthcare requires overcoming obstacles and understanding organizational and cultural factors to make their implementation successful. Ensuring that the complex algorithms used in big data analytics are interpretable and explainable is critical to building trust and understanding their predictions. Establishing strong information management frameworks and standards facilitates effective information sharing and collaboration. Cost-effective resource management approaches and strategies are necessary for the sus tainable implementation of big data analytics in healthcare. Continued research and innovation are essential to meet these challenges and harness the full potential of big data analytics to improve patient care and healthcare management.

Abstract

Big data analytics can revolutionize healthcare by using vast amounts of data to improve patient care and healthcare management. However, several challenges must be overcome to fully exploit its benefits. The first challenge is to integrate and harmonize data from different sources, ensuring interoperability of electronic health records, medical devices, wearable sensors, and control systems. Ensuring the quality and reliability of health data is very important for accurate analysis and decision-making, as incomplete or incorrect data can lead to incorrect conclusions. Privacy and security are critical concerns when handling sensitive patient data, requiring strong measures to anonymize data, store it securely, and control access. Scalability and performance issues arise when large data sets and complex algorithms are processed in real time or near real time. Developing accurate predictive models and decision support systems that can use big data to improve patient care is a research challenge. Ethical and legal considerations related to consent, data ownership, transparency, and algorithms must be considered to ensure the ethical use of patient data. Implementing big data analytics solutions in healthcare requires overcoming obstacles and understanding organizational and cultural factors to make their implementation successful. Ensuring that the complex algorithms used in big data analytics are interpretable and explainable is critical to building trust and understanding their predictions. Establishing strong information management frameworks and standards facilitates effective information sharing and collaboration. Cost-effective resource management approaches and strategies are necessary for the sus tainable implementation of big data analytics in healthcare. Continued research and innovation are essential to meet these challenges and harness the full potential of big data analytics to improve patient care and healthcare management.

Chapters in this book

  1. Frontmatter I
  2. About the book V
  3. Preface VII
  4. Foreword IX
  5. Contents XI
  6. List of contributors XV
  7. Chapter 1 The impact of blockchain technology on the healthcare system 1
  8. Chapter 2 The role of metaverse in transforming healthcare: blockchain approach 33
  9. Chapter 3 Blockchain-empowered metaverse healthcare systems and applications 61
  10. Chapter 4 Role of artificial intelligence in disease diagnosis 89
  11. Chapter 5 Machine learning for twinning the human body 105
  12. Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges 131
  13. Chapter 7 An emerging trends of bioinformatics and big data analytics in healthcare 159
  14. Chapter 8 Digital twins in medicine: leveraging machine learning for real-time diagnosis and treatment 189
  15. Chapter 9 Nanorobots in healthcare 209
  16. Chapter 10 Semantic-based approach for medical cyber-physical system (MCPS) with biometric authentication for secured privacy 237
  17. Chapter 11 Integration of cognitive computing and AI for smart healthcare 267
  18. Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope 293
  19. Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector 317
  20. Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning 343
  21. Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis 365
  22. Index 375
https://doi.org/10.1515/9783111398549-006

Chapters in this book

  1. Frontmatter I
  2. About the book V
  3. Preface VII
  4. Foreword IX
  5. Contents XI
  6. List of contributors XV
  7. Chapter 1 The impact of blockchain technology on the healthcare system 1
  8. Chapter 2 The role of metaverse in transforming healthcare: blockchain approach 33
  9. Chapter 3 Blockchain-empowered metaverse healthcare systems and applications 61
  10. Chapter 4 Role of artificial intelligence in disease diagnosis 89
  11. Chapter 5 Machine learning for twinning the human body 105
  12. Chapter 6 Improving patient care and healthcare management using bigdata analytics presents several research challenges 131
  13. Chapter 7 An emerging trends of bioinformatics and big data analytics in healthcare 159
  14. Chapter 8 Digital twins in medicine: leveraging machine learning for real-time diagnosis and treatment 189
  15. Chapter 9 Nanorobots in healthcare 209
  16. Chapter 10 Semantic-based approach for medical cyber-physical system (MCPS) with biometric authentication for secured privacy 237
  17. Chapter 11 Integration of cognitive computing and AI for smart healthcare 267
  18. Chapter 12 An overview of recommender systems in the healthcare domain: significant contributions, challenges, and future scope 293
  19. Chapter 13 Advancements and challenges of using natural language processing in the healthcare sector 317
  20. Chapter 14 Intraocular pressure monitoring system for glaucoma patients using IoT and machine learning 343
  21. Chapter 15 A machine learning approach to voice analysis in Parkinson’s disease diagnosis 365
  22. Index 375
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