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Chapter 4 Education and training in smart pharma

  • Rishabha Malviya , Shristy Verma , Sonali Sundram and Harshil Shah
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Digital Blockchain
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Abstract

A “connected health” movement is being sparked by the digitalization of healthcare, which also lowers per capita costs, enhances care quality, and improves patient outcomes. The pharmaceutical industry is expanding at a rapid rate due to digitalization, which uses information shared via social media, mobile phones, and other technologies to comprehend consumer demand and optimize supply chain efficiency. Machine learning algorithms can retrieve drug-drug interactions and predict their effects. Big data analysis enables digital health care by eliminating the need for in-person visits and guaranteeing that clinicians have immediate access to all medical records. The physical networking of software, embedded systems, and electronic sensors that permit data sharing across a network from any location is known as the Internet of Things (IoT). The pharmaceutical industry is using blockchain technology to expedite regulatory approval, drug development, and discovery. The Internet of Things is also being used to monitor health problems, predict events about patients, alter data, reduce expenses, and save lives. The chapter offers state-of-the-art training approaches for the smart pharmaceutical industry.

Abstract

A “connected health” movement is being sparked by the digitalization of healthcare, which also lowers per capita costs, enhances care quality, and improves patient outcomes. The pharmaceutical industry is expanding at a rapid rate due to digitalization, which uses information shared via social media, mobile phones, and other technologies to comprehend consumer demand and optimize supply chain efficiency. Machine learning algorithms can retrieve drug-drug interactions and predict their effects. Big data analysis enables digital health care by eliminating the need for in-person visits and guaranteeing that clinicians have immediate access to all medical records. The physical networking of software, embedded systems, and electronic sensors that permit data sharing across a network from any location is known as the Internet of Things (IoT). The pharmaceutical industry is using blockchain technology to expedite regulatory approval, drug development, and discovery. The Internet of Things is also being used to monitor health problems, predict events about patients, alter data, reduce expenses, and save lives. The chapter offers state-of-the-art training approaches for the smart pharmaceutical industry.

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Foreword VII
  4. Contents IX
  5. Chapter 1 Introduction to smart pharma: foundation of AI and big data 1
  6. Chapter 2 Emerging technology in pharma and the role of AI and blockchain 19
  7. Chapter 3 Partnership and collaboration in the pharmaceutical industry 37
  8. Chapter 4 Education and training in smart pharma 55
  9. Chapter 5 Drug manufacturing and quality control with artificial intelligence 77
  10. Chapter 6 Drug development and clinical trial via artificial intelligence 97
  11. Chapter 7 AI-driven clinical decision support systems for pharma executives 121
  12. Chapter 8 AI-based pharmacovigilance and drug safety 143
  13. Chapter 9 Intellectual property and data privacy in the pharmaceutical sector 163
  14. Chapter 10 Regulatory affairs and compliance in the pharmaceutical sector 183
  15. Chapter 11 AI and big data in post-marketing surveillance 201
  16. Chapter 12 Challenges and ethical considerations 219
  17. Chapter 13 Future trends and innovations of AI in healthcare 237
  18. Index 253
https://doi.org/10.1515/9783111574288-004

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Foreword VII
  4. Contents IX
  5. Chapter 1 Introduction to smart pharma: foundation of AI and big data 1
  6. Chapter 2 Emerging technology in pharma and the role of AI and blockchain 19
  7. Chapter 3 Partnership and collaboration in the pharmaceutical industry 37
  8. Chapter 4 Education and training in smart pharma 55
  9. Chapter 5 Drug manufacturing and quality control with artificial intelligence 77
  10. Chapter 6 Drug development and clinical trial via artificial intelligence 97
  11. Chapter 7 AI-driven clinical decision support systems for pharma executives 121
  12. Chapter 8 AI-based pharmacovigilance and drug safety 143
  13. Chapter 9 Intellectual property and data privacy in the pharmaceutical sector 163
  14. Chapter 10 Regulatory affairs and compliance in the pharmaceutical sector 183
  15. Chapter 11 AI and big data in post-marketing surveillance 201
  16. Chapter 12 Challenges and ethical considerations 219
  17. Chapter 13 Future trends and innovations of AI in healthcare 237
  18. Index 253
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