AI in Pharmaceutical Companies

Name: AI in Pharmaceutical Companies | Enabling Efficient, Responsible and Human-Centered Solutions for Medical Affairs
Brand: Tectum Wissenschaftsverlag
Price: 52.0 EUR
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Enabling Efficient, Responsible and Human-Centered Solutions for Medical Affairs

Michael F. Gleixner(Autor*in)

Tectum Wissenschaftsverlag

1. Auflage

Erschienen am 7. April 2022

260 Seiten

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978-3-8288-7816-7 (ISBN)

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Inhalt

Intro
Abstract
Zusammenfassung
Acknowledgement
List of abbreviations
Introductory remarks
1. Introduction
1.1. Initial situation and problem statement
1.2. Introduction of Merck & Co.
1.3. Research scope and approach
1.4. Focus, aim and research question
2. Digital transformation of the medical affairs business model in pharmaceutical companies meeting artificial intelligence
2.1. Digital transformation
2.1.1. Definition and understanding
2.1.2. Pharmaceutical companies and intellectual setting of medical affairs
2.1.3. Conceptual understanding
2.2. Business models and their innovation
2.2.1. Business model
2.2.2. Business model innovation
2.2.3. Framework elaboration for investigation
2.2.4. Changing business model of medical affairs
Medical affairs business function and activities
Drivers and required capabilities of the changing medical affair's business
2.3. Artificial intelligence
2.3.1. Definition and development in the historical context
Historical development
Definition
2.3.2. Conceptual understanding
2.4. Relevance of responsible artificial intelligence innovations within the pharmaceutical medical affairs context
2.4.1. Conceptual understanding of responsible artificial intelligence in pharmaceutical companies' medical affairs department
2.4.1.1. Definition and categorization
2.4.1.2. Potential and challenges of responsible artificial intelligence
2.4.2. Framework with challenges for safety and ethical responsibility considerations in artificial intelligence solutions
2.4.3. Artificial intelligence antecedents: new requirements and drivers for innovation
2.4.3.1. Technology
2.4.3.2. People/ Human
2.4.3.3. Organization
2.5. Outline of the corporate artificial intelligence use cases, agenda and sets for methodology of proof-of-concept and adaption
2.5.1. Background and approach to define and select the use cases
Background on global medical affairs AI journey
2.5.2. Introducing the human-centered artificial intelligence canvas
2.5.3. Use case descriptions
gPrime: AI HCP Medical Knowledge Discovery and Education
Advisory Board Expert Input Forum Artificial Intelligence Program
Scientific Congress and Abstract AI Analytics
Medical Information Artificial Intelligence Compendia
Medical Information Inquiries
Augmented Documentation Global Review Tool (GRT)
3. Research methodology
3.1. Research design and preparation
3.2. Triad of methodological principles
3.3. Design of the interview guideline
3.4. Sampling
3.5. Conduct of expert interviews
3.6. Transcription
3.7. Data analysis
3.7.1. Structured qualitative content analysis
3.7.2. Structured steps of data analysis
4. Results
Comprehension of AI within the business model innovation context of medical affairs
Dissolving the business AI framework of Dobbe et al. (2019) from a stage-based approach to an iterative domain-based approach
Development & design domain (value creation)
Training domain (value delivery)
Deployment domain (value capture)
Role of management to balance domains
Challenges
Manager as value enabler
Human-centered AI canvas
Jobs to be done
AI promise
Benefits for humans
Human activities (including skills, capabilities and competencies)
Machine activities
Collaboration
Human reinforcement
Critical thinking and biases
Critical/ complex decisions
Considerations and implications
Change management
Information policy
5. Discussion
Comprehension of AI within the BMI context of medical affairs
Value enablement as new BMI value part and role of the manager
The balancing activities of the manager as value enabler in the context of TOP antecedents and human-centered AI solutions
Deeper remarks on responsibility and ethical considerations based on three fundamental management principles
6. Conclusion
6.1. Summary
6.2. Limitations
6.3. Outlook
7. References
8. Appendix
Section A: Use case overview
Section B: Interview guideline
Section C: Interview sample overview
Section D: Detailed category development and coding process
Composition of the category system
Definition of categories and anchor examples
Material run-through with applied coding method
Section E: Code overview with anchor example references

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