Artificial Intelligence for Healthcare Interdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World
Book contents
- Artificial Intelligence for Healthcare
- Artificial Intelligence for Healthcare
- Copyright page
- Contents
- Contributors
- Preface
- Introduction
- Part I Personalized Medicine
- Part II Optimizing Healthcare Systems
- 5 Using Algorithmic Solutions to Address Gatekeeper Training Issues for Suicide Prevention on College Campuses
- 6 Optimizing Defibrillator Deployment
- 7 Optimization of Biomarker-Based Prostate Cancer Screening Policies
- 8 Analytics-Driven Capacity Management
- 9 Practical Advice for Clinician–Engineer Partnerships for the Use of AI, Optimization, and Analytics for Healthcare Delivery
- References
9 - Practical Advice for Clinician–Engineer Partnerships for the Use of AI, Optimization, and Analytics for Healthcare Delivery
from Part II - Optimizing Healthcare Systems
Published online by Cambridge University Press: 21 April 2022
Book contents
- Artificial Intelligence for Healthcare
- Artificial Intelligence for Healthcare
- Copyright page
- Contents
- Contributors
- Preface
- Introduction
- Part I Personalized Medicine
- Part II Optimizing Healthcare Systems
- 5 Using Algorithmic Solutions to Address Gatekeeper Training Issues for Suicide Prevention on College Campuses
- 6 Optimizing Defibrillator Deployment
- 7 Optimization of Biomarker-Based Prostate Cancer Screening Policies
- 8 Analytics-Driven Capacity Management
- 9 Practical Advice for Clinician–Engineer Partnerships for the Use of AI, Optimization, and Analytics for Healthcare Delivery
- References
Summary
This chapter offers lessons from engineering and other industries that promise developments in healthcare, and practical guidance for clinician-engineer partnerships. Section 1 provides guidance on how to establish a shared vocabulary and common understanding between engineers and clinicians of what terms such as AI and ML do and don’t mean. Section 2 identifies challenges clinician-engineering partnerships must overcome to deliver sustained value and ways to avoid common causes of failure. Section 3 provides specific advice on how to design projects to produce value at a series of stages rather than rely on the success of one, ambitious final model. Section 4 concludes by drawing on cautionary lessons from healthcare and other industries.
- Type
- Chapter
- Information
- Artificial Intelligence for HealthcareInterdisciplinary Partnerships for Analytics-driven Improvements in a Post-COVID World, pp. 182 - 192Publisher: Cambridge University PressPrint publication year: 2022
References
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