Automatic Idea Detection: Implementing artificial intelligence in medical technology innovation (AID)
A new way to seize innovative healthcare practices is to screen online health platforms to identify novel and feasible solutions. Artificial intelligent algorithms can screen vast amounts of information and automatically detect user‐contributed ideas. This algorithm is called automatic idea detection or AID.
Healthcare-associated infections (HAIs) are among the major causes of death of hospitalized patients. HAIs led to patient suffering, hospital budget overruns, and several economic and social challenges such as antibiotic control policies, prolonged stays, and infection control programs with high alternative costs for staff and resources. Preventing and controlling HAIs are extremely difficult because of a) the complexity of implementing sustained improvements in hospitals, b) lack of ways to analyze staff behaviour in real-time, and c) presence of emergent pathologies that require constant innovative prevention and control practices.
A proposed way to better understand HAIs prevention and control practices is to use Automatic Idea Detection (AID) systems. AID system refers to classification algorithms that can screen large amounts of information and identify those likely to contain ideas/solutions. AID system can be used to scan healthcare online platforms and identify innovative ideas/solutions. It can yield a range of benefits, such as acceleration of medical discovery, identification of emergent practices, systematic scanning of databases, and greater efficiency in revealing novel procedures. Despite these benefits, healthcare organizations face immense challenges in developing and implementing AID systems, given the move's systemic transformation.
This multidisciplinary research project addresses these issues from an ecosystem's perspective by focusing on value creation and the role and nature of complementarities in developing an AID system. The project partners include Halmstad University and other global healthcare organizations engaged in preventing and controlling HAIs.
About the project
April 2021 to March 2024
The Knowledge Foundation
Fábio Gama, Senior Lecturer in Healthcare Innovation, Halmstad University
Other participating researchers:
- Peyman Mashhadi, Senior Lecturer in Machine Learning
- Mahmoud Rahat, Associate Senior Lecturer in Natural Language Processing
- Jens Nygren, Professor in Health Innovation
- Magnus Holmén, Professor in Business model
- Slawomir Nowaczyk, Professor in Machine Learning
- Carina Göransson, Senior Lecturer in Healthcare
- Hanna Johnsson, Master Student
- Chaithanya Anjanappa, Master Student
- Manisha Gurung, Master Student
- Maj-Britt Voldby, Master Student
- Amir Gharaie, PhD Student, Linköping University
- Zahra Kharazian, PhD Student, Stockholm University
- Håkan Lindström, Global Technical Innovation Manager
- Peter Blomström, Global Brand Service Director
- Jens Backman, Clinician
- Gama, F., & Magistretti, S. (2023). “Lost in Red Tape? Conforming Medical Device Developments to Adaptive Regulations.” IAMOT 2023.
- Gama, F., & Holmén, M. (2022). “Ideation and Machine Learning: Problem Finding in Disruptive Innovation.” R&D 2022 Management Conference, June 9–13, Trento. RADMA, Research and Development Management.
- Gama, F., Florén, H., & Sjödin, D. (2021). “Artificial Intelligence Capabilities as Enablers for Digital Innovation Processes: A Systematic Literature Review.” R&D 2021 Management Conference, June 9–13, Digital Conference. RADMA, Research and Development Management.
- Kharazian, Z., Rahat, M., Gama, F., Mashhadi, P.S., Nowaczyk, S., Lindgren, T., & Magnusson, S. (2023). “AID4HAI: Automatic Idea Detection for Healthcare-Associated Infections (HAIs) from Twitter, A Framework based on Active Learning and Transfer Learning.” Symposium on Intelligent Data Analysis (IDA).