|
|
Artificial intelligence-enhanced care pathway planning and scheduling system : content validity assessment of required functionalities |
|---|---|
| Author: | Jansson, Miia1; Ohtonen, Pasi2; Alalääkkölä, Timo3; |
| Organizations: |
1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland 2Research Unit of Surgery, Anesthesia and Intensive Care, Oulu University Hospital, University of Oulu, Oulu, Finland 3Testing and Innovations, Oulu University Hospital, Oulu, Finland
4Division of Orthopedic and Trauma Surgery, Department of Surgery, Medical Research Center, Oulu University Hospital, Oulu, Finland
5Oulu University Hospital, Oulu, Finland 6Department of Anesthesiology, Oulu University Hospital, Oulu, Finland 7MRC Oulu, Research Group of Anesthesiology, Oulu, Finland 8Sense Organ Diseases Centre, Oulu University Hospital, Oulu, Finland |
| Format: | article |
| Version: | published version |
| Access: | open |
| Online Access: | PDF Full Text (PDF, 1.2 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe202301021129 |
| Language: | English |
| Published: |
Springer Nature,
2022
|
| Publish Date: | 2023-01-02 |
| Description: |
AbstractBackground: Artificial intelligence (AI) and machine learning are transforming the optimization of clinical and patient workflows in healthcare. There is a need for research to specify clinical requirements for AI-enhanced care pathway planning and scheduling systems to improve human–AI interaction in machine learning applications. The aim of this study was to assess content validity and prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system. Methods: A prospective content validity assessment was conducted in five university hospitals in three different countries using an electronic survey. The content of the survey was formed from clinical requirements, which were formulated into generic statements of required AI functionalities. The relevancy of each statement was evaluated using a content validity index. In addition, weighted ranking points were calculated to prioritize the most relevant functionalities of an AI-enhanced care pathway planning and scheduling system. Results: A total of 50 responses were received from clinical professionals from three European countries. An item-level content validity index ranged from 0.42 to 0.96. 45% of the generic statements were considered good. The highest ranked functionalities for an AI-enhanced care pathway planning and scheduling system were related to risk assessment, patient profiling, and resources. The highest ranked functionalities for the user interface were related to the explainability of machine learning models. Conclusion: This study provided a comprehensive list of functionalities that can be used to design future AI-enhanced solutions and evaluate the designed solutions against requirements. The relevance of statements concerning the AI functionalities were considered somewhat relevant, which might be due to the low level or organizational readiness for AI in healthcare. see all
|
| Series: |
BMC health services research |
| ISSN: | 1472-6963 |
| ISSN-E: | 1472-6963 |
| ISSN-L: | 1472-6963 |
| Volume: | 22 |
| Issue: | 1 |
| Article number: | 1513 |
| DOI: | 10.1186/s12913-022-08780-y |
| OADOI: | https://oadoi.org/10.1186/s12913-022-08780-y |
| Type of Publication: |
A1 Journal article – refereed |
| Field of Science: |
316 Nursing 113 Computer and information sciences |
| Subjects: | |
| Funding: |
This study is a part of an AICCELERATE-project (https://aiccelerate.eu/) which has received funding from the European Union’s Horizon 2020 research and innovation program (nº 101016902). The funder has not influenced the design, conduct, analysis or reporting of the study. |
| Copyright information: |
© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
|
https://creativecommons.org/licenses/by/4.0/ |