2022
DOI: 10.1186/s12913-022-08780-y
|View full text |Cite
|
Sign up to set email alerts
|

Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities

Abstract: Background 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. … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
1
0
1

Year Published

2022
2022
2024
2024

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 12 publications
(4 citation statements)
references
References 21 publications
0
1
0
1
Order By: Relevance
“…64 Additional source data validation and auditing may be necessary to ensure high-quality and well-annotated data for training and updating artificial intelligence algorithms. 65 After implementation, infrastructure should support continuous assessment of model performance and its impact on clinical practice to avoid unintended adverse effects, also referred to as “algorithmovigilance.” 66 Deployed systems should also enable collaboration and interoperability among different stakeholders, including clinicians, researchers, data scientists, and information technology professionals. 67…”
Section: Challengesmentioning
confidence: 99%
“…64 Additional source data validation and auditing may be necessary to ensure high-quality and well-annotated data for training and updating artificial intelligence algorithms. 65 After implementation, infrastructure should support continuous assessment of model performance and its impact on clinical practice to avoid unintended adverse effects, also referred to as “algorithmovigilance.” 66 Deployed systems should also enable collaboration and interoperability among different stakeholders, including clinicians, researchers, data scientists, and information technology professionals. 67…”
Section: Challengesmentioning
confidence: 99%
“…An interesting intelligent artificial tool that analyses patient health portfolios and supports patient profiling was presented by Gehani and Panda ( 20 ). Machine learning has been used to plan treatment pathways based on patient profiles ( 21 ). In addition to using traditional statistical clustering methods to assess patient profiles in diabetes, preferred communication channels have also been used and it has been shown, that effective interaction between healthcare providers and patients can lead to better treatment outcomes ( 22 ).…”
Section: Introductionmentioning
confidence: 99%
“…Der Fachkräftemangel verschärft diese Problematik zusehends und schafft die Notwendigkeit für einen effizienteren Einsatz der verfügbaren Ressourcen. Einige Projekte befassen sich mit dem Management von Patientenströmen und integrieren dabei perspektivisch auch das zu erwartende Auftreten von Notfalleingriffen, die verfügbaren postoperativen Ressourcen, das individuelle Patientenrisiko und die Personalbedarfsplanung[8]. Abb.…”
unclassified