BMC palliative care · 2025
Artificial intelligence-based approaches for advance care planning: a scoping review.
СтатьяScoping ReviewНазвание и аннотация: Перевод в очереди
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Arioz Umut, Allsop Matthew John, Goodman William D, Timmons Suzanne, Simbirtseva Kseniya, Mlakar Izidor, Mocnik Grega
Аннотация
BACKGROUND: Advance Care Planning (ACP) empowers individuals to make informed decisions about their future healthcare. However, barriers including time constraints and a lack of clarity on professional responsibilities for ACP hinder its implementation. The application of artificial intelligence (AI) could potentially optimise elements of ACP in practice by, for example, identifying patients for whom ACP may be relevant and aiding ACP-related decision-making. However, it is unclear how applications of AI for ACP are currently being used in the delivery of palliative care.
OBJECTIVES: To explore the use of AI models for ACP, identifying key features that influence model performance, transparency of data used, source code availability, and generalizability.
METHODS: A scoping review was conducted using the Arksey and O'Malley framework and the PRISMA-ScR guidelines. Electronic databases (Scopus and Web of Science (WoS)) and seven preprint servers were searched to identify published research articles and conference papers in English, German and French for the last 10Â years' records. Our search strategy was based on terms for ACP and artificial intelligence models (including machine learning). The GRADE approach was used to assess the quality of included studies.
RESULTS: Included studies (N = 41) predominantly used retrospective cohort designs and real-world electronic health record data. Most studies (n = 39) focused on identifying individuals who might benefit from ACP, while fewer studies addressed initiating ACP discussions (n = 10) or documenting and sharing ACP information (n = 8). Among AI and machine learning models, logistic regression was the most frequent analytical method (n = 15). Most models (n = 28) demonstrated good to very good performance. However, concerns remain regarding data and code availability, as many studies lacked transparency and reproducibility (n = 17 and n = 36, respectively).
CONCLUSION: Most studies report models with promising results for predicting patient outcomes and supporting decision-making, but significant challenges remain, particularly regarding data and code availability. Future research should prioritize transparency and open-source code to facilitate rigorous evaluation. There is scope to explore novel AI-based approaches to ACP, including to support processes surrounding the review and updating of ACP information.
OBJECTIVES: To explore the use of AI models for ACP, identifying key features that influence model performance, transparency of data used, source code availability, and generalizability.
METHODS: A scoping review was conducted using the Arksey and O'Malley framework and the PRISMA-ScR guidelines. Electronic databases (Scopus and Web of Science (WoS)) and seven preprint servers were searched to identify published research articles and conference papers in English, German and French for the last 10Â years' records. Our search strategy was based on terms for ACP and artificial intelligence models (including machine learning). The GRADE approach was used to assess the quality of included studies.
RESULTS: Included studies (N = 41) predominantly used retrospective cohort designs and real-world electronic health record data. Most studies (n = 39) focused on identifying individuals who might benefit from ACP, while fewer studies addressed initiating ACP discussions (n = 10) or documenting and sharing ACP information (n = 8). Among AI and machine learning models, logistic regression was the most frequent analytical method (n = 15). Most models (n = 28) demonstrated good to very good performance. However, concerns remain regarding data and code availability, as many studies lacked transparency and reproducibility (n = 17 and n = 36, respectively).
CONCLUSION: Most studies report models with promising results for predicting patient outcomes and supporting decision-making, but significant challenges remain, particularly regarding data and code availability. Future research should prioritize transparency and open-source code to facilitate rigorous evaluation. There is scope to explore novel AI-based approaches to ACP, including to support processes surrounding the review and updating of ACP information.