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Expectations and Requirements of Surgical Staff for an AI-Supported Clinical Decision Support System for Older Patients: Qualitative Study

Expectations and Requirements of Surgical Staff for an AI-Supported Clinical Decision Support System for Older Patients: Qualitative Study

Main categories (1-3), primary codes, and subcodes emerging from inductive analysis of the interviews. a The total sum of quotes belonging to the subcodes. b AI-CDSS: artificial intelligence–supported clinical decision support system. c In the following section, wishes for and chances of an AI-CDSS are presented together due to their overlapping subcodes. d HIS: hospital information system.

Adriane Uihlein, Lisa Beissel, Anna Hanane Ajlani, Marcin Orzechowski, Christoph Leinert, Thomas Derya Kocar, Carlos Pankratz, Konrad Schuetze, Florian Gebhard, Florian Steger, Marina Liselotte Fotteler, Michael Denkinger

JMIR Aging 2024;7:e57899

An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study

An Automatic Ontology-Based Approach to Support Logical Representation of Observable and Measurable Data for Healthy Lifestyle Management: Proof-of-Concept Study

Different research groups have conducted different studies on e Health ontology modeling for chronic illness, health monitoring, and ontology-based clinical decision support system (CDSS). For example, Kim et al [16] developed an ontology model for obesity management with the nursing process in the mobile device domain for spontaneous participant engagement and continuous weight monitoring.

Ayan Chatterjee, Andreas Prinz, Martin Gerdes, Santiago Martinez

J Med Internet Res 2021;23(4):e24656

Clinical Decision Support System Used in Spinal Disorders: Scoping Review

Clinical Decision Support System Used in Spinal Disorders: Scoping Review

The following review questions were answered: (1) Which CDSS tools can be identified in the current literature on spinal disorders? (2) What are the different purposes that the CDSS tools serve for spinal disorders? (3) How are these CDSS tools developed and assessed for effectiveness? and (4) What are the user’s perceptions and experiences regarding the use of CDSS tools?

Zheng An Toh, Bjørnar Berg, Qin Yun Claudia Han, Hwee Weng Dennis Hey, Minna Pikkarainen, Margreth Grotle, Hong-Gu He

J Med Internet Res 2024;26:e53951

Physicians’ Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform

Physicians’ Perspectives on AI in Clinical Decision Support Systems: Interview Study of the CURATE.AI Personalized Dose Optimization Platform

A clinical decision support system (CDSS) is a widely established tool to enhance health system efficiency. Administered through electronic medical records and other computerized workflows, a CDSS has been established to improve clinical practices [1]. For example, patient health outcomes from treatment presented through visual prebuilt reports can provide insights to physicians regarding patterns of care and patient responses, thereby improving the experience of treatment provision.

Smrithi Vijayakumar, V Vien Lee, Qiao Ying Leong, Soo Jung Hong, Agata Blasiak, Dean Ho

JMIR Hum Factors 2023;10:e48476

A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study

A Personalized Ontology-Based Decision Support System for Complex Chronic Patients: Retrospective Observational Study

Shortlife et al [18] reported that biomedical informaticians had identified the following characteristics necessary to achieve good acceptance of a CDSS: users, in this case, health care professionals, must understand the basis and reasoning behind the CDSS recommendations; the CDSS developed in the health care environment should be intuitive and easy to use; the CDSS should support the health care professional by providing advice but always respecting their experience and knowledge; and the recommendations

Esther Román-Villarán, Celia Alvarez-Romero, Alicia Martínez-García, German Antonio Escobar-Rodríguez, María José García-Lozano, Bosco Barón-Franco, Lourdes Moreno-Gaviño, Jesús Moreno-Conde, José Antonio Rivas-González, Carlos Luis Parra-Calderón

JMIR Form Res 2022;6(8):e27990

Implementation of a Clinical Decision Support System for Antimicrobial Prescribing in Sub-Saharan Africa: Multisectoral Qualitative Study

Implementation of a Clinical Decision Support System for Antimicrobial Prescribing in Sub-Saharan Africa: Multisectoral Qualitative Study

Overall, the axes integrate the organization or environment in which the CDSS is implemented, the individual behaviors related to the prescriber or patient, and the CDSS itself and its functionalities. Within these broad axes, there are numerous factors, such as practitioner acceptance of new technology, integration of the CDSS into workflows, and access to technical support [21-23]. These factors guide the analysis of potential barriers to CDSS implementation.

Nathan Peiffer-Smadja, Sophie Descousse, Elsa Courrèges, Audrey Nganbou, Pauline Jeanmougin, Gabriel Birgand, Séverin Lénaud, Anne-Lise Beaumont, Claire Durand, Tristan Delory, Josselin Le Bel, Elisabeth Bouvet, Sylvie Lariven, Eric D'Ortenzio, Issa Konaté, Marielle Karine Bouyou-Akotet, Abdoul-Salam Ouedraogo, Gisèle Affoue Kouakou, Armel Poda, Corinne Akpovo, François-Xavier Lescure, Aristophane Tanon

J Med Internet Res 2024;26:e45122

Effects of Computerized Decision Support Systems on Practitioner Performance and Patient Outcomes: Systematic Review

Effects of Computerized Decision Support Systems on Practitioner Performance and Patient Outcomes: Systematic Review

Artificial intelligence, software, or algorithms able to perform tasks that normally require human intelligence are integrated into CDSS processes. Data mining, a process usually assisted by AI, is often used by CDSSs to identify new data patterns from large data sets (like patient EHRs) [3]. The conclusions reached by AI used for data mining can be used by both non–knowledge-based CDSSs and knowledge-based CDSSs [3].

Clemens Scott Scott Kruse, Nolan Ehrbar

JMIR Med Inform 2020;8(8):e17283

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