The implementation of a multidisciplinary, electronic health record embedded care pathway to improve structured data recording and decrease electronic health record burden

Ebbers, Tom; Takes, Robert P.; Smeele, Ludi E.; Kool, Rudolf B.; van den Broek, Guido B.; Dirven, Richard

doi:10.1016/j.ijmedinf.2024.105344

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…The perceived benefits of AI in enhancing diagnostic efficiency and accuracy likely contributed to this positive attitude, aligning with the findings of a meta-analysis by Sun et al [22], which demonstrated the superior performance of AI-based diagnostic systems compared to traditional methods [22]. However, challenges such as compatibility issues with existing EMR systems and the need for extensive training were also identified, corroborating the findings of studies by Afzal et al [23] and Ebbers et al [24], which emphasized the importance of addressing usability concerns to ensure successful technology adoption in healthcare settings. These findings underscore the importance of addressing usability concerns and providing adequate training and support to facilitate the effective adoption and utilization of AI tools in ENT practice, ultimately enhancing patient care and outcomes [25].…”

Section: Discussionsupporting

confidence: 82%

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

Alshehri,

Alahmari,

Alasiry

2024

JPM

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The integration of Artificial Intelligence (AI) into healthcare has the potential to revolutionize medical diagnostics, particularly in specialized fields such as Ear, Nose, and Throat (ENT) medicine. However, the successful adoption of AI-assisted diagnostic tools in ENT practice depends on the understanding of various factors; these include influences on their effectiveness and acceptance among healthcare professionals. This cross-sectional study aimed to assess the usability and integration of AI tools in ENT practice, determine the clinical impact and accuracy of AI-assisted diagnostics in ENT, measure the trust and confidence of ENT professionals in AI tools, gauge the overall satisfaction and outlook on the future of AI in ENT diagnostics, and identify challenges, limitations, and areas for improvement in AI-assisted ENT diagnostics. A structured online questionnaire was distributed to 600 certified ENT professionals with at least one year of experience in the field. The questionnaire assessed participants’ familiarity with AI tools, usability, clinical impact, trust, satisfaction, and identified challenges. A total of 458 respondents completed the questionnaire, resulting in a response rate of 91.7%. The majority of respondents reported familiarity with AI tools (60.7%) and perceived them as generally usable and clinically impactful. However, challenges such as integration with existing systems, user-friendliness, accuracy, and cost were identified. Trust and satisfaction levels varied among participants, with concerns regarding data privacy and support. Geographic and practice setting differences influenced perceptions and experiences. The study highlights the diverse perceptions and experiences of ENT professionals regarding AI-assisted diagnostics. While there is general enthusiasm for these tools, challenges related to integration, usability, trust, and cost need to be addressed for their widespread adoption. These findings provide valuable insights for developers, policymakers, and healthcare providers aiming to enhance the role of AI in ENT practice.

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Section: Discussionsupporting

confidence: 82%

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

Alshehri,

Alahmari,

Alasiry

2024

JPM

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Design principles for patient flow improvement in care pathways: a paradox theory perspective

Vallandingham,

De Boer,

Dreyer

2024

BPMJ

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PurposeThe patient flow performance achievable by care pathways is constrained by competing flow and resource efficiency, which can negatively impact improvements. This paper probes the divergence between resource and flow efficiency and how care pathways can lead to improved patient flow. By framing the problem through the lens of paradox theory, a set of design principles is proposed to assist decision-makers in care pathway implementation. Implications are derived for research and practice.Design/methodology/approachThe authors used conceptual research to develop design principles for care pathways based on a systematic review of relevant care pathway research. The initial search contained 515 unique articles, resulting in a final sample of 56 studies.FindingsWhen applying care pathways, patient flow may be negatively affected in relation to the dimensions of bottlenecks, non-value-adding activities, and variability. However, the findings also indicate methods that can be applied to manage organizational paradoxes, which can contribute to more efficient patient flow along each of the three dimensions.Research limitations/implicationsThe study is limited to care pathways and therefore could have missed relevant studies in similar fields, such as care coordination.Practical implicationsHealth care managers, politicians, and IT developers can apply the proposed design principles when developing, implementing, and improving care pathways and supporting technologies.Originality/valueWhile existing research has studied care pathways from a medical perspective, this is the first paper to the author’s knowledge that addresses care pathways directly by considering paradox theory and in light of the operations management literature.

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Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions

Chen,

Lehmann,

Malin

2024

J Med Internet Res

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The integration of digital technologies into health care has significantly enhanced the efficiency and effectiveness of care coordination. Our perspective paper explores the digital information ecosystems in modern care coordination, focusing on the processes of information generation, updating, transmission, and exchange along a patient’s care pathway. We identify several challenges within this ecosystem, including interoperability issues, information silos, hard-to-map patient care journeys, increased workload on health care professionals, coordination and communication gaps, and compliance with privacy regulations. These challenges are often associated with inefficiencies and diminished care quality. We also examine how emerging artificial intelligence (AI) tools have the potential to enhance the management of patient information flow. Specifically, AI can boost interoperability across diverse health systems; optimize and monitor patient care pathways; improve information retrieval and care transitions; humanize health care by integrating patients’ desired outcomes and patient-reported outcome measures; and optimize clinical workflows, resource allocation, and digital tool usability and user experiences. By strategically leveraging AI, health care systems can establish a more robust and responsive digital information ecosystem, improving care coordination and patient outcomes. This perspective underscores the importance of continued research and investment in AI technologies in patient care pathways. We advocate for a thoughtful integration of AI into health care practices to fully realize its potential in revolutionizing care coordination.

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The implementation of a multidisciplinary, electronic health record embedded care pathway to improve structured data recording and decrease electronic health record burden

Cited by 5 publications

References 26 publications

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

A Comprehensive Evaluation of AI-Assisted Diagnostic Tools in ENT Medicine: Insights and Perspectives from Healthcare Professionals

Design principles for patient flow improvement in care pathways: a paradox theory perspective

Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions

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