Big data accessibility issues for key medical personnel

Liat Toderis, Iris Reychav, Roger McHaney, Itai Gueta, Gal Ben-Haim, Ronen Loebstein

פרסום מחקרי: פרסום בכתב עתמאמר מכנסביקורת עמיתים

תקציר

Healthcare systems rely on vast data repositories serving a variety of purposes which include business functions and patient medical records. With recent advances in big data analytics, the latent value for all this data has become more apparent yet operational use has lagged behind. Healthcare operations suffer from multiple and complex issues making harnessing the potential of big data more important than ever. This article describes use of the ArchiMate® modeling language to adopt an organizational architecture language for the purpose of building a model for personalization of healthcare-related big data. This model describes how data can be made accessible to healthcare professionals in a way that allows clinicians to generate clinical, operational, and managerial value from the data with minimal involvement of additional data professionals. The article concludes with a case study proof-of-concept demonstrating the value of the approach and suggestions for implementation.

שפה מקוריתאנגלית
עמודים (מ-עד)453-461
מספר עמודים9
כתב עתProcedia Computer Science
כרך239
מזהי עצם דיגיטלי (DOIs)
סטטוס פרסוםפורסם - 2024
אירוע2023 International Conference on ENTERprise Information Systems, CENTERIS 2023 - International Conference on Project MANagement, ProjMAN 2023 - International Conference on Health and Social Care Information Systems and Technologies, HCist 2023 - Porto, פורטוגל
משך הזמן: 8 נוב׳ 202310 נוב׳ 2023

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