How Sensor, Signal, and Imaging Informatics May Impact Patient Centered Care and Care Coordination

Summary

Objective: This synopsis presents a selection for the IMIA (International Medical Informatics Association) Yearbook 2015 of excellent research in the broad field of Sensor, Signal, and Imaging Informatics published in the year 2014, with a focus on patient centered care coordination.

Methods: The two section editors performed a systematic initial selection and a double blind peer review process to select a list of candidate best papers in the domain published in 2014, from the PubMed and Web of Science databases. A set of MeSH keywords provided by experts was used. This selection was peer-reviewed by external reviewers.

Results: The review process highlighted articles illustrating two current trends related to care coordination and patient centered care: the enhanced capacity to predict the evolution of a disease based on patient-specific information can impact care coordination; similarly, better perception of the patient and his treatment could lead to enhanced personalized care with a potential impact on care coordination.

Conclusions: This review shows the multiplicity of angles from which the question of patient-centered care can be addressed, with consequences on care coordination that will need to be confirmed and demonstrated in the future.

Keywords

Medicine personalization - care coordination - innovative technologies - biomedical imaging - patient centered modelling

 

• References

• 1 Mi H, Petitjean C, Dubray B, Vera P, Ruan S. Prediction of lung tumor evolution during radio-therapy in individual patients with PET. IEEE Trans Med Imaging 2014; Apr 33 (Suppl. 04) 995-1003.
• 2 Colin T, Cornelis F, Jouganous J, Martin M, Saut O. Patient specific image driven evaluation of the aggressiveness of metastases to the lung. Med Image Comput Comput Assist Interv 2014; 17 (Suppl. 01) 553-60.
• 3 Wong KCL, Summers RM, Kebebew E, Yao J. Tumor growth prediction with hyperelastic biomechanical model, physiological data fusion, and nonlinear optimization. Med Image Comput Comput Assist Interv 2014; 17 (Suppl. 02) 25-32.
• 4 Hsieh Y-Z, Su M-C, Wang C-H, Wang P-C. Prediction of survival of ICU patients using computational intelligence. Comput Biol Med 2014; 47: 13-9.
• 5 Tanter M, Fink M. Ultrafast imaging in biomedical ultrasound. IEEE Trans Ultrason Ferroelectr Freq Control 2014; Jan 61 (Suppl. 01) 102-19.
• 6 Hsu M, Gupta M, Su L-M, Liao JC. Intraoperative optical imaging and tissue interrogation during urologic surgery. Curr Opin Urol 2014; 24 (Suppl. 01) 66-74.
• 7 Fu Y, Zhang W, Mandal M, Meng MQ-H. Computer-aided bleeding detection in WCE video. IEEE J Biomed Health Inform 2014; 18 (Suppl. 02) 636-42.
• 8 Amir-Khalili A, Peyrat J-M, Abinahed J, Al-Alao O, Al-Ansari A, Hamarneh G. et al. Auto localization and segmentation of occluded vessels in robot-assisted partial nephrectomy. Med Image Comput Comput Assist Interv 2014; 17 (Suppl. 01) 407-14.
• 9 Chen HL, Varshney LR, Varshney PK. Noise-Enhanced Information Systems. Proc IEEE 2014; 102 (Suppl. 10) 1607-21.
• 10 Liu Y, Xu L, Zhu H, Liu SS-Y. Technical procedures for template-guided surgery for mandibular reconstruction based on digital design and manufacturing. Biomed Eng Online 2014; 13: 63.
• 11 Scuderi GR, Fallaha M, Masse V, Lavigne P, Amiot L-P, Berthiaume M-J. Total knee arthroplasty with a novel navigation system within the surgical field. Orthop Clin North Am 2014; 45 (Suppl. 02) 167-73.
• 12 Quellec G, Charrière K, Lamard M, Droueche Z, Roux C, Cochener B, and Cazuguel G.. et al. Real-time recognition of surgical tasks in eye surgery videos. Med Image Anal 2014; 18 (Suppl. 03) 579-90.
• 13 Pheiffer TS, Thompson RC, Rucker DC, Simpson AL, Miga MI. Model-based correction of tissue compression for tracked ultrasound in soft tissue image-guided surgery. Ultrasound Med Biol 2014; Apr 40 (Suppl. 04) 788-803.