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Internet of Things for Smart Healthcare and Digital Well-Being
Published in Utpal Chakraborty, Amit Banerjee, Jayanta Kumar Saha, Niloy Sarkar, Chinmay Chakraborty, Artificial Intelligence and the Fourth Industrial Revolution, 2022
The AI-Pathway CompanionS: Developed by Siemens Healthi-neers. It helps in data integration and evaluation. It also provides aggregated patient history and findings, projections on the patient’s status in the clinical pathway following clinical guidelines, and suggestions for the next steps to follow.
Process ontology technology in modeling clinical pathway information system
Published in International Journal of Computers and Applications, 2020
Jian Ma, Runtong Zhang, Xiaomin Zhu, Runqi Cao
The clinical pathway aims to control the cost of medical treatment, standardize the diagnosis and treatment, improve the quality of medical services, speed up postoperative rehabilitation, and ensure medical safety [1]. In the clinical pathway of our work, reflecting the use of clinical pathways low, of which different factors [2]. Clinical pathway information system (CPIS) as an infrastructure guarantee plays an important role in the development and implementation of clinical pathway. For the design of CPIS, some studies have given solutions, but all have their own shortcomings. Li used ontology engineering to model clinical pathway, which is essentially a formalized representation of clinical knowledge semantics and solved the phenomenon that traditional clinical pathways are not complete and standardized [3]. However, the research lacks a systematic review of the clinical pathway and does not explain the working mechanism of the clinical pathway from a systematic perspective.
Prediction of the healthcare resource utilization using multi-output regression models
Published in IISE Transactions on Healthcare Systems Engineering, 2018
Liwen Cui, Xiaolei Xie, Zuojun Shen, Rui Lu, Haibo Wang
Higher efficiency in the healthcare system and improved payment policy are strongly desired worldwide. Predictive analytics of the healthcare resource utilization is instrumental for better allocation and management of medical resources. Furthermore, prediction models with high accuracy can facilitate decision making about pricing and reimbursement policy. Recently, there has been growing interest in healthcare cost prediction using machine learning techniques. However, it is insufficient to use cost as the only measure for healthcare resource utilization. For instance, the Diagnosis Related Group (DRG) system, a widely used management tool to group patients with similar consumption of medical resources, uses both cost and length of stay (LoS) to measure the resource utilization by patients. Moreover, the clinical pathway, created to reduce variations in care delivery to improve care quality and efficiency, also focuses on cost and LoS. These motivate us to jointly consider multiple measures in prediction models, depending on the implementation background and practical purposes (Zhou et al., 2011; Lee et al., 2010).
Addressing healthcare operational deficiencies using stochastic and dynamic programming
Published in International Journal of Production Research, 2019
Na Geng, Xiaolan Xie, Zheng Zhang
Each patient requires one or multiple health care services including consultation, medical examinations, surgeries and medical treatments. They can be represented by a so-called ‘clinical pathway’ that describes what health care service to perform and when.