Background: In this paper, we give an overview of methadone treatment in Ireland and outline the rationale for designing an electronic health record (EHR) with extensibility, interoperability and decision support functionality. Incorporating several international standards, a conceptual model applying a problem orientated approach in a hierarchical structure has been proposed for building the EHR.
Methods: A set of archetypes has been designed in line with the current best practice and clinical guidelines which guide the information-gathering process. A web-based data entry system has been implemented, incorporating elements of the paper-based prescription form, while at the same time facilitating the decision support function.
Results: The use of archetypes was found to capture the ever changing requirements in the healthcare domain and externalises them in constrained data structures. The solution is extensible enabling the EHR to cover medicine management in general as per the programme of the HRB Centre for Primary Care Research.
Conclusions: The data collected via this Irish system can be aggregated into a larger dataset, if necessary, for analysis and evidence-gathering, since we adopted the openEHR standard. It will be later extended to include the functionalities of prescribing drugs other than methadone along with the research agenda at the HRB Centre for Primary Care Research in Ireland.
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http://dx.doi.org/10.1186/1472-6947-11-5 | DOI Listing |
Surg Endosc
December 2024
Cancer Center Amsterdam, Amsterdam, Netherlands.
Background: The surgical management of complicated diverticulitis varies across Europe. EAES members prioritized this topic to be addressed by a clinical practice guideline through an online questionnaire.
Objective: To develop evidence-informed clinical practice recommendations for key stakeholders involved in the treatment of complicated diverticulitis; to improve operative and perioperative outcomes, patient experience and quality of life through a systematic evidence-to-decision approach by a diverse, multidisciplinary panel.
Sci Rep
December 2024
School of Public Administration, Guangzhou University, Guangzhou, 510006, China.
With the accelerated urbanization and economic development in Northwest China, the efficiency of urban wastewater treatment and the importance of water quality management have become increasingly significant. This work aims to explore urban wastewater treatment and carbon reduction mechanisms in Northwest China to alleviate water resource pressure. By utilizing online monitoring data from pilot systems, it conducts an in-depth analysis of the impacts of different wastewater treatment processes on water quality parameters.
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December 2024
Advanced Research Institute for Digital-Twin Water Conservancy, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.
Traditional hydraulic structures rely on manual visual inspection for apparent integrity, which is not only time-consuming and labour-intensive but also inefficient. The efficacy of deep learning models is frequently constrained by the size of available data, resulting in limited scalability and flexibility. Furthermore, the paucity of data diversity leads to a singular function of the model that cannot provide comprehensive decision support for improving maintenance measures.
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December 2024
Chongqing Jianzhu College, Chongqing, 400072, China.
Prefabricated construction involves manufacturing components in a factory and then transporting them to a construction site for assembly, yielding resource savings and improved efficiency. However, the large size and weight of prefabricated components, along with strict delivery requirements, introduce logistical challenges, such as increased carbon emissions during transport and site congestion. This study addresses the dual-objective vehicle scheduling problem for prefabricated components.
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December 2024
Department of Computer Sciences and Industries, Universidad Católica del Maule, Talca, Chile.
Antimicrobial resistance (AMR) poses a significant global health challenge, necessitating advanced predictive models to support clinical decision-making. In this study, we explore multi-label classification as a novel approach to predict antibiotic resistance across four clinically relevant bacteria: E. coli, S.
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