提高儿童急性淋巴细胞白血病化疗方案依从性的临床决策支持系统

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Cancer Informatics Pub Date : 2022-01-01 DOI:10.1177/11769351221084812

H. Moghaddasi, Rezvan Rahimi, Alireza Kazemi, Khadijeh Arjmandi Rafsanjani, G. Bahoush, Forough Rahimi

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引用次数: 0

摘要

目的:设计并评价基于协议的化疗处方决策支持系统(CPDSS)，以减少ALL患儿化疗过程中的用药错误。方法:由软件开发团队根据医生治疗ALL患儿的方案提取CPDSS算法。ASP。系统的开发采用了。Net MVC和SQL Server 2016编程语言。对德黑兰两家儿童医院设计的CPDSS进行了三步评估(技术、回顾性和用户满意度)。数据采用描述性统计进行分析。在技术评价阶段，用户提供的建议被纳入系统。结果:在回顾性CPDSS评价步骤中，系统共录入了涉及30例患者的1281个处方剂量。CPDSS检测到735例方案偏差和57例(95% CI = 1.25-2.55)急性淋巴细胞白血病儿童处方化疗错误。在用户满意度评价中，用户对系统的用户界面和功能两个维度进行了认可。结论:CPDSS可提高患者对化疗方案的依从性，减少化疗处方错误，提高患者安全。

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A Clinical Decision Support System for Increasing Compliance with Protocols in Chemotherapy of Children with Acute Lymphoblastic Leukemia

Objective: In this survey, a protocol-based Chemotherapy Prescription Decision Support System (CPDSS) was designed and evaluated to reduce medication errors in the chemotherapy process of children with ALL. Methods: The CPDSS algorithm was extracted by the software development team based on the protocol used by doctors to treat children with ALL. The ASP.Net MVC and SQL Server 2016 programming languages were used to develop the system. A 3-step evaluation (technical, retrospective, and user satisfaction) was performed on CPDSS designed at 2 children’s hospitals in Tehran. The data were analyzed using descriptive statistics. At the technical evaluation step, users provided recommendations included in the system. Results: In the retrospective CPDSS evaluation step, 1281 prescribed doses of the drugs related to 30 patients were entered into the system. CPDSS detected 735 cases of protocol deviations and 57 (95%, CI = 1.25-2.55) errors in prescribed chemotherapy for children with ALL. In the user satisfaction evaluation, the users approved two dimensions of the user interface and functionality of the system. Conclusions: With the provision of alerts, the CPDSS can help increase compliance with chemotherapy protocols and decrease the chemotherapy prescribing errors that can improve patient safety.

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来源期刊

Cancer Informatics Medicine-Oncology

CiteScore

3.00

自引率

5.00%

发文量

审稿时长

8 weeks

期刊介绍： The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.