Enhancing Patient Care in Radiotherapy: Proof-of-Concept of a Monitoring Tool

Author:

Beldjoudi Guillaume1ORCID,Eugène Rémi2ORCID,Grégoire Vincent1,Tanguy Ronan1ORCID

Affiliation:

1. Radiotherapy Department, Centre Léon Bérard, 28 Rue Laennec, 69008 Lyon, France

2. Elekta SAS, Immeuble Dom Inno, 19–21 Rue du Dôme, 92100 Boulogne-Billancourt, France

Abstract

Introduction: A monitoring tool, named Oncology Data Management (ODM), was developed in radiotherapy to generate structured information based on data contained in an Oncology Information System (OIS). This study presents the proof-of-concept of the ODM tool and highlights its applications to enhance patient care in radiotherapy. Material & Methods: ODM is a sophisticated SQL query which extracts specific features from the Mosaiq OIS (Elekta, UK) database into an independent structured database. Data from 2016 to 2022 was extracted to enable monitoring of treatment units and evaluation of the quality of patient care. Results: A total of 25,259 treatments were extracted. Treatment machine monitoring revealed a daily 11-treatement difference between two units. ODM showed that the unit with fewer daily treatments performed more complex treatments on diverse locations. In 2019, the implementation of ODM led to the definition of quality indicators and in organizational changes that improved the quality of care. As consequences, for palliative treatments, there was an improvement in the proportion of treatments prepared within 7 calendar days between the scanner and the first treatment session (29.1% before 2020, 40.4% in 2020 and 46.4% after 2020). The study of fractionation in breast treatments exhibited decreased prescription variability after 2019, with distinct patient age categories. Bi-fractionation once a week for larynx prescriptions of 35 × 2.0 Gy achieved an overall treatment duration of 47.0 ± 3.0 calendar days in 2022. Conclusions: ODM enables data extraction from the OIS and provides quantitative tools for improving organization of a department and the quality of patient care in radiotherapy.

Publisher

MDPI AG

Reference29 articles.

1. McAfee, A., and Brynjolfsson, E. (2012). Big data: The management revolution. Harv. Bus. Rev., 90.

2. The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts;Mittelstadt;Sci. Eng. Ethics,2016

3. Machine Learning for Knowledge Extraction from PHR Big Data;Poulymenopoulou;Stud. Health Technol. Inform.,2014

4. An ontology-based approach to designing a NoSQL database for semi-structured and unstructured health data;Sen;Clust. Comput.,2023

5. Des Mass Data aux Big Data, changements ou « déjà-vu » pour le contrôle de gestion;Ciampi;ACCRA,2021

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3