An Automated Patient Chart Error Detection System for Radiation Therapy

用于放射治疗的自动患者图表错误检测系统

基本信息

批准号：
10015207
负责人：
Junyi Xia
金额：
$ 85.5万
依托单位：
INFONDRIAN, LLC
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-06 至 2023-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10015207
关键词：
Affect Area Artificial Intelligence Cancer Patient Cessation of life Clinic Clinical Collaborations Communities Computer software Data Data Analytics Databases Detection Development Effectiveness Goals Health Care Costs Human Human Resources Individual Information Systems Innovation Corps Insurance Intelligence Knowledge Lead Letters Machine Learning Manuals Medical Medical Technology Methods Paralysed Patients Pattern Performance Phase Prevention Radiation Oncology Radiation therapy Records Safety Site Small Business Technology Transfer Research Software Framework System Technology Time Training Translations United States United States National Institutes of Health Vendor Work base commercialization cost data sharing data streams data structure design flexibility improved industry partner maltreatment medical specialties member novel patient safety prevent programs prospective prototype research and development research clinical testing software development software systems structured data success treatment center treatment planning treatment site tumor

项目摘要

Project Summary/Abstract Every year, approximately 1,200 severe mistreatments happen in radiation therapy. Radiation therapy lawsuits rank in the top third of all medical specialties with an average of $313,000 per claim settled or litigated. The current method for detecting treatment errors is by a weekly patient chart check, where each treatment record is manually reviewed on a weekly basis. This labor-intensive and inefficient method prevents us from detecting the treatment error at an early stage. Here we propose a novel software system, ChartAlert, for automating patient chart checking. ChartAlert is a prospective real-time adaptive electronic checking system that can be configured to support different clinical workflows and perform “smart” check using artificial intelligence. It supports two major treatment databases (Elekta MOSAIQ and Varian ARIA) in radiotherapy. In Phase I project, we have successfully developed ChartAlert for MOSAIQ prototype that is under clinical testing in two treatment centers. Our preliminary results demonstrated the significant improvement of effectiveness in patient chart checking and the flexibility of supporting different workflows. In this Phase II proposal, we will continue the ChartAlert development. We will demonstrate the feasibility of the ChartAlert approach and its advantages over the standard manual checking method. We will develop a prospective checking module, develop the ARIA data translation module for ChartAlert for ARIA, design and implement an AI-based “smart” check module, and verify the proposed system at the partner sites. Successful completion of these aims will demonstrate the feasibility and commercial potential of the ChartAlert approach. Ultimately, this work will result in an intelligent patient chart checking software, which will increase patient chart check efficiency, save staff time, improve cancer patient treatment safety, and preventing potential lawsuits.

项目摘要/摘要每年，放射治疗中大约有1,200次严重的虐待。放射治疗诉讼在所有医学专业的前三名中排名平均每张索赔313,000美元。这当前检测治疗错误的方法是通过每周的患者图表检查，每个治疗记录每周手动审查。这种实验室密集型和效率低下的方法阻止了我们检测早期的治疗误差。在这里，我们提出了一个新颖的软件系统ChartAlert，用于自动化患者图表检查。 ChartAlert是一种前瞻性的实时自适应电子检查系统，可以是配置为支持不同的临床工作流程并使用人工智能执行“智能”检查。它在放射疗法中支持两个主要的治疗数据库（Elekta Mosaiq和Varian Aria）。在第一阶段项目中，我们已经成功地开发了Mosaiq原型的ChartAlert，该原型正在两种治疗中进行临床测试中心。我们的初步结果表明患者图表中有效性有显着提高检查和支持不同工作流的灵活性。在此第二阶段提案中，我们将继续 ChartAlert开发。我们将证明ChartAlert方法的可行性及其优势标准手动检查方法。我们将开发一个预期的检查模块，开发ARIA数据用于ARIA的ChartAlert的翻译模块，设计和实施基于AI的“智能”检查模块，以及在合作伙伴站点验证拟议的系统。这些目标的成功完成将证明 ChartAlert方法的可行性和商业潜力。最终，这项工作将导致智能患者图表检查软件，这将提高患者图表检查效率，节省员工时间，改善癌症患者治疗安全，并防止潜在的诉讼。