# An Automated Patient Chart Error Detection System for Radiation Therapy

> **NIH NIH R42** · INFONDRIAN, LLC · 2020 · $855,016

## Abstract

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.

## Key facts

- **NIH application ID:** 10015207
- **Project number:** 5R42CA195819-03
- **Recipient organization:** INFONDRIAN, LLC
- **Principal Investigator:** Junyi Xia
- **Activity code:** R42 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $855,016
- **Award type:** 5
- **Project period:** 2016-09-06 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10015207

## Citation

> US National Institutes of Health, RePORTER application 10015207, An Automated Patient Chart Error Detection System for Radiation Therapy (5R42CA195819-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10015207. Licensed CC0.

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