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An Automated Patient Chart Error Detection System for Radiation Therapy

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 2R42CA195819-02A1
Agency Tracking Number: R42CA195819
Amount: $1,750,003.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: 101
Solicitation Number: PA18-575
Timeline
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-09-10
Award End Date (Contract End Date): 2021-08-31
Small Business Information
2500 CROSSPARK RD STE E152
Coralville, IA 52241-4710
United States
DUNS: 079272783
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 JUNYI XIA
 (319) 356-2146
 junyi-xia@uiowa.edu
Business Contact
 JUNYI XIA
Phone: (212) 824-9476
Email: contact@infondrian.com
Research Institution
 UNIVERSITY OF IOWA
 
112 North Capitol Street
IOWA CITY, IA 52242-1320
United States

 Nonprofit college or university
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.Project Narrative
Treatment errors in radiation oncology occur at a rate of 2% per patient, and radiation therapy lawsuits rank in
the top third of all medical specialties with regard to claims made, claims paid, and damages. Current methods
of detecting treatment errors are manual and inefficient. There is a critical need for efficient treatment error
detection in order to improve patient safety and save cost. We propose to develop a scalable and
comprehensive software system (ChartAlert) for automated patient chart error detection in radiation therapy.
ChartAlert can be extended to other types of patient charts to check treatment and prescription consistency
and improve patient safety.

* Information listed above is at the time of submission. *

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