LEMRinterface

Simple EMR System. This code can be used for Electronic Medical Record Research.

This system was built to facilitate the development a Learning EMR (LEMR) system.

Getting Started

First, have a look in the screenshots directory to become familiar with the interface design.

Prerequisites

Python 3 or Docker Compose

Installing

Python

1. Clone repository
2. cd into project directory
3. enter "pip install -r requirements.txt"

Docker

1. Clone repository

Python installation notes

It is strongly recommended that you use a virtual environment.

1. if you do not have virtualenv, enter "pip install virtualenv"
2. enter "python -m venv name_of_environment"
3. enter "name_of_environment/Scripts/activate"
4. then follow the Python installation instructions shown above
5. to terminate the virtual environment, enter "deactivate"

Deployment

Python

1. cd into project directory
2. enter "python manage.py runserver"
3. open web browser to http://127.0.0.1:8000/LEMRinterface/
4. terminate using ctrl+c

Docker

1. cd into project directory
2. enter "docker-compose up"
3. open web browser to http://127.0.0.1:8000/LEMRinterface/
4. terminate using ctrl+c

Notes

The LEMRinterface in meant to run in full screen mode on a 1920 x 1080 resolution monitor. Responsive html is not currently supported.

Secondary Use

Included data

The repository includes three synthetic patient cases. They were created by scrambling a larger set of safe-harbor (de-identified) patient cases. If data does not make logical sense, it is because of the scrambling process.

Included case data can be found at (https://github.com/ajk77/SimpleEMRSystem/tree/master/resources/demo_study)

Importing custom patient data

To use your own cases either:

• Edit the case data found in the resources folder
• Connect to your own database and edit settings.py, models.py, and loaddata.py.

Eye-tracking research

Components related to eye-tracking are turned off in this version because accuracy across different environments can not be guaranteed. If you are interested in using LEMRinterface with a remote eye-tracking device, please see the following:

• EyeBrowserPy (https://github.com/ajk77/EyeBrowserPy)
• Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study (https://www.jmir.org/2020/4/e15876/)
• Eye-tracking for clinical decision support: A method to capture automatically what physicians are viewing in the EMR (https://www.ncbi.nlm.nih.gov/pubmed/28815151)

Versioning

Version 2.0. For the versions available, see https://github.com/ajk77/SimpleEMRSystem

Authors

• Andrew J King - Doctoral Candidate (at time of creation)
  • Website (https://www.andrewjking.com/)
  • Twitter (https://twitter.com/andrewsjourney)
• Shyam Visweswaran - Principal Investigator
  • Website (http://www.thevislab.com/)
  • Twitter (https://twitter.com/Shyam_Vis)
• Gregory F Cooper - Doctoral Advisor

Impact

This interface has been used in the following studies:

• King AJ, Cooper GF, Clermont G, Hochheiser H, Hauskrecht M, Sittig DF, Visweswaran S. Leveraging Eye Tracking to Prioritize Relevant Medical Record Data: Comparative Machine Learning Study. J Med Internet Res 2020;22(4):e15876. (https://www.jmir.org/2020/4/e15876/)
• King AJ, Cooper GF, Clermont G, Hochheiser H, Hauskrecht M, Sittig DF, Visweswaran S. Using Machine Learning to Selectively Highlight Patient Information. J Biomed Inform. 2019 Dec 1;100:103327. (https://www.sciencedirect.com/science/article/pii/S1532046419302461)
• King AJ, Cooper GF, Hochheiser H, Clermont G, Hauskrecht M, Visweswaran S. Using machine learning to predict the information seeking behavior of clinicians using an electronic medical record system. AMIA Annu Symp Proc. 2018 Nov 3-7; San Francisco, California p 673-682. [Distinguished Paper Nomination] (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371238/)
• King AJ, Hochheiser H, Visweswaran S, Clermont G, Cooper GF. Eye-tracking for clinical decision support: A method to capture automatically what physicians are viewing in the EMR. AMIA Joint Summits. 2017 Mar 27-30; San Francisco, California p 512-521. [Best Student Paper] (https://www.ncbi.nlm.nih.gov/pubmed/28815151)
• King AJ, Cooper GF, Hochheiser H, Clermont G, Visweswaran S. Development and preliminary evaluation of a prototype of a learning electronic medical record system. AMIA Annu Symp Proc. 2015 Nov 14-18; San Francisco, California p.1967-1975. [Best Student Paper] (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765593/)

License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

Acknowledgments

• Harry Hochheiser
  • Twitter (https://twitter.com/hshoch)
• Gilles Clermont
• Milos Hauskrecht