Simple EMR System

This code can be used for Electronic Medical Record

Importing custom patient data

To import custom data:

1. Prepare your data in the same JSON format as the sample studies
2. Place data files in resources/<study_id>/ directory
3. Run python manage.py load_resources to populate the database

The system expects the following structure:

• data_layout.json: Defines the UI layout
• variable_details.json: Variable metadata
• med_details.json: Medication information
• user_details.json: User assignments
• case_details.json: Case metadata
• cases_all/<case_id>/: Individual case data files

Getting Started

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

Prerequisites

Python 3.8+

Installing

Automated Installation

Windows:

install.bat

Linux/Mac:

chmod +x install.sh
./install.sh

Interactive Setup:

python setup_wizard.py

Manual Installation

1. Clone repository
2. cd into project directory
3. Create virtual environment: python -m venv semr_env
4. Activate environment: semr_env\Scripts\activate (Windows) or source semr_env/bin/activate (Linux/Mac)
5. Install dependencies: pip install -r requirements.txt
6. Run database migrations: python manage.py migrate
7. Load sample data: python manage.py load_resources
8. Start server: python manage.py runserver

Deployment

Python

1. cd into project directory
2. Activate virtual environment
3. Run python manage.py runserver
4. Open web browser to http://127.0.0.1:8000/SEMRinterface/
5. Terminate using ctrl+break

Database Setup

The system uses SQLite database for data storage. The load_resources management command populates the database with sample studies and cases from the resources/ directory.

Notes

The SEMRinterface 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

Two example studies are included in the Simple EMR System repository: demo_study and synthea_study. Demo_study includes three synthetic patient cases created by scrambling the contents of de-identified records from a larger set of patients from a hospital’s Cerner EMR system. Synthea_study includes 25 intensive care unit (ICU) encounters from the Synthea COVID-19 dataset (available at https://synthea.mitre.org/downloads).

Included case data can be found at:

1. https://github.com/ajk77/SimpleEMRSystem/tree/master/resources/synthea_study
2. https://github.com/ajk77/SimpleEMRSystem/tree/master/resources/demo_study

Importing custom patient data

To preprocess Synthea cases for display on the Simple EMR System:

1. Download the source data and extract it into the ‘resources’ folder.
2. Adjust parameters, such as the input and output directories, in the Synthea data loading script (‘loaddata_synthea.py’) and execute the script.
3. View details about each processed case in ‘list_case_dicts.json’.
4. Select cases to use in the study and add them to ‘case_details.json’
5. assign them to users in ‘user_details.json’.
6. adjust display groups, medication routes, display names, and display locations in ‘variable_details.json,’ ‘med_details.json,’ and ‘data_layout.json.’

To load in data from other sources, you will need to create a custom 'loaddata' file. If your data source is a database, consider usings Django's Models.py (https://docs.djangoproject.com/en/3.1/topics/db/models/).

You may also edit JSON file directly in order to create custom cases.

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 SEMRinterface 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 2024.2. 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.thevisweswaran.com/)
  • Twitter (https://twitter.com/Shyam_Vis)
• Gregory F Cooper - Doctoral Advisor

Citation

King AJ, Calzoni L, Tajgardoon M, Cooper G, Clermont G, Hochheiser H, Visweswaran S. A simple electronic medical record system designed for research. JAMIA Open. 2021 July 31;4(3):ooab040. (https://academic.oup.com/jamiaopen/article/4/3/ooab040/6332673)

Impact

This interface has been used in the following studies:

• Payne VL, Sattar U, Wright M, Hill E, Butler JM, Macpherson B, Jeppesen A, Del Fiol G, Madaras-Kelly K. Clinician perspectives on how situational context and augmented intelligence design features impact perceived usefulness of sepsis prediction scores embedded within a simulated electronic health record. J Am Med Inform Assoc. 2024; ocae089. (https://doi.org/10.1093/jamia/ocae089)
• 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