Author: Facilitator

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Drishti | An Open mHealth sense-plan-act framework based on FHIR!

Post author By Facilitator
Post date August 18, 2019
No Comments on Drishti | An Open mHealth sense-plan-act framework based on FHIR!

Originally published by Bell Eapen at nuchange.ca on August 13, 2019. If you have some feedback, reach out to the author on Twitter, LinkedIn or Github.

TL;DR: Here is an open-source mHealth framework based on FHIR! and here is the paper and my presentation at ICSE!

Pervasive health monitoring is becoming less and less intrusive with better sensors, and more and more useful with machine learning and predictive analytics.

MHealth (mobile health) could play an important part in pervasive health monitoring. It is difficult for clinicians to efficiently use the data from disparate apps that do not communicate with each other. For example, if a clinician has to monitor a patient’s blood sugar, blood pressure and physical activity, the clinician may have to check data from multiple apps. Another challenge is the difficulty in communicating clinical requirements to app developers and it is difficult to test and approve the clinical validity of these apps. Besides, there are always privacy and security concerns with personal health information.

Open mHealth is a framework introduced to manage the problem of interoperability between apps. It is an open-source project. Open mHealth project provides interfaces for cloud services such as GoogleFit and Fitbit and converts the data into a common data format. BIT model deals with the communication problem between clinicians and developers during app development. Drishti incorporates Open mHealth framework into the BIT model using FHIR as the common data model.

The BIT model is based on the Sense-Plan-Act paradigm from robotics. The BIT model encourages conceptualizing mHealth apps as three distinct components: Profilers that sense data on various physiological parameters such as blood pressure, planners that create a clinical intervention plan and actors that deliver the plan to the users as alerts or messages on their mobile devices. Drishti adopts the BIT model as a design model with all components sharing a central data repository. Drishti makes sharing of information with the doctors easy, by integrating it into an EMR. The central data repository also makes big data applications possible.

The central data repository in Drishti uses FHIR schema for storage. FHIR is a schema for health data created by HL7 that defines ‘Resources’ that can be exchanged as json or xml using RESTful interfaces. Resources support 80% of common use cases and the rest can be supported using extensions. For example, age and gender are defined for a Patient resource, while skin type that is not commonly used is defined through an extension if required. Drishti uses the ‘Observation’ resource for storing data from profilers and the ‘CarePlan’ resource for the planner and actor components.

Open mHealth is the profiler in Drishti. All data from the various cloud services are converted to FHIR Observations and stored in the Drishti-Cog. The Drishti-Planner can take data stored in the cog and create a careplan and the actor can deliver it to the patient. Drishti uses OpenMRS EMR for managing access, both for clinicians and patients. We have developed an OpenMRS module for integration with Drishti. The javascript visualization library called hGraph provides a consolidated view of the data pulled from sensors to the clinician.

In the current implementation, the cog is a FHIR server based on the HAPI java library. Planner and actor components are just stubs that can be extended for several use cases. The planner is a python flask app and the viewer is a Vue App that can be used as a native mobile app. Both are templates that can be extended. The entire stack is available on GitHub along with pre-built Docker containers for quick prototyping.

Here is a typical use case. Depression is a common mental health problem, characterized by loss of interest in activities that you normally enjoy. Patients with depression are typically treated with anti-depressant drugs. The clinicians need to track the patient’s activity to assess progress along with medication compliance. The patient can use an activity tracker app and a medication tracker app, both sending data to the cog as FHIR observations. The clinicians can have a consolidated view in their EMR and create alerts or messages (plan) that can be delivered to the patient’s mobile device. The interventions can also be created by AI systems.

Drishti was presented at Software Engineering in Healthcare conference in Montreal and selected for FHIR devdays. Please cite Drishti as below:

Bell Raj Eapen, Norm Archer, Kamran Sartipi, and Yufei Yuan. 2019. Drishti: a sense-plan-act extension to open mHealth framework using FHIR. In Proceedings of the 1st International Workshop on Software Engineering for Healthcare (SEH ’19). IEEE Press, Piscataway, NJ, USA, 49-52. DOI: https://doi.org/10.1109/SEH.2019.00016

Tags FHIR, Health Level 7, Sensors

OpenSource Resources

Hephestus: Health data warehousing tool for public health and clinical research

Post author By Facilitator
Post date July 16, 2019
2 Comments on Hephestus: Health data warehousing tool for public health and clinical research

Originally published by Bell Eapen at nuchange.ca on November 3, 2018. If you have some feedback, reach out to the author on Twitter, LinkedIn or Github.

Health data warehousing is becoming an important requirement for deriving knowledge from the vast amount of health data that healthcare organizations collect. A data warehouse is vital for collaborative and predictive analytics. The first step in designing a data warehouse is to decide on a suitable data model. This is followed by the extract-transform-load (ETL) process that converts source data to the new data model amenable for analytics.

The OHDSI – OMOP Common Data Model is one such data model that allows for the systematic analysis of disparate observational databases and EMRs. The data from diverse systems needs to be extracted, transformed and loaded on to a CDM database. Once a database has been converted to the OMOP CDM, evidence can be generated using standardized analytics tools that are already available.

Each data source requires customized ETL tools for this conversion from the source data to CDM. The OHDSI ecosystem has made some tools available for helping the ETL process such as the White Rabbit and the Rabbit In a Hat. However, health data warehousing process is still challenging because of the variability of source databases in terms of structure and implementations.

Hephestus is an open-source python tool for this ETL process organized into modules to allow code reuse between various ETL tools for open-source EMR systems and data sources. Hephestus uses SqlAlchemy for database connection and automapping tables to classes and bonobo for managing ETL. The ultimate aim is to develop a tool that can translate the report from the OHDSI tools into an ETL script with minimal intervention. This is a good python starter project for eHealth geeks.

Anyone anywhere in the world can build their own environment that can store patient-level observational health data, convert their data to OHDSI’s open community data standards (including the OMOP Common Data Model), run open-source analytics using the OHDSI toolkit, and collaborate in OHDSI research studies that advance our shared mission toward reliable evidence generation. Join the journey! here

Disclaimer: Hephestus is just my experiment and is not a part of the official OHDSI toolset.

SSH URL
Clone URL

Tags Datawarehouse, public health

HIS

Serverless on FHIR: Management guidelines for the semi-technical clinician!

Post author By Facilitator
Post date July 9, 2019
No Comments on Serverless on FHIR: Management guidelines for the semi-technical clinician!

Originally published at nuchange.ca on February 12, 2018. If you have some feedback, reach out to the author on Twitter, LinkedIn or Github.

Serverless is the new kid on the block with services such as AWS Lambda, Google Cloud Functions or Microsoft Azure Functions. Essentially it lets users deploy a function (Function As A Service or FaaS) on the cloud with very little effort. Requirements such as security, privacy, scaling, and availability are taken care of by the framework itself. As healthcare slowly yet steadily progress towards machine learning and AI, serverless is sure to make a significant impact on Health IT. Here I will explain serverless (and some related technologies) for the semi-technical clinicians and put forward some architectural best practices for using serverless in healthcare with FHIR as the data interchange format.

artificial intelligence — Serverless on FHIR

Let us say, your analyst creates a neural network model based on a few million patient records that can predict the risk for MI from BP, blood sugar, and exercise. Let us call this model r = f(bp, bs, e). The model is so good that you want to use it on a regular basis on your patients and better still, you want to share it with your colleagues. So you contact your IT team to make this happen.

This is what your IT guys currently do: First, they create a web application that can take bp, bs and e as inputs using a standard interface such as REST and return r. Next, they rent a virtual machine (VM) from a cloud provider (such as DigitalOcean). Then they convert this application into a container (docker) and deploy it in the VM. You now can use this as an application from your browser (chrome) or your EMR (such as OpenMRS or OSCAR) can directly access this function. You can share it with your colleagues and they can access it in their browsers and you are happy. The VM can support up to 3 users at a time.

In a couple of months, your algorithm becomes so popular that at any one time hundreds of users try to access it and your poor VM crashes most of the time or your users have to wait forever. So you call your IT guys again for a solution. They make 100 copies of your container, but your hospital is reluctant to give you the additional funding required.

Your smart resident notices that your application is being used only in the morning hours and in the night all the 100 containers are virtually sleeping. This is not a good use of the funding dollars. You contact your IT guys again, and they set up Kubernetes for orchestrating the containers according to usage. So, what is Serverless? Serverless is a framework that makes all these so easy that you may not even need your IT guys to do this. (Well, maybe that is an exaggeration)

My personal favourite serverless toolset (if you care) is Kubernetes + Knative + riff. I don’t try to explain what the last two are or how to use them. They are so new that they keep changing every day. In essence, your IT team can complete all the above tasks with few commands typed on the command line on the cloud provider of your choice. The application (function rather) can even scale to zero! (You don’t pay anything when nobody uses it and add more containers as users increase, scaling down in the night as in your case).

Best Practices

What are the best practices when you design such useful cloud-based ‘functions’ for healthcare that can be shared by multiple users and organizations? Well, here are my two cents!

First, you need a standard for data exchange. As JSON is the data format for most APIs, FHIR wins hands down here.

Next, APIs need a mechanism to expose their capabilities and properties to the world. For example, r = f(bp, bs, e) needs to tell everyone what it accepts (bp, bs, e) and what it returns (at the bare minimum). FHIR has a resource specifically for this that has been (not so creatively) named as an Endpoint. So, a function endpoint should return a FHIR Endpoint resource with information about itself if there is no payload.

What should the payload be? Payload should be a FHIR Bundle that has all the FHIR Resources that the function needs (bp, bs and e as FHIR Observations in your case). The bundle should also include a FHIR Subscription resource that points to the receiving system (maybe your EMR) for the response ( r ).

So, what next?

Take the phone and call your IT team. Tell them to take
Kubernetes + Knative + riff for a spin! I might do the same and if I do, I will share it here.

Research

eHealth against antimicrobial resistance

Post author By Facilitator
Post date July 3, 2019
No Comments on eHealth against antimicrobial resistance

This article was first published on Brighter World. Read the original article.

A forward-looking McMaster donor is investing $7 million in a new research centre dedicated specifically to tackle the growing global threat of antimicrobial resistance.

David Braley, whose gifts to the university include a $50-million investment in McMaster teaching, learning and health-care research and delivery, has allocated $7 million from that 2007 gift towards the new David Braley Centre for Antibiotic Discovery.

The centre will operate from the Michael G. DeGroote Institute for Infectious Disease Research, whose labs and offices are located on campus in the Michael G. DeGroote Centre for Learning and Discovery.

Researchers associated with the new David Braley Centre for Antibiotic Discovery. Photo by Georgia Kirkos.

“This is a very timely investment,” says Paul O’Byrne, dean and vice- president, Faculty of Health Sciences. “This provides fresh resources to a team of researchers who are among the world’s leaders in their field. Creating this centre gives them the chance to do their best work at a time in history when it’s needed most.”

The funding comes from a portion of Braley’s 2007 gift that had been designated for emerging health-care research priorities.

The David Braley Centre for Antibiotic Discovery will be home to McMaster’s leading researchers in the field of antimicrobial resistance, or AMR. The new resources will allow the team to concentrate more specific effort on that problem.

“Antimicrobial resistance is a slow-moving catastrophe, but make no mistake: within the next 30 years, it will kill millions, strangle our health-care systems and significantly alter life as we know it unless we develop new ways to attack the problem,” says Gerry Wright, who heads both the David Braley Centre for Antibiotic Discovery and the Institute for Infectious Disease Research. “The opportunity to open this centre is a hopeful sign, and we are grateful for Mr. Braley’s vision and his vote of confidence. This problem must be solved, and it can be solved.”

Dr. Gerry Wright standing at a podium in a hallway in front of a black curtain at the opening of the David Braley Centre for Antibiotic Discovery — Gerry Wright, director of the Institute for Infectious Disease Research and the new David Braley Centre for Antibiotic Discovery, addresses the crowd at the opening of the new research centre. Photo by Georgia Kirkos.

The waning effectiveness of traditional antibiotics gives urgency to the search for new forms of antibiotics and other ways to boost the effectiveness of existing drugs.

Widespread use of antibiotics in agriculture and medicine has accelerated resistance to penicillin and its related medicines, as bacteria evolve to meet the threat.

Infection control and treatment without antibiotics could cast the world back to the early 1900s, when infectious diseases routinely killed people, Wright says.

Today, at least 700,000 people around the world – including 2,000 in Canada – die each year as a result of drug-resistant diseases. The global total is expected to rise to 10 million deaths per year by 2050 if no new solutions are found.

The medical costs associated with AMR are predicted to reach $100 trillion within that same time frame.

Close-up of a lab coat that reads "David Braley Centre for Antibiotic Discovery." — Photo by Georgia Kirkos.

This year, the United Nations published a report projecting that without immediate global action, AMR could force up to 24 million people into extreme poverty by the year 2030.

Tags Antibiotics, Bioinformatics, EHealth

Research Resources

McMaster’s start-up incubator to receive $1.2 million from FedDev Ontario

Post author By Facilitator
Post date December 21, 2018
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This article was first published on Daily News. Read the original article.

The Government of Canada, through FedDev Ontario, is providing McMaster with $1.2 million to expand The Forge, a collaborative makerspace where entrepreneurs can access advanced equipment to design and build innovative new products.

The Honourable Filomena Tassi, Minister of Seniors and Member of Parliament for Hamilton West-Ancaster-Dundas, made the announcement today on behalf of the Honourable Navdeep Bains, Minister of Innovation, Science and Economic Development and minister responsible for FedDev Ontario.

“FedDev Ontario’s funding is providing invaluable support to the innovation community in Hamilton,” said Tassi. “The government of Canada is proud to support McMaster — one of Canada’s premier research-intensive universities — to expand The Forge’s makerspace and allow more companies to develop and bring new products to market.”

The funding will allow The Forge to expand its makerspace as it moves into a 10,000 square-foot facility shared with partner Innovation Factory. It will also purchase additional 3D printers and other fabricating equipment, and increase support to entrepreneurs through mentoring. As a result, the number of companies supported will almost double from 24 to up to 40 annually, with up to 75 new jobs created as a result.

“This strategic investment from the Government of Canada will strengthen the entrepreneurial capacity of our region by providing McMaster’s students and the wider Hamilton community access to the centralized expertise and infrastructure so essential for creating start-ups and business growth opportunities,” said Karen Mossman, Acting Vice-President of Research at McMaster and chair of the McMaster Innovation Park board of directors.

More than 105 tech companies have graduated from The Forge since its founding in 2014, with more than 300 employees hired and $20 million of private and public investment raised.

The Forge’s expansion further enhances McMaster’s entrepreneurial ecosystem and reputation as a leader in developing innovative manufacturing assets, in particular within the McMaster Innovation Park, which is also home to the McMaster Automotive Research Centre (MARC) and the Centre for Biomedical Engineering and Advanced Manufacturing (BEAM).

This article was first published on Daily News. Read the original article.

Tags McMaster University

HIS

How to create a Neural Network model for business in 10 minutes

Post author By Facilitator
Post date July 1, 2018
No Comments on How to create a Neural Network model for business in 10 minutes

Neural Network and deep-learning are the buzzwords lately. Machine learning has been in vogue for some time, but the easy availability of storage and processing power has made it popular. The interest is palpable in business schools as well. The ML related techniques have not percolated much from the IT departments to business, but everybody seems to be interested. So, let us build a Neural Network model in 10 minutes.

This is the scenario:

You have a collection of independent variables (IV) that predict a dependent variable (DV). You have a theoretical model and want to know if it is good enough. Remember, we are not testing the model. We are just checking how good the IVs are in predicting DV. If they are not good predictors to start with, why waste time conjuring a fancy model! Sounds familiar? Let’s get started.

Setup

Do you have some preliminary knowledge of python? If not spend another 10 minutes here learning python. Now you have to spend some time to set up your system once. Just follow these instructions.

Code

The first step is to import few modules. If you don’t know what these are, just copy paste and ignore. Consider them as a header that you require.

# Modules
import sys
import numpy
from imblearn.over_sampling import RandomOverSampler
from keras.layers import Dense
from keras.models import Sequential
from pandas import read_csv

Create a CSV file with your data with the last column as your DV. Now import that file.

# Import data
dataset = read_csv(sys.argv[1], header=1)
(nrows, ncols) = dataset.shape

nrows and ncols are the numbers of rows and columns. Now separate DV (y) from IVs (X) as below.

# Separate DV from IVs 
values = dataset.values
X = values[:, 0:ncols-1]
y = values[:, ncols-1]

In most cases, you will be trying to predict a rare event. So add some oversampling for taste 🙂

# Oversampling
ros = RandomOverSampler(random_state=0)
X_R, y_R = ros.fit_sample(X, y)

Create, compile and fit the model.

# create model
model = Sequential()
model.add(Dense(12, input_dim=vnum, kernel_initializer='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X_R, y_R, epochs=150, batch_size=10, verbose=2)

The three model.add statements represent the three layers in Neural Network. The number after Dense is the number of neurons in each layer. You can play with these values a bit. These settings should work in most business cases. Read this for more information.

Now evaluate the model.

# evaluate the model
scores = model.evaluate(X_R, y_R)
print("\n")
print("\n Accuracy of the model")
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1] * 100))
print("\n --------------------------------------------------")

Put this code in a file (say nnet.py) and use it as below.

python nnet.py mydata.csv

TL;DR

Just use QRMine. nnet.py is in there.

Operationalizing Neural Network models

Shortly, I will show you how to operationalize a model using flask.

…

Originally published at nuchange.ca on June 21, 2018. If you have some feedback, reach out to the author on Twitter, LinkedIn or Github.

Tags Neural Network

Research

LaTex template for eHealth Thesis

Post author By Facilitator
Post date March 29, 2016
No Comments on LaTex template for eHealth Thesis

I have shared below the latex template for the MSc eHealth thesis (McMaster University). At the outset, let me state the standard disclaimer: This is not an official template, use this at your own risk. If you find any mistakes, fork it on GitHub and improve it.

Latex is a system where “What You Get Is What You Mean“. So you can precisely “program” the typesetting and save lots of time in formatting large documents. However, it may not be ideal for small documents. Though you can install LaTex in you system by following the instructions here, there is an excellent free online service that you can use called http://sharelatex.com. Sharelatex also has the IEEE template available in their library.

Please be aware that your supervisor might ask you to submit revisions in a Word document for tracking changes. So LaTeX formatting is typically done before final submission and not during supervisor readings. You need to collect references in the BibTeX format. Mendeley reference manager has a BibTex export facility. I recommend creating a folder for your thesis references and using Mendeley’s web importer.

Steps to use eHealth Thesis template

1. Download the zip file here. (Please star the repository if you have a GitHub account.)
2. Register at Share latex, Create a ‘New Project’ and ‘Upload project’.
3. Add the details on the preliminaries.tex file
4. replace references.bib with your references. Retain the file name.
5. Cut and paste your thesis content to the respective ‘Chapters’.
6. See ‘Materials and Methods’ file for the correct way of inserting and referencing figures, tables, equations, and citations. Table of contents, the list of figures and tables, etc. would be automatically generated.
7. Compile it and download the pdf.

The template is open-source. Feel free to improve it. Pull-requests to merge your improvements are welcome. Contact me if you are in trouble on our IRC channel ##ehealth

Do you believe in the open-source eHealth culture? Join Us on PRO{DENTS} and contribute to the Wiki Textbook of eHealth.

Tags ehealth thesis, latex, McMaster University

Research

Research Project on 3D Food Printing

Post author By Facilitator
Post date February 11, 2016
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Students at Humber College are completing a research project on 3D Food Printing. The purpose of this research project is to explore the current knowledge and attitudes millennials possess regarding 3D food printing technology. The study will examine key areas of interest and concern to the participants, as well as assess participant’s potential buying intent.

This research project aims to provide keen insight for potential stakeholders of this revolutionary technology including: (1) the developers, researchers, and investors; (2) the consumer population; and (3) the participants of the study.

Take our quick 5-minute anonymous survey and enter a draw for a chance to win a $50 VISA! We would like to hear from anyone living in the Greater Toronto Area (GTA) between the ages of 18-35 regarding your opinions on this innovative new technology.

To access the survey, please click here: https://humber.qfimr.com/SU0JVOIV6N4

Once you complete the survey, you will be asked to enter the VISA draw. Your participation is important to us.  We greatly appreciate your help and time!

Thank you,

Juhi Agarwal
Jason Szymanski
Tracey Haefele
Anjali Sharma
Ankita Singh

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mHealth

HL10: A proposal for an mHealth framework

Post author By Facilitator
Post date September 29, 2015
No Comments on HL10: A proposal for an mHealth framework

Behaviour Intervention Technologies (BITs) are a subset of eHealth and mHealth interventions that support users in changing behaviour and cognitions related to health. Several psychological models guide the implementation of BITs. However, these psychological models such as social cognitive theory and theory of planned behaviour have a clinical focus and are incapable of guiding the design and coding.

Mohr et.al proposed the BIT model [ [ref] Mohr DC, Schueller SM, Montague E, Burns MN, Rashidi P. The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions. J Med Internet Res 2014;16(6):e146 [/ref] ] to address these limitations by systematizing why, how (conceptual and technical), what and when of BIT. ‘Why’ translates to clinical aims such as sun protection and weight reduction. Examples of conceptual ‘how’ are education, goal setting, monitoring and feedback. Technical ‘how’ indicates the medium of delivery and the complexity of delivery. ‘What’ corresponds to alerts, logs, messaging and data collection. ‘When’ indicates the workflow that can be user defined or based on time/event rules. The model proposes a sense-plan-act paradigm based on robotics with sense-act coupling in reactive models.

HL10 (Hamilton) is an attempt to take the BIT model and the sense-plan-act paradigm to the next level of a software framework. HL10 is a proposal for an mHealth specific mobile application frameworks that can be easily extended to create any type of app. The framework should take care of overarching concerns such as privacy and security of patient data, communication with electronic health record (EHR) systems and population health.

Ultimately HL10 framework would be available as an mHealth boilerplate or a Yeoman generator that can be easily modified to create any mHealth BIT. HL10 would try to segregate the sense-plan-act layers and would propose fundamental rules of communication between these layers though standardizing is not its primary intent. Privacy would be built into the framework by design. External communication with EMR and other HIS would be negotiated through fire! (FHIR)

HL10 is still a concept and would greatly benefit from ideas and contributions from domain experts. Though I am ‘opinionated’ to a certain extent, this preliminary post is intentionally left ‘non-opinionated’ to encourage the flow of ideas. Do give me a shout if you find this interesting. I have created a group on GitHub for this: https://github.com/E-Health

Some of the ideas are influenced by AppsForHealth at Mohawk, especially the keynote lecture by Dr. Ann Cavoukian on privacy by design, and the introductory lecture on FHIR followed by the connectathon demos.

Please site this page as below if you expand on this concept.

Eapen BR. HL10 (Hamilton) – An mHealth behaviour intervention technology framework. NuChange Informatics Blog (2015). Available from: http://nuchange.ca/2015/07/hl10-from-model-to-framework.html

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Recent Posts

Guest Author

By Bell Eapen, an eHealth graduate student at McMaster University.

Getting your Laptop ready for eHealth

Post author By Facilitator
Post date September 29, 2015
No Comments on Getting your Laptop ready for eHealth

OSCAR EMR and OpenMRS EHR — Image credit USDA @ Flikr (Image altered and text added)

EMRs and EHRs are of vital importance to health informatics students. Though there are several popular open-source products such as OSCAR EMR, installing them on your laptop can be cumbersome.They are designed for server installations with several prerequisites such as a backend database and a servlet container such as Tomcat. Though OSCAR EMR has an old Windows version, it has been marked as deprecated. If you are not a Linux geek, here is how you can install an EMR (OSCAR), an EHR (OpenMRS) and the popular statistical package R with R-Studio server. That is everything you need for your eHealth sojourn!

Installation instructions

There are ways of creating a virtual Linux machine in your laptop (Mac and Windows). Virtualization leaves your operating system untouched, and the virtual machine can be removed without a trace after use. Without further ado, you can install this in 5 easy steps using my puppet script. Obviously, this is for testing only and not for production.

1. Install VirtualBox.
2. Install Vagrant.
3. Download and extract the zip file below to any folder.

GitHub v1.1.0

This is the first public release

4. Windows users double-click run.bat. Mac users run the following command from the download folder.

vagrant up

(The script takes approximately 30 minutes to setup the machine. However, no response is needed from your part. An internet connection is required.)
5. Access in your browser:

Access OpenMRS at http://127.0.0.1:8001/openmrs (mysql password: mysql) (login:admin password:Admin123)
Access OSCAR at http://127.0.0.1:8001/Oscar10_12 (default login password on screen)
Access R Studio Server at http://127.0.0.1:8002/ (login:vagrant password:vagrant)

To stop the machine on windows, use stop.bat and on Mac:

vagrant suspend

You can restart the machine as step 4 above. Restarting the machine does not require an internet connection.

If you want to destroy (uninstall) the virtual machine, use the following command in the script folder.

vagrant destroy --force

My puppet script is open-source. Feel free to fork and improve the script. Pull-requests are welcome. Join E-Health on GitHub if you want direct write access to the repository.

If you like it please share this post and take 2 minutes to answer this survey.

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Guest Author

By Bell Eapen, an eHealth graduate student at McMaster University.