Bob Hoyt

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Bob Hoyt

Bob Hoyt MD FACP has become a well-known figure in the health informatics space due to his work in writing and publishing educational informatics materials for college students. A decade ago Dr. Read More »

Informatics Education

Informatics Education was created in 2007 as the business entity in support of the first edition of our textbook Health Informatics: Practical Guide for Healthcare and Information Technology Professionals. Newer editions were published every 1-2 years with the seventh edition published in June 2018...Since the inception of Informatics Education, the vision has been to support informatics students and faculty.

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Machine Learning in Healthcare: Part 1 - Learn the Basics

This article is the first in a three-part series that will discuss how machine learning impacts healthcare. The first article will be an overview defining machine learning and explaining how it fits into the larger fields of data science and artificial intelligence. The second article will discuss machine learning tools available to the average healthcare worker. The third article will use a common open source machine learning software application to analyze a healthcare spreadsheet. Part I was written to help healthcare workers understand the fundamentals of machine learning and to make them aware that there are simple and affordable programs available that do not require programming skills or mathematics background...

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Machine Learning in Healthcare: Part 2 - Tools Available to the Average Healthcare Worker

A variety of machine learning tools are now available that can be part of the armamentarium of many industries, to include healthcare. Users can choose from commercial expensive applications such as Microsoft Azure Machine Learning Studio, SAS Artificial Intelligence Solutions or IBM SPSS Modeler. Academic medical centers and universities commonly have licenses for commercial statistical/machine learning packages so this may be their best choice. The purpose of this article is to discuss several free open source programs that should be of interest to anyone trying to learn more about machine learning, without the need to know a programming language or higher math.

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Machine Learning in Healthcare: Part 3 - Time for a Hands-On Test

Every inpatient and outpatient EHR could theoretically be integrated with a machine learning platform to generate predictions, in order to alert clinicians about important events such as sepsis, pulmonary emboli, etc. This approach may become essential when genetic information is also included in the EHR which would mandate more advanced computation. However, using machine learning and artificial intelligence (AI) in every EHR will be a significant undertaking because not only do subject matter experts and data scientists need to create and validate the models, they must be re-tested over time and tested in a variety of patient populations. Models could change over time and might not work well in every healthcare system. Moreover, the predictive performance must be clinically, and not just statistically significant, otherwise, they will be another source of “alert fatigue.”

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The Story of How our Health Informatics Textbook Came into Being

I have been asked many times how and why I became interested in Health Informatics and how that led to the writing and self-publication of our textbook, Health Informatics: Practical Guide. The textbook is now in its 7th edition and has been adopted by a large number of universities for their health informatics courses. More co-authors have come on board, and we are now looking at publishing other textbooks. Thus we thought this would be a good point to tell the story.

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Using LibreHealth EHR for Education in Academic Settings

Traditionally, access to EHRs has been viewed as important only for software training, particularly order entry. What seems to be overlooked is the potential for education, analytics and research. Additionally, one could argue that there should be an open-source “EHR Sandbox” so multiple external EHR integrations could be studied and reported. Furthermore, many EHR users view the software as a means to enter or extract data on one patient at a time and fail to see the benefit in analyzing their entire clinic population (population health). The following diagram displays how an EHR could be used for education, training, analytics and research.

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