Abstract
This first chapter provides a brief history of some, but certainly not all, of the key subdomains within the health informatics field and further explains the potential significance of the FHIR standard that will occupy much of the rest of the book. To do this, the chapter begins with a discussion of early electronic records and clinical decision support tools and then shifts gears to introduce the concept of health information exchange. Later, we discuss interoperability challenges that date back decades and the various ways that existing technologies have been used, sometimes with limited success, to simplify and coordinate the sharing of information among providers. The chapter ends with the premise that widespread adoption of modern web technologies (and FHIR in particular) is transforming health informatics. To help illustrate this the chapter ends with a demonstration FHIR app developed by a team of Georgia Tech students did using these emerging technologies to help predict the onset of a life threatening condition in ICU patients.
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Machine Learning is the branch of artificial intelligence focused on the development of algorithms to do things such as classifying or clustering items of interest. In supervised learning input and output data are labeled and an algorithm learns the mapping function from the input data to the output labels by comparing its output to the correct output. In unsupervised learning there is only unlabeled input data and so the model is left to discover and present interesting structures in the data. In medicine, these techniques can use EHR data to classify patients into those that have diabetes and those that do not have it or to cluster diabetic patients into different diabetic subtypes.
In classical machine learning algorithms, the focus is on converting input data into features for supervised and unsupervised learning (feature extraction or feature engineering). There is currently great interest in “deep learning ” a subfield that uses massive amounts of labeled data and the power of modern computers to automatically generate useful features and classification models to obtain previously unachievable levels of accuracy.
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Braunstein, ML, The Computer in a Family Practice Center: A “Public” Utility for Patient Care, Teaching and Research, Medical Data Processing, pp 761–68, Laudet, M, Anderson, J, and Begon, F, Editors. London, Taylor and Francis, 1976.
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A written description of how a user will use a software system to accomplish a task or goal.
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The answer is yes. The hematocrit of 45 is within the normal range of 39–49 and the erythrocyte count of 4.94 is within the normal range of 4.3–5.9.
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Buchman TG, Coopersmith CM, Meissen HW, Grabenkort WR, Bakshi V, Hiddleson CA, Gregg SR. Innovative interdisciplinary strategies to address the intensivist shortage. Critical care medicine. 2017 Feb 1;45(2):298–304.
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Braunstein, M.L. (2018). A Brief History and Overview of Health Informatics. In: Health Informatics on FHIR: How HL7's New API is Transforming Healthcare . Springer, Cham. https://doi.org/10.1007/978-3-319-93414-3_1
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DOI: https://doi.org/10.1007/978-3-319-93414-3_1
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