Layering Maps and Data At Algorex Health, we make a lot of maps and get a lot of questions about the tools we use to make them. So, I thought I would briefly describe our process and the tools we use. I covered the basics of geographic charting in a past blog post and some of that terminology will be repeated here. We use to two primary data visualization systems at Algorex Health both of which were chosen for their support withthin the Python/Jupyter stack, ease of use, and/or aesthetic flexibility:
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Flying to Orlando

When is Data Big? This week the health world turns to Orlando for the annual conference of the Health Information Management Systems Society annual conference. it is absolutely one of the biggest events in health IT and one of the top 25 trade shows in the entire country. How many people attend HIMSS each year? We could search one of hundreds of press releases about the event from exhibitors. We could even visit the link above from Trade Show news.
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This post is part of a larger series of posts that examines the use of declarative logic programming in implementing a healthcare-specific risk score called the HCC Risk Score. The scope of this series runs through such topics as healthcare-intensive dissection of SAS code and translation into Python and PyDatalog a generic introduction to Logic Programming with Prolog and SQL (this post) discussions on the role of declarative programming in a technical organization the difference between forward-chaining and backward-chaining as an implementation strategy in logical/declarative technologies the choice of a forward-chaining embedded language called CHR for re-implementing our code from PyDatalog This post has the following characteristics:
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The following post is an introduction and summary on what will be a series of posts about the use of declarative programming, specifically logical programing, within one of Algorex’s more important libraries – the HCC (hierarchical code categories) risk calculator. It is our hope that this series of posts might be interesting to several classes of reader: The imperative programmer (regardless of field) who has never encountered the term ‘logical programming’ before.
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Calculating Growth and Obesity from FHIR Messages When parents bring their kids to the pediatrician, one of the most important checks is to have the child’s height, weight, and BMI plotted on growth charts to understand their development. The charts, a image of which appears below, are ubiquitous and critical to the tracking the health of kids. Sure enough, many many EHRs automatically show the graph and the patient’s position on the chart right inside the workflow making it easy for physicians and patients to review.
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Everything You Never Wanted to Know about Geographic Charting Before the start of seventh grade, my family moved, and I started a new school that had as required module, cartography. Over a few weeks, we painstakingly drew maps of states, continents and eventually the entire world. My backpack became filled with various stencils, a compass, colored pencils, pens of multiple weights, and even tracing paper. It was one of the more frustrating parts of my schooling and I remember thinking “Why would I ever need this?
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Almost 10 PieCharts 10 Python Libraries Here is a follow-up to our “10 Heatmaps 10 Libraries” post. For those of you who don’t remember, the goal is to create the same chart in 10 different python visualization libraries and compare the effort involved. All of the Jupyter notebooks to create these charts are stored in a public github repo Python-Viz-Compared. Each Jupyter notebook will contain one chart (bar, scatter etc) and then up to 10 different ways of implementing them.
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10 Heatmaps 10 Libraries I recently watched Jake VanderPlas’ amazing PyCon2017 talk on the landscape of Python Data Visualization. That presentation inspired this post. In programming, we often see the same ‘Hello World’ or Fibonacci style program implemented in multiple programming languages as a comparison. In Jake’s presentation, he shows the same scatter plot in several of the libraries he featured. Below, I am following the same formula. I am recreating a heatmap about airline flights, in ten different python visualization libraries.
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The CMS division of HHS produces a freely available algorithm for determining the diagnosis risk of cost for beneficiaries. The problem with this freely available algorithm is that it is written in a technology, SAS , that is not freely usable.1 We at Algorex Health have reimplemented the HCC algorithm in Python with two aims in mind. First, we want to promote truly free algorithms for value-based analytics, contributing to a community ethos of open-source population health libraries.
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“Social Determinants” are fast becoming the new buzzword in population health. Recent findings have reiterated what many intuitively know. 60% of a patient’s total health is determined not by their clinical conditions but by other factors such as where they live, their household status, and their behaviors - collectively SDOH. source The following chart from the link above summarizes the social determinants by category: Health organizations including insurance plans and hospital systems don’t have great data on these factors.
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Algorex Health Technologies

A blog for technology, policy, and grievances in the Open Health World

Opening the Healthcare Technology Doors

Boston, Massachusetts