Month: May 2015

Big Data

Great “Big Data is Big Deal” Infographic.

Posted on by ERDataDoc

I found this on BICorner.com, a blog that I follow.  However I was not able to reblog the post, so will post directly here, with credit and thanks to them.

What I particularly like about this infographic, is that it outlines ways that big data could potentially transform healthcare.  We need more information about the use cases for big data analytics in healthcare.

URL for infographic:  http://www.healthcareitconnect.com/healthitconnect2/wp-content/uploads/2012/10/Infographic-BIG-DATA.jpg

BICorner URL: http://bicorner.com/2015/05/22/infographic-when-it-comes-to-healthcare-big-data-is-a-big-deal/

Infographic-BIG-DATA

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Healthcare Analytics

Operationalizing healthcare analytics: A ‘plecosystem’ approach

Posted on by ERDataDoc

The TAO of Health by Paddy Padmanabhan

In my work with healthcare enterprises, I have realized that the term “analytics” means all kinds of things depending on who you speak with, and the term “predictive modeling” tends to carry a certain mystique about it. The former usually means Business Intelligence (BI), which more often than not simply means “reports.” The latter, on the other hand, conjures up images of geeks in labs (people with a PhD in applied math, for instance), toiling away at complicated statistical models for extended periods of time, and finally producing an algorithm, or a formula that “predicts” an outcome. The term also implies an expectation that some absolute truth will be revealed by a statistical model that can be a panacea for a vexing problem – such as high rates or readmissions in a hospital.
Savvy business stakeholders say “so what,” because they are unclear about how to use these complex models…

View original post 855 more words

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Becoming a Healthcare Data Scientist, Predictive Analytics

It’s official now, I have been accepted into Northwestern University’s Master of Science in Predictive Analytics Program

Posted on by ERDataDoc

Just a brief update as I attempt to record significant events/thoughts on my journey to become a data scientist.

I just received my official confirmation that I have been accepted into Northwestern University’s Master of Science in Predictive Analytics (MSPA) program.  I am due to start in the fall.  Here is the link if you are unfamiliar with the program.

http://sps.northwestern.edu/program-areas/graduate/predictive-analytics/index.php

2015-05-18_9-00-59

I selected this program for many reasons. First, it is oriented towards what I really want to do – predictive analytics.  This should give me a solid foundational background upon which I can build the specific skills and specialization I need to develop smarter bedside monitoring systems and help predict patient outcomes.

Second, this is an online curriculum.  At this point in my life I am not easily able to move or change jobs.  I am a practicing Emergency Physician.  I am a Chief Medical Information Officer for my health system.  And I am the Medical Director for our flight service.  I really enjoy all of these pursuits, and am not willing to give this up to move and go back to school full time.  Nor could I with my family obligations.   So the online option works best for me.

Third, Northwestern has a great reputation in this field.  I did my due diligence and researched the program, and the feedback I received was overwhelmingly positive.  As an aside, I did most of my undergraduate degree at Northwestern in the 1980’s (I did not graduate, but would love to finally have a degree from them, so this will hopefully allow me to do this!).

So the easy part is over.  I have been accepted.  Now I will have to do the hard work of learning all of the new material and keep up with the coursework, while still performing my regular day jobs.

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Becoming a Healthcare Data Scientist

Greetings from Billings Montana!

Posted on by ERDataDoc

This is my first entry in my new blog.  Let me tell you a little about myself, and why I decided to start this blog.

My name is Randy Thompson.  I am an Emergency Physician and I practice at the Billings Clinic in Billings Montana.   I am also the Chief Medical Information Officer (CMIO) for the Billings Clinic.  In my role as CMIO, I act as a bridge between the world of clinical medicine on one hand, and the Information Technology world on the other.  As part of this effort I am branching out into the world of social media, and have established linked in and twitter accounts, and now have this blog.  The information and views expressed in this blog will be mine, and do not reflect the views or policy of the Billings Clinic.

There are many things I would like to blog about, but the main reason for establishing this blog is to document the subjects that I feel very passionate about.   These include my quest to become a data scientist, my interest in predictive monitoring (developing smarter bedside physiological monitors), and my interest in complexity science as it applies to human health and disease, as well as organizational dynamics and development.

One of my biggest goals in blogging is to chronicle my journey to become a data scientist.  I have a huge interest in healthcare analytics in general and big data analytics in particular.  We have had difficulties in funding, recruiting, and hiring data scientists, so I thought the best solution for me and my organization was for me to become one.  Initially I tried to do this informally with books and online courses, but this Spring made the decision to do this formally.  I will be starting an online Master’s program this Fall to accomplish this goal.  I would like to document this journey from analytic novice to data scientist on this blog.

I also plan on documenting the field of predictive monitoring as time goes on.   I will blog more about this in the future, but will briefly describe this effort.  The bedside physiologic monitors used in healthcare have not fundamentally changed since the 1970’s.   They provide information about what is happening to the patient now, and you can retrieve some information about what happened to them in the past.  However, there are only a few monitoring systems that can predict what will happen to the patient in the future.  Will they get better?  Will they get worse?  Will they develop sepsis(an overwhelming systemic infection)?  Will they deteriorate and require a ventilator to help them breathe?  These are the questions we would like the monitors to help us answer.  In order to do this, we need to develop predictive algorithms that could be incorporated into these monitors, making them much smarter and more clinically relevant.

Hopefully, you will find this journey interesting and informative.

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