This is a copy of my first post that gives some background information about me and why I want to 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.

5 thoughts on “About

  1. Hi Dr. Thompson, I really enjoyed reading your blog, and I share your enthusiasm about the MSPA program. Although I work in medical informatics, I am not physician, and I think it would be very helpful to others if they can hear your perspective about some individual examples of data science in healthcare – both where it can succeed and where it is likely to fail.

    I personally believe it is very challenging to gain useful insight that can help a physician (or other member of the care team) at the point of care. I think it is a fair criticism to say that anything that works in a black box will be challenged to become effective in the majority of medical situations, simply because physicians have to be able to back up their findings with solid rationale, and a black box that simply gives an answer without greater detail isn’t likely to be beneficial to a medical provider.

    I also think that context is incredibly important in healthcare, and data mining of progress notes (or other text data) is always going to be challenging because it is difficult to maintain the context of a situation across multiple patients. As an example, I would expect a fever to be interpreted much differently by a pediatrician than an oncologist.

    Finally, I do think there are areas where big data can succeed. I think it could be helpful to identify communication breakdowns among staff (which is a known cause of many medical errors), but there are likely other opportunities in areas such as imaging. I would love to hear your own personal thoughts on some of these issues.



    1. Joey, thanks for your thoughtful comments. I am hoping to blog a little more this summer since I will not be taking classes. I like your ideas, and will consider them when I think about my next posts.
      I do agree that physicians are skeptical about adopting new technology, and new ways to analyze data. I do not see “black boxes” as being impossible to overcome, if they demonstrate value and positive results. That will be the challenge, showing meaningful results because of advanced analytics (or core analytics for that matter).
      Your comments about context are spot on, and must be considered each and every time a project is being developed. I agree with one of the touted values of data science, that the data scientist/analyst have core business knowledge so that context can be considered.


  2. Dr. Thompson,

    A few hours ago I got the good news that I have been accepted to the MSPA Program at Northwestern. Your blog has been a very, very useful tool in my efforts thus far while applying. Thank you for all of the effort you put into the course reviews as well! They have been fantastic to review.

    Coincidentally I currently work in Analytics at a healthcare startup. Seems we have both arrived at this program from two different directions!

    I am very excited to get started in the program. I wish you well on your own journey through the coursework as well!

    Best Regards,


    1. Congratulations Peter! I remember how excited I was when I learned that I had been accepted as well. I am still excited, despite the amount of work that this program demands, it has all been worth it.
      I was recently at a Chief Data Officer summit, and one of the CDO’s said his chief data scientist was a graduate of this program, and he was top notch. He couldn’t say enough good things about him, and this is from someone whose institution is doing great work in analytics, and is ahead of the field.


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