Health IT and the Limits to Analogies

Many who write and talk about health IT, including myself, are fond of using analogies. One of the most common analogies that we use is that of the banking industry. I have noted that I can insert my Wells Fargo ATM card into just about any ATM in the world and receive out local currency. This is all made possible by a standard adopted worldwide by the banking industry. Of course, there is another reason for banking interoperability that does not exist in healthcare, which is that the financial incentives are all aligned. Each time we make an ATM transaction, a fee goes to both the bank that owns the ATM and (if the machine is owned by a bank different from our own) our bank. While most of us grumble about ATM fees, we usually pay them, not only because we have to, but also because of the convenience.

Another common analogy we use for how health IT could be better is to discuss the aviation industry. There is no question that healthcare could learn more from not only the IT of the aviation industry, but also the relationships between all the players who insure that planes take off and land safely [1]. With regards to the IT of aviation, there is definitely more human factors and usability analysis that go into the design of cockpit displays for flying these complex machines than is done in healthcare.

An addition to the analogy list I hear with increasing frequency is the smartphone. In particular, many ask, why can’t the electronic health record (EHR) be as simple as a smartphone? Again, there is much to learn from the simplicity and ease of use of smartphones, especially their organization as allowing “substitutable” apps on top of a common data store and set of features, such as GPS [2]. However, there are also limitations to the smartphone analogy. First, the uses of the EHR are much more complex than most smartphone apps. There is a much larger quantity and diversity of data in the patient’s record. Second, the functions of viewing results, placing orders, and other actions are much more complex than our interactions with simple apps.

I look at my own smartphone usage and note that I spend a great deal of time (probably too much) using it. But there are many things I do with my laptop that I cannot do with my smartphone. For example, my phone is fine for reading email and typing simple replies. However, composing longer replies or working with attachments is not feasible, at least for me, on my phone. Likewise, writing documents, creating presentations, and carrying out other work requiring more than a small screen is also not possible on my phone with its limited screen, keyboard, and file storage capabilities.

While a key challenge of informatics is to make the EHR simpler and easier to use, it will never approach the simplicity of a highly focused smartphone app. Analogies can be helpful in elucidating problems, but we also must recognize their limitations.

References

1. Pronovost, PJ, Goeschel, CA, et al. (2009). Reducing health care hazards: lessons from the commercial aviation safety team. Health Affairs. 28: w479-w489.
2. Mandl, KD, Mandel, JC, et al. (2012). The SMART Platform: early experience enabling substitutable applications for electronic health records. Journal of the American Medical Informatics Association. 19: 597-603.

60 Years of Informatics: In the Context of Data Science

Like many academic health science universities, my institution has undertaken a planning process around data science. In the process of figuring how to merge our various data-related silos, we tried to look at what other universities were doing. One high-profile effort has been launched at the University of Michigan, and the formation of their program and those of others inspired a statistician, David Donoho, to look at data science from the purview of his field 50 years after famed statistician John Turkey had called for reformulation of the discipline into a science of learning from data. Donoho’s resulting paper [1] motivated me to look at data science from the purview of my field, biomedical and health informatics.

Statistics has of course been around for centuries, although this author drew from an event 50 years ago, a lecture by George Tukey. The informatics field has not been in existence for as many centuries, but one summary of its history by Fourman credits the origin of the term to Philip Dreyfus in 1962 [2]. However, the Wikipedia entry for informatics attributes the term to a German computer scientist Karl Steinbuch in 1956. Fourman also notes that the heaviest use of the term informatics comes from its attachment to various biomedical and health terms [2].

If the informatics field is indeed 60 years old, I have been working in it for about half of its existence, since I started my National Library of Medicine (NLM) medical informatics fellowship in 1987. I have certainly devoted a part of my career to raising awareness of the term informatics, making the case for it as a discipline [3]. Clearly the discipline has become recognized, with many academic departments, mostly in health science universities, and a new physician subspecialty devoted to it [4].

And now comes data science. What are we in informatics to make of this new field? Is it the same as informatics? If not, how does it differ? I have written about this before.

Donoho’s paper does offer some interesting insights [1]. I get a kick out of one tongue-in-cheek definition he gives of a data scientist, whom he defines as a “person who is better at statistics than any software engineer and better at software engineering than any statistician.” Perhaps we could substitute informatician for software engineer, i.e., a data scientist is someone who is better at statistics than any informatician and is better at informatics than any statistician?

Donoho does later provide a more serious definition of data science, which is that it is “the science of learning from data; it studies the methods involved in the analysis and processing of data and proposes technology to improve methods in an evidence-based manner.” He goes on to further note, “the scope and impact of this science will expand enormously in coming decades as scientific data and data about science itself become ubiquitously available.”

Donoho goes on to note six key aspects (he calls them “divisions” of “greater data science”) that I believe further serve to define the work of the field:

• Data Exploration and Preparation
• Data Representation and Transformation
• Computing with Data
• Data Modeling
• Data Visualization and Presentation
• Science about Data Science

Clearly data is important to informatics. But is it everything? We can being to answer this question by thinking about the activities of informatics where data, at least not “Big Data,” is not central. While I suppose it could be argued that all applications of informatics make use of some amount of data, there are aspects of those applications where data is not the central element. Consider the many complaints that have emerged around the adoption of electronic health records, such as poor usability, impeding of workflow, and even concerns around patient safety [5]. Academic health science leaders can lead the charge in use of data but must do so in the context of a framework that protects the rights of patients, clinicians, and others [6].

Like many informaticians, I do remain enthusiastic for the prospect of the growing quantity of data to advance our understanding of human health and disease, and how to treat the latter better. But I also have some caveats. I have concerns that some data scientists read too much into correlations and associations, especially in the face of so much medical data capture being imprecise, our lack of adoption of standards, and its inaccessibility when not structured well (which can lead us to try to “unscramble eggs”).

It is clear that informatics cannot ignore data science, but our field must also be among the leaders in determining its proper place and usage, especially in health-related areas. We must recognize the overlap as well as appreciate the areas where informatics can be synergistic with data science.

References

1. Donoho, D (2015). 50 years of Data Science. Princeton NJ, Tukey Centennial Workshop. https://dl.dropboxusercontent.com/u/23421017/50YearsDataScience.pdf.
2. Fourman, M (2002). Informatics. In International Encyclopedia of Information and Library Science, 2nd Edition. J. Feather and P. Sturges. London, England, Routledge: 237-244.
3. Hersh, W (2009). A stimulus to define informatics and health information technology. BMC Medical Informatics & Decision Making. 9: 24. http://www.biomedcentral.com/1472-6947/9/24/.
4. Detmer, DE and Shortliffe, EH (2014). Clinical informatics: prospects for a new medical subspecialty. Journal of the American Medical Association. 311: 2067-2068.
5. Rosenbaum, L (2015). Transitional chaos or enduring harm? The EHR and the disruption of medicine. New England Journal of Medicine. 373: 1585-1588.
6. Koster, J, Stewart, E, et al. (2016). Health care transformation: a strategy rooted in data and analytics. Academic Medicine. Epub ahead of print.