How are Data Science and Pharmacy Informatics Different? (FAQ 003)
Informatics is easily one of the hottest areas in pharmacy these days. As health systems have implemented new Electronic Health Record (EHR) systems and other advanced technology, there has been an increase in the need for pharmacists who have a specialized technology skillset to maintain these systems. I work in data science but I find myself being approached frequently with questions about informatics. It’s easy to see how these two fields get mistaken for each other considering how little education pharmacists and (especially) pharmacy students get about these two fields.
In this article, I am going to highlight how pharmacy informatics and data science differ in order to help you, the reader, determine which career path might be the one that fits your interests. This in no way is a complete list of all of the differences (that would take weeks to write 😉) between the two fields but will give high-level differences. We’ll cover the difference in end-users of the two fields, different skill sets needed for each, and previous experience you’ll need to get a position in each field.
User Bases
The first difference I’ll focus on is the difference in types of end-users of the work that data scientists and pharmacy informaticists produce.
There can be a wide variety of activities that a pharmacy informaticist works on depending on their employer but common types of work that pharmacy informaticists do include managing order sets in the EHR, working with automated dispensing cabinets (like Pyxis and Omnicell), bar-coded medication administration systems, and EHR building. These responsibilities generally have one thing in common, they tend to focus on technology related to the pharmacy department at hospitals and clinics. Therefore, end-users tend to be pharmacists. Systems like bar-coded medication administration can also involve other healthcare professionals like nurses but even in that case, pharmacy informatics tends to focus on working with healthcare professionals.
Data science, on the other hand, has a much more diverse user base than pharmacy informatics because it expands outside of only working with healthcare professionals. My data science team has tools that are being used by individuals in our organization who work in Human Resources, front desk staff at clinics, and supply chain team members in addition to the healthcare professionals directly taking care of patients. The wide variety of individuals that I get to work with daily is one of my favorite parts of my job. Trained as a healthcare provider and pharmacist, I enjoy getting to work with nurses, physicians, and pharmacists but working with system leaders and other non-clinical personnel helps me to build better communication skills. Something important that I want to mention to my fellow pharmacists (and pharmacy students) out there is the fact that data science work tends not to focus on pharmacy projects. I have been able to work with some pharmacy leaders recently but the vast majority of our work and tools have a population health focus and the heavy users are not traditionally pharmacists. I see this changing in the future but it is an important thing to consider if you’re interested in data science.
Is pharmacy informatics or data science for me? If you see yourself preferring to work with mostly pharmacists and pharmacy leaders at your institution, pharmacy informatics may be the route for you. If you would rather work with a wide variety of clinical and non-clinical folks (and be ok with not frequently working with pharmacists), check out data science as a career path.
Skills Needed
The next major category of differences to cover is the skills that you’ll need for roles in data science and pharmacy informatics. I see the main difference between these two fields, in terms of skills, being the types of tools that you’ll need to train on.
Pharmacy informatics skills tend to be product-focused as in working with a specific EHR vendor or a specific automated dispensing cabinet. For example, EHR companies like Epic and Cerner have their own training programs and intricacies that require specialized knowledge. While each vendor has medication order sets and ways to add modules to the EHR, knowing one system does not help you implement the same feature in the other because of the proprietary nature of the systems. This would apply to the different vendors of automated dispensing cabinets like Omnicell and Pyxis. Pharmacy informaticists will go through specific training programs to learn each new technology that they encounter. Pharmacy informatics skillsets also focus more on IT concepts, like getting different systems and tools talking to each other, building user interfaces in the EHR, and maintaining options that healthcare professionals are allowed to choose within the EHR.
Data science skillsets tend to favor programming and statistics over IT concepts like computer networking and the others mentioned above. A number of the tools that we use in data science are also open-source (read: not proprietary). I spend a good chunk of my workday programming in languages like SQL, R, or Python. If you’re interested in learning more about the programming languages in data science, check out this article I wrote discussing them. This is one of the reasons that I enjoy my job as much as I do. I have been interested in programming ever since I took my first programming class in high school and I would encourage everyone to learn how to program because it’s a skill set that will be in high demand in the future and can help you become more efficient in your job. Even if you take a few lessons to try out programming, I think that a lot of pharmacists would enjoy the cognitive and problem-solving skills that are involved. In data science, we also work a lot with visualization tools like Tableau to share the results of our machine learning model or other key data pieces, something not prevalent in informatics.
Is pharmacy informatics or data science for me? If you’ve tried programming and really enjoy it, a career in data science will give you more opportunities to use programming in your day-to-day work. If you’re more interested in working with the EHR or machines and robotics, informatics would suit you well.
Experience Needed
The last major difference to cover is the type of experience that informatics and data science require you to have.
A job in pharmacy informatics requires a strong background in pharmacy workflow within a hospital or healthcare facility. As previously mentioned, pharmacy informaticists work frequently with automated dispensing cabinets and manage medication order sets. This requires an informaticist to have previous experience working with these systems so that they can understand how the systems fit into the pharmacy workflow. Without previous experience, an informaticist could actually make pharmacist’s jobs more difficult. If a pharmacy informatics position requires working with or building EHR modules, it’s also important to have previous experience working with an EHR. That EHR experience comes even before the specialized training that EHR companies provide for informaticists to learn how to modify EHR implementations. As you can see, pharmacy informaticists must have intimate knowledge of how pharmacists do their work to be successful.
I wouldn’t say that it is the opposite for data scientists but there is less workflow experience required especially less pharmacy workflow experience. Data science is less about workflow and data surrounding workflow and more about data collection being done in your system. To be a great data scientist though, you should have a good understanding of how healthcare works and what type of data is collected at a health system for example. Data science is unique from pharmacy informatics also in that you often can rely more on a clinician stakeholder to educate you on how a process works or what data might be useful to predict an outcome. Data science also does not require you to be familiar with the EHR. Instead, you’ll become incredibly familiar with the data environment that exists behind the EHR within what are called relational databases that will allow you to apply predictive analytics and machine learning. The familiarity with databases will require you to have a background in programming languages like R, Python, and SQL, which can all be attained with resources and websites like DataCamp, Udemy, and Codecademy.
Is pharmacy informatics or data science for me? If you’re interested in working within the pharmacy workflow and improving the efficiency and safety of pharmacists, pharmacy informatics might be a good path for you. If you’re more interested in working with the behind-the-scenes data and general healthcare data, consider learning more about data science.
Wrap Up
In this article, we cycled through a few of the main differences between pharmacy informatics and data science. The primary differences I highlighted are the different user bases of both field’s work, the skills needed in each field, and the prior experience you’ll need to be successful as a pharmacy informaticist or data scientist.
Both of these career fields are highly rewarding and neither is a bad choice for a career. What you’re interested in though, may help to decide which path you’d rather take. Make sure to take time and consider the last paragraph in each section that asks you to reflect on what your skills and desires are and which path may best fit that.
Let me know what other data science topics you’d like to see covered!
