This is part of our data migration blog series – a range of topics intended to help organizations who are migrating from one EHR to another.
My previous blog posts have focused on oncology data and the ways in which it can change the foundation of medical research however I haven’t discussed what it’s like to work with oncology data or even an oncology EMR.
You may ask yourself why do certain organizations use an oncology specific EMR instead of your standard multi-specialty EMR? Well, the answer lies within the patients being seen at a given organization. Cancer patient, Jane Smith at Org A, for example, will require a unique patient care plan isn’t necessary for John Smith at Org B who is visiting for a routine checkup. Again, you may ask yourself why would Jane Smith require a different EMR from John Smith? Or maybe I should rephrase your question to “How will Jane Smith benefit from an oncology EMR vs the EMR being used during John Smith’s appointment at Org B?” Think of it this way:
The Oncology specialty is comprised of complex protocols, cancer specific templates and workflows that are essential to clinical trials, reporting and the prognosis or outcome for a given patient. Since each type of cancer is unique, oncology EMRs aid in the creation of specialized templates, workflows and treatment plans. Not to mention the data is stored in one centralized location. Ultimately, Oncology EMRs enhance patient safety and efficiency when ordering and administering chemotherapy treatments for cancer patients—hence why Jane Smith would benefit from one being utilized.
2 Halves Make A Whole
There are two sides to an Oncology EMR, Radiation Oncology (RAD ONC) and Medical Oncology (MED ONC). RAD ONC focuses on the treatment of cancer via radiation therapy while MED ONC focuses on the treatment of cancer via chemotherapy, hormone therapy, or targeted therapy. When working with an oncology EMR, be sure to ask questions regarding each facet of the EMR because there are probably flags or identifiers that differentiate RAD ONC data from MED ONC. In some cases, the RAD ONC and MED ONC data may even be separated into two different databases. This was the case when I worked with Aria. Aria stored the RAD ONC data in one database while the remaining MED ONC data lived in another. Even though each side may appear to be different or may even be separated into different locations, you shouldn’t forget both sides work together to coordinate and develop a patient’s cancer care plan.
Challenges & Lessons Learned
Over the course of two years I worked with two oncology EMRs: Aria and Mosaiq. Both proved to be challenging in various ways however one of the more challenging aspects revolved around distinguishing Chemo/treatment medications from ambulatory or OTC medications. In terms of Aria, the medication table contained a column entitled “Drug Class.” I was able to differentiate Chemo or treatment medications from ambulatory medications by searching for the drug class “Chemo.” Seems simple, right? Well using this oncology data experience, I assumed the same would occur in Mosaiq but I was wrong. For Mosaiq, medications were separated into 5 different types, in house treatment, Pick Up, take at home, medications tied to treatment plans, and outdated or inactive medications. I learned this by viewing the front-end application. The trickiest part was locating the identifiers associated to each medication type in the database. In House Treatment medications proved to be the hardest medication to understand because old in house treatment medications were associated to one identifier or code while newer in house treatment medications were associated to another. The difference in identifiers and codes turned out to be related to the version of Mosaiq. As I encountered additional EMR quirks, I made sure to take notes and create documentation. Not only will the documentation help your colleagues, it may even help you one day in the future.
Hopefully now it makes sense as to why Jane Smith would benefit from an oncology specific EMR. Whether you’re working on an oncology data migration project or reporting task, you may need to do your own research when it comes to understanding the nuances of oncology data. So yes, you may need to do your homework. Make sure to catch our full data migration blog series.
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