When I first started working in health informatics, I was confused by some of the acronyms and terms I commonly encountered. In this blog post I hope to de-mystify some of the common health informatic terms. Without further ado let’s get into it!
EMR – Electronic Medical Record is a digitized patient record usually specific to a clinic or family practice. These records are not generally shared as widely as Electronic Health Records. The electronic medical record might contain information on the patient’s weight and weight. The information that was recorded in paper records in doctor’s offices is usually translated to the electronic medical record of the patient.
EHR – Electronic Health Record these are digitized patient records which can be shared with other healthcare organizations (e.g. hospitals, pharmacies, labs) and can contain information such as, patient symptoms, history and treatment plan.
Health Information Exchange – Speaking of sharing data, the process of sharing medical records between different healthcare organizations such as pharmacies and the labs is defined as health information exchange and is facilitated by using standards such as Health Level 7 (HL7) to transform data into a standard form, for this process to be facilitated.
HL7 & FHIR: Health Level 7 is an organization which develops a framework for the sharing and receiving of health information in a secure and private form. The standards are used internationally and are known as HL7. HL7 recently developed FHIR (fast health interoperability resources) which is the next level of interoperability of sharing healthcare data. The resources that are developed are essentially templates that allow you to share administrative and clinical data, a collection of these resources together creates the complete medical record of the patient.
Data wrangling – Is the process of transforming your raw unstructured data (e.g. patient data that was manually entered in) to a clean form which is ready for advanced analytics. This could consist of many steps, as it is quoted in some articles that data cleaning takes up the majority of the analysts time. This could look like unpivoting your data, concatenating text data, dealing with incorrect date formats and much more.
Aggregated data: With aggregated data, only counts or summations are included in your dataset and identifiers are removed. However, sometimes with healthcare data depending on what is being collected, there could be a very small population of individuals who are diagnosed with a particular rare disease and this could be identifying. Therefore, further limits need to be placed on what data will be revealed depending on the population size.
De-identified data: De-identified data although identifiers are removed (e.g. date of birth, name) one has to be cautious of potential privacy breaches. Bad actors might be able to re-identify your de-identified dataset by using another dataset which has information on the patient and individual and then they could use the de-identified data and their dataset to re-identify the dataset you previously de-identified.
Anonymous data: For the highest level of privacy and security, anonymized data is data that has all identifying data removed and data cannot be identified. One way to anonymize your data is to include ranges instead of actual numbers, for instance, you can do a range for the age of the individual instead of their actual age.
These are some common health informatic terms de-mystified, comment down below, what health informatic terms confused you when you first encountered them! Have a wonderful holiday season and best wishes for next year 😀
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