A data dictionary contains metadata; in other words, it is a collection of elements in a database with a detailed description. It is helpful in the healthcare system, as “a catalog of available healthcare data is a great advantage to reusing routinely collected data for secondary purposes” (Bruland et al., 2017, p. 1). In creating a patient data dictionary for a free clinic, it is necessary to include fields with the personal information of patients (where they live, their names, and relatives). There should also be a field with information about the patient’s state of health. The data types would be an integer, floating-point, string, enumerated type, date, and time.
Patient data dictionary use is prevalent now, which underlines their significance. Kuo et al. (2019) note that “Three billion of simulated health raw data was constructed and cross-referenced with patient data profiles” (p. 232). Some of the fields in such dictionaries are required, and some of them are optional. It required: a place of actual residence, name, and surname, a diagnosis, and a result of treatment. Optional: personal information about relatives, previous medical history of the patient, official place of residence. The place of actual residence is more important than the official one because it is the first destination the ambulance would go to. Name and surname are necessary for identification, a diagnosis, and a result for treatment – for understanding the patient’s state of health that can help with the possible next illness. However, the previous medical history is optional, for there can be nothing to add, for instance, when there is a new patient.
Some of the described fields of the patient data dictionary would be linked to a different database. It means that each field would be linked to a database with the appropriate information it displays. For example, names, places of residence, and other personal information are in one database, and medical history (a diagnosis and a result of treatment) is in another. Ultimately, at least two databases would be needed, but the number could increase.
References
Bruland, P., Doods, J., Storck, M., & Dugas., M. (2017). What information does your EHR contain? Automatic generation of a clinical metadata warehouse (CMDW) to support identification and data access within distributed clinical research networks. In a A. V. Gundlapalli, D. Zhao, & M.-C. Jaulent (Eds.), MEDINFO 2017: Precision Healthcare Through Informatics (pp. 313-317). IOS Press.
Kuo, A., Chrimes, D., Qin, P., Zamani, H. (2019). A hadoop/MapReduce based platform for supporting health big data analytics. In a F. Lau, G. Bliss, J.A. Bartle-Clar (Eds.), Improving Usability, Safety and Patient Outcomes with Health Information Technology (pp. 229-235). IOS Press.