Although a seemingly basic question, the difference between a dataset and a database has important implications for how data is applied in practice—how it is viewed, extracted, and importantly for the nurse informaticist, how it is exchanged. In this Discussion, you examine this difference.
This Discussion has two components. The first component prompts you to consider different types of datasets or databases within professional practice.
The second component aligns with the Assignment in this module, where you will interview a professional nurse informaticist. In this component, you post draft questions in the Discussion for feedback from your colleagues.To Prepare
Review the Resources and consider the differences between datasets and databases.
Reflect on the types of data obtained and how they are used in sharing across health information systems.
Review the Module 2 Assignment.
Review the requirements of the Assignment and the guidelines for developing interview questions.
Develop a set of draft questions to post as the second component of your Discussion.You are also required to generate three questions of your own. To do so, reflect on the following:
Based on the interview subject’s expertise, what questions could expand your knowledge base regarding data and how it is shared?
What tools are used by the expert in their field to obtain research information, including bioinformatics, genetics, and genomics?
Datasets and Databases
Part 1
The healthcare industry must ensure the effective use and management of data collected across health information systems in this digital age. It is for this reason that nurse informaticists need to understand the difference between datasets and databases as they apply to their profession and health practice in general (Nibbelink et al., 2018). Randell et al. (2017) define a dataset as an organized collection of data that has been collected in relation to a specific body of work. On the other hand, a collection of multiple datasets is stored in a central place known as a database. Nurse informaticists deal with different types of datasets and databases within their professional practice. For example, there are databases for literary publications, genomes, genes, proteins, chemicals, and clinical datasets. Literature databases such as PubMed and Medline contain datasets for scientific and medical journals. Genomic databases such as Nucleotide contain datasets obtained from GenBank with data for DNA and RNA sequences. ClinVar is a clinical database with datasets that show human variations of clinical significance such as those that relate to a particular disease. PubChem BioAssay is a Chemicals database with datasets for data collected from bioactivity screening studies (Sayers et al., 2022; Wu et al., 2021). Nurse informaticists often share this data across health information systems to be used in decision-making.
Part 2
One of the best ways to understand the implications of datasets and databases to nurse informaticists is to interview professionals in the field. After interviewing a professional nurse informaticist, the following set of draft questions was developed to help expand knowledge base regarding data and how it is shared across health information systems;
- What types of data do you often collect in your practice setting?
- What measures do you use to ensure privacy and confidentiality when sharing this data electronically?
- What types of tools do you use to obtain research information, including bioinformatics, genetics, and genomics from different databases?
References
Nibbelink, C. W., Young, J. R., Carrington, J. M., & Brewer, B. B. (2018). Informatics solutions for application of decision-making skills. Critical Care Nursing Clinics of North America, 30(2), 237–246. https://doi.org/10.1016/j.cnc.2018.02.006.
Randell, I., Cornet, R., & McCowan, C. (2017). Informatics for health: Connected citizen-led wellness and population health. IOS Press.
Sayers, E. W., Bolton, E. E., Brister, J. R., Canese, K., Chan, J., Comeau, D. C., Connor, R., Funk, K., Kelly, C., Kim, S., Madej, T., Marchler-Bauer, A., Lanczycki, C., Lathrop, S., Lu, Z., Thibaud-Nissen, F., Murphy, T., Phan, L., Skripchenko, Y., Tse, T., … Sherry, S. T. (2022). Database resources of the national center for biotechnology information. Nucleic Acids Research, 50(D1), D20–D26. https://doi.org/10.1093/nar/gkab1112
Wu, W. T., Li, Y. J., Feng, A. Z., Li, L., Huang, T., Xu, A. D., & Lyu, J. (2021). Data mining in clinical big data: the frequently used databases, steps, and methodological models. Military Medical Research, 8(1), 44. https://doi.org/10.1186/s40779-021-00338-z.
Yang, J., Li, Y., Liu, Q., Li, L., Feng, A., Wang, T., Zheng, S., Xu, A., & Lyu, J. (2020). Brief introduction of medical database and data mining technology in big data era. Journal of Evidence-based Medicine, 13(1), 57–69. https://doi.org/10.1111/jebm.12373