In your Module 2 Assignment, you create a database diagram for the database that you proposed in the Week 3 Discussion and built upon in the Week 4 Discussion. You also create a PICOT question to be used to search your proposed database. Finally, you develop an analysis of your database design, explaining the proposed structure and design elements while justifying your approach.
To prepare:
Review the Learning Resources.
Review your posts from the Weeks 3 and 4 Discussions and the scenario on which they are based.
Consider a clinical research question you might ask that could be informed by the data in your database.
Restate your clinical question as a PICOT question.
Assignment (2-page paper and a diagram of a database as an appendix):
Using the scenario from Week 3, create a diagram of your proposed database using Microsoft Word.
Include the additions made in Week 4.
Complete the diagram first, but place it as the final page or pages of your submission. The diagram is separate from the required page count.
In the narrative portion of the assignment:
Explain how your diagram articulates your planned design.
Explain the principles behind selecting key fields and defining relationships. Be specific and support your response with evidence.
Write a sample PICOT question (i.e., a query) you might ask based on the information in the database created during Weeks 3 and 4 to demonstrate your understanding of the connection between data and research.
List the tables in the database that you would need to include when answering your question
Diagramming a Database Design
Name
Institution
Course
Instructor
Date
Diagramming a Database Design
Every study requires ease of data collection, storage, and retrieval. According to Davis (2018), a well-designed database provides access to up-to-date, accurate information. A database can take many models to show the relationships and constraints that determine how data can be stored and accessed (Goelman & Dietrich, 2018). This discussion gives an example of a database of a study on medical errors reported in a hospital.
PICOT Questions
The impacts of a 6-month tracking of medical administration errors and adverse events occurring to patients on identifying, managing, and reducing these risks.
The Diagram
| INPATIENT AREA | OUTPATIENT CLINICS | HOME HEALTH | |||
| Month –
Total number of incidences – |
Month –
Total number of incidences – |
Month –
Total number of incidences – |
|||
| Incidence type s
– – – |
Number of each incidence
– – – |
Incidence type s
– – – |
Number of each incidence
– – – |
Incidence type s
– – – |
Number of each incidence
– – – |
| Description of each incidence
Name of error – Patient involves – Patients Ages – Doctor involved – Date – Section – Consequence – |
Description of each incidence
Name of error – Patient involves – Patients Ages – Doctor involved – Date – Section – Consequence – |
Description of each incidence
Name of error – Patient involves – Patients Ages – Doctor involved – Date – Section – Consequence – |
|||
Explaining how the diagram articulates the planned design
This design will be in a tabular form to provide ease of tracking of the data to be collected for the study. The data will require adequate information about medication administration errors occurring in the selected hospital. Such a process may be complex considering the relevant details and relations required for better comprehension. This table enables easy identification of the key parameters of the research and their levels of applicability.
The Principles Behind Selecting Key Fields and Defining Relationships
This table identifies the necessary data to be collected while doing the research. The research study will require specific details about medical errors. These parameters include Medication administration error or near miss, cause of the error, and harm or death (Van Cott, 2018). The database design has three levels of assortment. The first level is the identification of the month and the total number of medical errors that occurred during the same month. This information simply tries to show whether errors occurred during that month or not.
The first level then informs the second level of information by allowing the specific categories of errors that occurred to be identified. It will specify the number of reported cases under each error category.
Level three of the database will be informed by the level 2 information provided. Each error identified will be described in detail, explaining how, when, why, and where it happened (Rodziewicz, Houseman, & Hipskind, 2022). Level 3 provides fields, including the name of the error, the patient involved, the patient’s age, the doctor involved, the date of occurrence, the section, and the consequences of the error. Such details will help put the error into perspective and make it traceable. In the third bracket, more spaces will be created downwards depending on the number of errors reported that month.
The assumption here is, errors are not common incidences in a hospital, and not so many can occur in a single month, particularly in a single section (Makary & Daniel, 2016). The study anticipates 0-2 errors in each section per month. The whole table will only account for one month with the columns categorizing the errors depending on the locations from which they are reported – Inpatient Area, Outpatient Clinics, and Home Health. Therefore, there will be five more similar tables, each representing each month to capture the 6 months recommended for study.
It is much easier to capture the data types to be collected for a research study in a database. This design also provides ease of logical accessibility and evaluation of the data. Row 2 of this design can easily be transferred into an excel sheet for ease of statistical evaluation. Every research study should come up with a database design to facilitate data collection and storage.
References
Davis, K. C. (2018, October). Teaching physical database design. In International Conference on Conceptual Modeling (pp. 165-175). Springer, Cham. https://10.1007/978-3-030-01391-2_22
Goelman, D., & Dietrich, S. W. (2018, February). A Visual introduction to conceptual database design for all. In Proceedings of the 49th ACM Technical Symposium on Computer Science Education (pp. 320-325). https://doi.org/10.1145/3159450.3159555
Makary, M. A., & Daniel, M. (2016). Medical error—the third leading cause of death in the US. Bmj, 353. https://doi.org/10.1136/bmj.i2139
Rodziewicz, T. L., Houseman, B., & Hipskind, J. E. (2022). Medical error reduction and prevention. In StatPearls [Internet]. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK499956/
Van Cott, H. (2018). Human errors: Their causes and reduction. In Human error in medicine (pp. 53-65). CRC Press. https://www.taylorfrancis.com/chapters/edit/10.1201/9780203751725-4/human-errors-causes-reduction-harold-van-cott