Blueprints are used by others besides architects. New home buyers may use the blueprint of their future home to better understand the architect’s vision and to consult with the architect on changes. Municipalities will use the blueprint to issue building permits. Builders will use it to plan construction and estimate costs.

Likewise, you don’t have to be a database designer to apply visual tools such as database diagrams. For example, creating or studying a database diagram can help you, as a leader in a healthcare organization, understand the data needs of the organization or of your team.

This in turn can create efficiencies and drive improvements at varied levels of patient care. As a nurse, database diagrams can help you analyze the data structure and how to make best use of the available data and functionality in the provision of care. At every level of an organization, an understanding of database diagrams can help in providing feedback to database managers that in turn can help drive improvements.

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.

Assignment (2- to 3-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.

Week 3: Discussion

Database Design for Errors

Scenario 1: You are working in risk management and need to track medication administration errors and adverse events for patients over a 6-month period. You are receiving information from the inpatient areas, outpatient clinics, and home health.

In the case of the first scenario, the adequate database design has to meet the specific requirements for recognizing and reporting medication administration errors and other adverse events. The database would include several tables:

1. Patient Information Table: This table may include fields like Patient ID, title, date of birth, gender, health record number, and others. These fields help safeguard that patient-specific data is connected to errors or adverse events without replication.
2. Incident Details Table: This table would document details of the error or adverse event about incident ID, date and time the event occurred, and location, which could be inpatient, outpatient, or home health type of error like wrong dosage, wrong medication, severity and description of the event. These fields allow risk management teams to classify and evaluate events systematically (Oner et al., 2021).
3. Medication Information Table: This table will capture details of the medication, including the medication identification number, its name, dose, administration route, and who prescribed the medication. To be able to find patterns and problems, medication data must be precise.
4. Staff Information Table: The fields in this table are staff ID, role, department, shift time, and training history. Incidents, when linked to staff, identify the areas of the staff that might need more practice or more reinforcement (Manias et al., 2020).
5. Follow-Up Actions Table: Each incident’s reaction, including remedial actions, patient outcomes, and preventative tactics, will be tracked in this table.

Transferring this data from paper to an electronic form of storage has various advantages. Computerized resources can be used to pull out and assess data quickly and, in turn, make comparisons and find evident patterns in various contexts (Tsai et al., 2020). For instance, organizing interventions based on the data regarding medication errors in these particular shifts will be possible. Furthermore, electronic databases enhance data security, and the probability of mistakes in data management is minimized.

References

Manias, E., Kusljic, S., & Wu, A. (2020). Interventions to reduce medication errors in adult medical and surgical settings: a systematic review. Therapeutic advances in drug safety, 11, 2042098620968309.

Oner, B., Zengul, F. D., Oner, N., Ivankova, N. V., Karadag, A., & Patrician, P. A. (2021). Nursing‐sensitive indicators for nursing care: A systematic review (1997–2017). Nursing open, 8(3), 1005-1022.

Tsai, C. H., Eghdam, A., Davoody, N., Wright, G., Flowerday, S., & Koch, S. (2020). Effects of electronic health record implementation and barriers to adoption and use: a scoping review and qualitative analysis of the content. Life, 10(12), 327.

 Week 4: Discussion

Initial Post

     The identification and gross configuration of a database, as proposed in the Week 3 Discussion, require additional planning and fine-tuning to meet more exacting concerns, such as one-to-many relations and data types. For instance, in the case of medication administration errors and adverse event tracking databases, the level of association between the tables, such as the Patient Information Table and the Incident Details Table, must reflect a one-to-many relationship. One patient may have several accidents in six months; therefore, the Patient ID in the Patient Information Table should be a primary key, and foreign keys should be contained in the Incident Details Table. This ensures all incidents connected with certain patients are associated, but no patient data is entered twice (Manias et al.,  2020).
     This principle also applies to the Staff Information Table and Incident Details Table. A staff member may be present in multiple incidents. However, their information should not be duplicated (Oner et al., 2021). However, the Staff Information Table’s Staff ID should be used to provide keys to the Incident Details to ensure conformity.
     Giving input data validation rules makes it possible to minimize the potential cases of poor data integrity and eradicate data duplication. For instance, unique constraints can help to exclude the duplication of patient IDs in patients’ tables or staff IDs in staff tables. Fields should also be created with proper data types, for example, integer and unique identifiers for IDs, datetime for incidents, and text or varchar for descriptions (Tsai et al., 2020). Some standardization should be maintained for fields where the values should not differ, such as the name of the medicine or even the patient’s details. Normalization of the database also guarantees that there will not be extra copies of similar data within the database. 

References

Manias, E., Kusljic, S., & Wu, A. (2020). Interventions to reduce medication errors in adult medical and surgical settings: a systematic review. Therapeutic advances in drug safety, 11, 2042098620968309.

Oner, B., Zengul, F. D., Oner, N., Ivankova, N. V., Karadag, A., & Patrician, P. A. (2021). Nursing‐sensitive indicators for nursing care: A systematic review (1997–2017). Nursing open, 8(3), 1005-1022.

Tsai, C. H., Eghdam, A., Davoody, N., Wright, G., Flowerday, S., & Koch, S. (2020). Effects of electronic health record implementation and barriers to adoption and use: a scoping review and qualitative analysis of the content. Life, 10(12), 327.