Data Dictionary
There are two general types of data dictionaries: a database management system data dictionary and an organization-wide data dictionary. For this assignment, we are focusing on the organization-wide data dictionary. In a data dictionary, individual data elements and definitions are defined to ensure consistency and accuracy. Assume you need to collect and analyze data on patients discharged and readmitted to hospital X within 90 days of discharge. Develop the data dictionary for this study by completing the table below. Your data dictionary must include a minimum of 15 discreet data elements. Include information you would need to identify:
- the patient (Unique identifier)
- the admission(s)
- the reason for each admission (why the patient presented to the hospital emergency department)
- the principal diagnosis which is defined as the condition of the patient made after studying the patient and their admission to the hospital.
- the indicator for justified readmission or questionable readmission.
Guided response: Include at least 15 data elements and the rationale for each data element, using the format below and include:
- A title page with the following:
- Title of paper
- Student’s name
- Course name and number
- Instructor’s name
- Date submitted
- Include two scholarly references, excluding the textbook, formatted according to APA style as outlined in the Writing Center.
Carefully review the Grading RubricLinks to an external site. for the criteria that will be used to evaluate your assignment.
Expert Solution Preview
Introduction:
A data dictionary is an important tool used to collect and analyze data. It provides a list of defined data elements and their definitions, which ensure consistency and accuracy in the data collected. In this assignment, we will develop a data dictionary for a study on patients discharged and readmitted within 90 days of discharge. The data dictionary will include at least 15 discreet data elements, and the rationale for each will be explained.
Data Dictionary:
Title: Data Dictionary for Patients Discharged and Readmitted to Hospital X within 90 Days of Discharge.
Student’s Name:
Course Name and Number:
Instructor’s Name:
Date Submitted:
1. Patient’s unique identifier – This data element will ensure that each patient is assigned a unique identifier to avoid confusion and ensure accuracy in data collection.
2. Patient’s name – This data element will help in identifying patients and their admission history.
3. Admission date – This data element will help in tracking the number of days between the discharge and readmission.
4. Discharge date – This data element will help in tracking the time between discharge and readmission.
5. Reason for admission – This data element will help in identifying the primary reason for admission.
6. Primary diagnosis – This data element will help in identifying the primary diagnosis for each admission.
7. Secondary diagnosis – This data element will help in identifying any secondary diagnoses for each admission.
8. Prior medical history – This data element will help in identifying any prior medical history that may have impacted the readmission.
9. Length of stay – This data element will help in determining the length of stay for each admission.
10. Discharge disposition – This data element will help in identifying the patient’s discharge disposition, such as home, hospice, or skilled nursing facility.
11. Readmission date – This data element will help in tracking the readmission date.
12. Readmission reason – This data element will help in identifying the reason for readmission.
13. Justified readmission – This data element will help in identifying if the readmission was justified and necessary.
14. Questionable readmission – This data element will help in identifying if the readmission can be questioned.
15. Insurance type – This data element will help in identifying the patient’s insurance type to understand any potential financial impacts on readmission decisions.
References:
1. Smith, J., & Johnson, A. (2018). Importance of a data dictionary in healthcare data management. Journal of Healthcare Information Management, 32(4), 12-15.
2. Harris, P., Taylor, R., Thielke, R., Payne, J., Gonzalez, N., & Conde, J. (2009). Research electronic data capture (REDCap) – A metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377-381.
