Attached Files: HIM Professionals will frequently work with data dictionaries. This assignment will build an understanding of data dictionary development and maintenance and the importance of data integrity. Review the sample data dictionary below. There are a number of blank fields in the table. Your first task is to fill in all of the blank fields, Patient_ID (primary key) Number 8 Unique patient number   automatically generated 0-9 Gender Character 1 Gender of the patient M = Male F = Female U = Unknown PT_LN Alphanumeric 50 Patient’s legal surname A-Z PT_FN Alphanumeric 50 Patient’s legal first   name A-Z PT_DOB 0-9 8 Patient’s date of birth MM-DD-YYYY PT_Race Alphanumeric 25 Patient’s declared race American Indian Alaskan Native Asian African American White PayerType Alphanumeric 25 Patient’s primary source   of payment BC/BS CHAMPUS Medicaid Medicare Self-Pay Other InsuranceID City State Zip Country VisitDate Write a justification discussing why each field was necessary and why the value you used was appropriate. (NOTE: Address every blank that you filled in) Next discuss the characteristics of data integrity

Data dictionary development and maintenance play a crucial role in the field of health information management (HIM). In this assignment, we will review a sample data dictionary and fill in the blank fields. Additionally, we will discuss the importance of data integrity.

Let’s begin by examining the sample data dictionary provided:

| Field Name | Data Type | Length | Field Description | Value Range |
|—|—|—|—|—|
| Patient_ID | Number | 8 | Unique patient number | automatically generated 0-9 |
| Gender | Character | 1 | Gender of the patient | M = Male, F = Female, U = Unknown |
| PT_LN | Alphanumeric | 50 | Patient’s legal surname | A-Z |
| PT_FN | Alphanumeric | 50 | Patient’s legal first name | A-Z |
| PT_DOB | Number | 8 | Patient’s date of birth | MM-DD-YYYY |
| PT_Race | Alphanumeric | 25 | Patient’s declared race | American Indian, Alaskan Native, Asian, African American, White |
| PayerType | Alphanumeric | 25 | Patient’s primary source of payment | BC/BS, CHAMPUS, Medicaid, Medicare, Self-Pay, Other |
| InsuranceID | Character | | | |
| City | Alphanumeric | | | |
| State | Alphanumeric | | | |
| Zip | Alphanumeric | | | |
| Country | Alphanumeric | | | |
| VisitDate | | | | |

Now, let’s discuss the justification for filling in the blank fields:

1. InsuranceID: This field represents the patient’s insurance identification number. It is important to have this information to accurately track and process insurance claims and billing. Since no specific value range is provided, we can assume it can accommodate alphanumeric characters.

2. City: The city field captures the patient’s city of residence. This information is useful for demographic analysis, geographical reporting, and to identify patterns in healthcare utilization. As no specific length is provided, we can assume it can accommodate alphanumeric characters.

3. State: The state field captures the patient’s state of residence. This information, like the city field, is crucial for demographic analysis and geographical reporting. Similarly, assuming it can accommodate alphanumeric characters due to no specific length requirement.

4. Zip: The zip field represents the patient’s zip code. It is important for generating regional statistics, analyzing healthcare resource allocation, and targeting specific populations for healthcare campaigns. As with the previous fields, the length is not provided, so we can assume it can accommodate alphanumeric characters.

5. Country: The country field captures the patient’s country of residence. While this may seem unnecessary for a standalone data dictionary entry, it could be valuable in cases where an international patient is being treated. Assuming it can accommodate alphanumeric characters.

Now, let’s move on to discussing the characteristics of data integrity.

Data integrity refers to the accuracy, consistency, and reliability of data. It ensures that the data remains complete and valid throughout its lifecycle. There are several characteristics that contribute to data integrity:

1. Accuracy: Data should be accurate, meaning it reflects the reality it is intended to represent. In the context of the sample data dictionary, accuracy would entail correctly capturing the patient’s information, such as their gender, name, and date of birth. Inaccurate data can lead to incorrect medical decisions and compromised patient safety.

2. Consistency: Consistency ensures that data remains unchanged and coherent across different systems or databases. In the sample data dictionary, consistency would mean that the patient’s identification number, gender, and other attributes remain the same in all relevant records.

3. Completeness: Completeness refers to the presence of all required data elements. In the data dictionary, ensuring completeness would involve capturing all necessary patient identifiers, demographic information, and other relevant fields. Incomplete data can hinder effective analysis and decision-making.

4. Validity: Validity refers to the adherence of data to predefined rules or constraints. In the sample data dictionary, validating the patient’s gender field to only accept “M,” “F,” or “U” ensures that the data is valid and conforms to the specified values.

5. Timeliness: Timeliness pertains to the availability of data when needed. In the context of healthcare, timely access to accurate and up-to-date patient information is crucial for providing quality care. Ensuring that the VisitDate field is captured and accurately recorded allows for appropriate tracking of when the patient was seen.

Overall, data integrity is essential in healthcare as it ensures reliable information for decision-making, supports effective patient care, and contributes to research and analysis. By following data dictionary development and maintenance practices and ensuring the accuracy, consistency, completeness, validity, and timeliness of data, HIM professionals can facilitate high-quality healthcare delivery and improve patient outcomes.