You Will Be Designing And Creating A DBMS For The Field Of H

You will be designing and creating a DBMS for the field of Healthcare

You will be designing and creating a DBMS for the field of Healthcare. You will be creating SQL queries based on common queries that a common end-user would make to a healthcare system. In addition, identify when to use No-SQL. Not only are you building multi-table databases for different components of a healthcare system, but you will also incorporate and create image databases that store real and publicly available health data like cancer images and how databases play a role in building machine learning models. GitHub All the files will be stored and organized on GitHub.

The healthcare field has very unique data requirements and processes tons of different types of health information daily. Your project will include the following requirements:

1. Build an Entity-Relationship Diagram that incorporates all the different types of entities, actions, attributes, connecting lines, and cardinality of data within your DBMS.
2. Based on your ER Diagram, create a Relational Schema that will identify all the components of your table, highlighting your primary and foreign keys.
3. Create your DBMS within an SQL database, including:
   1. Create a database dump of your database and upload that into GitHub.
   2. Each table should have no less than 20 tuples, and you can use open-source health datasets instead of creating all the data yourself.
4. Create a list of SQL queries and document their output for common queries that an end-user might have, including:
   1. Aggregate Functions
   2. Dates
   3. SQL Logical Operators like IN, AND, LIKE, NOT, OR, ANY, BETWEEN, EXISTS, etc.
   4. Regular Expressions
   5. Formula Expressions
   6. Subqueries
5. Identify and develop infrastructure diagrams incorporating SQL and No-SQL DBMS, specifying the advantages of one over the other in your final report.
6. Identify, define, compare, and contrast the different types of databases available and their uses, creating a table for this information.
7. In your final statement, specify why you selected a specific type of database and why it meets your needs.
8. Demonstrate and document a DBMS query.

Health Data Repositories include: National Cancer Institution, Imaging Data Commons; Healthdata.gov.

Paper For Above Instructions

The advent of technology in the healthcare sector has led to significant enhancements in the management and utilization of health data. Database Management Systems (DBMS) play a crucial role in organizing, storing, and retrieving data efficiently. This paper outlines the design and creation of a DBMS tailored for the healthcare field, focusing on unique data requirements and the implementation of SQL and No-SQL databases.

Entity-Relationship Diagram (ERD)

The first step in designing a DBMS is to create an Entity-Relationship Diagram (ERD). The ERD illustrates the various entities within the healthcare domain, such as Patients, Physicians, Appointments, Medical Records, and Billing. Each entity will have specific attributes; for example, the Patient entity will include attributes such as PatientID, Name, DateOfBirth, and ContactInfo. The relationships among these entities will be defined with cardinalities to make clear how data points are interrelated. For instance, one Patient can have many Appointments, but each Appointment is associated with only one Patient.

Relational Schema

After defining the ERD, the next step is to create a Relational Schema. This schema organizes the data into tables based on the defined entities in the ERD. Each table will have primary and foreign keys. For example:

• Patients: Primary Key (PatientID)
• Appointments: Primary Key (AppointmentID), Foreign Key (PatientID)
• MedicalRecords: Primary Key (RecordID), Foreign Key (PatientID)
• Billing: Primary Key (BillID), Foreign Key (PatientID)

Every table should be populated with at least 20 tuples, ensuring the database is representative of real-world scenarios.

Creating the SQL Database

The next step in this project involves creating the DBMS within an SQL database. This includes writing scripts to create tables and ensuring data integrity through constraints. After populating the tables, a database dump will be created and uploaded to GitHub for organization and future access. The use of open-source healthcare datasets is encouraged to populate the database effectively without solely relying on fabricated data.

Common SQL Queries

Common SQL queries that an end-user might perform include:

1. Aggregate Functions: Queries that calculate summaries, such as the average age of patients or total billing.
2. Dates: Queries that analyze data based on date ranges, like retrieving all appointments from a specific month.
3. Logical Operators: Using operators such as IN, AND, LIKE to refine search results.
4. Regular Expressions: If the database supports it, queries could include validating fields like phone numbers or email addresses.
5. Formula Expressions: To calculate values such as total costs based on various charges.
6. Subqueries: Queries within queries to obtain more refined data.

No-SQL and SQL Infrastructure Diagrams

As part of the analysis of our DBMS, it's essential to understand the differences between SQL and No-SQL databases. SQL databases are relational and structured, making them suitable for predefined schemas where data integrity is critical. No-SQL databases, on the other hand, offer flexibility with unstructured data, making them advantageous for handling large volumes of data from various sources.

Comparative Analysis of Database Types

It is critical to identify the major types of databases available to ensure the correct choice for the specific healthcare use case. These include:

For this project, an SQL database is preferred due to the structured nature of patient data and the necessity for data integrity, which aligns with healthcare regulations.

Demonstrating a DBMS Query

To illustrate the practical application of the DBMS, consider a query that retrieves all appointments for a specific patient:

SELECT Appointments.AppointmentID, Appointments.Date, Appointments.Time
FROM Appointments
JOIN Patients ON Appointments.PatientID = Patients.PatientID
WHERE Patients.Name = 'John Doe';

Conclusion

The design and implementation of a DBMS for healthcare is a multifaceted endeavor that requires careful planning and execution. By combining SQL with No-SQL considerations and understanding the nuances of healthcare data, a robust system can be developed that meets both functional and regulatory requirements.

References

• National Cancer Institute. (n.d.). Imaging Data Commons. Retrieved from https://imaging.datacommons.cancer.gov
• Healthdata.gov. (n.d.). Health Data Resources. Retrieved from https://healthdata.gov
• Elmasri, R., & Navathe, S. B. (2016). Fundamentals of Database Systems. Pearson.
• Connolly, T. M., & Begg, C. E. (2015). Database Systems: A Practical Approach to Design, Implementation, and Management. Pearson.
• Rob, P., & Coronel, C. (2017). Database Systems: Design, Implementation, & Management. Cengage Learning.
• Chaudhuri, S., & Narasayya, V. (2007). Database Management Systems: Current Research and Future Directions. IEEE Data Engineering Bulletin.
• Stonebraker, M., & Çetintemel, U. (2005). “One Size Fits All”: An Idea Whose Time Has Come and Gone. Proceedings of the 21st International Conference on Data Engineering.
• Han, J., Kamber, M., & Pei, J. (2011). Data Mining: Concepts and Techniques. Morgan Kaufmann.
• Miskovic, A., & Sankar, K. (2017). Data Science for Healthcare: Methodologies and Applications. Springer.
• Weber, H. (2018). SQL and No-SQL Databases: Key Differences and Use Cases. Journal of Computer and Communications.