Create An Dss Decision Support Systemone Page Per Diagram 4 To 5 Pa ✓ Solved

Create an DSS (Decision Support System) One page per diagram, 4 to 5 pages. Must include (A) logical design, (B) the physical design, (C) background and discussion of the table contents, keys and relationships, implementation details. References and title page. Data: 4 hospitals of Kaiser, hospital table, patient table, visits by patient to a hospital table
staff table for doctors and therapists and more. (Be creative and send screenshots of some examples) A. .
 Logical Data Warehouse Design Diagram Required Elements: 1. Entity names 2.

Attributes 3. Data type suggested B. Physical Data Warehouse Model Diagram Required elements: 1. specification all tables and columns 2. foreign keys to identify relationships between tables 3. denormalization based on user requirements 4. physical considerations that cause it to be different from the logical model C. Discussion 1. Presents a clear, logical data warehouse diagram that shows what the data is in depth and includes links between data sources.

Includes all required elements. However, it does not include information on implementation. The diagram is made digitally using appropriate software and graphic techniques. Additionally, contains links to additional information that will enhance the readers understanding. 2.

Presents a clear, physical data warehouse diagram that shows how the model will be built in the database. It includes in-depth, clear and accurate information on implementation including relationships and all required elements. The diagram is made digitally using appropriate software and graphic techniques. Additionally, contains links to additional information that will enhance the readers understanding. 3.

Explanations of 2 sample queries 4. Demonstrates best practices in use of font, words on the page, and linking.

Paper for above instructions

Title: Design and Implementation of a Decision Support System for Kaiser Hospitals
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Abstract
This report discusses the design and implementation of a Decision Support System (DSS) tailored for four hospitals within the Kaiser organization. The DSS is built around a detailed logical and physical structure, focusing on enhancing patient care and hospital management through a structured data warehouse. The design aims to facilitate effective decision-making by healthcare professionals.
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1. Logical Data Warehouse Design
The logical data warehouse design outlines the key entities and their attributes required for the DSS. It utilizes an Entity-Relationship Model (ERD) to illustrate the entities related to the hospital system, including the Hospital, Patient, Visit, and Staff tables.
1.1 Entity Relationship Diagram (ERD)
- Entities and Attributes
- Hospital
- Hospital_ID (INTEGER) - Primary Key
- Name (VARCHAR)
- Location (VARCHAR)
- Total_Beds (INTEGER)
- Patient
- Patient_ID (INTEGER) - Primary Key
- Name (VARCHAR)
- Date_of_Birth (DATE)
- Gender (VARCHAR)
- Contact_Info (VARCHAR)
- Visit
- Visit_ID (INTEGER) - Primary Key
- Patient_ID (INTEGER) - Foreign Key
- Hospital_ID (INTEGER) - Foreign Key
- Visit_Date (DATE)
- Diagnosis (VARCHAR)
- Staff
- Staff_ID (INTEGER) - Primary Key
- Name (VARCHAR)
- Role (VARCHAR)
- Hospital_ID (INTEGER) - Foreign Key
The relationships are established as follows:
- Each Patient can have multiple Visits.
- Each Visit belongs to one Hospital and one Patient.
- Hospitals employ multiple Staff members.
Diagram Visualization
A visual representation of the ERD employs circles for entities connected via lines indicating relationships.
2. Physical Data Warehouse Model
The physical data warehouse model translates the logical design into a database schema, specifying tables and columns using SQL-based standards. This section delves into the table implementations, data types, and any necessary denormalization.
2.1 Database Schema Design
- Table Definitions
Creating the tables involves specifying all attributes along with their data types based on requirements. Suggestions might include:
- CREATE TABLE Hospital
```sql
CREATE TABLE Hospital (
Hospital_ID INTEGER PRIMARY KEY,
Name VARCHAR(100),
Location VARCHAR(100),
Total_Beds INTEGER
);
```
- CREATE TABLE Patient
```sql
CREATE TABLE Patient (
Patient_ID INTEGER PRIMARY KEY,
Name VARCHAR(100),
Date_of_Birth DATE,
Gender VARCHAR(10),
Contact_Info VARCHAR(150)
);
```
- CREATE TABLE Visit
```sql
CREATE TABLE Visit (
Visit_ID INTEGER PRIMARY KEY,
Patient_ID INTEGER REFERENCES Patient(Patient_ID),
Hospital_ID INTEGER REFERENCES Hospital(Hospital_ID),
Visit_Date DATE,
Diagnosis VARCHAR(150)
);
```
- CREATE TABLE Staff
```sql
CREATE TABLE Staff (
Staff_ID INTEGER PRIMARY KEY,
Name VARCHAR(100),
Role VARCHAR(50),
Hospital_ID INTEGER REFERENCES Hospital(Hospital_ID)
);
```
2.2 Relationships
The foreign keys establish relationships between tables, detailing how data interrelates and ensuring data integrity across related entities. For example, the Patient_ID in the Visits table links to the Patient table, providing a way to associate visits directly with patients.
2.3 Denormalization & Physical Considerations
Denormalization may be considered for enhancing read performance when querying patient visits by combining the Patient and Visit tables. For instance, if a query frequently requires patient's personal information alongside visit history, they could be consolidated.
3. Discussion of Tables, Keys, and Relationships
This section presents a detailed account of each table's contents, outlining how they operate and depend on one another.
3.1 Background
The Hospital table safeguards essential details regarding each hospital, facilitating location-based queries. The Patient table, serving as the core table, stores vital patient information necessary for effective healthcare delivery and follow-ups. The Visits table logs patient visits, documenting their history, while the Staff table identifies healthcare professionals and their affiliations to hospitals.
3.2 Sample Queries Detailing Implementation
A discussion on the relationships' importance reflects in the following SQL queries:
- Query 1: Retrieve all visits for a specific patient.
```sql
SELECT Visit.Visit_Date, Visit.Diagnosis, Hospital.Name
FROM Visit
JOIN Patient ON Visit.Patient_ID = Patient.Patient_ID
JOIN Hospital ON Visit.Hospital_ID = Hospital.Hospital_ID
WHERE Patient.Name = 'John Doe';
```
- Query 2: Count patient visits per hospital.
```sql
SELECT Hospital.Name, COUNT(Visit.Visit_ID) AS Total_Visits
FROM Hospital
LEFT JOIN Visit ON Hospital.Hospital_ID = Visit.Hospital_ID
GROUP BY Hospital.Name;
```
These queries exemplify how the system effectively utilizes structured data to derive insightful reports that support healthcare administrative decision-making.
4. Implementation Details
The implementation will occur in stages; initially, schema design will be executed, followed by populating the tables with real-time data. Testing will ensure data accuracy and integrity while enforcing relationships through foreign keys.
Conclusion
In conclusion, the presented design of the Decision Support System will optimize data utilization across Kaiser’s hospitals, promoting improved patient care, operational efficiency, and better decision-making by healthcare staff. Adhering to best practices, including the appropriate linking of entities and straightforward navigability of queries, will be paramount in ensuring user satisfaction.
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References
1. Kimball, R., & Ross, M. (2016). The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling. Wiley.
2. Inmon, W. H. (2005). Building the Data Warehouse. Wiley.
3. O'Brien, J. A., & Marakas, G. M. (2011). Management Information Systems. McGraw-Hill.
4. Turban, E., Sharda, R., & Delen, D. (2011). Decision Support and Business Intelligence Systems. Prentice Hall.
5. Connolly, T., & Begg, C. (2010). Database Systems: A Practical Approach to Design, Implementation, and Management. Pearson.
6. Date, C. J. (2012). Database Design: Principles and Practices. Addison-Wesley.
7. Elmasri, R., & Navathe, S. B. (2016). Fundamentals of Database Systems. Pearson.
8. Haefliger, S., & Dufour, C. (2015). Business Intelligence in Healthcare: Data Warehousing and Data Mining Techniques. Springer.
9. Prakash, S., & Kumar, S. (2015). Healthcare Data Warehousing and Mining: An Integrated Approach. Journal of Healthcare Engineering.
10. Chen, H., & Zhuang, K. (2015). Data Management: Databases and Data Warehouses in the Cloud. IEEE Transactions on Emerging Topics in Computing.