BMI 603 Health Informatics Database Modeling And Application
Bmi 603 Health Informatics Database Modeling And Applicationassignme
Review the database provided for this week’s assignment. Please generate the following data reports using SQL. All reports should be generated inside the provided database as queries. Name your queries with numbers 1 to 5 to match with the questions below. Grading will be based on correct SQL statement and the data sheet generated from each query.
A list of all patients from outside of the State of Arizona, with their first name, last name, and state. A detailed report on patient visits that occurred before the year of 2009, with patient first name, patient last name, visit date, provider first name, and provider last name. Using nested queries to generate a report on healthcare providers who provided care to Jane Doe, with their first name and last name. Using aggregate operators to generate a report on the total number of patient visits before the year of 2009. Using GROUP BY clause to generate a report on patients’ average account balance by ethnicity.
Paper For Above instruction
The following paper provides detailed solutions to the specified SQL queries based on the provided health informatics database. Each query aligns with a particular reporting requirement, employing various SQL techniques such as simple selection, nested queries, aggregate functions, and grouping. These reports serve to illustrate different methods of extracting meaningful healthcare data, which is vital for clinical analysis, administrative decision-making, and healthcare policy development.
1. List of Patients Outside of Arizona
The first report aims to identify all patients who reside outside the state of Arizona. This involves a simple SELECT statement filtering patients based on their address or state attribute. Assuming the database has a 'Patients' table with 'FirstName', 'LastName', and 'State' columns, the SQL query would look like this:
SELECT FirstName, LastName, State
FROM Patients
WHERE State 'Arizona';
This query returns a list of patients with their names and the state, excluding those from Arizona. It leverages the WHERE clause with the not-equals operator () to filter out Arizona residents. Such data is essential for state-specific healthcare planning and resource allocation.
2. Patient Visits Before 2009
The second report focuses on patient visits that occurred prior to the year 2009, providing details about patient and provider names along with visit dates. Assuming the relevant tables are 'Visits', 'Patients', and 'Providers', with 'VisitDate' in 'Visits', and foreign key relationships set up accordingly, the query might be:
SELECT p.FirstName AS PatientFirstName, p.LastName AS PatientLastName, v.VisitDate, pr.FirstName AS ProviderFirstName, pr.LastName AS ProviderLastName
FROM Visits v
JOIN Patients p ON v.PatientID = p.PatientID
JOIN Providers pr ON v.ProviderID = pr.ProviderID
WHERE v.VisitDate
This query joins multiple tables to gather comprehensive details about each qualifying visit. The date condition filters records before January 1, 2009, facilitating retrospective healthcare analyses or trend assessments.
3. Healthcare Providers Who Served Jane Doe (Nested Query)
The third report utilizes nested queries to identify healthcare providers who cared for a specific patient, Jane Doe. The innermost query finds the PatientID for Jane Doe, and the outer query retrieves provider details linked via visits:
SELECT DISTINCT pr.FirstName, pr.LastName
FROM Providers pr
WHERE pr.ProviderID IN (
SELECT v.ProviderID
FROM Visits v
JOIN Patients p ON v.PatientID = p.PatientID
WHERE p.FirstName = 'Jane' AND p.LastName = 'Doe'
);
This approach employs a subquery to filter providers associated with Jane Doe. The DISTINCT keyword ensures unique provider entries if multiple visits exist. This method demonstrates the use of nested queries for relational data navigation.
4. Total Number of Visits Before 2009 (Aggregate Functions)
The fourth report calculates the total number of patient visits that occurred before 2009, employing aggregate functions such as COUNT(). The corresponding query:
SELECT COUNT(*) AS TotalVisitsBefore2009
FROM Visits
WHERE VisitDate
This simple yet effective query provides a count of qualifying visits, aiding in workload measurement, capacity planning, or historical trend analysis concerning healthcare utilization before 2009.
5. Average Patient Account Balance by Ethnicity (GROUP BY)
The fifth report groups patients by ethnicity and computes the average account balance for each group. Assuming the 'Patients' table includes 'Ethnicity' and 'AccountBalance', the SQL statement is:
SELECT Ethnicity, AVG(AccountBalance) AS AverageBalance
FROM Patients
GROUP BY Ethnicity;
This query summarizes financial data across different ethnic groups, highlighting disparities or informing targeted health programs. The GROUP BY clause clusters data, and AVG() computes the mean balances, valuable for socioeconomic healthcare analyses.
Conclusion
The assembled queries demonstrate core SQL capabilities essential for healthcare database management: filtering data with WHERE, joining tables with JOIN, using nested queries for complex filtering, applying aggregate functions for summarization, and grouping data with GROUP BY for comparative analysis. These techniques collectively empower healthcare professionals and administrators to extract actionable insights, enhance decision-making, and support evidence-based practices.
References
- Coronel, C., & Morris, S. (2015). Database Systems: Design, Implementation, & Management. Cengage Learning.
- Relational Database Design and Implementation. Morgan Kaufmann.