Understanding the Problem Statement

As a technical blogger, I’m here to help you tackle a challenging SQL query problem. The question at hand involves returning patients who have not had a “Kept” (K) appointment in the last 3 years from a proprietary database that is closest to MySQL.

The given tables are PATIENT and APPT, with the PATUNIQUE column serving as the primary key for both tables. The APPT table has multiple status codes, but we’re only interested in the ‘K’ status.

Breaking Down the Query

The original query is provided below:

SELECT p.PATUNIQUE, a.BILLUNIQUE as "Acct#", a.STATUS, a.DATE
FROM PATIENT p
INNER JOIN APPT a
ON p.PATUNIQUE = a.PATUNIQUE
WHERE (a.DATE >= CURDATE()-1095 AND a.DATE < CURDATE())
AND (a.LOCATION = :LOCATION)
AND (a.PATUNIQUE NOT IN 
    (SELECT **ap.PATUNIQUE, COUNT(ap.STATUS)**
    FROM APPT ap 
    WHERE ap.STATUS in ('K')
    Group by ap.PATUNIQUE))
ORDER BY a.DATE

The query attempts to address the problem by joining the PATIENT and APPT tables on the PATUNIQUE column. It then filters the results based on several conditions:

• The appointment date is within the last 3 years, calculated using the CURDATE() function.
• The location matches the provided :LOCATION.
• The patient does not have any “K” appointments in the past 3 years.

However, there are a couple of issues with this query. Firstly, it uses an inner join, which would exclude patients who do not have any ‘K’ appointments. Secondly, the use of subqueries for filtering might be problematic if the database does not support correlated subqueries or is limited in its SQL functionality.

Alternative Approaches

The answer provided by the Stack Overflow user suggests two alternative approaches to tackle this problem.

Using NOT EXISTS

SELECT p.*
FROM patient p
WHERE NOT EXISTS (
    SELECT 1 
    FROM appt a
    WHERE 
        a.patunique = p.patunique 
        and a.location = :location 
        and a.date >= current_date - interval 3 years
        and a.status = 'K'
)

This approach uses the NOT EXISTS clause to exclude patients who have at least one “K” appointment in the past 3 years. The key feature of this method is the correlated subquery, which allows access to columns defined in the outer FROM clause (p.patunique). However, it’s essential to verify if your database supports this feature.

Using Anti-Left Join

SELECT p.*
FROM patient p
LEFT JOIN appt a 
    on  a.patunique = p.patunique 
    and a.location = :location 
    and a.date >= current_date - interval 3 years
    and a.status = 'K'
WHERE a.patunique IS NULL

This approach uses an anti-left join, where the pattern involves joining with a “K” appointment (LEFT JOIN) and then filtering out rows where there was no match (WHERE clause). Although this method might be less efficient than using NOT EXISTS, it’s still worth considering as a viable alternative.

Understanding Not EXISTS and Anti-Left Join

Let’s dive deeper into these two approaches, discussing the inner workings of both methods in detail.

NOT EXISTS

The NOT EXISTS clause is a standard SQL construct that allows you to exclude rows from your query result based on whether a corresponding row exists in another table or set. In this case, we’re using it to check if there’s at least one “K” appointment for each patient within the last 3 years.

Here are some key aspects of the NOT EXISTS clause:

• Correlated subquery: The SQL in the NOT EXISTS clause has access to columns defined in the outer FROM clause. In this case, we’re using p.patunique for correlation.
• Simplification: Using NOT EXISTS can simplify your query and make it more readable.

However, there are some potential limitations:

• Database support: Some databases may not support correlated subqueries or have specific requirements for their usage. In this case, we need to verify if the database supports this feature.
• Performance impact: Depending on the database and the complexity of your query, NOT EXISTS might have a slightly slower performance compared to other methods.

Anti-Left Join

An anti-left join is similar to an inner left join but is used when you want to exclude rows that don’t match any records in the other table. In this case, we’re using it to filter out patients who have at least one “K” appointment within the last 3 years.

Here’s a breakdown of how it works:

• Anti-left join pattern: We’re trying to join with a “K” appointment (LEFT JOIN) and then filtering out rows where there was no match.
• Simplification: Anti-left joins can simplify your query, making it more concise and easier to read.

However, anti-left joins also have some potential limitations:

• Performance impact: Depending on the database and the complexity of your query, an anti-left join might be less efficient than other methods.
• Limited flexibility: You’re limited to joining with a specific condition (a.date >= current_date - interval 3 years).

Choosing the Right Approach

When deciding between NOT EXISTS, anti-left joins, or other approaches like inner joins or subqueries, consider the following factors:

• Database support: Verify that your database supports correlated subqueries and the necessary features for the approach you choose.
• Query performance: Evaluate the potential performance impact of each method on your query. Some methods might be faster or slower depending on the database and data complexity.
• Readability and conciseness: Consider how readable and concise each approach is, as these factors can also influence your decision.

By carefully evaluating these factors and understanding the inner workings of NOT EXISTS and anti-left joins, you can make an informed choice about which method to use for this particular problem.

Last modified on 2023-05-19