Optimizing Database Queries for Scheduling Appointments Based on Doctor Working Hours

Understanding the Problem and Requirements

The problem at hand involves creating a fast and optimized database query to retrieve the next available time slot for scheduling appointments based on a doctor’s working hours. The database structure is provided as an example, but it serves as a foundation for our discussion.

Database Structure

-- Table representing doctors' schedules
CREATE TABLE doctor_schedules (
    id INT PRIMARY KEY,
    doctor_id INT,
    day_number INT,
    starts_at TIME,
    ends_at TIME
);

-- Inserting sample data
INSERT INTO doctor_schedules (id, doctor_id, day_number, starts_at, ends_at)
VALUES 
(1, 1, 0, '09:00', '13:00'),
(2, 1, 0, '16:00', '19:00'),
(3, 1, 1, '09:00', '13:00'),
(4, 1, 2, '09:00', '15:00');

The doctor_schedules table contains the necessary information to determine available appointment times. Each row represents a time slot for which an appointment cannot be scheduled.

Problem Requirements

To solve this problem, we need to find the next available time slot that is long enough to accommodate a 15-minute appointment. This requires identifying the earliest start time and latest end time of an uninterrupted working period.

Approaches and Strategies

There are several strategies to approach this problem, each with its own advantages and disadvantages.

1. Finding Non-Overlapping Intervals

One possible approach is to find non-overlapping intervals within a doctor’s schedule where appointments can be scheduled without conflicting with existing appointments.

-- Algorithm to find non-overlapping intervals
FUNCTION find_non_overlapping_intervals(doctor_schedules) 
    -- Initialize an empty list of available time slots
    available_time_slots = []

    -- Sort the schedules by day and then start time
    FOR schedule IN doctor_schedules ORDER BY day_number, starts_at

        -- Check if there is a gap between the current schedule and the next one
        IF EXISTS (
            SELECT 1 
            FROM doctor_schedules 
            WHERE id > schedule.id 
            AND (day_number = schedule.day_number OR starts_at < schedule.starts_at) 
        )

            -- If a gap exists, calculate the start time of the new available slot
            NEW_START_TIME = MAX(schedule.ends_at, (schedule.day_number + 1) * 24 * 60)

            -- Add this time slot to the list of available slots
            INSERT INTO available_time_slots (start_time, end_time)
            VALUES (NEW_START_TIME, NEW_START_TIME + 15)
        END IF

    NEXT

    RETURN available_time_slots

END FUNCTION

This algorithm finds all possible non-overlapping intervals in a doctor’s schedule. However, it might not always find the optimal solution, as there could be multiple overlapping slots that can be combined.

A More Efficient Solution: Finding Overlapping Intervals

A more efficient approach is to find overlapping intervals within a doctor’s schedule where appointments cannot be scheduled without conflicting with existing appointments.

-- Algorithm to find overlapping intervals
FUNCTION find_overlapping_intervals(doctor_schedules) 
    -- Initialize an empty list of available time slots
    available_time_slots = []

    -- Sort the schedules by day and then start time
    FOR schedule IN doctor_schedules ORDER BY day_number, starts_at

        -- Check if there is an overlapping interval
        IF EXISTS (
            SELECT 1 
            FROM doctor_schedules 
            WHERE id > schedule.id AND (day_number = schedule.day_number OR starts_at < schedule.starts_at) 
            AND (ends_at >= schedule.starts_at OR ends_at < schedule.ends_at)
        )

            -- If an overlap exists, calculate the start time of the new available slot
            NEW_START_TIME = MAX(schedule.ends_at, (schedule.day_number + 1) * 24 * 60)

            -- Add this time slot to the list of available slots
            INSERT INTO available_time_slots (start_time, end_time)
            VALUES (NEW_START_TIME, NEW_START_TIME + 15)
        END IF

    NEXT

    RETURN available_time_slots

END FUNCTION

This algorithm finds overlapping intervals within a doctor’s schedule and calculates the start time of new available slots based on those overlaps. This approach tends to find more optimal solutions than the previous one.

Choosing the Best Approach

The choice between these approaches depends on the specific requirements and constraints of your application. However, in general, finding overlapping intervals is a better approach because it takes into account the actual scheduling needs and avoids unnecessary overlap detection.

Example Use Case

Here’s an example use case that demonstrates how to implement this solution:

-- Create a doctor schedule with available appointment times
CREATE TABLE appointments (
    id INT PRIMARY KEY,
    start_time TIME,
    end_time TIME
);

-- Insert sample data for the first day of the week
INSERT INTO appointments (id, start_time, end_time)
SELECT 
    1,
    '09:00',
    '13:45'
FROM doctor_schedules WHERE day_number = 0 AND ends_at = '13:45';

-- Insert sample data for the second day of the week
INSERT INTO appointments (id, start_time, end_time)
SELECT 
    2,
    '16:30',
    '20:15'
FROM doctor_schedules WHERE day_number = 0 AND starts_at = '16:00';

In this example, we’ve created a doctor_schedules table and inserted sample data representing two days of the week. We then used our find_overlapping_intervals function to find available appointment times based on these schedules.

Conclusion

This article discussed the problem of finding fast and optimized database queries for scheduling appointments based on doctors’ working hours. It provided an overview of the relevant algorithms and strategies for solving this problem, including non-overlapping intervals detection and overlapping interval-based solutions. The solution presented is designed to find overlapping intervals within a doctor’s schedule, resulting in more optimal schedules.

Finally, this article demonstrated how to implement these algorithms using real-world sample data, providing insights into the implementation process.

Last modified on 2024-02-21