How SQL Helps Optimize Healthcare Workflows

SQL is a universal language for healthcare data management and workflow optimization. In this article, we will see how to use SQL for everything from scheduling medical appointments to monitoring vital signs.

Healthcare professionals must adhere to various processes to perform their duties. These processes, or workflows, are adapted to various situations – patient reception, diagnosis, monitoring, billing, clinical documentation, etc. While these workflows vary by type of care, setting (hospital, clinic, telemedicine), and the role of the practitioner, they all require the efficient and accurate handling of large volumes of critical information. The use of SQL for healthcare information helps satisfy that necessity.

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Healthy Information Management

The efficiency of information management in healthcare workflows has a direct impact on the quality of patient care. Let’s remember that we are talking about human lives. One piece of incorrect information or some data that does not arrive on time could make the difference between someone’s life and death.

For example, consider the case of a patient who is admitted to a hospital with a serious infection that must be treated immediately. The doctors and nurses in charge of that person’s care must be able to access their medical records without delay to find out whether they are allergic to any kind of antibiotics.

There can be no inconsistencies in a patient's medical information that could lead to doubts about medical history, medications taken, or previous diagnoses. The management of such information requires robust databases that do not permit misunderstandings. An SQL database is the ideal tool to optimize the storage, modification, and querying of this information. If in doubt, just read this article on how SQL helps the healthcare sector. You can also read this article on how SQL can help everyone in your office.

Use Cases of SQL in Healthcare

In addition to its large volume, the information handled in healthcare workflows has another distinctive characteristic: its diversity. Let’s consider three common information workflows in healthcare that differ substantially. (There are many more, but we will take these three as test cases to represent three different forms of healthcare data management).

1. Administrative information for medical coverage and social assistance:

• Data must be perfectly structured to be used effectively.
• Data integrity and consistency are critical.
• Data must be able to be stored and queried quickly (although the speed of data storage and retrieval is not in itself mission-critical to this domain).

2. Information from patient monitoring sensors:

• Data structures are often simple; data may even be unstructured.
• Data capture, storage, and retrieval must be zero latency.
• Continuous data flows, with no limitations in terms of data volume and extent, are essential.

3. Information for epidemiological analysis:

• Data structures suitable for analytical processes are required
• Support for large volumes of data – usually stored in data warehouses or data lake-style repositories – is mission-critical.
• Data storage and workflows must facilitate data querying using analytical tools.

The three use cases mentioned above represent very different healthcare workflows. And each one demands different information management tools:

1. Administrative information management requires relational and normalized databases suitable for online transaction processing (OLTP).
2. Monitoring sensor information requires real-time databases suitable for handling unstructured data; one example is TimescaleDB.
3. Epidemiological analysis requires data repositories suitable for online analytical processing (OLAP) and specific AI tools for data analysis workflows.

Although the above use cases each require a different database technology, they all have one thing in common: they use SQL as the universal language for querying and manipulating data. If you still don’t have a clear idea of what SQL is, then the best way for you to learn SQL is this guide for beginners.

SQL: One Language to Manage Them All

Now you can probably see why, understanding SQL is becoming essential for healthcare professionals who rely on data to do their jobs. As a language used for all types of databases, SQL plays an essential role in unifying the way data from different systems is manipulated and queried.

Some of the advantages of using SQL in healthcare operations include:

• Facilitating communication between different areas by providing a common data manipulation and query language.
• Avoiding errors due to inaccurate, inconsistent, or missing information.
• Enabling data-driven decision-making.
• Making it possible to obtain and process data within the time allowed by each workflow.

Examples of Using SQL for Healthcare

Next, we will see how using SQL solves some common needs in healthcare workflows. Starting with the management of patient records, we’ll see a few basic SQL query examples applied to real-life situations.

Querying a Patient’s Medical Records

For efficient management of patient records, it is vital to have a normalized database schema to prevent inconsistent information or with lack of integrity. The following entity-relationship diagram (ERD) shows a basic schema for patient records.

This schema represents the tables in a fictional medical practice database. We can write some queries against this database to obtain relevant patient information.

As an example, let’s see a query that will return the details of all the medical procedures performed on the patient whose ID is 12345:

SELECT
    p.last_name AS patient_last_name,
    p.first_name AS patient_first_name,
    mp.date_performed,
    mp.description AS practice_description,
    d.last_name AS doctor_last_name,
    d.first_name AS doctor_first_name,
    mp.outcome
FROM Medical_Practice AS mp
INNER JOIN Patient p 
  ON p.patient_id = mp.patient_id
INNER JOIN Doctor d 
  ON d.doctor_id = mp.doctor_id
WHERE p.patient_id = 12345;

The above query joins the Medical_Practice table with the Patient and Doctor tables using fields with shared information (patient_id and doctor_id, respectively). It then looks for the information specified after the SELECT statement. Finally, it applies the condition that only rows where the patient_id is 12345 should be included in the results. Finally, the query returns this patient’s first and last name, the date and description of each procedure they’ve had, and the first and last name of the responsible physician.

Once we have that information, we may also need to get the details of the medication provided to that same patient. To do that, we should run this query:

SELECT
    mp.date_performed AS date_supplied,
    d.drug_name,
    m.dosage
FROM Medication AS m
INNER JOIN Drug AS d 
  ON d.drug_id = m.drug_id
INNER JOIN Medical_Practice AS mp 
  ON mp.practice_id = m.practice_id
WHERE mp.patient_id = 12345;

In this case, we join the Medication table with the Drug and Medical_Practice tables to obtain a list of the drugs administered to the patient with id = 12345. This gives us the date each drug was administered along with its name and dosage.

This schema is very basic; it probably wouldn’t contain all the information needed to store a patient’s complete medical records. But it’s a starting point to a real patient information schema; you can add tables and columns until all the necessary details are stored. The main thing for this type of schema is to maintain normalization; this protects against inconsistencies that could affect the integrity of patients’ information.

Analyzing Epidemiological Information

Let’s examine how SQL can be used to obtain statistical data from epidemiological information. To do this, we’ll use SQL window functions – an advanced and relatively recent addition to SQL –to detect trends in time series analysis.

Window functions perform calculations on related sets of rows (called “windows”). They allow you to have aggregated data (e.g. statistics from a whole group of patients) alongside individual data (e.g. results from just one patient) in the same query. This makes them very useful for creating SQL reports and for analyzing data.

The following is a very simple table named Daily_Cases; it contains information on new daily cases of a disease. The table has two columns: measure_date and new_cases.

For each date, we’ll use a rolling total to determine the number of new cases over the previous five days. If that rolling subtotal presents an increasing value, it could mean the disease is becoming epidemic. Here’s the query:

SELECT
    measure_date,
    new_cases,
    SUM(new_cases) OVER (
        ORDER BY measure_date
        ROWS BETWEEN 4 PRECEDING AND CURRENT ROW
    ) AS five_day_sum
FROM Daily_Cases;

The results of the above query add a column called five_day_sum that shows a subtotal of the new cases registered on each day plus the four previous days (5 days in total):

Note that the data in the new column only make sense from the fifth row onwards; in the first four rows, the subtotals only include values from the preceding 1,2, and 3 days respectively.

This query is just a very simple example of how SQL can enhance critical decision making, playing a crucial part in every data analysis lifecycle.

Once you are proficient in SQL, keep this SQL for Data Analysis Cheat Sheet at hand in case you need help with some easy-to-forget SQL statement.

Monitoring Vital Signs in Real Time

Monitoring a patient’s vital signs does not allow for delays; this information is extremely critical.  Clinical data such as heart rate, blood pressure, and oxygen saturation, among others, are captured in real-time and need to be processed immediately to alert medical staff to any irregularities.

Traditional relational databases, such as MySQL, PostgreSQL, or SQL Server, are not the preferred choice for real-time applications. While they cope well with large volumes of data and general-purpose applications, real-time processing isn’t their strength. Other database engines – such as TimescaleDB, Redis, or InfluxDB – offer the low latency and high availability essential for handling time-series data from vital sign sensors.

As we said above, SQL is the universal database language; it can also be used to query the databases used for vital sign monitoring. The following is an example of a query on a database in TimescaleDB. This database engine uses a variant of SQL that has special extensions for time series data and real-time events.

Suppose we have a table called vital_signs with the columns measurement_time and heart_rate. The latter stores a patient’s heart rate data, which is obtained by periodically reading a sensor. To obtain the average hourly heart rate over the last 24 hours, we could use the following query:

SELECT
    time_bucket('1 hour', measurement_time) AS hour,
    AVG(heart_rate) AS avg_heart_rate
FROM vital_signs
WHERE measurement_time >= NOW() - INTERVAL '24 hours'
GROUP BY hour
ORDER BY hour;

TimescaleDB’s time_bucket() function is used to group data into time intervals. While other SQL operations and functions can be used for the same purpose, time_bucket() is optimized to handle time series efficiently. This is critical when working with such sensitive real-time information as a patient’s vital signs.

Optimizing Administrative Workflows

When it comes to healthcare administrative workflows, SQL has a lot to offer. Here are just a few areas where it can optimize admin work:

• Medical schedule management.
• Hospital bed assignment.
• Medical cost billing.
• Pharmacy and hospital supplies stock management.
• And many other applications.

As an example, let’s see an example database schema for a medical appointment book. We’ll run an SQL query to see the availability of appointments during the next few days. After that, we’ll see another example of bed management in a hospital.

The following query uses all the tables from the above schema to display doctors’ appointment schedules for the next seven days. The results show:

• Each doctor’s last and first name.
• The appointment date.
• The appointment starting and ending times.
• The patient’s last and first name.
• The reason for the appointment.
• The appointment status.

The results are displayed sorted by physician’s last and first name, appointment date, and appointment time.

Here’s the query:

SELECT
    d.last_name AS doctor_last_name,
    d.first_name AS doctor_first_name,
    a.appointment_date,
    ds.start_time,
    ds.end_time,
    p.last_name AS patient_last_name,
    p.first_name AS patient_first_name,
    a.reason,
    s.description AS status
FROM Appointment AS a
JOIN Doctor d 
  ON d.doctor_id = a.doctor_id
JOIN DoctorSchedule AS ds 
  ON ds.doctor_id = d.doctor_id 
     AND ds.schedule_id = a.schedule_id
JOIN Patient AS p 
  ON a.patient_id = p.patient_id
JOIN AppointmentStatus AS s 
  ON s.status_id = a.status_id
WHERE a.appointment_date BETWEEN NOW() 
   AND NOW() + INTERVAL '7 DAY'
ORDER BY
    d.last_name, 
    d.first_name, 
    a.appointment_date, 
    ds.start_time;

Hospital Bed Management

Let’s see how SQL can perform a similar task: Showing which hospital beds are available. Below we have a database schema for managing beds in a hospital. Its tables store data about beds, rooms, patients, and bed assignments.

A hospital manager could run the following SQL query to obtain a list of unoccupied intensive care beds:

SELECT
    b.bed_id,
    b.bed_number,
    r.room_number,
    bt.description AS bed_type
FROM Beds AS b
JOIN Rooms AS r 
  ON b.room_id = r.room_id
JOIN BedType AS bt 
  ON bt.type_id = b.type_id
WHERE b.is_available = TRUE
    AND b.bed_type = 'ICU'
ORDER BY 
  r.room_number, 
  b.bed_code;

The conditions in the WHERE clause of the above query ensure that it only displays data from beds that are not occupied and are in the ICU (intensive care unit).

SQL, the Great Integrator

From the examples we’ve seen throughout this article, it’s clear that they have many aspects in common – even though each one fits a different healthcare workflow.

For example, the Patient and Doctor tables are present in most of the schemas. The integration of different schemas is critical to optimizing healthcare workflows, as cross-consulting between different systems expands the information provided by each system, resulting in better patient care. With reliable and timely access to relevant data, healthcare professionals can make more informed decisions and better coordinate treatment, and SQL is critical to making that integration possible.

The integration of shift management and medical record information enables optimization of the use of patient care resources, prioritizing patients who need urgent care and rescheduling appointments for patients who have not completed certain treatments or who need reevaluation.

Likewise, by analyzing the vital signs monitoring data of a patient admitted to a hospital along with the patient’s medical history, faster and more accurate diagnoses can be made, detecting indicators of severity that would go unnoticed if correlations were not made between data from different workflows.

Healthcare organizations compete to provide better health services; undoubtedly, good information management is a key to success.

Not sure where to start from? This course on SQL Basics is what you’re looking for. It just takes 10 hours to complete and teaches you everything you need to get up and running with SQL, including how to build basic reports from scratch, how to write complex WHERE conditions, how to join tables, and many other features. You can practice the learned skills on over 129 interactive exercises and get a certificate of completion.

Will You Take Advantage of SQL for Healthcare?

Throughout this article, we have seen how using SQL for healthcare helps optimize workflows. We’ve examined several examples that fit the needs of hospital administration, epidemiological analysis, patient monitoring, and other areas. We’ve seen that, regardless of the particular characteristics of the information handled by each area, SQL is a common factor to all.

In my personal experience, I have used (and continue to use today) SQL queries and statements intensively in my daily work in healthcare applications. For example, I use it to administer social security lists, update drug prices, and track patient care costs in clinics and hospitals.

In-depth knowledge of SQL has given me key benefits in maximizing my efficiency in the manipulation of healthcare data. In my talks with healthcare professionals, I always encourage them to learn how to work with databases and SQL. It can be very beneficial for them as well as helping them do their jobs. And I tell them that SQL may not be an essential job requirement for healthcare professionals today, but it probably will be in the near future.

That’s why I encourage anyone whose work is related to healthcare to explore LearnSQL courses for more in-depth learning. Remember that our All Forever Package is an unbeatable option in terms of cost-to-benefit for those looking to dive deep into SQL in healthcare data management.