At Teladoc Health, SQL does the heavy lifting for analyzing telehealth data trends and optimizing patient health record management. Unsurprisingly this is why Teladoc Health often tests SQL questions in interviews for Data Science, Data Engineering and Data Analytics jobs.
As such, to help you ace the Teladoc Health SQL interview, we'll cover 11 Teladoc SQL interview questions – can you answer each one?
Given a dataset, Write a SQL query to find the total number of consultations each doctor has had each month, and the average rating they've received for these consultations. Also, the doctor's total number of consultations and their average rating to date for all preceding months.
consultation_id | doctor_id | consultation_date | rating |
---|---|---|---|
111 | 867 | 2022-06-08 | 4 |
112 | 442 | 2022-06-10 | 3 |
113 | 867 | 2022-06-18 | 5 |
114 | 442 | 2022-07-26 | 2 |
115 | 867 | 2022-07-05 | 3 |
We can solve this problem with a SQL query using the clause, which is a window function. Window functions perform a calculation across a set of table rows that are somehow related to the current row.
This SQL window function calculates the total number of consultations and average rating by doctor for each month (, ) and provides running totals (, ) of the monthly consultation counts and ratings from the beginning of the data set to the current row, inclusive. This data can be useful in tracking the monthly and overall performance of each doctor.
For more window function practice, try this Uber SQL problem within DataLemur's online SQL code editor:
Teladoc Health offers telemedicine services, allowing patients to consult with healthcare professionals remotely over video call or voice call. They are interested in understanding the patients' interaction with their services and the type of health issues reported.
In this scenario, you are given two tables: and . Our task is to find out how many patients consulted for each health issue category each month.
The table stores patient information,with each row representing a patient. The columns are , , and .
The table represents each consultation a patient made, with columns , , , and .
patient_id | patient_name | age |
---|---|---|
1 | John | 23 |
2 | Emma | 45 |
3 | Aiden | 39 |
4 | Olivia | 27 |
consultation_id | patient_id | consult_date | health_issue |
---|---|---|---|
1001 | 1 | 08/12/2023 | Fever |
1002 | 3 | 12/14/2023 | Diabetes |
1003 | 1 | 12/19/2023 | Cold |
1004 | 2 | 01/02/2024 | Fever |
1005 | 4 | 01/10/2024 | Diabetes |
query_month | health_issue | num_patients |
---|---|---|
2023-12 | Fever | 1 |
2023-12 | Diabetes | 1 |
2023-12 | Cold | 1 |
2024-01 | Fever | 1 |
2024-01 | Diabetes | 1 |
Here's the SQL query to generate the required information.
Our approach here is to make use of the function to get the month, and group by and . We use distinct count of to get the number of unique patients who consulted for each health issue in a particular month. The result is ordered by in descending order.
A is a field in a table that references the of another table. It creates a link between the two tables and ensures that the data in the field is valid.
For example, if you have a table of Teladoc Health customers and an orders table, the customer_id column in the orders table could be a that references the id column (which is the primary key) in the Teladoc Health customers table.
The constraint helps maintain the integrity of the data in the database by preventing the insertion of rows in the table that do not have corresponding entries in the table. It also enforces the relationship between the two tables and prevents data from being deleted from the table if it is still being referenced in the table.
As an analyst at Teladoc Health, you are tasked with determining the average duration of medical consultations conducted over a specific period. For each doctor, calculate the average call duration for consultations in the month of October 2022.
consultation_id | doctor_id | patient_id | start_time | end_time |
---|---|---|---|---|
1001 | 789 | 1201 | 10/04/2022 09:15:00 | 10/04/2022 09:45:00 |
1002 | 789 | 1035 | 10/07/2022 15:30:00 | 10/07/2022 15:50:00 |
1003 | 345 | 1290 | 10/10/2022 11:00:00 | 10/10/2022 11:30:00 |
1004 | 345 | 1109 | 10/15/2022 14:00:00 | 10/15/2022 14:25:00 |
1005 | 561 | 1345 | 10/20/2022 10:30:00 | 10/20/2022 11:00:00 |
doctor_id | avg_duration_minutes |
---|---|
789 | 25.00 |
345 | 27.50 |
561 | 30.00 |
This query works by first calculating the duration of each consultation in minutes using the function to get the epoch (unix timestamp), which is the number of seconds since '1970-01-01 00:00:00' UTC. The difference between the and gives the consultation duration in seconds, which is then divided by 60 to convert to minutes. The function is then used to compute the average duration for each doctor. The condition on restricts the consultations to those conducted in October 2022.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating total duration with datetime fields or this Verizon International Call Percentage Question which is similar for calculating a percentage within a set time period.
As the name implies, the window function ranks each row within your window/partition. If two rows have the same rank, the next number in the ranking will be the previous rank plus the number of duplicates. For example, if you've got three records tied for 5th place, they'll get the values 5, 6, and 7, and the next row after this 3-way tie will have a rank of 8.
The function assigns a distinct rank to each row within a partition based on the provided column value with no gaps. This function will assign the same rank to two rows if they have the same rank, and give the next row the next rank number. To make this more concrete, imagine you had three records at rank 5 – then, the next rank would be 6.
Suppose, as a data analyst at Teladoc Health, you are required to find out the average consultation time by each doctor for every month.
Useful for the company: This data can provide insights about the doctors' capacity, allowing the company to better manage appointments and waiting times.
consultation_id | doctor_id | consultation_date | consultation_time_in_minutes |
---|---|---|---|
101 | 1 | 06/01/2022 00:00:00 | 30 |
102 | 2 | 06/07/2022 00:00:00 | 20 |
103 | 1 | 06/15/2022 00:00:00 | 25 |
104 | 2 | 06/20/2022 00:00:00 | 30 |
105 | 1 | 07/02/2022 00:00:00 | 35 |
mth | doctor_id | avg_consultation_time |
---|---|---|
6 | 1 | 27.5 |
6 | 2 | 25.0 |
7 | 1 | 35.0 |
The above SQL query accesses average consultation time for each doctor in each month by extracting the month from the field, and using an function to calculate the average of . The clause groups the results by the month and to provide a clear, easily understandable output.
Constraints are used to specify the rules concerning data in the table. It can be applied for single or multiple fields in an SQL table during the creation of the table or after creating using the ALTER TABLE command. The constraints are:
Say you were storing sales analytyics data from Teladoc Health's CRM inside a database. Here's some example constraints you could use:
PRIMARY KEY constraint: You might use a PRIMARY KEY constraint to ensure that each record in the database has a unique identifier. For example, you could use the "opportunity_id" field as the primary key in the "opportunities" table.
FOREIGN KEY constraint: You might use a FOREIGN KEY constraint to link the data in one table to the data in another table. For example, you could use a foreign key field in the "opportunities" table to reference the "account_id" field in the "accounts" table.
NOT NULL constraint: You might use a NOT NULL constraint to ensure that a field cannot contain a NULL value. For example, you could use a NOT NULL constraint on the "opportunity_name" field in the "opportunities" table to ensure that each opportunity has a name.
UNIQUE constraint: You might use a UNIQUE constraint to ensure that the data in a field is unique across the entire table. For example, you could use a UNIQUE constraint on the "email" field in the "contacts" table to ensure that each contact has a unique email address.
CHECK constraint: You might use a CHECK constraint to ensure that the data in a field meets certain conditions. For example, you could use a CHECK constraint to ensure that the "deal_probability" field in the "opportunities" table is a value between 0 and 100.
DEFAULT constraint: You might use a DEFAULT constraint to specify a default value for a field. For example, you could use a DEFAULT constraint on the "stage" field in the "opportunities" table to set the default value to "prospecting"
Suppose you are working with Teladoc Health's patient records database and your task is to find patient records where the described symptoms match a specific pattern. For this question, please write an SQL query that retrieves all patient records with 'Cough' mentioned anywhere in the symptoms column.
patient_id | name | admission_date | symptoms |
---|---|---|---|
101 | John | 01/10/2022 00:00:00 | Cough, Fever, Sore throat |
102 | Anna | 02/18/2022 00:00:00 | Headache, Dizziness |
103 | Sam | 03/16/2022 00:00:00 | Cough, Chest pain |
104 | Nicole | 04/20/2022 00:00:00 | Fever, Fatigue |
105 | Troy | 05/19/2022 00:00:00 | Cough, Difficulty in breathing |
patient_id | name | admission_date | symptoms |
---|---|---|---|
101 | John | 01/10/2022 00:00:00 | Cough, Fever, Sore throat |
103 | Sam | 03/16/2022 00:00:00 | Cough, Chest pain |
105 | Troy | 05/19/2022 00:00:00 | Cough, Difficulty in breathing |
This SQL query works by scanning the 'symptoms' column in the 'patients' table for any entries that contain the string 'Cough'. The percent sign (%) is a wildcard character in SQL that can represent zero, one, or multiple characters. This allows the query to find 'Cough' whether it is at the beginning, middle, or end of the symptoms text.
At Teladoc Health, we have a database with information about customers and their appointments with healthcare professionals. For our analysis, we are specifically interested in analyzing the duration of online appointments in relation to the specialist's area.
Within our database, we have two tables: and .
The table has the following structure:
customer_id | first_name | last_name | membership_date | |
---|---|---|---|---|
3021 | Jane | Doe | 01/02/2019 | jane.doe@email.com |
4025 | John | Smith | 05/18/2020 | john.smith@email.com |
5278 | Mary | Johnson | 08/08/2018 | mary.johnson@email.com |
6352 | James | Brown | 03/25/2021 | james.brown@email.com |
8517 | Patricia | Garcia | 11/17/2020 | patricia.garcia@email.com |
The table has the following structure:
appointment_id | customer_id | specialist_area | appointment_date | appointment_duration_min |
---|---|---|---|---|
5171 | 3021 | Cardiology | 06/28/2022 10:00:00 | 30 |
7802 | 4025 | Dermatology | 06/29/2022 16:00:00 | 15 |
8293 | 5278 | Psychiatry | 07/02/2022 13:00:00 | 45 |
9352 | 6352 | Orthopedics | 07/06/2022 14:00:00 | 20 |
3247 | 8517 | Dermatology | 07/07/2022 11:00:00 | 15 |
The task is to write a SQL query that will join these two tables and compute the total duration (in minutes) of online appointments, grouped by specialist_area and month of the appointment, for each calendar year.
This PostgreSQL query joins the customers and appointments tables on the customer_id field. It then groups the data by year, month, and specialist area, and sums up the total appointment duration for each group. The resulting data is ordered by year, month, and total duration in descending order. This will give us an overview of the total duration of appointments split by timeline and specialist area. This information can be useful to analyze resource utilization and can further assist in decision making related to capacity planning and scheduling.
Because join questions come up frequently during SQL interviews, try this Snapchat JOIN SQL interview question:
A foreign key is a field in a database table that links to the primary key of another table, establishing a connection between the two tables. For example, let's analyze Teladoc Health's Google Ads campaigns data:
:
+------------+------------+------------+------------+ | ad_id | campaign_id| keyword | click_count| +------------+------------+------------+------------+ | 1 | 201 | Teladoc Health reviews | 120 | | 2 | 202 | Teladoc Health pricing | 150 | | 3 | 101 | buy Teladoc Health | 65 | | 4 | 101 | Teladoc Health alternatives | 135 | +------------+------------+------------+------------+
is a foreign key that connects to the of the corresponding Google Ads campaign. This establishes a relationship between the ads and their campaigns, enabling easy querying to find which ads belong to a specific campaign or which campaigns a specific ad belongs to.
The table may also have multiple foreign keys that reference primary keys in other tables. For example, and foreign keys could be used to link each ad to its ad group and the Google Ads account that the campaigns belong to, respectively.
Teladoc Health has a database containing a table of Doctors with their details and a table containing their working hours. The working hours of doctors are much like those often found in call center environments, whereby their shift can span past midnight. The database department needs to calculate the duration of their shifts. {#Question-11}
Let's consider the following tables:
doctor_id | first_name | last_name | specialization |
---|---|---|---|
1 | John | Doe | Cardiologist |
2 | Mary | Janes | Pediatric |
3 | Paul | Johnson | Dermatologist |
record_id | doctor_id | shift_start_time | shift_end_time |
---|---|---|---|
1 | 1 | 08:00:00 | 20:00:00 |
2 | 2 | 22:00:00 | 10:00:00 |
3 | 3 | 09:00:00 | 21:00:00 |
Here is a question that can make use of various PostgreSQL math functions and operators:
"How would you write a query to calculate the shift duration in hours for each doctor, taking into account that shifts can go past midnight?"
SQL Block:
The SQL query above joins the and tables on . Then it calculates the shift duration. If the shift ends on the same day (the shift end time is more than the start time), it simply subtracts the start time from end time and divides it by 3600 to convert seconds into hours. If the shift goes past midnight (the end time is less than the start time), it calculates the remaining hours from the to midnight, then adds the hours from midnight to .
Please note, the times here are assumed to be in 24-hour format and the output rounded to 2 decimal points for neatness.
To practice a very similar question try this interactive Amazon Server Utilization Time Question which is similar for calculating total time duration or this Tesla Unfinished Parts Question which is similar for data extraction based on time frame.
The best way to prepare for a Teladoc Health SQL interview is to practice, practice, practice. Besides solving the earlier Teladoc Health SQL interview questions, you should also solve the 200+ DataLemur SQL Interview Questions which come from companies like FAANG (FB, Apple, Amazon, Netflix, Google).
Each interview question has multiple hints, step-by-step solutions and best of all, there's an interactive coding environment so you can instantly run your query and have it executed.
To prep for the Teladoc Health SQL interview it is also wise to solve SQL questions from other tech companies like:
However, if your SQL skills are weak, don't worry about jumping right into solving questions – go learn SQL with this DataLemur SQL tutorial.
This tutorial covers things like using ORDER BY and Subquery vs. CTE – both of these pop up often during SQL interviews at Teladoc Health.
Besides SQL interview questions, the other types of problems to practice for the Teladoc Health Data Science Interview are:
The best way to prepare for Teladoc Health Data Science interviews is by reading Ace the Data Science Interview. The book's got: