At Humana, SQL is used for analyzing patient data, helping them design and implement effective health programs that cater to diverse patient populations. They also use SQL to manage large insurance databases, ensuring that policy management is efficient and streamlined for better customer service, tat is the reason why Humana asks SQL questions during interviews for Data Science, Data Engineering, and Data Analytics jobs.
As such, to help you prepare for the Humana SQL interview, we'll cover 10 Humana SQL interview questions – can you solve them?
Humana is a healthcare company, so a power user, in this context, could be defined as a customer who frequently enrolls in high-cost plans.
Suppose we have a customer database that holds customer details, plan details, and their enrollment details in three separate tables. Your task is to write a SQL query to retrieve the top 10 customers () who have enrolled the most number of times in the 'Premium' or 'Ultra-Premium' plans in the last year.
The following are the sample tables:
user_id | customer_name | sign_up_date |
---|---|---|
1 | John Doe | 01/01/2019 |
2 | Jane Smith | 05/12/2020 |
3 | Jason Bourne | 22/07/2018 |
plan_id | plan_name | plan_cost |
---|---|---|
100 | Basic | 120 |
101 | Premium | 220 |
102 | Ultra-Premium | 320 |
enrollment_id | user_id | plan_id | enroll_date |
---|---|---|---|
1001 | 1 | 100 | 03/20/2021 |
1002 | 1 | 101 | 04/15/2021 |
1003 | 2 | 101 | 05/19/2021 |
1004 | 2 | 102 | 05/20/2021 |
1005 | 3 | 100 | 06/24/2021 |
This query first selects the premium and ultra-premium plan IDs. Then it filters the enrollments table based on these plan IDs and the date range. It groups the result by user ID and orders the counts of enrollments in descending order. Finally, it limits the result to top 10 entries.
To solve a related customer analytics question on DataLemur's free interactive SQL code editor, try this Microsoft Azure Cloud SQL Interview Question:
Learn about the exciting partnership between Humana and Google aimed at reducing healthcare costs and enhancing member experiences! This collaboration highlights the importance of technology in improving healthcare delivery and can inspire ideas for future innovations in the industry.
Given a table of Humana employee salaries, write a SQL query to find the 2nd highest salary among all employees.
employee_id | salary |
---|---|
1 | 2500 |
2 | 800 |
3 | 1000 |
4 | 1200 |
second_highest_salary |
---|
1200 |
Solve this problem directly within the browser on DataLemur:
You can find a detailed solution with hints here: 2nd Highest Salary.
The operator merges the output of two or more statements into a single result set. The two statements within the must have the same number of columns and the data types of the columns are all compatible.
For example, if you were a Data Analyst on the marketing analytics team at Humana, this statement would return a combined result set of both Humana's Google and Facebook ads that have more than 300 impressions:
Humana is a healthcare company, so their dataset is more likely to revolve around healthcare-related data. For this question, consider that Humana conducts survey among patients for different healthcare providers. Write a SQL query to determine the average patient satisfaction score for each healthcare provider within each year-month, along with the rank of each provider's average score within that month. Use SQL window function to provide a relative ranking.
Let's take the example table:
score_id | patient_id | submit_date | provider_id | satisfaction_score |
---|---|---|---|---|
1252 | 1001 | 01/12/2022 00:00:00 | 105 | 4.5 |
1376 | 1002 | 01/15/2022 00:00:00 | 207 | 3.8 |
1986 | 1003 | 02/05/2022 00:00:00 | 207 | 3.9 |
2773 | 1004 | 02/10/2022 00:00:00 | 105 | 4.0 |
3526 | 1005 | 03/06/2022 00:00:00 | 207 | 4.2 |
year_month | provider_id | avg_satisfaction_score | rank |
---|---|---|---|
2022-01 | 105 | 4.5 | 1 |
2022-01 | 207 | 3.8 | 2 |
2022-02 | 105 | 4.0 | 2 |
2022-02 | 207 | 3.9 | 1 |
2022-03 | 207 | 4.2 | 1 |
In this table, patients are submitting scores for different healthcare providers. The scores are from 1 to 5.
This query first converts the to a year-month format and groups by that along with . It calculates average satisfaction score for each provider within that year-month. Finally, it uses a window function to calculate the rank of each provider's average score within each year-month, ordering by average score in descending order. The result is sorted by year-month and rank.
To solve a related window function SQL problem on DataLemur's free interactive coding environment, solve this Google SQL Interview Question:
To find records in one table that aren't in another, you can use a and check for values in the right-side table.
Here is an example using two tables, Humana employees and Humana managers:
This will return all rows from Humana employees where there is no matching row in managers based on the column.
You can also use the operator in PostgreSQL and Microsoft SQL Server to return the records that are in the first table but not in the second. Here is an example:
This will retrieve all rows from employees that do not appear in managers. The operator works by retrieving the rows that are returned by the first query, but not by the second.
Please note that is not supported by all DBMS systems, such as MySQL and Oracle (however, you can use the operator to achieve a similar outcome).
Humana, a health insurance company, would like to analyze the distribution of age, gender, and health conditions among its subscribers who are enrolled in different health care plans. In order to do so, Humana needs to design a database that will allow it to efficiently query this demographic and health-related information.
The company records include:
patient_id | name | age | gender |
---|---|---|---|
101 | John Doe | 45 | M |
102 | Jane Doe | 23 | F |
103 | Mary Johnson | 35 | F |
104 | James Smith | 55 | M |
plan_id | description | cost |
---|---|---|
201 | Basic Plan | 200 |
202 | Premium Plan | 400 |
record_id | patient_id | health_condition | date_diagnosed |
---|---|---|---|
301 | 101 | Diabetes | 2019-05-01 |
302 | 102 | Hypertension | 2020-08-15 |
303 | 103 | Asthma | 2021-01-10 |
record_id | patient_id | plan_id | subscription_date |
---|---|---|---|
401 | 101 | 201 | 2018-01-01 |
402 | 102 | 202 | 2019-06-01 |
403 | 103 | 202 | 2020-08-01 |
404 | 104 | 201 | 2021-01-01 |
Design a PostgreSQL query to find out the average age of male and female patients enrolled in the Premium Plan.
This query first joins the , , and tables on the appropriate keys. It then calculates the average age of male and female patients who are subscribed to the 'Premium Plan'. The result is grouped by gender.
A transaction is a one or more SQL commands which are executed as a singular unit if the transaction -commits- (or no execution happens if the transaction -aborts-).
For transactions, a DBMS is supposed to enforce the follwing ACID properties: Atomicity, Consistency, Isolation, & Durability.
Here's what each one means:
As you can see, it's pretty important for the multiple databases where Humana store's it's data to be ACID-compliant!
As an health insurance firm, Humana is interested in understanding their claims data. Therefore, you are asked to find the average number of insurance claims per client over the past year.
claim_id | client_id | claim_date | policy_id | claim_amount |
---|---|---|---|---|
8971 | 6721 | 06/08/2021 | 30001 | 1050 |
6782 | 3452 | 06/10/2021 | 30001 | 1200 |
5373 | 6721 | 06/18/2021 | 30001 | 900 |
2732 | 3456 | 07/16/2021 | 30001 | 2100 |
8252 | 7831 | 07/05/2021 | 30001 | 1250 |
client_id | policy_id | start_date |
---|---|---|
6721 | 30001 | 01/10/2020 |
3452 | 30001 | 02/15/2020 |
3456 | 30001 | 06/10/2020 |
7831 | 30001 | 07/01/2020 |
This SQL script joins the and tables on and identifies the number of claims for the past year. The function counts the total number of claims, and this is divided by the unique number of , giving the average number of claims per client over the specified period.
To practice a very similar question try this interactive Twitter Histogram of Tweets Question which is similar for calculating per user data or this Walmart Histogram of Users and Purchases Question which is similar for analyzing user transaction data.
You are a data analyst at Humana health insurance company. You've been asked to filter customer records stored in the database in order to collect a list of all customers whose first name starts with 'Jan'. In addition, the management team wants to know about the city and state where these customers live.
The table is structured as follows:
customer_id | first_name | last_name | city | state | |
---|---|---|---|---|---|
1001 | Jane | Doe | jane.doe@humana.com | Louisville | KY |
1002 | Janice | Smith | janice.smith@humana.com | Chandler | AZ |
1003 | Steven | Johnston | steven.johnston@humana.com | Madison | WI |
1004 | Janet | Wilson | janet.wilson@humana.com | Omaha | NE |
1005 | Sebastian | Wright | sebastian.wright@humana.com | Bellevue | WA |
The required output should display the customer's first name, last name, city, and state in this format:
first_name | last_name | city | state |
---|---|---|---|
Jane | Doe | Louisville | KY |
Janice | Smith | Chandler | AZ |
Janet | Wilson | Omaha | NE |
Provide the SQL query that you would use to extract the required data in PostgreSQL.
Now, the query above would filter the customers whose first names start with 'Jan' and return their full name, city of living, and the state. The SQL keyword is used in the clause to search for a specified pattern. The percentage sign next to 'Jan' acts as a wildcard, signifying any characters following 'Jan'.
The function can take in multiple parameters, and returns the first input parameter that is not null. If all arguments are null, the function will return null too.
Suppose you have a table of Humana salespeople and the number of deals they closed. If a salesperson didn't close any deals, the data from the 3rd-party CRM system exports a NULL value.
sales_person | closed_deals |
---|---|
Jason Wright | NULL |
Drew Jackson | 3 |
Chris Ho | NULL |
Adam Cohen | 2 |
Samantha Perez | 4 |
To change these NULLs to zeros, you can use the function in the following way:
This would result in the following data:
sales_person | closed_deals |
---|---|
Jason Wright | 0 |
Drew Jackson | 3 |
Chris Ho | 0 |
Adam Cohen | 2 |
Samantha Perez | 4 |
The key to acing a Humana SQL interview is to practice, practice, and then practice some more! Besides solving the earlier Humana SQL interview questions, you should also solve the 200+ SQL coding questions which come from companies like Amazon, JP Morgan, and healthcare and pharmaceutical companies like Humana.
Each problem on DataLemur has multiple hints, full answers and crucially, there is an online SQL coding environment so you can instantly run your SQL query and have it checked.
To prep for the Humana SQL interview it is also useful to solve SQL questions from other healthcare and pharmaceutical companies like:
However, if your SQL query skills are weak, don't worry about diving straight into solving questions – go learn SQL with this DataLemur SQL tutorial.
This tutorial covers SQL topics like cleaning text data and handling NULLs in SQL – both of these come up routinely during Humana interviews.
Beyond writing SQL queries, the other types of questions tested in the Humana Data Science Interview are:
To prepare for Humana Data Science interviews read the book Ace the Data Science Interview because it's got:
Don't ignore the behavioral interview – prep for it using this Behavioral Interview Guide for Data Scientists.