How SQL Helps the Healthcare Sector

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Why does the healthcare industry need SQL? Find out how you can use SQL for healthcare in this article.

The digital age has ushered in a new era where data is at the heart of every industry. From finance to retail, data drives decision-making and strategy, shaping the way businesses operate and grow. The healthcare sector, with its vast and complex data sets, is no exception to this trend . The rise of digital health records, patient databases, and health informatics has underscored the importance of efficient data management in healthcare.

SQL, or Structured Query Language, has emerged as a powerful tool in this context. It has revolutionized the way health professionals access, analyze, and manage patient data.

With SQL, healthcare providers can quickly retrieve patient information, track treatment plans, and monitor patient outcomes. This efficiency in data management has significantly improved the operational aspects of healthcare, enabling providers to deliver timely and effective care.

This article delves into the role of SQL in the healthcare sector. It will explore how SQL contributes to various aspects of healthcare, from patient care to administrative tasks. By understanding the applications of SQL in healthcare, we can appreciate its potential in driving improvements in patient care and operational efficiency.

I will also show employees in the healthcare sector why they should learn SQL and how to start to achieve the best results. If you already know you want to learn SQL, start with our SQL Basics track. And if you want to go all-in, I recommend the All Forever Package , which is all of our SQL courses. But let’s start with the basics.

What Is SQL?

You've probably heard such terms as data science or being data-driven . SQL is a programming language that has become a cornerstone in the world of data management. It's a language that allows us to communicate with databases – specifically, with relational databases, where data is organized into tables with rows and columns. These tables are interconnected through predefined relationships, making SQL an essential tool for efficiently storing and retrieving data. You can read more on that in our article What Is SQL? .

Whether it's a small bookstore or a multinational corporation like Amazon, SQL plays a crucial role in managing vast amounts of data.

The history of SQL dates back to the 1970s, when the concept of the relational database was first introduced by Edgar Frank (Ted) Codd , an English computer scientist at IBM. For over 50 years, SQL has evolved and become a standard in IT and the entire world of data. Without SQL, real data analysis or data engineering would not take place. It's no coincidence that SQL still holds up; it's just a brilliant tool.

sql in healthcare

SQL is now used far beyond its original purpose of managing relational databases. Today, it's used in various technologies for data access, including distributed data processing systems like Apache Spark, search engines, and common spreadsheet applications like Excel or Google Sheets . Even smartphone apps use a version of SQL (called SQLite) for data storage.

Learning SQL is not as difficult as you might think. The syntax of SQL is based on the English language, making it relatively easy to understand and learn. With commands like SELECT , INSERT , UPDATE , and DELETE , you can retrieve, add, modify, and remove data from a database. Check out how easy it is to learn SQL syntax .

As the digital age continues to evolve, the importance of SQL in managing and analyzing data will only increase, making it a valuable skill for anyone working with data. That means if you are working in the healthcare sector, you should learn SQL. Here’s why.

SQL in Healthcare: A Game Changer

One of the primary benefits of SQL for healthcare is its ability to streamline data management. Hospitals and healthcare providers deal with vast amounts of data daily, including patient records, treatment plans, and billing information. SQL databases provide a structured and efficient way to store, retrieve, and manage this data.

SQL in healthcare also plays a crucial role in enhancing patient care. By analyzing patient data, healthcare providers can identify patterns and trends, predict health risks, and personalize treatment plans. This data-driven approach to healthcare can significantly improve patient outcomes.

Insurance companies in the healthcare sector also collect a lot of data. Patient data is processed and analyzed so that the system can function and work for the benefit of users. Do you work in such a company? Most likely, you've already come into contact with SQL reports or you know that they could improve your daily tasks. It’s worth introducing SQL to the educational process in your team .

Real-World Applications of SQL in Healthcare

Let's take a look at how SQL can help the healthcare industry. I have collected a few examples here, which are only the tip of the iceberg of possible applications.

Electronic Health Records (EHRs)

SQL is often used to manage electronic health records, or EHRs. These digital versions of patient charts contain a patient's medical history, diagnoses, medications, treatment plans, and more. SQL databases make it easy to store, update, and retrieve this information, improving the efficiency and accuracy of patient care.

sql in healthcare

Until recently, doctors and medical staff used mostly paper documents. As long as the treatment took place within one facility, this was feasible. Currently, however, many therapies take place in several locations, such as a doctor’s office, an outpatient clinic, and through at-home followup. Hence, there’s a need to digitize data so it’s available across locations. EHRs are faster and more reliable than sharing paper records, especially if the specialist needs immediate access to the patient's treatment history. Different EHRs have varied features. Choose one that suits your facility. Working with known care coordination software vendors offers more options to find the best fit.

Medical Research and Clinical Trials

SQL plays a pivotal role in the realm of medical research and clinical trials. Its ability to manage and analyze large datasets makes it an indispensable tool for researchers. They can use SQL to sift through the vast amounts of data generated during clinical trials, helping them make sense of complex information.

By using SQL, researchers can identify patterns within the data. These could be correlations between different variables, trends over time, or anomalies that stand out from the norm. Such patterns can provide valuable insights into the efficacy of treatments or the progression of diseases.

The conclusions drawn from SQL analysis can significantly accelerate the development of new treatments and therapies. By understanding the data at a deeper level, researchers can make informed decisions, refine their hypotheses, and focus their efforts more effectively. This ultimately leads to advancements in medical science and improved patient care.

Healthcare Analytics

Healthcare analytics is another area where SQL shines. By analyzing healthcare data, providers can gain insights into patient behavior, treatment effectiveness, and operational efficiency. These insights can inform decision-making and strategy, leading to improved patient care and business performance.

sql in healthcare

Public Health

SQL is a vital tool in the field of public health, enabling the efficient management and analysis of extensive health-related datasets. These datasets encompass a wide range of information, including disease prevalence, health behaviors, environmental health factors, and more. Using SQL, public health organizations can structure this data into accessible databases, facilitating easy retrieval and analysis. This is crucial for tracking public health trends and identifying disparities in health outcomes.

The use of SQL extends to the analysis of this data, providing insights that can inform public health strategies. By identifying patterns and trends within the data, public health professionals can gain a deeper understanding of health issues within a population. This knowledge can guide the development of targeted interventions and health promotion strategies, ultimately improving community health outcomes. Some examples of this include:

  • Tracking obesity among citizens and creating strategies to counteract it.
  • Measuring smog and pollution, understanding their consequences, and developing a plan for cleaner air.
  • Studying epidemics and vaccines and preparing countermeasures.

If you are interested in the topic, read my article Where Can I Find Free Online Data Sets to Practice SQL? There you will find a link to Data.gov databases with gigantic data sets of this type.

Learning SQL for Healthcare

Have I convinced you to learn SQL? Great, now I'll tell you how to start.

For beginners or those who have never heard about SQL, LearnSQL.com offers an interactive course titled SQL Basics . This course is designed to introduce you to the fundamental concepts and commands of SQL. You'll learn how to retrieve data from an SQL database and build simple reports. The course includes 129 interactive exercises that will help you solidify your new skills. No prior experience is needed and you can learn at your own pace, making it an excellent starting point for your SQL learning journey.

sql in healthcare

For those who are ready to delve deeper and want to use SQL to analyze healthcare data or prepare sophisticated reports, LearnSQL.com provides a comprehensive learning path titled SQL from A to Z .  It covers everything from basic queries to advanced SQL functions and features. You'll learn how to combine data from multiple tables using SQL JOINs, write complex SQL queries using aggregation, subqueries, and set operations, and much more.

By the end of this track, you'll be equipped with the tools to freely and efficiently work with any type of data in SQL.

And here you have some simple tips how to make your learning experience even smoother:

  • 5 Tips for Learning SQL for Beginners
  • How to Learn SQL Without Any Programming Knowledge
  • Is it Difficult to Learn SQL?
  • How to Stay Healthy When Learning SQL

SQL Is a Valuable Tool for Healthcare

The use of SQL in healthcare has transformed the industry, enabling more efficient data management, enhanced patient care, and valuable insights. As healthcare continues to evolve, the role of SQL is likely to become even more critical. By understanding and leveraging the power of SQL, healthcare providers can stay at the forefront of this exciting and rapidly changing field.

Whether you're a healthcare professional looking to leverage data in your work or you're planning to enter the field of healthcare data analysis, mastering SQL can open up new opportunities and enhance your career prospects.

Don't wait to equip yourself with this valuable skill. Start your journey with SQL today and stay ahead in the rapidly evolving healthcare industry.

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5 Healthcare Data Analyst Projects You Need In Your Portfolio

Discover How Big Data Analytics Boosts the Healthcare Industry by Working on These Unique Healthcare Data Analyst Projects | ProjectPro

5 Healthcare Data Analyst Projects You Need In Your Portfolio

Data is crucial for businesses to remain competitive and flexible and to make better decisions. Likewise, the healthcare sector is not an exception. The global big data market in the healthcare industry is anticipated to reach $130,132.1 million by 2031. Big data analytics also allows healthcare data analysts to make data-driven decisions, receive better treatment response predictions, have a deeper understanding of the complex factors and how they interact to affect a patient's health, and so much more.

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For all the data analysts who are willing to enter the healthcare industry, adding these health analytics projects to your portfolio will help you land a better job opportunity. So, let's get started and help you carve a career as a successful healthcare data analyst!

Top 5 Healthcare Data Analyst Projects

Check out these five unique data analytics in healthcare examples that will help you understand the various applications of data analytics in healthcare .

Healthcare Data Analyst Projects

1. Heart Disease Prediction

The Heart Disease Prediction project is one of the most popular Python healthcare projects. This project seeks to contribute to the detection of the occurrence and potential risk of heart attacks, coronary artery disease, and other cardiovascular disorders. Using the UCI Heart Disease dataset, which has 14 columns and more than 300 samples, you can try out how well various prediction models work. Start by loading Python's essential libraries such as Pandas, Scikit-Learn , etc. Build the different machine learning models such as k neighbors classifier, SVM, Random Forest, and Decision Tree using Scikit Learn. Additionally, you will conduct exploratory data analysis (EDA) on the data using the Pandas library. You will observe that K-nearest neighbors perform the best on the UCI dataset after experimenting with five machine learning models to predict heart disease. 

ProjectPro Free Projects on Big Data and Data Science

2. Twitter Trends Analysis On COVID-19 Vaccinations

This project aims to extract data from tweets about the COVID vaccine (between January and April 2021), where opinions are very unstructured, diverse, supportive, critical, or neutral and find factors that influence people’s sentiments. You can use this project to visualize the sentiment trends among Twitter users and the level of interest in the topics. Collect tweets between January and April 2021 and perform data cleaning using the bag-of-words algorithm to separate individual tweets from corporate and automated tweets. Use unsupervised LDA to decipher the cryptic abstract topics in the tweets. Employ VADER to perform sentiment analysis to determine how vaccinations affected users' sentiments throughout the epidemic. Apply Normalized Topic Correlation (NTC) to show correlations inside a single document related to a specific topic. Additionally, you will create graphs, charts, and heat maps using Excel and Tableau to accomplish data visualization. 

3. Hospital Treatment Package Pricing

This health analytics project intends to develop a predictive model to calculate the cost of treatment based on the clinical parameters available at the time of admission. Use the Mission Hospital package pricing dataset , which is available on GitHub. The model will help you decide between package and standard pricing and formulate a plan that will accurately estimate the package price at the time of admission. Given the clinical circumstances at the time of entry, it will also evaluate the cost of the treatment plan. The project handles variable data correlation, addresses NULL values, and performs feature engineering on critical variables. In addition, you will conduct statistical analysis to determine the impact of dependent factors on the target variable and develop the best possible multiple regression models to calculate the cost of treatment.

Build a Job Winning Data Engineer Portfolio with Solved End-to-End Big Data Projects .

4. Covid-19 Time Series Forecasting

The main goal of this healthcare data analyst project is to understand how the cases of COVID-19 are developing, first individually in each location and eventually globally. Use the Covid-19 dataset , available on GitHub and provides data about covid-19 progression. Use the ARIMA model to address this challenge by importing the library- from statsmodels.tsa.arima model import ARIMA. With the use of rolling mean and standard deviation charts, examine the distribution of the cases. Additionally, you can use the Dickey-Fuller test to determine whether the time series is stationary.

5. Liver Disease Prediction

In this project, you will use a patient's lifestyle and medical history to determine if they will develop liver cirrhosis. Use the Cirrhosis Prediction Dataset available on Kaggle for this project. The first step in this project is to perform data cleaning and handle outliers and null values in the dataset. Perform some exploratory data analysis and visualizations to better comprehend the data. Use Stratified K-fold to ensure that the target variable is evenly distributed across all test and training splits when splitting the data into training and test sets. You can import the classification report, accuracy score, precision score, and recall score metrics from sklearn.metrics to evaluate the performance metrics. You can implement the XGBoost Classifier and the Random Forest Classifier to build this model.

Access Solved Big Data and Data Science Projects

About the Author

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Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill

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What Is SQL? The Basics of SQL for Healthcare Professionals

Rehan

Data is an essential asset for all healthcare organizations. With the right information and the right tools, the quality of treatment can improve , along with the outcomes for patients.

One of the most popular ways to organize and analyze data in the healthcare space is with SQL. With SQL on your side, you can access and harness information from a database quickly and efficiently.

If you are new to the world of SQL and unaware of its advantages, here is a look at the basics and the benefits it brings to the table for healthcare professionals.

What is SQL?

SQL is an acronym that stands for ‘structured query language’ but is more usually pronounced as ‘sequel’ to help it roll off the tongue a little easier.

It is a programming language that was designed back in the mid-1970s and continues to be used to this day to orchestrate information within a database.

In particular, it is intended to work as part of a relational database, which is a platform that contains structured data.

As a programming language, SQL uses standard English words to form longer queries, allowing users to extract information contained within a database and manipulate it as required.

In many cases, the functions of SQL are hidden from the end-users by another layer of software. Additional solutions, such as SentryOne SQL Sentry , can help streamline the performance of a database by highlighting the causes of existing problems.

Of course, if you want to use a database that relies on SQL, you need to get to grips with the underlying language. Keep reading to find out how it functions and why it is relevant in a healthcare context.

How does SQL work?

Discussing the ins and outs of SQL can take a long time, so let’s focus on the aspect that will be most important for most users – queries.

As mentioned, queries are a form of a statement in SQL which allows you to ask a question of the database and receive an answer, according to the parameters you define.

Every SQL query has to start with the clause ‘SELECT,’ which you can then use to set the parameters for the particular columns of a table within a database you want to search. This could be the first or last name of a patient, for example.

The ‘FROM’ clause follows up on this, which pinpoints a table for the query to look within. Finally, there is the ‘WHERE’ clause, which lets you narrow down your search even further so that the query results only include entries that meet your needs. This could be the specific ID number of a given patient or a more broad category to include multiple entries on the table in the results.

Queries are just one part of how SQL works, but hopefully, you can now see that the language itself is comparatively simple. The syntax is logically constructed so that anyone can get a handle on it with little instruction and experience.

As you progress and learn, you will be able to use SQL to modify, delete and move data freely within a database, meaning that the potential applications of this language are almost limitless.

Why is SQL important for healthcare professionals?

SQL can provide many perks to healthcare professionals if used correctly, some of which we have touched on already.

Firstly, it makes storing large volumes of patient information a breeze and does a much better job than basic spreadsheet software in this respect. If you find that your Excel files are getting unwieldy and confusing, leaping an SQL-powered database could be ideal.

Secondly, it gives you the ability to drill down into the available data and harness it efficiently and effectively from day to day. This makes it easier to manage patients, see to their needs and improve their satisfaction levels.

Finally, it allows you to capture, model, and analyze information to improve and enhance the underlying processes within your organization. Tapping into data to extract insights would usually be impossible given the volumes involved. Still, SQL is just one of the tools that tear down the barriers to a better, brighter future for healthcare as a whole, from admin to diagnosis and beyond.

Furthermore, because you can express the analytical insights from your use of SQL using visualization tools, it is straightforward to convey the findings to others, regardless of their technical expertise.

SQL is a powerful solution to the data dilemmas that plenty of healthcare professionals face today. For smaller firms and large organizations alike, it offers a way to make the most of information rather than being overwhelmed by it.

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Improve the Data Quality for Healthcare, Clinical and Life Sciences Projects with Melissa Informatics

By: Jeremy Kadlec   |   Updated: 2024-01-02   |   Comments   |   Related: > Data Quality Services

All companies fundamentally have the need to improve data quality for decision making. With complex data sets it is even more difficult to improve data quality. The problem is exacerbated with numerous standards and synonyms around the globe with data in numerous systems, varying formats and industry specific terminology. For healthcare, clinical and life sciences organizations, merging, reconciling and enhancing data is challenging for electronic medical records, clinical trials and pharmaceutical applications. How can the technology teams at healthcare and life sciences organizations adopt a framework to improve the data quality for complex data sets for the medical professionals they support?

Although many database platforms have the means to support healthcare and life sciences organizations, the reality is the data is complex to consolidate, build relationships and find meaningful results from electronic medical records, clinical trials and more. Inconsistent data from numerous systems supporting multi-decade studies, data is housed in free form text fields or in unstructured formats, includes over 100 synonyms and spellings for specific terms, and requires significant financial resources and precious time from medical experts to derive value from the collected data.

Melissa is aware of these challenges in the healthcare field and has been delivering data quality solutions for 35 years including direct integration with relational database engines such as SQL Server and Oracle. Melissa provides high-quality tools for data profiling, validation, enrichment and more that many SQL Server Professionals are familiar with that seamlessly integrate with SQL Server Integration Services (SSIS). Melissa even has a user-based tool called Unison to enable power users to apply much of the same logic from the SSIS Data Quality tools and incorporate it into a user friendly tool to enable Power Users, who are intimately familiar with the data, to resolve data quality issues.

Melissa recognized these solutions solve broad data quality needs. In particular industries such as healthcare, some challenges remained unanswered. To compliment the Melissa Data Quality tools for SQL Server Integration Services, Melissa Informatics delivers Machine Reason and Machine Learning solutions for complex and dynamic data sets as well as mapping industry specific terminology for organizations across the globe. One implementation of this technology by Melissa Informatics is in the Healthcare and Life Sciences fields to improve data quality of complex data sets for clinical trials, drug validation, and more initiatives. Melissa Informatics goals are:

  • Improve the quality and completeness of data in the healthcare industry
  • Reduce costs and increase efficiency of data management
  • Enable interoperability and searchable integration of data

This article is intended to serve as a primer for database professionals who support medical researchers, analysts, physicians, clinicians, etc. to gain an understanding of Melissa Informatics capabilities who are looking to improve the organizational data management capabilities. Let’s dive into the Melissa Informatics products and services offerings for the healthcare industry, designed to significantly reduce costs for clinical trials and pharmaceutical applications.

Melissa Informatics Process

From a process perspective, Melissa recommends first implementing Data Quality best practices including data profiling, matching, enriching, etc. to build an accurate data set in the relational engine. This is accomplished with the Melissa integration with SSIS Tools or Unison for power users. Once the broad data quality best practices are implemented, if more advanced data integration and analysis is a goal, Melissa recommends leveraging the Sentient Suite of tools. These tools combine SQL and semantic database technologies including Machine Learning and Machine Reasoning algorithms. These semantic technologies provide advanced means to categorize and process data as well as discover new relationships that were previously difficult to ascertain.

Generally, the solution for health care and life sciences organizations from Melissa is a hybrid of relational and semantic technologies. The relational engine provides a consistent means to perform data cleansing and enrichment as well as high speed data access to support end users. The semantic model is used to understand and improve the complex data sets, easily build relationships and make new discoveries with the data. In the end, the final results are populated in a relational engine for end users to access the data in a relational engine due to the high performance. Let’s walk through each of the products Melissa Informatics delivers for a final solution.

Melissa Informatics Sentient Suite

Healthcare data is unusually complex with many types and forms of data. Unfortunately, this can easily lead to poor data quality and incomplete data sets leading to errors and inefficient decision making. To address these needs, Melissa Informatics has built the Sentient Suite which are data integration tools to enable data exploration and run queries to discover data relationships:

Sentient Knowledge Explorer

  • Sentient Server - Web Query
  • Sentient Server - Applied Semantic Knowledgebase
  • Sentient Server - Data Manager

The Sentient Suite architecture is dynamic, to support data discovery, harmonization and integration. This enables medical data scientists to correct incomplete data sets and errors to stop poor decision making, to create new data relationships and to discover data patterns for decision making - driving innovation and discovery, and increasing efficiency for data management.

Sentient Knowledge Explorer is a data integration and data exploration tool built on semantic technologies. This tool helps to build ontologies, which are models for data management and relationships. This enables the technology and medical professionals to build a unified knowledge database in order to perform data analysis.

Sentient Web Query

Sentient Web Query is a web-based interface for medical professionals to run queries against any relational database, web service or Sentient data store in a secure and compliant manner around the globe. This tool also enables medical professionals to also import data for comparison and further analysis.

Sentient Applied Semantic Knowledgebase

Sentient Applied Semantic Knowledgebase builds on customer data analysis to help researchers proactively address predictive biology and early stage drug development challenges with hypothesis testing, yielding better decision making.

Sentient Data Manager

Sentient Data Manager is a web-based application for data entry, importing, correction, delivery and reporting. This solution has the flexibility to work with data from unstructured text, image data, web services, SQL and NoSQL databases, medical devices, and other sources, providing medical professionals with the flexibility they need to efficiently conduct their research.

Melissa Informatics Knowledge Hub

Knowledge Hub is a massive knowledge database used for data enrichment in six key healthcare areas. This database includes content (verified integrated data), lexicons (industry specific terminology to normalize over 17 million terms) and ontologies (over 800 data models). The Knowledge Hub includes:

As an example, the Drugs Knowledge Hub includes data on 193,077 drugs to ensure your organization is using the correct terms in compliance with the FDA in the United States, RxNorm with NIH, SnoMed-CT internationally or other standards as needed. The data includes drugs that are commonly prescribed simultaneously, precautions when using a particular drug and relationships between drugs, genes, proteins and diseases. Further, you are able to append your drug data with links to 34,756 genes, 3,554 proteins and 58,351 diseases.

Below is a screen shot of one interactive Knowledge Hub visualization outlining the relationship between a drug, genes, ontologies and diseases. You can explore nodes, relationships and hierarchies.

melissa

The Melissa Informatics Knowledge Hub appends and enriches data ultimately giving healthcare organizations a more complete picture. Further, the Knowledge Hub validates and builds a coherent data set yielding accuracy and time savings.

Melissa Informatics Cloud APIs

Druginator is one of the Melissa Informatics Cloud APIs, which is a web service for validating drug names, variants, dosages and spellings. Druginator accepts user input for a drug term and validates the drug, verifies the preferred name and enriches the data to ensure the output terms are standardized for global interoperability, including:

  • Drug Cleansing and Append
  • Drug Identification and Profiling
  • Drug Enrichment
  • Drug Semantics and Harmonization

Druginator saves time researching drugs, ensures data accuracy and reduces errors when cleaning up dirty data from electronic medical records or internal data.

Melissa Informatics provides new opportunities for healthcare organizations facing data quality challenges resulting in poor decision making, missed opportunities and high data management costs. Melissa Informatics delivers comprehensive data discovery, harmonization, integration and research for healthcare with broad quality tools, including relational and semantic technologies for Machine Reasoning and Machine Learning, and Professional Services. This solution drives innovation and decision making at lower costs, enabling healthcare organizations to make data management a competitive advantage.

  • Learn more about Melissa Informatics
  • Get your free trial of Melissa Informatics
  • See Melissa Informatics in action with this Parkinson’s Case Study

MSSQLTips.com Product Spotlight sponsored by Melissa, makers of Melissa Informatics.

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11 Healthcare Data Science Projects (To Get Hired in 2023!)

This post may contain paid links to my personal recommendations that help to support the site!

It’s 2023, and healthcare data science projects are in high demand!

If you’re looking to get hired in data science in the health industry in 2023, you need to start working on some healthcare data science projects.

In this blog post, I’ll share the top 11 healthcare data science projects you should start with. I’ll also provide tips on how to complete these projects successfully.

So what are you waiting for? Read on to find out all about these data science project ideas!

1. Patient Risk Prediction

The first project in the list is about using machine learning algorithms to predict the risk of a healthcare patient for certain medical conditions.

Predicting a patient’s risk can rely on several key data points , such as age, gender, lifestyle habits, and medical history.

You’ll need to gather data from healthcare providers and hospitals to successfully complete this project.

You can use logistic regression, linear regression, Cox regression, and machine learning to determine a patient’s risk.

Tools to Get Started:

  • Scikitlearn

Project Tips:

  • Analyze data from different healthcare organizations and test your model on all of them
  • Think about what kind of healthcare patient risk factors you should focus on

You can also consider doing a similar project to predict the risk to a person’s mental health too.

2. Gene Cluster Analysis

Gene cluster analysis is another data science project you should try! This project involves bioinformatics work, which is a key area within the healthcare industry due to its large volumes of biological data.

This bioinformatics project looks at analyzing clusters of genes in order to better understand various healthcare conditions.

You’ll use techniques like clustering, hierarchical clustering, and PCA (principal component analysis) to analyze gene expressions across different groups.

You can also use unsupervised machine learning algorithms such as K-means clustering for further analysis.

  • Bioconductor
  • Focus on data sets related to a specific healthcare condition you want to study
  • Look for patterns only in the gene clusters associated with the healthcare condition you’ve chosen

3. Disease Outbreak Prediction

The healthcare industry needs some help predicting disease outbreaks through data analytics too!

With this data analytics project, you can perform disease predictive modeling that uses healthcare historical data to forecast the spread of a particular disease in a region.

You’ll need to work with data sets with information about demographics, healthcare costs, and other relevant factors related to healthcare.

  • Work on COVID-19 Datasets to get started since most of you will have a better understanding of its context

4. Pneumonia Detection From X-Rays

This data science project looks at using artificial intelligence to analyze medical imaging (X-ray) images to detect illnesses like pneumonia.

You’ll need to use convolutional neural networks (CNNs) and deep learning algorithms to build a predictive model to analyze the X-ray images and build your model.

A healthcare data scientist would typically use deep learning and image segmentation to predict the presence of pneumonia.

  • TensorFlow/PyTorch
  • You might need a powerful machine with enough RAM to process the medical imaging data. You should at least have 16 GB RAM.
  • You can consider using cloud processing to run your deep learning models.

This medical image analysis project requires knowledge of more advanced computer vision knowledge. If you’re beginner, you might give this one a miss.

5. Cancer Disease Prediction

Next up, you can try predicting cancer disease using genomic data. This is a huge area within the healthcare sector, as early cancer prediction can be critical in patient survival!

Genomics has improved tremendously since the Human Genome Project was completed and this has allowed the full potential big data and data science applications in cancer research.

You can use a combination of data science techniques to predict the onset of cancer.

These include supervised learning algorithms such as logistic regression, random forest, or decision trees.

  • Get genomic datasets from NCBI

Not only can you learn useful skills while learning data science , but you’ll also impress your employers if you’re looking to work in healthcare.

6. Drug Target Identification

Drug target identification is another healthcare data science project you should consider.

This project looks at using drug-target interactions to identify potential drugs for new diseases or healthcare conditions.

You’ll need to use bioinformatics data science skills such as genomic sequencing, gene expression analysis, and protein-protein interaction networks.

Many healthcare research scientists use these skills on a regular basis so this project would be very applicable to a real job task.

  • You can use healthcare data sets related to drug-target interactions like ChEMBL and DrugBank.
  • You can also use public repositories such as Kaggle or Github.

7. Healthcare Supply Chain Optimization

Healthcare supply chain optimization is a possible healthcare data science project you can try.

This is one project that can help you to stand out when applying for jobs in healthcare management!

You can use data sets related to healthcare costs from Kaggle and logistics to optimize the healthcare supply chain process.

You’ll need to use a machine learning algorithm such as linear regression to develop predictive models. You can also do exploratory data analysis and data cleaning to mine for insights.

  • Scikit-learn
  • You can use data sets from Kaggle or datasets from various government websites.
  • Do create a data visualization to present your project findings

8. Natural Language Processing for Clinical Notes

This healthcare data science project looks at using natural language processing (NLP) to analyze clinical notes.

Through this project, you’ll learn NLP, an essential machine-learning model many data scientists use!

You’ll need to use NLP techniques such as sentiment analysis and text mining to process and understand healthcare data.

Your machine learning models should be able to detect and categorize information into the various ICD clinical codes.

Although this project might require some clinical knowledge, a little research will be sufficient!

  • Try healthcare data sets related to clinical notes from Kaggle or healthcare datasets from government websites.
  • You can also use healthcare data sets related to medical codes and terminologies like SNOMED CT.

9. Healthcare Chatbot Development

Chatbots are becoming increasingly popular in healthcare.

With healthcare chatbot development, you can develop a healthcare chatbot that patients can use to access medical information and resources.

You’ll need to use natural language processing (NLP) techniques and deep learning algorithms such as recurrent neural networks (RNNs) or long-short term memory (LSTM) to build healthcare chatbots.

  • TensorFlow or PyTorch
  • You might need to get sufficient RAM of 16GB to run the algorithms
  • Get involved in a data science community to ask for help since this project is pretty tough

10. Health Insurance Fraud Detection

Health insurance fraud is a major healthcare problem.

One project you can try is health insurance fraud detection.

You’ll need to use supervised machine learning algorithms such as logistic regression, decision trees, or random forest to detect fraudulent healthcare claims.

  • Do explore different data sets to identify patterns and trends.

Through this project, you’ll be able to determine the relationship between the dependent variable and the target variable (fraud likelihood).

11. Clinical Decision Support System

In healthcare, clinical decision support systems(CDSS) use healthcare data to help healthcare professionals make better decisions.

This project explores developing a CDSS using machine learning algorithms.

You’ll need to use supervised learning algorithms such as logistic regression and decision trees to classify test results, diagnoses, and treatments.

  • Do refer to SNOMED to familiarize with clinical terms

Related Questions

How can data science be used in healthcare.

Data science can be used to improve access, reduce healthcare costs, and develop personalized healthcare solutions.

Examples include predictive modeling for diseases and patient risk factors, natural language processing for clinical notes, healthcare chatbot development, and healthcare supply chain optimization.

Final Thoughts

And that’s all the healthcare data science project ideas I have for you!

I hope this article inspires you to use data science to create solutions that can improve healthcare and save lives.

All the best in getting hired as a healthcare data scientist. Thanks for reading!

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sql healthcare projects

Want To Level-Up Your SQL? Get Building With These SQL Projects

In this article, I share the 15 best SQL projects in 2024 with source code.

Whether you’re looking to land a job as a database admin, enhance your portfolio, or boost your skills, I’ve included 15 SQL projects for beginners.

To help you build your skills, I’ve organized these SQL projects to be more challenging as you make your way through the list. This is great for leveling up and building your portfolio.

With a history spanning more than 25 years, SQL is still the standard language for relational databases, as shown by its number 4 ranking among developers.

And with the Bureau of Labor Statistics reporting an average salary of more than $100K for database administrators, building SQL projects can be very lucrative for your career.

So, if you’re ready, let’s dive into these SQL projects to help you further your database career.

  • Is SQL Dying Out?

No! SQL (Structured Query Language) is most certainly not dying out.

In fact, it remains a crucial and widely used language in database management, data analysis, and business intelligence.

Sure, there is a lot of buzz around NoSQL versus SQL , but a huge number of organizations continue to rely on relational databases and, by extension, SQL for the primary querying language.

So, SQL is still very much essential for data warehousing, analytics, data integration, and reporting. This means that the demand is as high as every for skilled SQL professionals, including those with SQL certifications .

And yes, while new technologies and languages continue to emerge in the data field, SQL continues to be a foundational skill and tool for data professionals.

  • Best SQL Projects For Beginners in 2024

One of the main benefits of SQL is that it’s easy to learn, meaning that anyone, including beginners with little programming experience, can learn SQL . 

That said, one of the very best ways to learn SQL is to get involved with SQL projects.

I'm a strong advocate for learning by building, as there's no substitute for creating something that could be used in real-world scenarios.

After all, if you're looking to learn SQL, chances are pretty high that you plan to be manipulating data in a meaningful way with a relational database, so why not learn to do this by building relatable projects?

Now, depending on your current skill level, you might be uncertain where to start.

If you are brand new to the world of SQL and databases, you might want to consider an SQL course to pair with your project building.

That said, I've organized these SQL projects so that you can gradually build up in difficulty as you make your way through the list.

So, have your SQL cheat sheet ready, and let's start building some SQL projects!

1. Blood Donation Management System

This beginner SQL project uses a database to store information about medical patients for a blood bank. When designing the database or thinking about the data you want to store or query, consider the patient's name, unique ID, blood type, medical history, and phone number as a starting point.

If these concepts seem a little fuzzy, consider refreshing your memory with an SQL book or online documentation.

It’s also a good idea to create an Entity-Relationship (E-R) diagram and a schema to start implementing these fields within a database before trying to normalize it.

Source code

2. Cooking Recipe Website

In this SQL project, you can design a website with a recorded procedure to list your cooking instructions under various headings. Here are some tips for displaying and storing your information.

  • Utilize HTML text editor to write a recipe post or blog
  • Highest rated/liked "Recipe of the Day"
  • Cooking videos viewed in the last five hours

You can also add a feature that allows individuals to leave feedback and review recipes and another that allows you to modify or remove a recipe in the admin area. Head over to the GitHub repository using the source code link below to get started.

3. Library Database Management System 

An online library management system is user-friendly for assigning books and viewing the many books and topics accessible under a category.

The C# programming language simplifies creating this kind of management information system (MIS). Additionally, rapid information retrieval is possible with SQL commands.

Consider your college library, where lecturers and students can check books out. Typically, both groups have different deadlines for returning the book, ranging from a few days to a few weeks.

Additionally, although they may be identical copies of the same book written by the same author, each has a separate ID. This means that every book in a library management system contains an entry that details who issued it, how long it was on loan, how much any fines came to, and other helpful information.

4. Online Retail Database Software

Online retail application databases are some of the most well-known SQL practice projects as the importance of e-commerce continues to grow. 

The application enables customers to sign up and make online purchases. Users also receive a unique client ID and password during the registration process, which gathers the user’s name, contact details, address, bank details, etc.

After making a purchase, a user bill is created based on the item's quantity, price, and applicable discounts. Before the item is sent to the chosen location, the customer must select a payment option to complete the transaction.

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5. Inventory Management System

Inventory management and control ensure that a company keeps enough materials and goods on hand to satisfy customer demands quickly.

By maintaining inventory at the ideal level, companies can increase profitability by avoiding undesirable understocking and overstocking scenarios. In addition, an inventory management and control system will keep the company informed of how many goods and services are in stock.

The design goals for an inventory control management database include securing the necessary items, improving inventory turnover, maintaining safe stock levels, acquiring raw materials at a lower price, lowering storage requirements, minimizing insurance, etc.

6. Voice Commands Transport Enquiry System

With this innovative tool, you can travel faster and avoid those long queues we’ve all seen at bus and train stations.

By using tech-powered systems for transport inquiries, transport operators can enjoy significant savings in time and labor. With this project, commuters can ask questions about their various transportation options.

To do this, you can create an automation process that takes voice commands and responds with speech to share information about bus stops, airports, and train stations. 

7. Carbon-Emission Calculator

Environmental preservation has garnered a great deal of attention in recent years. By creating a web app that calculates a building's carbon footprint, you can actively contribute to furthering the cause.

This carbon calculator combines information on floor space and annual working days with user-selected or custom attributes on building types, water fixture types, climate zones, etc.

You can then link emissions outputs to energy use, water usage, transportation, and solid waste disposal.

A similar tool was conceptualized by American business CTG Energetics Inc., which later converted it from an Excel file to a SQL web application. 

8. Railway Control System Database

This DBMS requires you to model various rail lines between connecting stations, train stations, train information (each train has a unique ID), rail routes, train schedules, and commuter schedules.

To simplify the project, you can suggest that every train travels to its destination in one day and runs every day. 

For recording purposes, concentrate on monitoring the following information for each station along a rail route.

  • When a train pulls into a station on schedule
  • When a train departs from a station (out-time)
  • The positioning of stations along the route

9. Student Database Management

This project can be used to help students with record-keeping. The SQL server would include general student data, such as attendance records, mark or score sheets, fee records, contact details, enrollment year, courses, etc.

An automated student database management system can significantly simplify a university's administrative processes.

10. Hospital Management System

This web-based application or system allows you to control how a hospital is run.

It establishes a uniform record of clients, physicians, and rooms that is only accessible to the administrator. In the database, every doctor and patient will get a unique ID and be connected based on the current treatments.

Separate modules will also be available for hospitalization, patient discharge summaries, nurse responsibilities, medical supplies, etc.

11. Payroll Management System

Based on how widely this system is used across many business sectors, this SQL project is among the most popular for beginners.

A business salary management system computes employees' monthly pay, tax rates, and social security benefits. It uses employee information (name, pay scale, designation, benefits, etc.) and time sheets, including leave taken, to calculate salaries.

The application outputs bank files and pay stubs based on specific formulas, and the tax office also receives a similar tax file, which is created and stored in the database.

12. Grocery Store Sales 

Every day, tens of thousands of consumers shop at supermarkets for groceries and household goods.

Depending on region or gender, we can use this data to understand customer preferences for payment methods, peak times for visits, and participation in loyalty programs. And we can use this to adjust grocery store policies to raise sales and customer satisfaction.

This project uses a dataset that contains information gathered over three months from three supermarket stores. We can answer a range of questions by examining this dataset.

  • Does the percentage of customers participating in the rewards program affect gross income or the mode of payment?
  • Which branch has the best performance in the rewards program?
  • Does customer feedback affect membership?
  • Are there any indicators that differ for men and women?

13. Centralized College Database 

This system is similar to the student DBMS system we discussed earlier.

A college has academic departments, including English, Mathematics, History, etc. Each department also provides a range of courses that teachers can oversee using this system. 

Consider the scenario where a professor teaches both statistics and calculus. A student may enroll in either of these classes if they are a mathematics major, but a given course can only have one teacher; otherwise, there would be unusual overlaps. 

14. Food Service Database SQL Project

Eateries extend their food services beyond their physical locations by offering online delivery options via website pages. 

Additionally, restaurants can accept phone orders and send delivery staff to deliver each order. However, delivery people staff can only deliver orders within a given zip code, and they cannot go outside of this. 

In this project, the main idea is to maintain records for previous customers so they can be offered discounts for future orders. 

15. Power Bill Database System

This SQL project idea is helpful for private-owned electric companies that require a database to manage their ever-growing datasets.

This project allows you to access the example database's user category and admin sections, which will enable you to experiment with the admin operations of a power company’s billing system.

What Is SQL? 

SQL is an acronym for Structured Query Language, a standard language used to communicate with databases. SQL commands are used to ask questions (queries) about data in a database, retrieve data from a database, and manipulate data in a database. 

It was initially called Structured English Query Language by IBM but later changed to Structured Query Language. 

SQL is used to write lines of code that query the database to fetch or store data.

When you send an SQL request to a database, the Database Management System (DBMS) processes the request and sends feedback to the user. SQL generally specifies how data is collected, organized, and extracted from/to the database. 

SQL is different from common programming languages because it focuses on what the computer should do rather than how it should do it . 

  • Features & Uses of SQL

SQL Features

Let’s take a look at the key features and applications of SQL in modern-day databases:

Main Features of SQL:

  • Data Querying: Use SQL queries to retrieve specific data from a database using SELECT statements, allowing for data filtering and sorting.
  • Data Modification: Operations like INSERT, UPDATE, and DELETE are used to add, modify, or remove data, ensuring database integrity.
  • Data Definition: Commands like CREATE TABLE, ALTER TABLE, and DROP TABLE  define and maintain table structures, including columns, data types, constraints, and indexes.
  • Data Integrity: Enforces data integrity through constraints like primary keys, foreign keys, unique constraints, and check constraints.
  • Transaction Control: Commands like COMMIT, ROLLBACK, and SAVEPOINT manage transactions and ensure data integrity with ACID properties.
  • Security: Administrators can grant or revoke permissions, ensuring only authorized users access and modify data.
  • Aggregation and Analysis: Aggregate functions (SUM, AVG, COUNT, etc.) facilitate data calculations and summarization for analysis and reporting.
  • Joins: Combine data from multiple tables, enabling complex data retrieval and analysis.
  • Subqueries: Use subqueries for advanced data retrieval and manipulation.
  • Views: Create virtual tables (views) to simplify complex queries and offer data access abstraction.

Typical Use Cases For SQL:

  • Database Management: SQL is essential for managing relational databases, including data creation, updates, and queries.
  • Reporting: SQL is crucial for generating reports and deriving insights from data and large datasets.
  • Web Development: SQL is integral to web applications, enabling data storage and retrieval for backend development.
  • Business Intelligence (BI): SQL is fundamental for building data warehouses, OLAP cubes, and dashboards used in BI for business data analysis.
  • Data Mining: SQL complements data mining and machine learning for extracting patterns from extensive datasets.
  • Data Migration: SQL is valuable for migrating data between databases, aiding system transitions and data consolidation.
  • E-commerce: SQL manages product catalogs, inventory, and customer data in e-commerce platforms.
  • Healthcare and Finance: SQL securely handles patient records, financial transactions, and sensitive data in healthcare and finance systems.
  • Mobile Apps: SQL databases enable data storage in mobile apps, supporting offline data interaction.
  • Data Warehousing: SQL is used to design and manage data warehouses for historical data used in analysis and reporting.
  • Wrapping Up

So there you have it, the 15 best SQL projects in 2024 for beginners. 

To help you build your skills, each of the SQL projects I’ve covered was designed to be more challenging as you make your way through the list. 

The idea here is to help you level up your SQL skills naturally while also enhancing your portfolio with these SQL projects.

So whether you’re starting in database admin or keen to enhance your portfolio, each of the SQL projects I’ve shared is ideal for doing just that!

Whichever SQL project you choose to build, I hope you have fun, and I wish you the best of luck with your database career!

This article has covered 15 different SQL projects for beginners that you can use to learn about databases while improving your SQL skills.

Looking for ways to take your SQL skills into a data science career? Check out:

Coursera's SQL for Data Science from UC Davis

  • Frequently Asked Questions

1. How Do I Create an SQL Project?

Projects with SQL are a collection of databases and tables that store data. You can create an SQL project in several steps, depending on your software. 

  • Go to File > New > Project
  • Select SQL Project from the list of templates (or select File > New > Database)
  • Enter a database name and click Save
  • Ensure you have the correct version of the SQL software you're working with

2. How Do I Put My SQL Project on My Resume?

Firstly, you can list the entire project. But you must make it easy for an employer to read by putting the most important parts at the top of each section. So, for example, if you implemented an online database with a form-filling module and reporting system, just focus on these two components.

Secondly, you can break your project into smaller sections like "User Interface Design", "Database Design", etc. This makes it easier for an employer to understand the time that went into each component of the project without having to read everything.

3. How Do I Practice an SQL Project?

SQL is a complex language; it can feel overwhelming to learn it all at once. It's better to start with SQL database projects for a DBMS like SQLite or look for MySQL projects for beginners. You can then practice by working on your own small project ideas, finding existing databases that need work, or trying out the simple SQL projects we’ve listed above.

1. Stack Overflow. Stack Overflow Developer Survey 2023: Most Popular Technologies [Internet]. Stack Overflow; [date unknown; cited 2024 Jan 15]. Available from: https://survey.stackoverflow.co/2023/#technology-most-popular-technologies

2. Bureau of Labor Statistics, U.S. Department of Labor. Occupational Employment and Wages, May 2022, 15-1242 Database Administrators and Architects [Internet]. [updated 2021 Mar 31; cited 2024 Jan 15]. Available from: https://www.bls.gov/oes/current/oes151242.htm

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In this article

  • What Is SQL? 
  • Download SQL Injection Cheat Sheet PDF for Quick References SQL Cheat Sheets
  • SQL vs MySQL: What’s the Difference and Which One to Choose SQL MySQL
  • What is SQL? A Beginner's Definitive Guide SQL

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    Click on the "Open Database" button, select the adni.sqlite file, and click "Open" to open the database. You can see the tables in the database by looking at the left hand side of the screen under Database Structure tab. Here you will see a list under "Tables.".

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    Clustering, regression, sentiment analysis, predictive modeling. Representation of outcome. Data model, Tableau or PowerBI visualization, tables or graphs. Note: The projects below are to inspire similar projects. The data used for projects may or may not be open-source. 1. The short project.

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    Also, we will refurbish our EDA skills too with this project. Diabetes Retinopathy: It is a computer vision problem. Computer vision is widely applied in the field of healthcare and is a must-have skill for anyone wishing to apply AI in healthcare mainly because a lot of healthcare data is in the form of diagnostic images e.g. MRI's, etc.

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    This project help shed light on a small portion of healthcare out there. I found out through this project that most patients stay less then 7 days with the majority staying between two and three.

  20. 11 Healthcare Data Science Projects (To Get Hired in 2023!)

    Project Tips: You can use healthcare data sets related to drug-target interactions like ChEMBL and DrugBank. You can also use public repositories such as Kaggle or Github. 7. Healthcare Supply Chain Optimization. Healthcare supply chain optimization is a possible healthcare data science project you can try.

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    2. Analyzing students' mental health in SQL. In the Analyzing Students' Mental Health in SQL project, you'll use your PostgreSQL skills to analyze the student data from a Japanese international university and spot one of the most influencing factors impacting the mental health of international students.

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