Data science and health informatics (sometimes called biomedical informatics) are two hot career choices for anyone seeking a career in the computer and IT fields. Employers can’t find enough of these keyboard wizards to advance their in-house IT teams.
There’s a reason these professionals are in short supply and high demand: they are no mere coders or troubleshooters. Data scientists and health informatics professionals boast serious programming and analytics chops, along with an arsenal of sophisticated tools to gather large sets of data and process them into actionable insights.
Companies recruit these pros aggressively for their ability to perform analytic functions on large data streams. Their work reveals growth opportunities and potential savings in operations and administrative costs. In short: what they do creates a lot of value.
Data scientists often look for key performance indicators (KPI) in data that align with or differ from previously held beliefs and trends. They can also find gaps in processes or operations to make processes more efficient. It’s a demanding but rewarding role for those with an aptitude for computer programming, mathematics, and analytical thinking.
Health informaticians perform many of the same tasks, but within a specialized healthcare setting. They use many of the same data science skills and tools to collect and manipulate healthcare data for medical facilities and healthcare systems. Health informatics insights can lead to greatly improved efficiencies in patient care as well as in medical recordkeeping and database systems.
Both of these careers are multi-disciplinary, requiring bachelor’s-degree-level skills and education at minimum. Specialized functions need extra training found with a master’s degree or advanced certificate programs.
It’s helpful to explore the similarities and differences between these two career paths. While they share much in common, health informatician is a specialized career choice for those interested in the medical field. However, data scientists are undoubtedly capable of becoming health informatic pros. Confused? Let’s take a closer look at the health informatics vs. data science question. We’ll do that by addressing:
According to IBM, “Data science is a multidisciplinary approach to extracting actionable insights from the large and ever-increasing volumes of data collected and created by today’s organizations.” This process involves “preparing data for analysis and processing, performing advanced data analysis, and presenting the results to reveal patterns and enable stakeholders to draw informed conclusions.” Data science is a wide-ranging field used in many practical applications within numerous industries, including manufacturing, logistics, financial services, and healthcare.
Those working in data science are analytics professionals looking patterns in large data sets to extract valuable insights to identify current problems and forecast future sales, market activity, and other profit-generating and efficiency-enhancing events. Manufacturers use data science to optimize their supply chains; financial services look to big data for more confident predictions in the stock market.
Data scientists design, build, and operate the programs needed to analyze large data sets. Strong skills in computer science, data mining, machine learning, and deep learning are required to excel. They also need knowledge of several complex programming languages and advanced statistical analytics. Other fields within data science include:
Some data scientists use such machine learning concepts and techniques as neural networks, supervised machine learning, decision trees, logistic regression, and adversarial learning. These specialized skills within data science solve complex real-world business or organizational problems.
There’s also work with unstructured data. Because some data don’t fit into neat rows and columns, unstructured data factors in wildcards like customer reviews, social media posts, images, and video. Data visualization takes structured and unstructured data and compiles it into easily digestible presentations using charts, graphs, and infographics.
Approximately 34,300 jobs in the health informatics medical records and health information openings will open each year from 2020 through 2030. (
A Master of Science in Health Informatics (MSHI) broadens your skill set and, as a result, your career options. ( )
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Perhaps it’s best to start by asking about informaticst itself. Often called the “human side” of data science, informatics uses technology to design and solve everyday issues. It takes big data queries associated with data science and creates real programs, healthcare databases, and systems for others to use.
Health informatics and biomedical informatics takes the basics of informatics and apply them to the healthcare industry. Because the medical field is a labyrinth of databases and records drawn from multiple sources, health informatics uses data science technology to organize these divergent sources into a singular system to be accessed by a wide spectrum of end-users, from doctors and nurses to medical administrators and insurance companies.
Management of these kinds of information systems requires specialized skills and training. Data must be kept secure in healthcare to protect patient privacy due to HIPPA laws without limiting critical provider access. Electronic health records (EHR) are now used throughout the healthcare industry. EHRs have radically changed patient recordkeeping but are often in diffrent formats and systems, creating challenges in navigating and unifying the data.
Health informatics and biomedical informatics use the predictive power of data science to find commonalities in medical data to treat patients. The ability to see trends in healthcare information can literally save lives. Scouting patient care records for likely outcomes or medical procedures or post-operative care provides medical professionals information not previously available.
Healthcare information comes from many sources and can be housed in various database systems. There is a wide array of healthcare in healthcare management roles as well. The most frequent health informatics positions include:
Health informatics is often described as “interdisciplinary,” encompassing health services, information technology, and medical science. This interdisciplinary approach utilizes healthcare delivery, management, and planning systems. One of the key goals of healthcare informatics is to help healthcare managers optimize care outcomes and performance.
The differences between data science and health informatics hinge on the question, “What can I do with all this data?” Data science is analytical. It examines data for actionable insights. Health informatics is the application of that analyzed information. The two play off one another in a healthcare setting, each helping the other towards a common goal of improving operational efficiencies and clinical care.
Health informatics professionals use computer science, programs, and databases to locate and manage data. Data analysts devise and execute data captures acquired by health information technology systems to report current and future predictive outcomes using written and visual presentations. Healthcare data analytics uses data streams to find patterns that produce predictive models for patients and hospital operations. They’re looking for insights into how healthcare organizations can improve efficiencies in clinical care and operations.
Health informaticists typically work at healthcare facilities or within healthcare systems, including clinical areas including emergency room care, physical therapy, and nursing. Others may work with EHRs or other medical computer software programs. There are also subsets of roles within health informatics, including bioinformatics, public health informatics, regulatory informatics, and health information management.
According to the Harvard Business Review, data scientists “lay a solid data foundation in order to perform robust analytics. Then they use online experiments, among other methods, to achieve sustainable growth.” They then “build machine learning pipelines and personalized data products to better understand their business and customers and to make better decisions.” In summary, “data science is about infrastructure, testing, machine learning for decision making, and data products.”
Indeed, employers that use big data are always searching for qualified data scientists. They find themselves in demand at numerous corporations, nonprofits, and governmental agencies. Recognizable companies like Amazon and Google use hundreds of data scientists in different configurations. Local, state, and federal agencies need big data to operate effectively. Big data is found just about everywhere in 2021, providing options galore for burgeoning data scientists across the US and abroad.
Data science and health informatics share a lot in common. They both dive deep into the world of data analytics and data processing to find actionable insights within the information. Skills in computer programming, mathematics, and statistics are essential. Soft skills like communication, critical thinking, and strategic planning are also necessary for both professions.
The difference between health informatics versus data science comes down to how and where the data is used. Data science offers more options to use your talents in a myriad of industries and fields. Health informatics is a specialized data science role within healthcare organizations.
If you are interested in broader data and higher-level theory and programming applications, data science is likely the better fit for you. Of the two, data science offers more opportunities for research and hands-on computing. However, if you have dual passions for healthcare and data, health informatics is a natural fit. Perhaps you already work in electronic medical records management and see the potential applications of healthcare analytics. A master’s in health informatics could be your ticket to applying data analytics to improve epidemiology, healthcare delivery management, and medical cost efficiencies.
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