How is Data Analytics Used in Healthcare?

Data is all around us; it comes in different sizes and from different sources. Each day, an estimated 2.5 quintillion bytes of data are created. However, until this data is sorted and analyzed, none of it is actionable. That’s where data analytics comes in.

Data analytics is the set of techniques used to analyze raw (unprocessed) data in order to extract relevant information, trends, and insights. This process includes collecting data, organizing it, and storing it, then performing statistical analysis on the data. Once the information is collected, conclusions can be drawn from it, which can be used for problem-solving, business processing, decision-making, as well as predictions that can inform what a company’s next steps should be. This process relies on disciplines like mathematics, statistics, and computer programming.

As our reliance on data continues to grow, so does the need for qualified Data Analysts in a variety of professions. This article will explore how data analytics is used in the healthcare industry in the form of health analytics.

Professional Applications of Data Analytics in Healthcare

An important part of the healthcare industry is the collection of data. Data provides healthcare organizations, practitioners, and workers with important insights into ways to offer more effective patient care, as well as better day-to-day operations. It’s a means of reviewing historical information, noticing current trends, and tracking outcomes.

The following are some of the most popular applications of healthcare analytics, as well as the benefits they provide stakeholders:

• Advancing research: Healthcare analytics is helping to advance clinical research, which can lead to better clinical guidelines, as well as registry-based clinical trials.

• Identifying high-risk patients: Rather than just treating a patient’s current symptoms, healthcare analytics allows practitioners to spot patients who are at a higher risk of chronic illness, which is an effective preventive means, as well as a cost-saver for all parties, including the patient, insurance provider, and practitioner.

• Reducing healthcare costs: As fee-for-healthcare is being replaced with patient-centric models, the use of predictive and preventive measures is growing in importance. Predictive and prescriptive analytics provide healthcare practitioners with comprehensive models for mitigating patient risk as well as reducing the cost of treatment. The field of health data analytics is also a valuable tool for preventing equipment malfunctions and cutting down on appointment no-shows.

• Incorporating predictive modeling: When applied to the healthcare setting, predictive analytics informs healthcare professionals of the possible risks in a situation. For example, studying behavioral data helps with predicting the possible risk of chronic illness as well as treatment outcomes. Predictive modeling also has large-scale applications. It’s used to manage the health of whole populations in terms of predicting outbreaks and offering insights about what can be done to prevent future incidence.

• Evaluating healthcare practitioners: When patients write reviews about visiting a doctor, the data from these reviews are collected and analyzed to indicate which areas can be improved to offer a better patient experience.

• Noticing anomalies in scans: Machine learning algorithms allow Healthcare Analysts to quickly analyze data. Since these algorithms can learn from data they have already received, when they are applied to situations such as finding differences between MRI scans, they can notice anomalies over a thousand times quicker than a human would be able to.

• Improving e-recordkeeping: Most of the hospitals in the US keep electronic health records of all their patients. These detailed records contain even minute details about a patient’s health, demographic information, and family history of illness. The main benefits of these files are that they can be stored for years without creating a loss of data, and they can be accessed immediately by various hospitals or providers without the need for one facility to send information to another.

• Providing insights about hospitalized patients: When a patient must be hospitalized, healthcare analytics can be applied to anticipate the risks of infection, as well as readmission.

• Helping with staffing concerns: One of the main concerns a medical facility faces pertains to making sure it is adequately staffed. If a hospital is understaffed, patients receive less care; if it is overstaffed, the hospital may accrue needless expenses. When applied to staffing considerations, healthcare analytics draws from prior months’ and years’ admission records to pinpoint patterns and offer predictions on what hospital admission may look like in a week or so. This allows hospitals to schedule additional staff to compensate for larger patient volumes and also cuts down on the wait time for patients.

The use of data in the healthcare industry has provided many benefits to practitioners, insurers, organizations, and patients. As new technologies become more available, such as machine learning, natural language processing, and artificial intelligence, the field of healthcare analytics is expected to continue to evolve and change the landscape of the healthcare industry.

Start Learning Data Analytics with Hands-On Classes

Are you interested in learning more about the field of data analytics? If so, Noble Desktop’s data analytics classes are a great starting point. Courses are currently available in topics such as Excel, Python, and data analytics, among others skills necessary for analyzing data.

In addition, more than 130 live online data analytics courses are also available from top providers. Courses range from three hours to six months and cost from $219 to $27,500.

Those who are committed to learning in an intensive educational environment may also consider enrolling in a data analytics or data science bootcamp. These rigorous courses are taught by industry experts and provide timely instruction on how to handle large sets of data. Over 90 bootcamp options are available for beginners, intermediate, and advanced students looking to master skills and topics like data analytics, data visualization, data science, and Python, among others.

For those searching for a data analytics class nearby, Noble’s Data Analytics Classes Near Me tool provides an easy way to locate and browse the 400 or so data analytics classes currently offered in the in-person and live online formats. Course lengths vary from three hours to 36 weeks and cost $119-$27,500.