Four Key Components of Data Analytics for Healthcare
Posted on: 10/11/2023
Test results, lab notes, medical images, financial reports and other materials accumulate rapidly in a wide variety of healthcare settings. In a 2020 report, the American Hospital Association noted that the healthcare field generates approximately 2,314 exabytes of data annually, representing an average annual growth rate of 47% over the previous seven years. It’s difficult to explain how much data 2,314 exabytes truly is: a single exabyte is equivalent to one billion gigabytes, and some technologists have estimated that all words ever spoken by humankind are equal to about five exabytes.
The exponential rise in data use points to an urgent need for well-trained data analysts to collect and manage the information. The U.S. Bureau of Labor Statistics predicts that data scientist and health information technologist roles will see 35% and 16% job growth, respectively, until 2032.
Healthcare-focused data analysts perform crucial behind-the-scenes work in order to improve patient care, save organizations money and time and organize patient and institutional data. Their work keeps this important data secure and accessible for doctors and other staff who need it.
If you’re considering a role in healthcare data analytics, read on to discover four key aspects of the field and how an advanced degree can help your career.
Four Components of Data Analytics and Their Importance in Healthcare
| Component | Purpose |
|---|---|
| Descriptive analytics | Identify trends and relationships based on historical and current data |
| Diagnostic analytics | Detect patterns and find why something is occuring |
| Prescriptive analytics | Outline a best course of action |
| Predictive analytics | Predict future outcomes and performance |
The foremost goal of data analytics in healthcare is improving patient outcomes. By leveraging data from electronic health records (EHRs), patient portals, e-prescribing systems and more, data analysts ensure care teams have the most current and accurate information. This equips physicians with the data necessary to make precise diagnoses and outline the most appropriate treatment plans. Robust data collection and analysis is also critical for public health emergencies, like the COVID-19 pandemic, so that experts can accurately predict their spread and respond effectively.
Healthcare organizations rely on data analytics to save money, time and even lives. Prescribing the wrong dosage of a medication or failing to predict an illness based on evident data are costly and dangerous errors. Besides risks to the patient’s health, the facility can lose money from lost productivity and litigation expenses from patient lawsuits. Investing in analytics and the personnel to use data properly protects patients and the facility.
Here’s a deeper look at how each of the data analytics components aid healthcare both for patients and medical organizations:
Descriptive analytics
Descriptive analytics improves patient outcomes in a number of specific ways, but one application is when a healthcare facility evaluates patient demographic information to understand the needs of diverse populations. A hospital may also use descriptive analytics by evaluating emergency department data to determine the cause of wait times, allowing the facility to solve the issue and treat patients quicker. At the population level, descriptive analytics can identify patterns in disease outbreaks and evaluate different treatments to help prevent the outbreak’s spread.
Descriptive analytics can also positively impact healthcare by:
- Optimizing allocation and reducing waste by analyzing the usage of equipment, supplies and staff resources
- Helping analysts understand the cost of providing services and identifying opportunities to save money by examining patterns in financial data
- Improving patient care by aggregating and dispersing patient satisfaction survey results
Diagnostic analytics
Diagnostic analytics looks to find the root cause of why something happened. Healthcare institutions employ diagnostic analytics platforms to delve into the reasons behind events, such as investigating why a patient was hospitalized or determining the factors contributing to an ineffective treatment plan.
While performing diagnostic analyses, it’s crucial to differentiate between correlation and causation. Controlled experiments are needed to fully understand the cause of certain patient outcomes. Regression analysis can be used as a tool in diagnostic analytics by examining historical data to understand and explain relationships between variables.
Examples of questions that diagnostic analytics can answer in the healthcare setting include:
- Why are some patients more likely to follow prescribed treatment plans?
- What leads to a patient being discharged early?
- Why do certain illnesses affect some demographics of people differently?
Prescriptive analytics
Data professionals utilizing prescriptive analytics derive recommendations for an optimal course of action based on current and historical data. For instance, in oncology, prescriptive analytics helps doctors create precise cancer treatments based on genetic profiling and patient history, increasing the chances of remission while minimizing side effects.
Additionally, prescriptive analytics aids hospital management in predicting admission rates and resource demands, allowing administrators to allocate staff and materials effectively. These capabilities can enhance satisfaction, reduce expenses and ultimately contribute to a more efficient and patient-focused healthcare system.
Predictive analytics
On December 30, 2019, BlueDot, a Canadian company specializing in predictive analytics and AI solutions, issued a warning regarding the increase in unfamiliar flu-like cases in Wuhan, China. Nine days later, the World Health Organization officially declared the emergence of the coronavirus. Predictive analytics helped identify COVID-19 before an outbreak had been announced, and it continues to be a great asset in making discoveries that aid disease prevention, reduce cost and improve patient care.
By combing through vast amounts of patient data, predictive analysis can forecast disease progression, identify high-risk patients and optimize treatment plans. With this information, providers can efficiently distribute resources and target patients in need, thereby reducing burden on the healthcare system. More precise research data equates to better treatment, more effective operations and healthy patients.
Examples of predictive analytics facilitating precision medicine include:
- Helping providers anticipate future patient complications so they may conduct early interventions with personalized care plans.
- Tailoring therapies based on patients’ genetic and demographic data to maximize efficacy and minimize side effects.
- Forecasting medical readmissions, medication availability, disease spread and other factors that impact patient care.
- Finding suitable patient cohorts for clinical trials, thereby fostering accurate results and reducing costly treatment failures.
Why Pursue Graduate Education in Data Analytics?
Whether you’re currently working in healthcare or data science, or if you are curious about pursuing a career at the intersection of these fields, an advanced degree can be vital preparation for a leadership role in healthcare data analysis. Besides understanding what each analytic method is for, analysts also need evidence-based knowledge about the best applications for each analysis and the correct ways to perform them. In advanced study, aspiring data analysts get expert guidance in analytic techniques and the materials with which to conduct them.
A program with a well-balanced curriculum provides students with the theoretical knowledge and hands-on training to excel in the field. During graduate study, you’ll develop skills in programming languages, like Python and Java, and statistical modeling software, like SAS, which are in high demand. With many candidates entering the field with only a bachelor’s degree, investing in a master’s degree can set you apart from the crowd and can even lead to a higher salary.
About Master of Science in Applied Statistics at University of Delaware
The University of Delaware offers a 100% online M.S. in Applied Statistics (ASTAT) for data professionals interested in earning an advanced degree without interrupting the rest of their careers.
University of Delaware’s ASTAT master’s program provides students with opportunities to develop and apply their skills to current, real-world problems. Distance learners benefit from close relationships the University of Delaware maintains with large, locally based companies in numerous sectors, including the financial services, healthcare, chemical, pharmaceutical, technology and farming industries.
Our statistics courses also offer hands-on experience with popular statistical software packages such as SAS, enabling students to develop advanced proficiency in skills they will need to evolve their careers.
Expert statisticians from these and other organizations were recruited to develop and instruct case-study based courses specifically for the online ASTAT. These full-time faculty members aptly prepare students for jobs with a median base salary of $80,000.
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To learn more about the University of Delaware’s Master of Science in Applied Statistics, fill out the fields below to download a free brochure. If you have additional questions, please call 844-247-6251 to speak with an Admissions Counselor.