What is Data Mining in Health Care?

Big data has become a central theme across all industries — health care included. Data analytics have achieved wide adoption and popularity in health care, and for good reason. The insights mined from such data can prove invaluable in improving care delivery, early diagnosis, disease identification and hospital staffing. There is nearly limitless potential for leveraging data across the spectrums of patient care and safety, as well as operational decision-making and academia.

But what exactly is data mining? And what is the value of data mining in health care? These are questions that today's nurses must answer. Data mining can be useful to nurse practitioners, nurse administrators, nurse leaders, nurse lobbyists and nurse executives, to name a few popular nursing careers. These professionals regularly interact with data sources, whether as caregivers taking patient histories with the help of electronic health records or as population health nurses who use large data sets.

The bottom line is data proficiency is increasingly becoming a necessity in the modern nursing profession. Health care data analytics is too important a topic to bypass. However, not all nurses may possess such highly technical knowledge. That's where earning a Doctor of Nursing Practice can help by exposing nurses to data concepts and mining strategies to make the most of the intelligence they gather.

What is data mining?

Data mining is basically the analysis of large data sets, looking for patterns and trends that can be extrapolated into insight. An example is scrutinizing thousands of MRI images to find commonalities that may influence how diagnoses are made or treatments are constructed. Data mining has always been around in a practical sense, but has not really ramped up until the 21st century. The rapid pace of technology innovation has enabled countless advances in health care data analytics. Now, applications like machine learning and artificial intelligence can be used to automate processes and sharpen interpretation of data. Instead of sorting through images by hand, nurses can use data mining solutions to automate the collection, organization and analysis of data, serving up actionable insights.

What do health care analytics look like?

There are a number of scenarios in which data analytics can be used for health care, and the list seems to grow by the day. However, understanding these uses of big data analytics for health care purposes means becoming familiar with the three main categories:

• Descriptive: This refers to the exploration of information contained in a data set. As the name implies, such analytics are used to describe vast sets, thereby functionally condensing them into digestible data points, graphs or reports that nurses can act on. Descriptive analytics uses historical and current data to quantify raw data. Hospitals, for example, might analyze readmissions to gain insight into trends that could reduce costs and improve care delivery.
• Predictive: Such analytics are used to make projections, like patient outcomes based on number and severity of risk symptoms presented. Predictive analytics uses that same historical data, but in a different way: a basis for forecasting events or outcomes in the future. This has clear value in health care, like population health. For example, nurses can look ahead to trends in chronic illnesses in rural populations and act accordingly.
• Prescriptive: This form of analytics takes predictive one step further, like examining the effect of different steps taken to reduce diabetes in the rural population. Stakeholders can predict multiple futures based on the preventive action taken and analyze the usefulness of each tactic compared to other options. Prescriptive analytics can help nurses understand which path of action is feasible and which offers the most upside, such as in constructing department-wide performance improvement goals as a nurse executive. Often, this helps with decision-making support or evidence to persuade policymakers.

How can nurses prepare?

Data mining is increasingly being woven into the job expectations for nurses, especially those in senior leadership roles. Here are a few best practices nurses should become familiar with:

• Data collection: Proper gathering of data is essential to analysis. Data quality begins with collection, and nurses should be knowledgeable about how to maintain accuracy and uniformity when inputting data into an EHR, for instance.
• Data tools: Effective analysis also depends on the correct use of the tools at hand. While Meaningful Use standards develop EHR familiarity, going beyond the basic requirement helps nurses become intimately familiar with data mining tools and how to utilize them for positive gain.
• Database maintenance: The right data storage approach preserves integrity and accuracy, both essential to quality analysis. Learning how to maintain databases and manipulate them with search query language (SQL) should be high on the priority list.
• Data compliance: Security of private health information is paramount, and nurses must be aware of regulations (such as HIPAA) and potential cyber threats.

Learn about health care analytics at Bradley

If you're looking for a way to develop this deep knowledge, earning an online DNP degree from Bradley could be an option. You can explore fundamental data mining concepts and their relevance to health care in courses like NUR 752 Advanced Health Informatics, ENC 510 Statistical Procedures, and CIS 576 Data Management. In that last class, you'll examine techniques and processes for collecting, organizing, storing, protecting and analyzing data. Insight into concepts like data governance and data science will help build competency in data mining.

Want to get more information about the Bradley online DNP program? Contact an enrollment advisor today.

Recommended Reading

Five Ways Big Data Is Changing Nursing

How Does Health Informatics Impact Nurse Practitioners?

Bradley University Online DNP Program

Sources

Halo - Descriptive, Predictive, and Prescriptive Analytics Explained