Applications of data analytics in healthcare.

An overview of Data Mining in Healthcare

Data mining in healthcare has great potential to play a significant role in the healthcare industry. This is mainly due to the fact that electronic health records of patients are increasingly getting popular among healthcare providers.

Doctors and physicians usually work with patients’ health data recorded in paper-based forms. With improved access to a large volume of patient data, it has become a big challenge for healthcare providers to shift to an efficient computerized data management system which would analyze and transform this mass of data into useful information most accurately and efficiently. This is where the use of healthcare data analytics has come to play a significant role. Healthcare data mining has the capability to discover hidden patterns or relationships among the objects in medical data.

Here in this post I have given an overview of applications of data analytics in healthcare in major areas and also highlighted the limitations of data mining in healthcare. But before that let’s first explain what is data mining healthcare and how it works.

What is healthcare data mining?

The data mining process is defined as the process of extracting useful information from the patterns of a large volume of stored data sets and using that information to build predictive models. The data mining process uses algorithms drawn from the field of statistics, machine learning and database management systems. The foundation of data mining encompasses three intertwined scientific disciplines – statistics, artificial intelligence (AI) and machine learning (ML).

The most basic definition of healthcare data mining is – extracting useful information from the patterns of a large volume of stored healthcare data sets and using that information to build predictive models. Simply put, the goals of data mining in healthcare are – prediction, modelling, and inference.

What is healthcare data analytics?

The processes of healthcare data analytics are categorized into descriptive and predictive. The descriptive analysis mines a huge amount of data generated by healthcare transactions and provides the latest information on past and recent events. On the other hand, predictive analysis plays a significant role in the prediction and diagnosis of diseases and in improved decision-making, by discovering patterns and trends from this large healthcare database. Since processing and analysis of such a large collection of healthcare data are too complex and voluminous by traditional methods, advanced data mining techniques are used today which analyze and transform this mass of data into useful information most accurately and efficiently. The application of data mining algorithms plays a significant role in these advanced techniques in the prediction and diagnosis of diseases and in decision-making.

The major role of healthcare data analytics.

Healthcare providers find it too complex and voluminous to handle and analyze the massive amounts of electronic health records of patients and their related administrative reports by traditional methods. Healthcare data analytics has the capabilities to carry out this data analysis most efficiently and transform the large volume of stored data into useful information to predict future outcomes. Simply put, the goals of data analytics in healthcare are – prediction, modelling, and inference.

Here are three major areas of applications of data mining in healthcare examples where data analytics in healthcare plays an important role are:

Evaluation of the effectiveness of medical treatments

Data mining can be used to evaluate the effectiveness of medical treatment for a particular illness or health condition.

By comparing the symptoms, causes and courses of drug treatments of similar diseases, the data mining process can carry out an analysis to decide which remedies would work best and would be most cost-effective for the specific ailments.

For example, the results of treatments of patient groups with different drugs for the same illness or condition can be compared and analyzed to find out which drug would give the best results for the particular disease or condition and would also save money. Moreover, results from such applications of data mining techniques in healthcare for a long period can help to standardize approaches to treatments for specific ailments, making diagnosis and treatment processes faster and simpler.

Managing customer relationship

As in the case of commercial organizations, customer relationship management is also very important for healthcare providers. Data mining healthcare services can be used to recognize patient preferences and their current and future needs to improve their level of satisfaction. Based on hundreds and thousands of healthcare transaction data of a large number of patients, healthcare data analytics can identify patients who can benefit most from specific healthcare services and encourage them to access the said services. Through this process, healthcare facilities can reach the right audiences for improved health and long-term patient relationships and loyalty. Eventually, this will result in more effective and efficient communications as well as increased revenue for healthcare providers.

Detecting fraud and misuse

Data mining healthcare combines powerful analytical techniques to detect healthcare fraud and abuse related to medical and insurance claims. Data mining in healthcare first establishes norms by analyzing the mass of data generated by millions of prescriptions, operations and treatment courses and building predictive models for finding fraudulent claims. These healthcare data analytics models are then applied to future claims to identify any abnormal patterns of medical claims by clinics and labs or inappropriate prescriptions or referrals by physicians and probable fraudulent insurance claims.

Besides the above, other areas of applications of data mining in healthcare examples are,

i. Evaluating the practitioners – Data collected regarding patients’ experiences with medical practitioners can be analyzed to know the areas for their improvement;

ii Analyzing data more quickly and efficiently – Data analytics in health care can analyze data more quickly and efficiently than humans with the help of machine-learning algorithms;

iii. Predicting the spread of illness – Data analytics can predict trends in the spread of illness, allowing to take adequate precautions beforehand;

Limitations of data mining healthcare

Although data mining applications in healthcare are very powerful tools, they cannot do everything by themselves. There are some limitations and challenges in the use of data mining in healthcare which creates a major obstacle to successful data mining. To be successful, data mining needs correct data and the help of an expert who can make objective conclusions from the extracted information. If there is a supply of incorrect or incomplete information, the output will be affected and the forecast will not be credible. Some of the major limitations of data mining healthcare are the reliability of medical data, data sharing across healthcare organizations and improper modelling leading to erroneous predictions.

Frequently asked questions

Before I conclude let me also address some of the related queries often made by people.

How can data mining be used in the healthcare system?

Data mining can be used to help doctors to provide effective treatments and better patient care, help healthcare organizations to improve customer relationships, help healthcare insurers to detect fraud and abuse and many more.

What is big data or data mining?

Big Data refers to the collection of a large amount of healthcare data. Whereas data mining refers to the extraction of useful information from the patterns of a large volume of stored healthcare data sets and to use that information to build predictive models.

How data is used in healthcare?

In recent years, healthcare data collection has moved into the digital sphere, making analysis faster and more accurate. Healthcare data is used to help healthcare in 4 major areas as mentioned below:

1. For managing the good health of a patient;

2. For providing more accurate treatment;

3. For preventing cases before they occur;

4. For reducing prescription errors;

Conclusion

From the above discussion, it is evident that data mining in healthcare has huge potential to play a significant role in the healthcare industry. Healthcare data analytics can quickly filter massive amounts of data to find treatment options for different diseases and provide accurate solutions based on historical information. It may also provide customized solutions to unique concerns for particular patients. This is going to improve patient care and reduce medical costs in a big way.

However, as already mentioned the success of data mining techniques in healthcare depends on the availability of correct healthcare data. It is, therefore, critical to be concerned about how data can be better captured, stored, prepared, and mined. Possible advice in this context may be, sharing of data across healthcare organizations to enhance the benefits of applications of healthcare data mining.