Data Mining Applications in Healthcare

Healthcare facilities are adopting Electronic Health Records (EHR) more rapidly than ever. EHRs allow healthcare facilities to improve quality and boost efficiency through the application of data mining on large pools of data.

In the 90s, organizations applied data mining for credit scoring and the detection of fraud. More healthcare providers are starting to realize the potential that lies in using data mining and predictive analysis in their organization.

Benefits of Data Mining in Healthcare

There are several areas in the healthcare industry that data mining has proven to be useful. Some of these areas include improving customer relations, development of predictive medicine, identifying fraud and neglect, overall healthcare management, as well as accurately determining the accuracy of certain elements within the system.

The main goal of data mining in the different industries where it is applied is to capture the patterns that are useful and meaningful by evaluating large data sets. Once these patterns are captured, they are used to predict trends in the industry and how to take advantage of these trends.

Data mining has proven useful, specifically in the healthcare sector particularly in the reduction of costs through the enhancement of efficiencies, enhancing care for the patient, and by saving more patients lives.

What Are the Applications of Data Mining?

In many industries, data mining has played a big role in enhancing the customer’s experience, improving safety and the convenience of using the products or services. Data mining is very useful in the healthcare industry in the development of medication, enhancement of customer experience and customer relationships, detecting and eliminating fraud within the system, and determining the viability of treatment options.

Here is a closer look at two of the applications as applied in the healthcare industry:

• Determining Viability of Treatments: Data mining is used here to make a comparison on the symptoms, causes and factors that can determine the best treatment option for a given condition or illness. This can be done by comparing patients under different treatment protocols and working out which treatment option is the most effective and less costly. Additionally, this process can be carried out on an ongoing basis with the aim of standardizing treatment protocols for certain diseases. The advantage of using data mining, in this case, is that the treatment procedures take less time and conditions are easier to execute.
• Detecting and Eliminating Fraud: By using data mining, healthcare providers are in a better position to pinpoint unusual patterns of behavior from clinics, labs, and other medical practitioners making various claims. Data mining can help identify cases of false medical claims, unsuitable referrals, as well as detect prescription and insurance fraud. One real-life example of data mining used for this application is the Texas Medicaid Fraud and Abuse Detection System. In 1998 the system was able to recover $2.2 million in funds lost through fraud and also produced a list of 1,400 suspects who should be investigated.

The Effects of Data Mining on Privacy

While data mining can be useful to the healthcare industry, the privacy concerns must be acknowledged. Patients may not be comfortable with their data being used for data mining processes. Many patients may feel the process could fail to protect their data and it could fall in the wrong hands. Many experts believe the benefits outweigh the risks.

To solve the issue of privacy concerns, it has also been proposed that patients should be allowed to decide if their information can be shared for data mining purposes. Those who opt to share information can enjoy the benefits of a tax break, which will encourage more people to participate.

Data Mining in the Future

The application of data mining in improving aspects of the healthcare industry has largely been facilitated by the transition from paper records and files to Electronic Health Records. Practitioners in the healthcare industry can dispense information across different sectors of healthcare. This has enabled the players in the healthcare sector to eliminate mistakes, develop more comprehensive documentation of processes and procedures, and enhance the level of patient care.

According to a report by McKinsey Global Institute, data mining is expected to help in cutting costs further. The report states that if big data is applied with the aim of enhancing quality and efficiency, its value could rise to about $300 billion annually.

Conclusion

Data mining is set to play a very important role in minimizing costs in healthcare, the development of best practices and treatment options, determining and enhancing efficiency, detecting false medical and insurance claims, and to ultimately raise the level of patient care.