Electronic health records (EHR) are quickly becoming more common among healthcare facilities. With increased access to a large amount of patient data, healthcare providers can now optimize the efficiency and quality of their organizations using data mining.
Since the 1990s, businesses have used data mining for things like credit scoring and fraud detection. Now, a number of healthcare organizations are also beginning to see the potential benefits of data mining and predictive analytics.
In healthcare, data mining has proven effective in areas such as predictive medicine, customer relationship management, detection of fraud and abuse, management of healthcare and measuring the effectiveness of certain treatments.
The purpose of data mining, whether it’s being used in healthcare or business, is to identify useful and understandable patterns by analyzing large sets of data. These data patterns help predict industry or information trends and then determine what to do about them.
In the healthcare industry specifically, data mining can be used to decrease costs by increasing efficiencies, improve patient quality of life, and perhaps most importantly, save the lives of more patients.
Proven application of data mining
Data mining has been used in many industries to improve customer experience and satisfaction and increase product safety and usability. In healthcare, data mining has proven effective in areas such as predictive medicine, customer relationship management, detection of fraud and abuse, management of healthcare and measuring the effectiveness of certain treatments.
Here is a short breakdown of two of these applications:
- Measuring Treatment Effectiveness –This application of data mining involves comparing and contrasting symptoms, causes and courses of treatment to find the most effective course of action for a certain illness or condition. For example, patient groups who are treated with different drug regimens can be compared to determine which treatment plans work best and save the most money. Furthermore, the continued use of this application could help standardize a method of treatment for specific diseases, thus making the diagnosis and treatment process quicker and easier.
- Detecting Fraud and Abuse – This involves establishing normal patterns, then identifying unusual patterns of medical claims by clinics, physicians, labs, or others. This application can also be used to identify inappropriate referrals or prescriptions and insurance fraud and fraudulent medical claims. The Texas Medicaid Fraud and Abuse Detection System is a good example of a business using data mining to detect fraud. In 1998, the organization recovered $2.2 million in stolen funds and identified 1,400 suspects for investigation. To recognize its success, the Texas system received a national award for its innovative use of technology.
Data mining and its effect on privacy
Data mining possesses great potential for the healthcare industry, but it also comes with a few privacy concerns. Massive amounts of patient data being shared during the data mining process may leave some patients worried that their personal information could fall into the wrong hands. However, experts argue that this is a risk worth taking.
“There will be criminals. There will be people who are bad actors. At some point, something is going to get out,” Thomas Graf, chief medical officer at Geisinger Health System told The Washington Post in a 2014 article. “It’s not an irrational fear. At the same time, people die driving every year and we still choose to drive cars, or most of us do. It’s a risk every person has to decide where they fall on the line.”
Others have suggested letting patients choose whether their information can be used for data mining purposes and then providing a tax break benefit to encourage patients to get involved.
“The goal in healthcare is not to protect privacy, the goal is to save lives,” David Castro, Director of the Center for Data Innovation told The Washington Post.
The future of data mining
The shift from written to electronic health records has played a huge part in the push to use patient data to improve areas of the healthcare industry. The adoption of electronic health records has allowed healthcare professionals to distribute knowledge across all sectors of healthcare, which in turn, helps reduce medical errors, provide thorough documentation and improve patient care and satisfaction.
Data mining is also projected to help cut costs. If the U.S. healthcare industry continues to use big data to drive efficiency and quality, the potential value could reach more than $300 billion per year, according to a 2011 report from the McKinsey Global Institute.
The future of healthcare may well depend on using data mining to decrease healthcare costs, identify treatment plans and best practices, measure effectiveness, detect fraudulent insurance and medical claims, and ultimately, improve the standard of patient care.