Question

Provide an example on how data mining can turn a large collection of data into knowledge that can help meet a current global challenge in order to improve healthcare outcomes.

Answer 1

Ans. Data mining holds great potential for the healthcare industry to enable health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce costs. Some experts believe the opportunities to improve care and reduce costs concurrently could apply to as much of overall healthcare spending.

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.

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. To recognize its success, the Texas system received a national award for its innovative use of technology.

Data mining is proving beneficial for healthcare, but it has also come with a few privacy concerns. Massive amounts of patient data being shared during the data mining process increases patient concerns that their personal information could fall into the wrong hands

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 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 have allowed healthcare professionals to distribute the knowledge across all sectors of healthcare, which in turn, helps reduce medical errors and improve patient care and satisfaction.

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.