Data mining has been utilized by corporations since the 1990s for a variety of purposes, including credit scoring and fraud. The use of data mining in healthcare is becoming increasingly prevalent as a result of an increase in the accessibility of huge amounts of patient data for healthcare providers in the modern era. These organizations are adopting the use of data mining with the goal of improving the quality and efficacy of their predictive analytics.
What is Data Mining
The goal of data mining is to locate meaningful patterns that can be comprehended easily by examining several data sets. This is true regardless of whether it is used in the field of medicine or in business. These data patterns help identify trends in the sector or in the information and then decide what should be done about them.
In the context of the healthcare business, data mining has the potential to reduce costs by improving operational efficiency, enhancing the quality of life for patients, and, perhaps most critically, saving the lives of numerous patients.
Examples of Data Mining in Healthcare
Mining customer data is a practice that has found application across a variety of business sectors, with the goals of enhancing customer experience and happiness, as well as enhancing product safety and usefulness. In the field of medicine, data mining has been shown to be useful in a number of applications, including predictive medicine, the management of customer relationships, the identification of abuse and fraud, the evaluation of the efficacy of certain therapies, and the management of healthcare.
The following is a concise comparison of two applications for healthcare data mining, followed by some examples of how both applications may be used in practice.
Measuring the Effectiveness of the Treatment
In this application of data mining in healthcare, symptoms, causes, and treatment options are compared and contrasted in order to determine which treatment option is likely to provide the best results for a particular sickness or condition. For instance, patient groups that are treated with various medication regimens may be compared to one another in order to establish which treatment plans are the most effective while also resulting in optimum cost savings. In addition, ongoing usage of this data mining application might contribute to the standardization of a technique of treatment for certain illnesses, which would make the process of diagnosis and treatment both more expedient and less difficult.
The Detection of Fraud and Misuse
This particular use of data mining in the healthcare industry entails establishing regular patterns and then recognizing unexpected patterns of medical claims made by clinics, doctors, laboratories, or other entities. Inappropriate prescriptions or referrals, as well as false medical claims and insurance fraud, may all be uncovered with the assistance of data mining. One company that successfully detects fraudulent activity via the use of data mining is the Texas Medicaid Fraud and Abuse Detection System. In 1998, the group was successful in recovering $2.2 million and identifying 1,400 individuals as potential candidates for further inquiry. A national award was given to the Texas system in recognition of its achievements in the creative use of technology.
Data Mining & Privacy of the Patients
Data mining is proven to be advantageous for the healthcare industry, but it has also brought with it certain concerns over patient privacy. Patients become more concerned that their personal information may be compromised throughout the data mining process since massive volumes of patient data are being exchanged with one another. However, experts believe that the risk is worth taking.
There will be some who commit crimes. There are going to be those who behave inappropriately. Something is going to slip out at some time. It’s not an illogical worry. Despite the fact that there are fatalities associated with driving every year, the majority of us continue to prefer to drive our automobiles.
Others have proposed giving patients the option of deciding whether or not their information may be used for the purpose of data mining, followed by the provision of a tax break advantage in order to encourage people to participate.
According to David Castro, Director of the Center for Data Innovation, “the goal in healthcare is not to protect privacy, the goal is to save lives.“
The Prospect for Data Mining In Healthcare
The movement away from paper-based health records and toward electronic health records has been a significant contributor to the ongoing effort to enhance many aspects of the healthcare business via the use of patient data. As a result of the widespread adoption of electronic health records, medical professionals now have the ability to share their knowledge with colleagues in all areas of the healthcare industry. This, in turn, contributes to the reduction of medical errors and the improvement of patient care as well as patient satisfaction.
It is also anticipated that data mining would assist reduce expenses. The potential benefit might be substantial if the healthcare business in the United States keeps relying on big data as a driver of efficiency and quality. Research conducted by McKinsey & Company indicates that initiatives to analyze data on a system-wide scale have the potential to reduce total healthcare expenditures by 12-17%.
In 2017, the total national expenditure on healthcare in the United States surpassed $3.5 trillion, as estimated by CMS and published expenditures statistics. If a savings rate of 12-17% is applied to that figure, the anticipated cost reduction that may be achieved by application-wide data analytics might range anywhere from $420 billion to $595 billion.
It is possible that the future of healthcare will be dependent on the use of data mining to reduce the costs of healthcare, measure effectiveness, identify best practices and treatment plans, detect fraudulent medical and insurance claims, and ultimately enhance the level of care provided to patients.
The Bottom Line
Data mining strategies should be used in hospitals in order to efficiently handle patient inquiries and perform big data analyses. The facility will utilize the data science to determine which patient circumstances are inefficient and then implement best practices in order to save expenses and provide better treatment.
The Ministry of Health and its affiliated institutes should make data mining one of their top priorities. By making the most of the latest technological advancements and positively classifying the sources of patient data, medical professionals may improve the quality of care they serve their patients. In a similar vein, a greater number of hospitals have to begin using Data Mining methods in order to carry out big data analytics and effectively handle patient queries.
A comprehensive grasp of the fundamental concepts, data mining processes, and data mining tools is required prior to the application of data mining. The user won’t be able to steer clear of the potential problems of data mining if they don’t have the necessary expertise.