DATA MINING IN HEALTHCARE

 

DATA MINING:

                

The process of extracting information to identify patterns, trends, and useful data that would allow the business to make the data-driven decision from huge sets of data is called Data Mining.

DATA MINING IN HEALTHCARE:

Data mining in healthcare 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.

Data mining techniques used in healthcare Source:

üSEQUENCE OR PATH ANALYSIS (i.e. finding patterns where one event leads to another later event), 

üCLASSIFICATION (i.e. looking for new patterns and predicting variables based on the factors the database contains), 

üCLUSTERING (i.e. grouping a set of objects and aggregating them based on their similarity to each other) 

HEALTHCARE DATA MINING AND ITS EFFECT ON PATIENT PRIVACY:

üData mining is proving beneficial for healthcare, but it has also come with a few patient privacy concerns..

üMassive amounts of patient data being shared during the data mining process increases patient concerns that their personal information could fall into the wrong hands.

ü However, experts argue that this is a risk worth taking

MEASURING TREATMENT EFFECTIVENESS:

üThis application of healthcare 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.

ü 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.

ü Furthermore, the continued use of this data mining 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:

üThis application of data mining in healthcare involves establishing normal patterns, then identifying unusual patterns of medical claims by clinics, physicians, labs, or others.

ü This application can also be used to identify inappropriate referrals or prescriptions and insurance fraud and fraudulent medical claims. 

THE FUTURE OF DATA MINING IN HEALTHCARE

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.

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 value could be significant. According to research from McKinsey and Company, system wide data analytics efforts could cut overall healthcare costs by 12-17%.

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.

The goal in healthcare is not to protect privacy, the goal is to save lives