Data Mining for Healthcare: A Proven Remedy for an Ailing Industry

When it comes to the challenges faced by hospitals and other medical facilities, there’s no better prescription than data mining for healthcare. The unpredictable ebb and flow of Medicare funding have made it absolutely imperative for healthcare facilities to provide the best service at all times.

Medicare and other evaluation organizations keep hospitals reaching for positive quality ratings. These competitive ratings are based on a number of criteria including patient satisfaction, preventive treatment measures, the volume of re-admitted patients, the number of deaths, etc.

Hospitals with high ratings will be rewarded with higher funding, whereas poorly rated facilities will suffer from a hit to their overall budgets.

With all this pressure on healthcare organizations, data mining has become an extremely valuable tool to measure what is truly happening in their facilities. By using healthcare data mining techniques, medical facilities are making positive changes to ensure high Medicare rankings. By properly employing data mining for healthcare, you can protect your budget and quality rating. Here’s how:

Smarter treatment methodologies

Data mining for healthcare is useful in evaluating the effectiveness of medical treatments. Through comparing and contrasting various causes, symptoms, and treatment methodologies, data mining can produce an analysis of which treatments correct specific symptoms most effectively.

Data mining can also help physicians discover which medications are the most cost-efficient while still working effectively. Still other data mining applications could be used to associate the most common side-effects of a medication, to collate typical symptoms in order to improve the accuracy of diagnosis, or to discover proactive steps to reduce the risk of affliction.

Improved healthcare management

In order to improve healthcare management, data mining applications are able to work to identify and track high-risk patients in order to design appropriate interventions as a means to lower the number of admissions, re-admissions, and claims.

For example, the Arkansas Data Network evaluates re-admissions and resource utilization, compares the data against current scientific literature, and then determines the best treatment options to lower spending.

Another example can be found in the Group Health Cooperative which sorts its patients by their demographic traits and medical conditions in order to discover which groups use the most resources. In this way, programs can be developed to help educate “problem” populations on how to better prevent or manage their conditions.

In other cases, data mining for healthcare has been used to decrease patient length-of-stay, avoid medical complications, improve patient outcomes, hospital infection control and early warning systems, etc.

Better customer relations

Customer relationships are invaluable within the healthcare industry - especially since your customers now have a say in your Medicare ranking. By understanding patient preferences, patterns, and characteristics, you can significantly improve their level of satisfaction.

Because of this, the Customer Potential Management Corp. has created a Consumer Healthcare Utilization Index that uses data mining for healthcare in order to indicate an individual patient’s propensity toward using specific healthcare services. This determination is defined by 25 major diagnostic categories and is based upon millions of healthcare transactions. The end result is an ability to identify patients who would receive the most benefit from specific services and to encourage these patients to do so.

Data mining is also helpful in finding patterns amongst patients surveyed as a means of setting reasonable wait time expectations, discovering what patients want from their healthcare providers, and finding ways to improve services.

Decreased insurance fraud

Another advantage to data mining for health care is the ability to detect and decrease insurance fraud. Data mining applications are able to establish norms and then identify any abnormal patterns and claims in order to eliminate inappropriate prescriptions or referrals, and fraudulent medical claims.

With the right tolls, the data mining for healthcare can significantly impact your Medicare quality rating and your overall budget.

Learn more about Data Mining for Healthcare

Interested in discovering more about big data and data mining? Our Resource Center is packed with free eBooks, whitepapers and videos to help you gain an edge in this growing industry.

IKANOW has the healthcare data mining resources and tools to keep your organization ahead of the curve. Our product experts are ready to give you a free one-on-one demonstration to show you how our Infinit.e open analytics platform can help you reach your goals with big data.