Businesses use data mining to study and predict customer behavior, and in credit scoring, fraud detection, and maintenance scheduling. In healthcare, mining relevant data allows providers to improve the quality of care.

1. Data Mining Improving the Quality of Healthcare
Data mining is helping researchers identify effective treatments and best practices in
the field of cardiology.
Businesses use data mining to study and predict customer behavior, and in credit
scoring, fraud detection, and maintenance scheduling. In healthcare, mining relevant
data allows providers to improve the quality of care.
Patient Data Mined to Study the Correlation Between CPR and Neurological
Outcomes
Mining clinical records is proving useful for cardiovascular researchers in the area of
patient diagnosis. Last November, it was reported that a group of Japanese
researchers concluded that continuing CPR (Cardiopulmonary Resuscitation) for
around half an hour after cardiac arrest could save the patient’s life. They claim that
around 38 minutes of CPR increased the chances of survival of victims and retained
brain function. These conclusions were reached by mining data related to more than
280,000 people who had experienced cardiac arrest outside a hospital with a
bystander present.
A data filtering process was then used to narrow down the previous group to 32,000
people, whose hearts started beating on their own (termed “return of spontaneous
circulation”) after resuscitation. Analysis of the data on these patients 30 days after
their cardiac arrest revealed that more than 27 percent had good brain function!
The final conclusion of the study was that the brain function was the best with a
return to spontaneous circulation by 13 minutes and that neurological outcomes
would probably be not as favorable beyond 38 minutes.
Though this study has not been peer-reviewed and the results cannot be used to
modify clinical practices, what’s important here is that such useful research would
not have been possible without data mining. One of the major challenges that
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2. healthcare organizations face is the provision of quality patient care at affordable
costs. Quality treatment and care depends on accurate diagnosis. To minimize the
costs of clinical testing, healthcare providers need to make appropriate use of
information derived from electronic health records (EHR) as well as decision support
systems. Digitization of health information offers immense opportunities to mine
patient data and use it to improve care at reduced costs.
Data Mining – An Iterative Process
Regardless of the field it is applied to, data mining is an iterative process which
involves certain typical steps:
• Understanding the problem — identifying the project’s objectives and
requirements from a business perspective and defining the data mining
problem.
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3. • Understanding data —collecting the initial data, understanding it, and
identifying quality problems
• Data preparation—building the final dataset from the raw data
• Data modeling— using data mining software to analyze the tagged data
• Evaluation— evaluating the final outcome of the project by comparing data
mining models and their results using a common benchmark
• Deployment—implementing the data mining results by exporting the results
into applications such as database tables
More and more businesses are making use of professional data mining services as
they have limited time to spare for this strenuous and time consuming task.
Outsourcing companies that specialize in data entry offer large volume data mining,
web data mining and online data acquisition projects at affordable cost.
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