Why were managed care organizations initially hesitant to use data mining applications?
One of the biggest hesitations would have to be cost to build an implement such a system. The technique depends on an organization having "clean" data to analyze, which requires data being scrubbed and moved to data warehouses. Many payers lack the money and manpower to build and maintain these warehouses. (Kongstvedt, P., Capagemini). In addition, internal politics and the numerous constituencies within a managed care organization can make it difficult to focus data mining efforts, says Scott Kozicki.
What has changed in this industry to adopt data mining?
The demand for more organizations to become more efficient, customers are demanding more and better services in shorter amounts of time. Another change would have to be HIPAA, which stands for Healthcare Information Portability and Accountability Act of 1996, it was a law that has many different facets to it one of which protects your private health information. The standards mandated by HIPAA have made the data "cleaner" and streamlines the analysis efforts.
What complexities arise when data mining is used in health care organizations?
One thing that makes data mining in health care organizations complex is just the same as what has helped get it going, HIPAA. Even though it has created standard rules for cleaning data, it requires that you encrypt information being transmitted over the internet, which adds costs to doing so. Some organizations only require it on certain transactions, but some want it done on every transaction. Doing this can increase the cost significantly causing it to raise the costs back up, making it not as feasible to use this practice.
Assume you are an employer and that your managed care organization raises your rate based on the results of data mining and predictive modeling software. What are your opinions? What would help make up your mind in regards to adopting these rate...
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