Data Mining - Mehmed Kantardzic [85]
The completed system was then run each night giving each new medical claim a fraud probability. The claims are then reviewed being sorted by the given probabilities. There were previously very few documented cases of fraud. After implementation there were approximately 75 rejected claims per month. These newly found cases of fraud accounted for nearly 10% of the raw overall costs to the company. Additionally, the culture of fraud detection changed. A taxonomy of the types of fraud was created and further improvements were made on the manual revision process. The savings covered the operational costs and increased the quality of health coverage.
Overall this project was a big success. The authors spent a lot of time first understanding the problem and second analyzing the data in detail, before the data was modeled. The final models produced were analyzed in terms of real business costs. In the end the results showed that the costs of the project were justified and Banmedica S.A. greatly benefited from the final system.
4.9.2 Improving Cardiac Care
CVD leads to nearly 1 million deaths (or 38% of all deaths) in the United States per year. Additionally, in 2005 the estimated cost of CVD was $394 billion compared with an estimated $190 billion on all cancers combined. CVD is a real problem that appears to be growing in the number of lives claimed and the percent of the population that will be directly affected by this disease. Certainly we can gain a better understanding of this disease. There already exist guidelines for the care of patients with CVD that were created by panels of experts. With the current load on the medical system, doctors are able to only spend a short amount of time with each patient. With the large number of guidelines that exists, it is not reasonable to expect that doctors will follow every guideline on every patient. Ideally a system would aid a doctor in following the given guidelines without adding additional overheads.
This case study outlines the use and deployment of a system called REMIND, which is meant both to find patients at need within the system, and to enable a better tracking of when patients are being cared for according to guidelines. Currently two main types of records are kept for each patient, financial and clinical. The financial records are used for billing. These records use standardized codes (e.g., ICD-9) for doctor assessments and drugs prescribed. This standardization makes it straightforward for computer systems to extract information from these records and used by data-mining processes. However, it has been found that these codes are accurate only 60–80% of the time for various reasons. One reason is that when these codes are used for billing, although two conditions are nearly identical in symptoms and prescriptions, the amount of money that will be paid out by an insurer may be very different. The other form of records kept is clinical records. Clinical records are made up of unstructured text, and allow for the transfer of knowledge about a patient’s condition and treatments from one doctor to another. These records are much more accurate, but are not in a form that is easily used by automated computer systems.
It is not possible that with great demands on the time of doctors and nurses that additional