EVIS delivers services to transform your data to information.

As in many other areas, also the medical care domain has to deal with large amounts of data. Unfortunately, data does not automatically lead to information. Even worse, just storing data, e.g. for legal reasons, only worsens the situation, and you first need to invest large amounts of money without a direct benefit. Instead, we propose that you should let your data 'work for you' and help you improve your business of delivering care.

Whether you are a hospital, an insurance company, a medical researcher or a patient, we all have strong interests in optimizing our processes: A hospital wants to minimize costs without reducing quality. An insurance company wants healthy clients who quickly get better. A medical researcher needs to sift through large sets of data to uncover unknown relationships. And a patient wants to have as little to do with hospitals and insurance companies as possible.

Analyzing huge amounts of data is not a task taken lightly. Service providers offering 'out-of-the-box' solutions are tempting, but often don't yield the desired results (source: K.K. Hirji, IBM Canada Ltd., 'A proposed Process for performing Data Mining projects', Ch.5. in "Managing Data Mining Technologies in Organizations"). Also the personel that you employ to manage and store your data is often not sufficiently skilled in analyzing it for you, the same way a nurse doesn't perform an operation.

How can we help?

EVIS is dedicated to helping you get most out of your data. Some example areas where we may assist you are:

• Compute performance indicators that show you how well your hospital is performing.
• Determining the most efficient, beneficial and cost-effective hospital for your clients, e.g. find the hospital that has a very good track record and a short waiting list for your client's ailment.
• Combining genotype information with environment factors and medical history to determine high risk cases that need special attention.
• Investigating the quality of care by analyzing the root causes for patient demise.
• Combining disparate data sets to a whole for knowledge management (such as dashboard applications).
• Determining fraudulent behaviour by comparing the normal process flow with fraudulent process flows.

 If you would like to find out more, please contact us.