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Bayser Fuzzy Matcher
You certainly do not look forward to manually match two lists of physicians or two lists of facilities that don't have a common ID. While the eye can easily tell "Dr. Jean - Patrick Tsang" and "J. P. Tsang, PhD" are the same person, "4709 Golf Road" and "4709 East Golf, Suite 803", are the same street address, or that "Skockie, Ilinnois 60076" has a few typos and should be spelled "Skokie, Illinois 60076", the eye gets strained well before a thousand matches. The other problem with the eye is it may not catch the fact that 60066 is a bogus zip code or that the only valid zip codes for Skokie are 60076 or 60077. At Bayser, we developed a Fuzzy Matcher tool based on Artificial Intelligence principles. The Fuzzy Matcher uses fuzzy logic and abstraction techniques on one hand and a host of dictionaries pertaining to people's names, facility names, states, cities, zip codes, area codes on the other, to automate the matching process. The Fuzzy Matcher matches databases that don't have common ids, spots errors, suggests fixes, and eliminates fuzzy duplicates. If you are consolidating multiple data sources to set up a target list, to assess the physician/facility overlap, or for whatever reason, you now have a time- and cost-efficient alternative: the Fuzzy Matcher. Not only will you save time and money, the results will be far more accurate. You don't even have to learn how to use our Fuzzy Matcher - just email your files to us and you'll get the results back to you by e-mail along with a cleansing/matching report.
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Testimonials: “One of the brightest individuals that I've met in my career. JP has an incredible skill regarding simplifying issues, and preparing presentations for senior management." -- Director, Large Pharmaceutical Company
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