A Fuzzy-Match Search Engine for Physician Directories
BackgroundA search engine to find physicians’ information is a basic but crucial function of a health care provider’s website. Inefficient search engines, which return no results or incorrect results, can lead to patient frustration and potential customer loss. A search engine that can handle misspe...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2014-11-01
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| Series: | JMIR Medical Informatics |
| Online Access: | http://medinform.jmir.org/2014/2/e30/ |
| _version_ | 1818382475779375104 |
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| author | Rastegar-Mojarad, Majid Kadolph, Christopher Ye, Zhan Wall, Daniel Murali, Narayana Lin, Simon |
| author_facet | Rastegar-Mojarad, Majid Kadolph, Christopher Ye, Zhan Wall, Daniel Murali, Narayana Lin, Simon |
| author_sort | Rastegar-Mojarad, Majid |
| collection | DOAJ |
| description | BackgroundA search engine to find physicians’ information is a basic but crucial function of a health care provider’s website. Inefficient search engines, which return no results or incorrect results, can lead to patient frustration and potential customer loss. A search engine that can handle misspellings and spelling variations of names is needed, as the United States (US) has culturally, racially, and ethnically diverse names.
ObjectiveThe Marshfield Clinic website provides a search engine for users to search for physicians’ names. The current search engine provides an auto-completion function, but it requires an exact match. We observed that 26% of all searches yielded no results. The goal was to design a fuzzy-match algorithm to aid users in finding physicians easier and faster.
MethodsInstead of an exact match search, we used a fuzzy algorithm to find similar matches for searched terms. In the algorithm, we solved three types of search engine failures: “Typographic”, “Phonetic spelling variation”, and “Nickname”. To solve these mismatches, we used a customized Levenshtein distance calculation that incorporated Soundex coding and a lookup table of nicknames derived from US census data.
ResultsUsing the “Challenge Data Set of Marshfield Physician Names,” we evaluated the accuracy of fuzzy-match engine–top ten (90%) and compared it with exact match (0%), Soundex (24%), Levenshtein distance (59%), and fuzzy-match engine–top one (71%).
ConclusionsWe designed, created a reference implementation, and evaluated a fuzzy-match search engine for physician directories. The open-source code is available at the codeplex website and a reference implementation is available for demonstration at the datamarsh website. |
| first_indexed | 2024-12-14T02:51:04Z |
| format | Article |
| id | doaj.art-c3dc28df1e2b402a90384769aeafe1eb |
| institution | Directory Open Access Journal |
| issn | 2291-9694 |
| language | English |
| last_indexed | 2024-12-14T02:51:04Z |
| publishDate | 2014-11-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Medical Informatics |
| spelling | doaj.art-c3dc28df1e2b402a90384769aeafe1eb2022-12-21T23:19:46ZengJMIR PublicationsJMIR Medical Informatics2291-96942014-11-0122e3010.2196/medinform.3463A Fuzzy-Match Search Engine for Physician DirectoriesRastegar-Mojarad, MajidKadolph, ChristopherYe, ZhanWall, DanielMurali, NarayanaLin, SimonBackgroundA search engine to find physicians’ information is a basic but crucial function of a health care provider’s website. Inefficient search engines, which return no results or incorrect results, can lead to patient frustration and potential customer loss. A search engine that can handle misspellings and spelling variations of names is needed, as the United States (US) has culturally, racially, and ethnically diverse names. ObjectiveThe Marshfield Clinic website provides a search engine for users to search for physicians’ names. The current search engine provides an auto-completion function, but it requires an exact match. We observed that 26% of all searches yielded no results. The goal was to design a fuzzy-match algorithm to aid users in finding physicians easier and faster. MethodsInstead of an exact match search, we used a fuzzy algorithm to find similar matches for searched terms. In the algorithm, we solved three types of search engine failures: “Typographic”, “Phonetic spelling variation”, and “Nickname”. To solve these mismatches, we used a customized Levenshtein distance calculation that incorporated Soundex coding and a lookup table of nicknames derived from US census data. ResultsUsing the “Challenge Data Set of Marshfield Physician Names,” we evaluated the accuracy of fuzzy-match engine–top ten (90%) and compared it with exact match (0%), Soundex (24%), Levenshtein distance (59%), and fuzzy-match engine–top one (71%). ConclusionsWe designed, created a reference implementation, and evaluated a fuzzy-match search engine for physician directories. The open-source code is available at the codeplex website and a reference implementation is available for demonstration at the datamarsh website.http://medinform.jmir.org/2014/2/e30/ |
| spellingShingle | Rastegar-Mojarad, Majid Kadolph, Christopher Ye, Zhan Wall, Daniel Murali, Narayana Lin, Simon A Fuzzy-Match Search Engine for Physician Directories JMIR Medical Informatics |
| title | A Fuzzy-Match Search Engine for Physician Directories |
| title_full | A Fuzzy-Match Search Engine for Physician Directories |
| title_fullStr | A Fuzzy-Match Search Engine for Physician Directories |
| title_full_unstemmed | A Fuzzy-Match Search Engine for Physician Directories |
| title_short | A Fuzzy-Match Search Engine for Physician Directories |
| title_sort | fuzzy match search engine for physician directories |
| url | http://medinform.jmir.org/2014/2/e30/ |
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