A record linkage protocol for a diabetes registry at ethnically diverse community health centers.
Community health centers serve ethnically diverse populations that may pose challenges for record linkage based on name and date of birth. The objective was to identify an optimal deterministic algorithm to link patient encounters and laboratory results for hemoglobin A1c testing and examine its var...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 12; no. 3; pp. 331 - 338 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
May/Jun2005
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=106542004&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106542004 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: May/Jun2005 vid: 12 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 106542004 106542004 2009054748 10.1197/jamia.m1696 NLM15684130 106542004 ppf: 331 ppct: 7 formats: tig: atl: A record linkage protocol for a diabetes registry at ethnically diverse community health centers. aug: au: Maizlish NA Herrera L affil: Community Health Center Network, 1320 Harbor Bay Parkway, Suite 250, Alameda, CA 94502; neilm@chcn-eb.org sug: subj: Medical Record Linkage Community Health Centers Algorithms Registries, Disease Diabetes Mellitus Ethnic Groups Glycated Hemoglobin Systems Design Sensitivity and Specificity Predictive Value of Tests Evaluation Research Chi Square Test Data Analysis Software Funding Source Human ab: Community health centers serve ethnically diverse populations that may pose challenges for record linkage based on name and date of birth. The objective was to identify an optimal deterministic algorithm to link patient encounters and laboratory results for hemoglobin A1c testing and examine its variability by health center site, patient ethnicity, and other variables. Based on data elements of last name, first name, date of birth, gender, and health center site, matches with >/=50% to < 100% of a maximum score were manually reviewed for true matches. Match keys based on combinations of name substrings, date of birth, gender, and health center were used to link encounter and laboratory files. The optimal match key was the first two letters of the last name and date of birth, which had a sensitivity of 92.7% and a positive predictive value of 99.5%. Sensitivity marginally varied by health center, age, gender, but not by ethnicity. An algorithm that was inexpensive, accurate, and easy to implement was found to be well suited for population-based measurement of clinical quality. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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