Patient Misidentification in the Neonatal Intensive Care Unit: Quantification of Risk
To quantify the potential for misidentification among NICU patients resulting from similarities in patient names or hospital medical record numbers (MRNs). A listing of all patients who received care in 1 NICU during 1 calendar year was obtained from the unit's electronic medical record system....
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Veröffentlicht in: | Pediatrics (Evanston) 2006-01, Vol.117 (1), p.e43-e47 |
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creator | Gray, James E Suresh, Gautham Ursprung, Robert Edwards, William H Nickerson, Julianne Shiono, Pat H Plsek, Paul Goldmann, Donald A Horbar, Jeffrey |
description | To quantify the potential for misidentification among NICU patients resulting from similarities in patient names or hospital medical record numbers (MRNs).
A listing of all patients who received care in 1 NICU during 1 calendar year was obtained from the unit's electronic medical record system. A patient day was considered at risk for misidentification when the index patient shared a surname, similar-sounding surname, or similar MRN with another patient who was cared for in the NICU on that day.
During the 1-year study period, 12186 days of patient care were provided to 1260 patients. The unit's average daily census was 33.4; the maximum census was 48. Not a single day was free of risk for patient misidentification. The mean number of patients who were at risk on any given day was 17 (range: 5-35), representing just over 50% of the average daily census. During the entire calendar year, the risk ranged from 20.6% to a high of 72.9% of the average daily census. The most common causes of misidentification risk were similar-appearing MRNs (44% of patient days). Identical surnames were present in 34% of patient days, and similar-sounding names were present in 9.7% of days. Twins and triplets contributed one third of patient days in the NICU. After these multiple births were excluded from analysis, 26.3% of patient days remained at risk for misidentification. Among singletons, the contribution to misidentification risk of similar-sounding surnames was relatively unchanged (9.1% of patient days), whereas that of similar MRNs and identical surnames decreased (17.6% and 1.0%, respectively).
NICU patients are frequently at risk for misidentification errors as a result of similarities in standard identifiers. This risk persists even after exclusion of multiple births and is substantially higher than has been reported in other hospitalized populations. |
doi_str_mv | 10.1542/peds.2005-0291 |
format | Article |
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A listing of all patients who received care in 1 NICU during 1 calendar year was obtained from the unit's electronic medical record system. A patient day was considered at risk for misidentification when the index patient shared a surname, similar-sounding surname, or similar MRN with another patient who was cared for in the NICU on that day.
During the 1-year study period, 12186 days of patient care were provided to 1260 patients. The unit's average daily census was 33.4; the maximum census was 48. Not a single day was free of risk for patient misidentification. The mean number of patients who were at risk on any given day was 17 (range: 5-35), representing just over 50% of the average daily census. During the entire calendar year, the risk ranged from 20.6% to a high of 72.9% of the average daily census. The most common causes of misidentification risk were similar-appearing MRNs (44% of patient days). Identical surnames were present in 34% of patient days, and similar-sounding names were present in 9.7% of days. Twins and triplets contributed one third of patient days in the NICU. After these multiple births were excluded from analysis, 26.3% of patient days remained at risk for misidentification. Among singletons, the contribution to misidentification risk of similar-sounding surnames was relatively unchanged (9.1% of patient days), whereas that of similar MRNs and identical surnames decreased (17.6% and 1.0%, respectively).
NICU patients are frequently at risk for misidentification errors as a result of similarities in standard identifiers. This risk persists even after exclusion of multiple births and is substantially higher than has been reported in other hospitalized populations.</description><identifier>ISSN: 0031-4005</identifier><identifier>EISSN: 1098-4275</identifier><identifier>DOI: 10.1542/peds.2005-0291</identifier><identifier>PMID: 16396847</identifier><language>eng</language><publisher>United States: Am Acad Pediatrics</publisher><subject>Humans ; Infant, Newborn ; Intensive Care Units, Neonatal ; Medical Errors ; Medical Records Systems, Computerized ; Milk, Human ; Patient Identification Systems ; Risk Assessment</subject><ispartof>Pediatrics (Evanston), 2006-01, Vol.117 (1), p.e43-e47</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c429t-9a1828ff1618a5aeae14adc8fa9c6f5e2829d5e814531a8dc502cda3fdaf905c3</citedby><cites>FETCH-LOGICAL-c429t-9a1828ff1618a5aeae14adc8fa9c6f5e2829d5e814531a8dc502cda3fdaf905c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,27929,27930</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16396847$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gray, James E</creatorcontrib><creatorcontrib>Suresh, Gautham</creatorcontrib><creatorcontrib>Ursprung, Robert</creatorcontrib><creatorcontrib>Edwards, William H</creatorcontrib><creatorcontrib>Nickerson, Julianne</creatorcontrib><creatorcontrib>Shiono, Pat H</creatorcontrib><creatorcontrib>Plsek, Paul</creatorcontrib><creatorcontrib>Goldmann, Donald A</creatorcontrib><creatorcontrib>Horbar, Jeffrey</creatorcontrib><title>Patient Misidentification in the Neonatal Intensive Care Unit: Quantification of Risk</title><title>Pediatrics (Evanston)</title><addtitle>Pediatrics</addtitle><description>To quantify the potential for misidentification among NICU patients resulting from similarities in patient names or hospital medical record numbers (MRNs).
A listing of all patients who received care in 1 NICU during 1 calendar year was obtained from the unit's electronic medical record system. A patient day was considered at risk for misidentification when the index patient shared a surname, similar-sounding surname, or similar MRN with another patient who was cared for in the NICU on that day.
During the 1-year study period, 12186 days of patient care were provided to 1260 patients. The unit's average daily census was 33.4; the maximum census was 48. Not a single day was free of risk for patient misidentification. The mean number of patients who were at risk on any given day was 17 (range: 5-35), representing just over 50% of the average daily census. During the entire calendar year, the risk ranged from 20.6% to a high of 72.9% of the average daily census. The most common causes of misidentification risk were similar-appearing MRNs (44% of patient days). Identical surnames were present in 34% of patient days, and similar-sounding names were present in 9.7% of days. Twins and triplets contributed one third of patient days in the NICU. After these multiple births were excluded from analysis, 26.3% of patient days remained at risk for misidentification. Among singletons, the contribution to misidentification risk of similar-sounding surnames was relatively unchanged (9.1% of patient days), whereas that of similar MRNs and identical surnames decreased (17.6% and 1.0%, respectively).
NICU patients are frequently at risk for misidentification errors as a result of similarities in standard identifiers. This risk persists even after exclusion of multiple births and is substantially higher than has been reported in other hospitalized populations.</description><subject>Humans</subject><subject>Infant, Newborn</subject><subject>Intensive Care Units, Neonatal</subject><subject>Medical Errors</subject><subject>Medical Records Systems, Computerized</subject><subject>Milk, Human</subject><subject>Patient Identification Systems</subject><subject>Risk Assessment</subject><issn>0031-4005</issn><issn>1098-4275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2006</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkE1vEzEQhi3UqgltrxyRT71t8Phj1-4NRXxUKrSg5mwN3jExTXbD2ini37OrRGpvnObVzDPv4WHsDYgFGC3f7ajNCymEqYR08IrNQThbadmYEzYXQkGlx-OMvc75lxBCm0aesRnUytVWN3O2useSqCv8S8qpHUOKKYyrvuOp42VN_Cv1HRbc8JuuUJfTE_ElDsRXXSrX_NseX_70kX9P-fGCnUbcZLo8znO2-vjhYfm5ur37dLN8f1sFLV2pHIKVNkaowaJBQgKNbbARXaijIWmlaw1Z0EYB2jYYIUOLKrYYnTBBnbOrQ-9u6H_vKRe_TTnQZoMd9fvs66YGAU78F5SikUqZegQXBzAMfc4DRb8b0haHvx6En4z7ybifjPvJ-Pjw9ti8_7Gl9hk_Kn5uXKef6z9poKkhYRlSyC8iQOPBk1bqH_mAjjA</recordid><startdate>20060101</startdate><enddate>20060101</enddate><creator>Gray, James E</creator><creator>Suresh, Gautham</creator><creator>Ursprung, Robert</creator><creator>Edwards, William H</creator><creator>Nickerson, Julianne</creator><creator>Shiono, Pat H</creator><creator>Plsek, Paul</creator><creator>Goldmann, Donald A</creator><creator>Horbar, Jeffrey</creator><general>Am Acad Pediatrics</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7U1</scope><scope>7U2</scope><scope>C1K</scope><scope>7X8</scope></search><sort><creationdate>20060101</creationdate><title>Patient Misidentification in the Neonatal Intensive Care Unit: Quantification of Risk</title><author>Gray, James E ; Suresh, Gautham ; Ursprung, Robert ; Edwards, William H ; Nickerson, Julianne ; Shiono, Pat H ; Plsek, Paul ; Goldmann, Donald A ; Horbar, Jeffrey</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c429t-9a1828ff1618a5aeae14adc8fa9c6f5e2829d5e814531a8dc502cda3fdaf905c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Humans</topic><topic>Infant, Newborn</topic><topic>Intensive Care Units, Neonatal</topic><topic>Medical Errors</topic><topic>Medical Records Systems, Computerized</topic><topic>Milk, Human</topic><topic>Patient Identification Systems</topic><topic>Risk Assessment</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gray, James E</creatorcontrib><creatorcontrib>Suresh, Gautham</creatorcontrib><creatorcontrib>Ursprung, Robert</creatorcontrib><creatorcontrib>Edwards, William H</creatorcontrib><creatorcontrib>Nickerson, Julianne</creatorcontrib><creatorcontrib>Shiono, Pat H</creatorcontrib><creatorcontrib>Plsek, Paul</creatorcontrib><creatorcontrib>Goldmann, Donald A</creatorcontrib><creatorcontrib>Horbar, Jeffrey</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><collection>Environmental Sciences and Pollution Management</collection><collection>MEDLINE - Academic</collection><jtitle>Pediatrics (Evanston)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gray, James E</au><au>Suresh, Gautham</au><au>Ursprung, Robert</au><au>Edwards, William H</au><au>Nickerson, Julianne</au><au>Shiono, Pat H</au><au>Plsek, Paul</au><au>Goldmann, Donald A</au><au>Horbar, Jeffrey</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Patient Misidentification in the Neonatal Intensive Care Unit: Quantification of Risk</atitle><jtitle>Pediatrics (Evanston)</jtitle><addtitle>Pediatrics</addtitle><date>2006-01-01</date><risdate>2006</risdate><volume>117</volume><issue>1</issue><spage>e43</spage><epage>e47</epage><pages>e43-e47</pages><issn>0031-4005</issn><eissn>1098-4275</eissn><abstract>To quantify the potential for misidentification among NICU patients resulting from similarities in patient names or hospital medical record numbers (MRNs).
A listing of all patients who received care in 1 NICU during 1 calendar year was obtained from the unit's electronic medical record system. A patient day was considered at risk for misidentification when the index patient shared a surname, similar-sounding surname, or similar MRN with another patient who was cared for in the NICU on that day.
During the 1-year study period, 12186 days of patient care were provided to 1260 patients. The unit's average daily census was 33.4; the maximum census was 48. Not a single day was free of risk for patient misidentification. The mean number of patients who were at risk on any given day was 17 (range: 5-35), representing just over 50% of the average daily census. During the entire calendar year, the risk ranged from 20.6% to a high of 72.9% of the average daily census. The most common causes of misidentification risk were similar-appearing MRNs (44% of patient days). Identical surnames were present in 34% of patient days, and similar-sounding names were present in 9.7% of days. Twins and triplets contributed one third of patient days in the NICU. After these multiple births were excluded from analysis, 26.3% of patient days remained at risk for misidentification. Among singletons, the contribution to misidentification risk of similar-sounding surnames was relatively unchanged (9.1% of patient days), whereas that of similar MRNs and identical surnames decreased (17.6% and 1.0%, respectively).
NICU patients are frequently at risk for misidentification errors as a result of similarities in standard identifiers. This risk persists even after exclusion of multiple births and is substantially higher than has been reported in other hospitalized populations.</abstract><cop>United States</cop><pub>Am Acad Pediatrics</pub><pmid>16396847</pmid><doi>10.1542/peds.2005-0291</doi></addata></record> |
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source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
subjects | Humans Infant, Newborn Intensive Care Units, Neonatal Medical Errors Medical Records Systems, Computerized Milk, Human Patient Identification Systems Risk Assessment |
title | Patient Misidentification in the Neonatal Intensive Care Unit: Quantification of Risk |
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