Incorporating scannable forms into immunization data collection processes: a mixed-methods study.
Identifieur interne : 000438 ( Main/Exploration ); précédent : 000437; suivant : 000439Incorporating scannable forms into immunization data collection processes: a mixed-methods study.
Auteurs : Christine L. Heidebrecht [Canada] ; Susan Quach ; Jennifer A. Pereira ; Sherman D. Quan ; Faron Kolbe ; Michael Finkelstein ; David L. Buckeridge ; Jeffrey C. KwongSource :
- PloS one [ 1932-6203 ] ; 2012.
Descripteurs français
- KwdFr :
- Collecte de données (), Collecte de données (instrumentation), Enquêtes et questionnaires, Grippe humaine (), Humains, Logiciel, Modèles statistiques, Ontario, Plan de recherche, Programmes de vaccination, Projets pilotes, Reproductibilité des résultats, Statistiques comme sujet, Vaccination (), Vaccins antigrippaux (usage thérapeutique), Études de faisabilité.
- MESH :
- usage thérapeutique : Collecte de données, Vaccins antigrippaux.
- Collecte de données, Enquêtes et questionnaires, Grippe humaine, Humains, Logiciel, Modèles statistiques, Ontario, Plan de recherche, Programmes de vaccination, Projets pilotes, Reproductibilité des résultats, Statistiques comme sujet, Vaccination, Études de faisabilité.
English descriptors
- KwdEn :
- Data Collection (instrumentation), Data Collection (methods), Electronic Data Processing (methods), Feasibility Studies, Humans, Immunization Programs, Influenza Vaccines (therapeutic use), Influenza, Human (prevention & control), Models, Statistical, Ontario, Pilot Projects, Reproducibility of Results, Research Design, Software, Statistics as Topic, Surveys and Questionnaires, Vaccination (methods).
- MESH :
- chemical , therapeutic use : Influenza Vaccines.
- instrumentation : Data Collection.
- methods : Data Collection, Electronic Data Processing, Vaccination.
- prevention & control : Influenza, Human.
- Feasibility Studies, Humans, Immunization Programs, Models, Statistical, Ontario, Pilot Projects, Reproducibility of Results, Research Design, Software, Statistics as Topic, Surveys and Questionnaires.
Abstract
INTRODUCTION
Individual-level immunization data captured electronically can facilitate evidence-based decision-making and planning. Populating individual-level records through manual data entry is time-consuming. An alternative is to use scannable forms, completed at the point of vaccination and subsequently scanned and exported to a database or registry. To explore the suitability of this approach for collecting immunization data, we conducted a feasibility study in two settings in Ontario, Canada.
METHODS AND FINDINGS
Prior to the 2011-2012 influenza vaccination campaign, we developed a scannable form template and a corresponding database that captured required demographic and clinical data elements. We examined efficiency, data quality, and usability through time observations, record audits, staff interviews, and client surveys. The mean time required to scan and verify forms (62.3 s) was significantly shorter than manual data entry (69.5 s) in one organization, whereas there was no difference (36.6 s vs. 35.4 s) in a second organization. Record audits revealed no differences in data quality between records populated by scanning versus manual data entry. Data processing personnel and immunized clients found the processes involved to be straightforward, while nurses and managers had mixed perceptions regarding the ease and merit of using scannable forms. Printing quality and other factors rendered some forms unscannable, necessitating manual entry.
CONCLUSIONS
Scannable forms can facilitate efficient data entry, but certain features of the forms, as well as the workflow and infrastructure into which they are incorporated, should be evaluated and adapted if scannable forms are to be a meaningful alternative to manual data entry.
DOI: 10.1371/journal.pone.0049627
PubMed: 23272047
Affiliations:
Links toward previous steps (curation, corpus...)
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Quan</name></author><author><name sortKey="Kolbe, Faron" sort="Kolbe, Faron" uniqKey="Kolbe F" first="Faron" last="Kolbe">Faron Kolbe</name></author><author><name sortKey="Finkelstein, Michael" sort="Finkelstein, Michael" uniqKey="Finkelstein M" first="Michael" last="Finkelstein">Michael Finkelstein</name></author><author><name sortKey="Buckeridge, David L" sort="Buckeridge, David L" uniqKey="Buckeridge D" first="David L" last="Buckeridge">David L. Buckeridge</name></author><author><name sortKey="Kwong, Jeffrey C" sort="Kwong, Jeffrey C" uniqKey="Kwong J" first="Jeffrey C" last="Kwong">Jeffrey C. Kwong</name></author></analytic><series><title level="j">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Data Collection (instrumentation)</term><term>Data Collection (methods)</term><term>Electronic Data Processing (methods)</term><term>Feasibility Studies</term><term>Humans</term><term>Immunization Programs</term><term>Influenza Vaccines (therapeutic use)</term><term>Influenza, Human (prevention & control)</term><term>Models, Statistical</term><term>Ontario</term><term>Pilot Projects</term><term>Reproducibility of Results</term><term>Research Design</term><term>Software</term><term>Statistics as Topic</term><term>Surveys and Questionnaires</term><term>Vaccination (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Collecte de données ()</term><term>Collecte de données (instrumentation)</term><term>Enquêtes et questionnaires</term><term>Grippe humaine ()</term><term>Humains</term><term>Logiciel</term><term>Modèles statistiques</term><term>Ontario</term><term>Plan de recherche</term><term>Programmes de vaccination</term><term>Projets pilotes</term><term>Reproductibilité des résultats</term><term>Statistiques comme sujet</term><term>Vaccination ()</term><term>Vaccins antigrippaux (usage thérapeutique)</term><term>Études de faisabilité</term></keywords><keywords scheme="MESH" type="chemical" qualifier="therapeutic use" xml:lang="en"><term>Influenza Vaccines</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Data Collection</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Data Collection</term><term>Electronic Data Processing</term><term>Vaccination</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Influenza, Human</term></keywords><keywords scheme="MESH" qualifier="usage thérapeutique" xml:lang="fr"><term>Collecte de données</term><term>Vaccins antigrippaux</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Feasibility Studies</term><term>Humans</term><term>Immunization Programs</term><term>Models, Statistical</term><term>Ontario</term><term>Pilot Projects</term><term>Reproducibility of Results</term><term>Research Design</term><term>Software</term><term>Statistics as Topic</term><term>Surveys and Questionnaires</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Collecte de données</term><term>Enquêtes et questionnaires</term><term>Grippe humaine</term><term>Humains</term><term>Logiciel</term><term>Modèles statistiques</term><term>Ontario</term><term>Plan de recherche</term><term>Programmes de vaccination</term><term>Projets pilotes</term><term>Reproductibilité des résultats</term><term>Statistiques comme sujet</term><term>Vaccination</term><term>Études de faisabilité</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>INTRODUCTION</b></p><p>Individual-level immunization data captured electronically can facilitate evidence-based decision-making and planning. Populating individual-level records through manual data entry is time-consuming. An alternative is to use scannable forms, completed at the point of vaccination and subsequently scanned and exported to a database or registry. To explore the suitability of this approach for collecting immunization data, we conducted a feasibility study in two settings in Ontario, Canada.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS AND FINDINGS</b></p><p>Prior to the 2011-2012 influenza vaccination campaign, we developed a scannable form template and a corresponding database that captured required demographic and clinical data elements. We examined efficiency, data quality, and usability through time observations, record audits, staff interviews, and client surveys. The mean time required to scan and verify forms (62.3 s) was significantly shorter than manual data entry (69.5 s) in one organization, whereas there was no difference (36.6 s vs. 35.4 s) in a second organization. Record audits revealed no differences in data quality between records populated by scanning versus manual data entry. Data processing personnel and immunized clients found the processes involved to be straightforward, while nurses and managers had mixed perceptions regarding the ease and merit of using scannable forms. Printing quality and other factors rendered some forms unscannable, necessitating manual entry.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSIONS</b></p><p>Scannable forms can facilitate efficient data entry, but certain features of the forms, as well as the workflow and infrastructure into which they are incorporated, should be evaluated and adapted if scannable forms are to be a meaningful alternative to manual data entry.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">23272047</PMID><DateCompleted><Year>2013</Year><Month>06</Month><Day>26</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>02</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>7</Volume><Issue>12</Issue><PubDate><Year>2012</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS ONE</ISOAbbreviation></Journal><ArticleTitle>Incorporating scannable forms into immunization data collection processes: a mixed-methods study.</ArticleTitle><Pagination><MedlinePgn>e49627</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0049627</ELocationID><Abstract><AbstractText Label="INTRODUCTION" NlmCategory="BACKGROUND">Individual-level immunization data captured electronically can facilitate evidence-based decision-making and planning. Populating individual-level records through manual data entry is time-consuming. An alternative is to use scannable forms, completed at the point of vaccination and subsequently scanned and exported to a database or registry. To explore the suitability of this approach for collecting immunization data, we conducted a feasibility study in two settings in Ontario, Canada.</AbstractText><AbstractText Label="METHODS AND FINDINGS" NlmCategory="RESULTS">Prior to the 2011-2012 influenza vaccination campaign, we developed a scannable form template and a corresponding database that captured required demographic and clinical data elements. We examined efficiency, data quality, and usability through time observations, record audits, staff interviews, and client surveys. The mean time required to scan and verify forms (62.3 s) was significantly shorter than manual data entry (69.5 s) in one organization, whereas there was no difference (36.6 s vs. 35.4 s) in a second organization. Record audits revealed no differences in data quality between records populated by scanning versus manual data entry. Data processing personnel and immunized clients found the processes involved to be straightforward, while nurses and managers had mixed perceptions regarding the ease and merit of using scannable forms. Printing quality and other factors rendered some forms unscannable, necessitating manual entry.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">Scannable forms can facilitate efficient data entry, but certain features of the forms, as well as the workflow and infrastructure into which they are incorporated, should be evaluated and adapted if scannable forms are to be a meaningful alternative to manual data entry.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Heidebrecht</LastName><ForeName>Christine L</ForeName><Initials>CL</Initials><AffiliationInfo><Affiliation>Infectious Diseases, Public Health Ontario, Toronto, Ontario, Canada.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Quach</LastName><ForeName>Susan</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Pereira</LastName><ForeName>Jennifer A</ForeName><Initials>JA</Initials></Author><Author ValidYN="Y"><LastName>Quan</LastName><ForeName>Sherman D</ForeName><Initials>SD</Initials></Author><Author ValidYN="Y"><LastName>Kolbe</LastName><ForeName>Faron</ForeName><Initials>F</Initials></Author><Author ValidYN="Y"><LastName>Finkelstein</LastName><ForeName>Michael</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Buckeridge</LastName><ForeName>David L</ForeName><Initials>DL</Initials></Author><Author ValidYN="Y"><LastName>Kwong</LastName><ForeName>Jeffrey C</ForeName><Initials>JC</Initials></Author><Author ValidYN="Y"><CollectiveName>Public Health Agency of Canada/Canadian Institutes of Health Research Influenza Research Network (PCIRN) Vaccine Coverage Theme Group</CollectiveName></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>4IRR96974</GrantID><Agency>Canadian Institutes of Health Research</Agency><Country>Canada</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal 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MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D005240" MajorTopicYN="N">Feasibility Studies</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017589" MajorTopicYN="N">Immunization Programs</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D007252" MajorTopicYN="N">Influenza Vaccines</DescriptorName><QualifierName UI="Q000627" MajorTopicYN="N">therapeutic use</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D007251" MajorTopicYN="N">Influenza, Human</DescriptorName><QualifierName UI="Q000517" MajorTopicYN="N">prevention & control</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="N">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D009864" MajorTopicYN="N">Ontario</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010865" 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ValidYN="Y"><LastName>Brien</LastName><ForeName>Stephanie</ForeName><Initials>S</Initials></Investigator><Investigator ValidYN="Y"><LastName>Buckeridge</LastName><ForeName>David</ForeName><Initials>D</Initials></Investigator><Investigator ValidYN="Y"><LastName>Chambers</LastName><ForeName>Larry</ForeName><Initials>L</Initials></Investigator><Investigator ValidYN="Y"><LastName>Crowcroft</LastName><ForeName>Natasha</ForeName><Initials>N</Initials></Investigator><Investigator ValidYN="Y"><LastName>Deeks</LastName><ForeName>Shelley</ForeName><Initials>S</Initials></Investigator><Investigator ValidYN="Y"><LastName>Finkelstein</LastName><ForeName>Michael</ForeName><Initials>M</Initials></Investigator><Investigator ValidYN="Y"><LastName>Guay</LastName><ForeName>Maryse</ForeName><Initials>M</Initials></Investigator><Investigator ValidYN="Y"><LastName>Hamid</LastName><ForeName>Jemila</ForeName><Initials>J</Initials></Investigator><Investigator ValidYN="Y"><LastName>Heidebrecht</LastName><ForeName>Christine</ForeName><Initials>C</Initials></Investigator><Investigator ValidYN="Y"><LastName>Kalailieff</LastName><ForeName>Donna</ForeName><Initials>D</Initials></Investigator><Investigator ValidYN="Y"><LastName>Kolbe</LastName><ForeName>Faron</ForeName><Initials>F</Initials></Investigator><Investigator ValidYN="Y"><LastName>Kwong</LastName><ForeName>Jeff</ForeName><Initials>J</Initials></Investigator><Investigator ValidYN="Y"><LastName>McGeer</LastName><ForeName>Allison</ForeName><Initials>A</Initials></Investigator><Investigator ValidYN="Y"><LastName>Nassif</LastName><ForeName>Jane</ForeName><Initials>J</Initials></Investigator><Investigator ValidYN="Y"><LastName>Pereira</LastName><ForeName>Jennifer</ForeName><Initials>J</Initials></Investigator><Investigator ValidYN="Y"><LastName>Quach</LastName><ForeName>Susan</ForeName><Initials>S</Initials></Investigator><Investigator 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IdType="pubmed">21147264</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>Canada</li></country></list><tree><noCountry><name sortKey="Buckeridge, David L" sort="Buckeridge, David L" uniqKey="Buckeridge D" first="David L" last="Buckeridge">David L. Buckeridge</name><name sortKey="Finkelstein, Michael" sort="Finkelstein, Michael" uniqKey="Finkelstein M" first="Michael" last="Finkelstein">Michael Finkelstein</name><name sortKey="Kolbe, Faron" sort="Kolbe, Faron" uniqKey="Kolbe F" first="Faron" last="Kolbe">Faron Kolbe</name><name sortKey="Kwong, Jeffrey C" sort="Kwong, Jeffrey C" uniqKey="Kwong J" first="Jeffrey C" last="Kwong">Jeffrey C. Kwong</name><name sortKey="Pereira, Jennifer A" sort="Pereira, Jennifer A" uniqKey="Pereira J" first="Jennifer A" last="Pereira">Jennifer A. Pereira</name><name sortKey="Quach, Susan" sort="Quach, Susan" uniqKey="Quach S" first="Susan" last="Quach">Susan Quach</name><name sortKey="Quan, Sherman D" sort="Quan, Sherman D" uniqKey="Quan S" first="Sherman D" last="Quan">Sherman D. Quan</name></noCountry><country name="Canada"><noRegion><name sortKey="Heidebrecht, Christine L" sort="Heidebrecht, Christine L" uniqKey="Heidebrecht C" first="Christine L" last="Heidebrecht">Christine L. 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