Publications search optimization: Comparison of a homegrown—API approach versus manual publication searches at an NCI designated cancer center

One measure of research productivity within the University of Kansas Cancer Center (KU Cancer Center) is peer-reviewed publications. Considerable effort goes into searching, capturing, reviewing, storing, and reporting cancer-relevant publications. Traditionally, the method of gathering relevant inf...

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Vydáno v:Health informatics journal Ročník 26; číslo 4; s. 3066 - 3071
Hlavní autoři: Cernik, Colin, Fife, John, Thompson, Jeffrey, Harlan-Williams, Lisa, Mudaranthakam, Dinesh Pal
Médium: Journal Article
Jazyk:angličtina
Vydáno: London, England SAGE Publications 01.12.2020
SAGE PUBLICATIONS, INC
Témata:
Application programming interface
Automation
Cancer
Copyright
Cost analysis
Cost control
Costs
Optimization
Productivity
Publication output
Retrieval
Savings
United States > US
Kansas
text mining
health information on the Web
databases and data mining
IT healthcare evaluation
data mining
ISSN:1460-4582, 1741-2811, 1741-2811
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Abstract One measure of research productivity within the University of Kansas Cancer Center (KU Cancer Center) is peer-reviewed publications. Considerable effort goes into searching, capturing, reviewing, storing, and reporting cancer-relevant publications. Traditionally, the method of gathering relevant information to the publications is done manually. This manuscript describes the efforts to transition KU Cancer Center’s publication gathering process from a heavily manual to a more automated and efficient process. To achieve this transition in the most customized and cost-effective manner, a homegrown, automated system was developed using open source API among other software. When comparing the automated and the manual processes over several years of data, publication search and retrieval time dropped from an average of 59 h to 35 min, which would amount to a cost savings of several thousand dollars per year. The development and adoption of an automated publications search process can offer research centers great potential for less-error prone results with a savings in time and cost.
AbstractList One measure of research productivity within the University of Kansas Cancer Center (KU Cancer Center) is peer-reviewed publications. Considerable effort goes into searching, capturing, reviewing, storing, and reporting cancer-relevant publications. Traditionally, the method of gathering relevant information to the publications is done manually. This manuscript describes the efforts to transition KU Cancer Center’s publication gathering process from a heavily manual to a more automated and efficient process. To achieve this transition in the most customized and cost-effective manner, a homegrown, automated system was developed using open source API among other software. When comparing the automated and the manual processes over several years of data, publication search and retrieval time dropped from an average of 59 h to 35 min, which would amount to a cost savings of several thousand dollars per year. The development and adoption of an automated publications search process can offer research centers great potential for less-error prone results with a savings in time and cost.
One measure of research productivity within the University of Kansas Cancer Center (KU Cancer Center) is peer-reviewed publications. Considerable effort goes into searching, capturing, reviewing, storing, and reporting cancer-relevant publications. Traditionally, the method of gathering relevant information to the publications is done manually. This manuscript describes the efforts to transition KU Cancer Center's publication gathering process from a heavily manual to a more automated and efficient process. To achieve this transition in the most customized and cost-effective manner, a homegrown, automated system was developed using open source API among other software. When comparing the automated and the manual processes over several years of data, publication search and retrieval time dropped from an average of 59 h to 35 min, which would amount to a cost savings of several thousand dollars per year. The development and adoption of an automated publications search process can offer research centers great potential for less-error prone results with a savings in time and cost.One measure of research productivity within the University of Kansas Cancer Center (KU Cancer Center) is peer-reviewed publications. Considerable effort goes into searching, capturing, reviewing, storing, and reporting cancer-relevant publications. Traditionally, the method of gathering relevant information to the publications is done manually. This manuscript describes the efforts to transition KU Cancer Center's publication gathering process from a heavily manual to a more automated and efficient process. To achieve this transition in the most customized and cost-effective manner, a homegrown, automated system was developed using open source API among other software. When comparing the automated and the manual processes over several years of data, publication search and retrieval time dropped from an average of 59 h to 35 min, which would amount to a cost savings of several thousand dollars per year. The development and adoption of an automated publications search process can offer research centers great potential for less-error prone results with a savings in time and cost.
Author Harlan-Williams, Lisa
Mudaranthakam, Dinesh Pal
Cernik, Colin
Thompson, Jeffrey
Fife, John
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Cites_doi 10.1007/s13187-016-1120-4
10.2196/jmir.3369
10.1186/1471-2105-11-S5-O5
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Keywords text mining
health information on the Web
databases and data mining
IT healthcare evaluation
data mining
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Snippet One measure of research productivity within the University of Kansas Cancer Center (KU Cancer Center) is peer-reviewed publications. Considerable effort goes...
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SubjectTerms Application programming interface
Automation
Cancer
Copyright
Cost analysis
Cost control
Costs
Optimization
Productivity
Publication output
Retrieval
Savings
Title Publications search optimization: Comparison of a homegrown—API approach versus manual publication searches at an NCI designated cancer center
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