Evidence map›Paper›PMID 24454586›Full record

ArticleApplied clinical informatics2013

Transforming user needs into functional requirements for an antibiotic clinical decision support system: explicating content analysis for system design.

T J Bright

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Article in Applied clinical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

1 author.

T J BrightColumbia University, Biomedical Informatics, New York , New York, United States.

Funding

Training in Biomedical Informatics at Columbia UniversityT15LM007079 · NLM · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI NOEMIE ELHADAD, GEORGE M HRIPCSAK · 1992 to 2026
$28.9M
Washington Heights Initiative Community-based Comparative Effectiveness ResearchR01HS019853 · AHRQ · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BAKKEN, SUZANNE · 2010 to 2010
$8.9M
Training in Interdisciplinary Research to Reduce Antimicrobial Resistance (TIRAR)T90NR010824 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LARSON, ELAINE LUCILLE · 2007 to 2011
$1.0M
AHRQ HHS R01 HS019853AHRQ HHS R01 HS019853-01NINR NIH HHS T90 NR010824NLM NIH HHS T15 LM007079
6 · The paper itself

Abstract

backgroundMany informatics studies use content analysis to generate functional requirements for system development. Explication of this translational process from qualitative data to functional requirements can strengthen the understanding and scientific rigor when applying content analysis in informatics studies.

objectiveTo describe a user-centered approach transforming emergent themes derived from focus group data into functional requirements for informatics solutions and to illustrate these methods to the development of an antibiotic clinical decision support system (CDS).

methodsTHE APPROACH CONSISTED OF FIVE STEPS: 1) identify unmet therapeutic planning information needs via Focus Group Study-I, 2) develop a coding framework of therapeutic planning themes to refine the domain scope to antibiotic therapeutic planning, 3) identify functional requirements of an antibiotic CDS system via Focus Group Study-II, 4) discover informatics solutions and functional requirements from coded data, and 5) determine the types of information needed to support the antibiotic CDS system and link with the identified informatics solutions and functional requirements.

resultsThe coding framework for Focus Group Study-I revealed unmet therapeutic planning needs. Twelve subthemes emerged and were clustered into four themes; analysis indicated a need for an antibiotic CDS intervention. Focus Group Study-II included five types of information needs. Comments from the Barrier/Challenge to information access and Function/Feature themes produced three informatics solutions and 13 functional requirements of an antibiotic CDS system. Comments from the Patient, Institution, and Domain themes generated required data elements for each informatics solution.

conclusionThis study presents one example explicating content analysis of focus group data and the analysis process to functional requirements from narrative data. Illustration of this 5-step method was used to develop an antibiotic CDS system, resolving unmet antibiotic prescribing needs. As a reusable approach, these techniques can be refined and applied to resolve unmet information needs with informatics interventions in additional domains.

Indexed as

Decision Support Systems, ClinicalHealth Services Needs and DemandData CollectionEvaluation Studies as TopicHumansUser-Computer Interfaceclinical decision support systemsData collection methodsfocus groupsneeds assessmentuser-computer interface

Identifiers

PMID24454586
PMCPMC3885919

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.