TY - JOUR
T1 - A multi-site cognitive task analysis for biomedical query mediation
AU - Hruby, Gregory W.
AU - Rasmussen, Luke V.
AU - Hanauer, David
AU - Patel, Vimla L.
AU - Cimino, James J.
AU - Weng, Chunhua
N1 - Funding Information:
This research was funded by NLM grant R01LM009886 (PI: Weng), and 5T15LM007079 (PI: Hripcsak), and CTSA award UL1 TR000040 (PI: Ginsberg). Its contents are solely the responsibility of the authors and do not necessarily represent the official view of NIH.
Publisher Copyright:
© 2016 Elsevier Ireland Ltd
PY - 2016/9/1
Y1 - 2016/9/1
N2 - Objective To apply cognitive task analyses of the Biomedical query mediation (BQM) processes for EHR data retrieval at multiple sites towards the development of a generic BQM process model. Materials and methods We conducted semi-structured interviews with eleven data analysts from five academic institutions and one government agency, and performed cognitive task analyses on their BQM processes. A coding schema was developed through iterative refinement and used to annotate the interview transcripts. The annotated dataset was used to reconstruct and verify each BQM process and to develop a harmonized BQM process model. A survey was conducted to evaluate the face and content validity of this harmonized model. Results The harmonized process model is hierarchical, encompassing tasks, activities, and steps. The face validity evaluation concluded the model to be representative of the BQM process. In the content validity evaluation, out of the 27 tasks for BQM, 19 meet the threshold for semi-valid, including 3 fully valid: “Identify potential index phenotype,” “If needed, request EHR database access rights,” and “Perform query and present output to medical researcher”, and 8 are invalid. Discussion We aligned the goals of the tasks within the BQM model with the five components of the reference interview. The similarity between the process of BQM and the reference interview is promising and suggests the BQM tasks are powerful for eliciting implicit information needs. Conclusions We contribute a BQM process model based on a multi-site study. This model promises to inform the standardization of the BQM process towards improved communication efficiency and accuracy.
AB - Objective To apply cognitive task analyses of the Biomedical query mediation (BQM) processes for EHR data retrieval at multiple sites towards the development of a generic BQM process model. Materials and methods We conducted semi-structured interviews with eleven data analysts from five academic institutions and one government agency, and performed cognitive task analyses on their BQM processes. A coding schema was developed through iterative refinement and used to annotate the interview transcripts. The annotated dataset was used to reconstruct and verify each BQM process and to develop a harmonized BQM process model. A survey was conducted to evaluate the face and content validity of this harmonized model. Results The harmonized process model is hierarchical, encompassing tasks, activities, and steps. The face validity evaluation concluded the model to be representative of the BQM process. In the content validity evaluation, out of the 27 tasks for BQM, 19 meet the threshold for semi-valid, including 3 fully valid: “Identify potential index phenotype,” “If needed, request EHR database access rights,” and “Perform query and present output to medical researcher”, and 8 are invalid. Discussion We aligned the goals of the tasks within the BQM model with the five components of the reference interview. The similarity between the process of BQM and the reference interview is promising and suggests the BQM tasks are powerful for eliciting implicit information needs. Conclusions We contribute a BQM process model based on a multi-site study. This model promises to inform the standardization of the BQM process towards improved communication efficiency and accuracy.
KW - Electronic health records
KW - Information storage and retrieval
KW - Task performance and analysis
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U2 - 10.1016/j.ijmedinf.2016.06.006
DO - 10.1016/j.ijmedinf.2016.06.006
M3 - Article
C2 - 27435950
AN - SCOPUS:84976358815
SN - 1386-5056
VL - 93
SP - 74
EP - 84
JO - International Journal of Medical Informatics
JF - International Journal of Medical Informatics
ER -