A Sequential Data Analysis Approach to Electronic Health Record Workflow.
Failure to understand clinical workflow across electronic health record (EHR) tasks is a significant contributor to usability problems. In this paper, we employed sequential data analysis methods with the aim of characterizing patterns of 5 clinicians' information-gathering across 66 patients. Two analyses were conducted. The first one characterized the most common sequential patterns as reflected in the screen transitions. The second analysis was designed to mine and quantify the frequency of sequence occurrence. We observed 27 screen-transition patterns that were employed from 2 to 7 times. Documents/Images and Intake/Output screens were viewed for nearly all patients indicating the importance of these information sources. In some cases, they were viewed more than once which may show that users are following inefficient patterns in the information gathering process. New quantitative methods of analysis as applied to interaction data can yield critical insights in robust designs that better support clinical workflow.
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.
David R
DR
Kaufman
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.
Stephanie K
SK
Furniss
Department of Biomedical Informatics, Arizona State University, Scottsdale, AZ, USA.
Maria Adela
MA
Grando
Department of Colon and Rectal Surgery, Mayo Clinic, Rochester, MN, USA.
David W
DW
Larson
Department of Colon and Rectal Surgery, Mayo Clinic, Rochester, MN, USA.
Matthew M
MM
Burton
D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006804:Ergonomics / Q000379:methods*; D016247:Information Storage and Retrieval / Q000706:statistics & numerical data*; D009664:Norway; D013996:Time and Motion Studies; D014584:User-Computer Interface; D057188:Workflow