Assessing performance of an Electronic Health Record (EHR) using Cognitive Task Analysis

被引:53
|
作者
Saitwal, Himali [1 ]
Feng, Xuan [2 ]
Walji, Muhammad [1 ]
Patel, Vimla [1 ]
Zhang, Jiajie [1 ]
机构
[1] Univ Texas Houston, Hlth Sci Ctr, Sch Hlth Informat Sci, Houston, TX 77030 USA
[2] Arizona State Univ, Dept Biomed Informat, Phoenix, AZ USA
关键词
Electronic Health Records; Cognitive Task Analysis; Distributed cognition; UFuRT; GOMS; KLM;
D O I
10.1016/j.ijmedinf.2010.04.001
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Background: Many Electronic Health Record (EHR) systems fail to provide user-friendly interfaces due to the lack of systematic consideration of human-centered computing issues. Such interfaces can be improved to provide easy to use, easy to learn, and error-resistant EHR systems to the users. Objective: To evaluate the usability of an EHR system and suggest areas of improvement in the user interface. Methods: The user interface of the AHLTA (Armed Forces Health Longitudinal Technology Application) was analyzed using the Cognitive Task Analysis (CTA) method called GOMS (Goals, Operators, Methods, and Selection rules) and an associated technique called KLM (Keystroke Level Model). The GOMS method was used to evaluate the AHLTA user interface by classifying each step of a given task into Mental (Internal) or Physical (External) operators. This analysis was performed by two analysts independently and the inter-rater reliability was computed to verify the reliability of the GOMS method. Further evaluation was performed using KLM to estimate the execution time required to perform the given task through application of its standard set of operators. Results: The results are based on the analysis of 14 prototypical tasks performed by AHLTA users. The results show that on average a user needs to go through 106 steps to complete a task. To perform all 14 tasks, they would spend about 22 min (independent of system response time) for data entry, of which 11 min are spent on more effortful mental operators. The inter-rater reliability analysis performed for all 14 taskswas 0.8 (kappa), indicating good reliability of the method. Conclusion: This paper empirically reveals and identifies the following finding related to the performance of AHLTA: (1) large number of average total steps to complete common tasks, (2) high average execution time and (3) large percentage of mental operators. The user interface can be improved by reducing (a) the total number of steps and (b) the percentage of mental effort, required for the tasks. (C) 2010 Elsevier Ireland Ltd. All rights reserved.
引用
收藏
页码:501 / 506
页数:6
相关论文
共 50 条
  • [1] Assessing Electronic Health Record (EHR) Use during a Major EHR Transition: An Innovative Mixed Methods Approach
    Brianne Molloy-Paolillo
    David Mohr
    Deborah R. Levy
    Sarah L. Cutrona
    Ekaterina Anderson
    Justin Rucci
    Christian Helfrich
    George Sayre
    Seppo T. Rinne
    Journal of General Internal Medicine, 2023, 38 : 999 - 1006
  • [2] Assessing Electronic Health Record (EHR) Use during a Major EHR Transition: An Innovative Mixed Methods Approach
    Molloy-Paolillo, Brianne
    Mohr, David
    Levy, Deborah R.
    Cutrona, Sarah L.
    Anderson, Ekaterina
    Rucci, Justin
    Helfrich, Christian
    Sayre, George
    Rinne, Seppo T.
    JOURNAL OF GENERAL INTERNAL MEDICINE, 2023, 38 (SUPPL 4) : 999 - 1006
  • [3] Analysis of the cognitive demands of electronic health record use
    Pfaff, Mark S.
    Eris, Ozgur
    Weir, Charlene
    Anganes, Amanda
    Crotty, Tina
    Rahman, Mohammad
    Ward, Merry
    Nebeker, Jonathan R.
    JOURNAL OF BIOMEDICAL INFORMATICS, 2021, 113
  • [4] Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis
    Shickel, Benjamin
    Tighe, Patrick James
    Bihorac, Azra
    Rashidi, Parisa
    IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, 2018, 22 (05) : 1589 - 1604
  • [5] Silence in the EHR: infrequent documentation of aphonia in the electronic health record
    Morris, Megan A.
    Kho, Abel N.
    BMC HEALTH SERVICES RESEARCH, 2014, 14
  • [6] Silence in the EHR: infrequent documentation of aphonia in the electronic health record
    Megan A Morris
    Abel N Kho
    BMC Health Services Research, 14
  • [7] Development and validation of an asthma exacerbation prediction model using electronic health record (EHR) data
    Martin, Alfred
    Bauer, Victoria
    Datta, Avisek
    Masi, Christopher
    Mosnaim, Giselle
    Solomonides, Anthony
    Rao, Goutham
    JOURNAL OF ASTHMA, 2020, 57 (12) : 1339 - 1346
  • [8] Evaluation of an Electronic Health Record (EHR) Tool for Integrated Behavioral Health in Primary Care
    Jetelina, Katelyn K.
    Woodson, Tanisha Tate
    Gunn, Rose
    Muller, Brianna
    Clark, Khaya D.
    DeVoe, Jennifer E.
    Balasubramanian, Bijal A.
    Cohen, Deborah J.
    JOURNAL OF THE AMERICAN BOARD OF FAMILY MEDICINE, 2018, 31 (05) : 712 - 723
  • [9] Missing clinical and behavioral health data in a large electronic health record (EHR) system
    Madden, Jeanne M.
    Lakoma, Matthew D.
    Rusinak, Donna
    Lu, Christine Y.
    Soumerai, Stephen B.
    JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 2016, 23 (06) : 1143 - 1149
  • [10] Primary Care Physicians’ Use of an Electronic Medical Record System: A Cognitive Task Analysis
    Aviv Shachak
    Michal Hadas-Dayagi
    Amitai Ziv
    Shmuel Reis
    Journal of General Internal Medicine, 2009, 24 : 341 - 348