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      Iterative User Interface Design for Automated Sequential Organ Failure Assessment Score Calculator in Sepsis Detection

      research-article
      , MD 1 , , , BS 2 , , MD 2 , , MD, PhD 3 , 4
      (Reviewer)
      JMIR Human Factors
      JMIR Publications
      automation, organ dysfunction scores, software design, user-computer interface

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          Abstract

          Background

          The new sepsis definition has increased the need for frequent sequential organ failure assessment (SOFA) score recalculation and the clerical burden of information retrieval makes this score ideal for automated calculation.

          Objective

          The aim of this study was to (1) estimate the clerical workload of manual SOFA score calculation through a time-motion analysis and (2) describe a user-centered design process for an electronic medical record (EMR) integrated, automated SOFA score calculator with subsequent usability evaluation study.

          Methods

          First, we performed a time-motion analysis by recording time-to-task-completion for the manual calculation of 35 baseline and 35 current SOFA scores by 14 internal medicine residents over a 2-month period. Next, we used an agile development process to create a user interface for a previously developed automated SOFA score calculator. The final user interface usability was evaluated by clinician end users with the Computer Systems Usability Questionnaire.

          Results

          The overall mean (standard deviation, SD) time-to-complete manual SOFA score calculation time was 61.6 s (33). Among the 24% (12/50) usability survey respondents, our user-centered user interface design process resulted in >75% favorability of survey items in the domains of system usability, information quality, and interface quality.

          Conclusions

          Early stakeholder engagement in our agile design process resulted in a user interface for an automated SOFA score calculator that reduced clinician workload and met clinicians’ needs at the point of care. Emerging interoperable platforms may facilitate dissemination of similarly useful clinical score calculators and decision support algorithms as “apps.” A user-centered design process and usability evaluation should be considered during creation of these tools.

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          Most cited references40

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          Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes.

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          Artificial neural networks are algorithms that can be used to perform nonlinear statistical modeling and provide a new alternative to logistic regression, the most commonly used method for developing predictive models for dichotomous outcomes in medicine. Neural networks offer a number of advantages, including requiring less formal statistical training, ability to implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect all possible interactions between predictor variables, and the availability of multiple training algorithms. Disadvantages include its "black box" nature, greater computational burden, proneness to overfitting, and the empirical nature of model development. An overview of the features of neural networks and logistic regression is presented, and the advantages and disadvantages of using this modeling technique are discussed.
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            IBM computer usability satisfaction questionnaires: Psychometric evaluation and instructions for use

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              Can electronic medical record systems transform health care? Potential health benefits, savings, and costs.

              To broadly examine the potential health and financial benefits of health information technology (HIT), this paper compares health care with the use of IT in other industries. It estimates potential savings and costs of widespread adoption of electronic medical record (EMR) systems, models important health and safety benefits, and concludes that effective EMR implementation and networking could eventually save more than $81 billion annually--by improving health care efficiency and safety--and that HIT-enabled prevention and management of chronic disease could eventually double those savings while increasing health and other social benefits. However, this is unlikely to be realized without related changes to the health care system.
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                Author and article information

                Contributors
                Journal
                JMIR Hum Factors
                JMIR Hum Factors
                JMIR Human Factors
                JMIR Human Factors
                JMIR Publications (Toronto, Canada )
                2292-9495
                Apr-Jun 2017
                18 May 2017
                : 4
                : 2
                : e14
                Affiliations
                [1] 1Mayo Clinic Department of Medicine, Division of General Internal Medicine Rochester, MNUnited States
                [2] 2Mayo Clinic Department of Information Technology Rochester, MNUnited States
                [3] 3Mayo Clinic Multidisciplinary Epidemiology and Translation Research in Intensive Care (METRIC) Rochester, MNUnited States
                [4] 4Mayo Clinic Department of Anesthesia and Perioperative Medicine Rochester, MNUnited States
                Author notes
                Corresponding Author: Christopher Ansel Aakre aakre.christopher@ 123456mayo.edu
                Author information
                http://orcid.org/0000-0001-9817-8533
                http://orcid.org/0000-0002-1399-5508
                http://orcid.org/0000-0001-8412-8084
                http://orcid.org/0000-0002-0214-0651
                Article
                v4i2e14
                10.2196/humanfactors.7567
                5454218
                28526675
                de30159f-500e-47f6-9c80-c2baef037a60
                ©Christopher Ansel Aakre, Jaben E Kitson, Man Li, Vitaly Herasevich. Originally published in JMIR Human Factors (http://humanfactors.jmir.org), 18.05.2017.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on http://humanfactors.jmir.org, as well as this copyright and license information must be included.

                History
                : 23 February 2017
                : 20 March 2017
                : 27 March 2017
                : 27 March 2017
                Categories
                Original Paper
                Original Paper

                automation,organ dysfunction scores,software design,user-computer interface

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