SYFSA: A framework for Systematic Yet Flexible Systems Analysis

  • Authors:

  • Todd R. Johnson;Eliz Markowitz;Elmer V. Bernstam;Jorge R. Herskovic;Harold Thimbleby

  • Affiliations:

  • The University of Texas School of Biomedical Informatics at Houston, 7000 Fannin Suite 600, Houston, TX 77030, USA and Division of Biomedical Informatics, Department of Biostatistics, College of P ...;The University of Texas School of Biomedical Informatics at Houston, 7000 Fannin Suite 600, Houston, TX 77030, USA and National Center for Cognitive Informatics and Decision Making, 7000 Fannin Su ...;The University of Texas School of Biomedical Informatics at Houston, 7000 Fannin Suite 600, Houston, TX 77030, USA and Department of Internal Medicine, The University of Texas Health Science Cente ...;The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA and National Center for Cognitive Informatics and Decision Making, 7000 Fannin Suite 165, Houston, TX 77030, USA;FIT Lab-Future Interaction Laboratory, Swansea University, Swansea, Wales, SA2 8PP, UK

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2013

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Abstract

Although technological or organizational systems that enforce systematic procedures and best practices can lead to improvements in quality, these systems must also be designed to allow users to adapt to the inherent uncertainty, complexity, and variations in healthcare. We present a framework, called Systematic Yet Flexible Systems Analysis (SYFSA) that supports the design and analysis of Systematic Yet Flexible (SYF) systems (whether organizational or technical) by formally considering the tradeoffs between systematicity and flexibility. SYFSA is based on analyzing a task using three related problem spaces: the idealized space, the natural space, and the system space. The idealized space represents the best practice-how the task is to be accomplished under ideal conditions. The natural space captures the task actions and constraints on how the task is currently done. The system space specifies how the task is done in a redesigned system, including how it may deviate from the idealized space, and how the system supports or enforces task constraints. The goal of the framework is to support the design of systems that allow graceful degradation from the idealized space to the natural space. We demonstrate the application of SYFSA for the analysis of a simplified central line insertion task. We also describe several information-theoretic measures of flexibility that can be used to compare alternative designs, and to measure how efficiently a system supports a given task, the relative cognitive workload, and learnability.