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Feblowitz J.C.,Brigham and Womens Hospital | Feblowitz J.C.,HealthCare Partners | Wright A.,Brigham and Womens Hospital | Wright A.,HealthCare Partners | And 9 more authors.
Journal of Biomedical Informatics | Year: 2011

Background: To provide high-quality and safe care, clinicians must be able to optimally collect, distill, and interpret patient information. Despite advances in text summarization, only limited research exists on clinical summarization, the complex and heterogeneous process of gathering, organizing and presenting patient data in various forms. Objective: To develop a conceptual model for describing and understanding clinical summarization in both computer-independent and computer-supported clinical tasks. Design: Based on extensive literature review and clinical input, we developed a conceptual model of clinical summarization to lay the foundation for future research on clinician workflow and automated summarization using electronic health records (EHRs). Results: Our model identifies five distinct stages of clinical summarization: (1) Aggregation, (2) Organization, (3) Reduction and/or Transformation, (4) Interpretation and (5) Synthesis (AORTIS). The AORTIS model describes the creation of complex, task-specific clinical summaries and provides a framework for clinical workflow analysis and directed research on test results review, clinical documentation and medical decision-making. We describe a hypothetical case study to illustrate the application of this model in the primary care setting. Conclusion: Both practicing physicians and clinical informaticians need a structured method of developing, studying and evaluating clinical summaries in support of a wide range of clinical tasks. Our proposed model of clinical summarization provides a potential pathway to advance knowledge in this area and highlights directions for further research. © 2011 Elsevier Inc. Source


Johnson T.R.,University of Houston | Johnson T.R.,University of Kentucky | Johnson T.R.,National Center for Cognitive Informatics and Decision Making | Markowitz E.,University of Houston | And 7 more authors.
Journal of Biomedical Informatics | Year: 2013

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. © 2013 Elsevier Inc. Source

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