Function allocation refers to strategies for distributing system functions and tasks across people and technology. We review approaches to function allocation in the context of human machine teaming with technology that exhibits high levels of autonomy (e.g., unmanned aerial systems). Although most function allocation projects documented in the literature have employed a single method, we advocate for an integrated approach that leverages four key activities: (1) analyzing operational demands and work requirements; (2) exploring alternative distribution of work across person and machine agents that make up a human machine team (HMT); (3) examining interdependencies between human and autonomous technologies required for effective HMT performance under routine and off-nominal (unexpected) conditions; and (4) exploring the trade-space of alternative HMT options. Our literature review identified methods to support each of these activities. In combination, they enable system designers to uncover, explore, and weigh a range of critical design considerations beyond those emphasized by the MABA–MABA (“Men are better at, Machines are better at”) and Levels of Automation function allocation traditions. Example applications are used to illustrate the value of these methods to design of HMT that includes autonomous machine agents.
Adoption of clinical decision support has been limited. Important barriers include an emphasis on algorithmic approaches to decision support that do not align well with clinical work flow and human decision strategies, and the expense and challenge of developing, implementing, and refining decision support features in existing electronic health records (EHRs). We applied decision-centered design to create a modular software application to support physicians in managing and tracking colorectal cancer screening. Using decision-centered design facilitates a thorough understanding of cognitive support requirements from an end user perspective as a foundation for design. In this project, we used an iterative design process, including ethnographic observation and cognitive task analysis, to move from an initial design concept to a working modular software application called the Screening & Surveillance App. The beta version is tailored to work with the Veterans Health Administration’s EHR Computerized Patient Record System (CPRS). Primary care providers using the beta version Screening & Surveillance App more accurately answered questions about patients and found relevant information more quickly compared to those using CPRS alone. Primary care providers also reported reduced mental effort and rated the Screening & Surveillance App positively for usability.
Naturalistic decision-making (NDM) research offers important guidance for designing information technology to be used by people who conduct extreme activities in extreme environments, such as military special operators. Recent advances in technology make it possible to deploy information technology in extreme environments (e.g., desert, arctic, high altitude, underwater) to support people who engage in complex and dangerous activities. This practitioner case integrates and operationalizes relevant NDM research including an NDM perspective, methods (cognitive task analysis, staged world exercise, and functional analysis), and frameworks (macrocognition, Laws that Govern Cognitive Work in Action, and Cognitive Performance Indicators) to inform design and identify the potential pitfalls associated with introducing information technology into extreme environments. A project conducted with the United States Air Force pararescue jumpers illustrates the complexity and relevance of NDM research in this challenging design space. Pararescue jumpers are military personnel who specialize in recovering personnel and administering life-saving medical treatment in a wide variety of operational environments. Practitioner pointsRecent technological advances make it possible to introduce information technology into domains requiring extreme activities in extreme environments. Naturalistic decision-making research offers perspective, methods, and frameworks relevant to designing for extreme activities in extreme environments. Naturalistic decision-making research provides guidance for anticipating potential pitfalls in introducing information technology.The Naturalistic decision-making (NDM) community has long-studied decision-making in situations characterized by high stakes, time pressure, team dynamics, organizational constraints, and dynamic conditions. In this practitioner case, we address a specialized *Correspondence should be addressed to Laura Militello, 5335 Far Hills Ave.,
We present a framework for using augmented reality (AR) to train sensemaking skills in combat medics and civilian emergency medical personnel. AR and other extended reality technologies create engaging training environments, but their effectiveness on training outcomes is not yet clear. One benefit of AR is that it can enhance simulation training with realism and context that naturalistic decision-making (NDM) models emphasize. We describe four key elements of sensemaking that leverage the strengths of AR: perceptual skills, assessment skills, mental models, and generating/evaluating hypotheses. We discuss how AR can be used to train each of these four elements, along with design implications. A focus on naturalistic tasks and environments while designing AR-based simulation training will likely lead to training that is not only engaging but also effective.
The results speak to the benefits of using the Decision-Centered Design approach in the analysis, design, and evaluation of Health Information Technology. Furthermore, the Screening and Surveillance App shows promise for filling decision support gaps in current electronic health records.
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