<h4>ABSTRACT</h4> <P>This article reviews the growing body of research on clinical judgment in nursing and presents an alternative model of clinical judgment based on these studies. Based on a review of nearly 200 studies, five conclusions can be drawn: (1) Clinical judgments are more influenced by what nurses bring to the situation than the objective data about the situation at hand; (2) Sound clinical judgment rests to some degree on knowing the patient and his or her typical pattern of responses, as well as an engagement with the patient and his or her concerns; (3) Clinical judgments are influenced by the context in which the situation occurs and the culture of the nursing care unit; (4) Nurses use a variety of reasoning patterns alone or in combination; and (5) Reflection on practice is often triggered by a breakdown in clinical judgment and is critical for the development of clinical knowledge and improvement in clinical reasoning. A model based on these general conclusions emphasizes the role of nurses' background, the context of the situation, and nurses' relationship with their patients as central to what nurses notice and how they interpret findings, respond, and reflect on their response.</P> <h4>AUTHOR</h4> <p>Dr. Tanner is A.B. Youmans-Spaulding Distinguished Professor, Oregon & Health Science University, School of Nursing, Portland, Oregon.</p> <p>Address correspondence to Christine A. Tanner, PhD, RN, A.B. Youmans-Spaulding Distinguished Professor, Oregon & Health Science University, School of Nursing, 3455 SW U.S. Veterans Hospital Road, Portland, OR 97239; e-mail: <a href="mailto:tannerc@ohsu.edu">tannerc@ohsu.edu</a>.</p>

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Approaching the interpretive process as systematically as possible within a nonlinear methodology streamlines and clarifies interpretations of the interview data.

Nurses' discourse about knowing the patient emerged as a recurring theme in an interpretive phenomenological study of the development of expertise in critical care nursing. The purpose of this article is to present analyses related to the meaning of knowing the patient, and its role in everyday nursing practice. Informants in the study were 130 nurses who practiced in adult, pediatric and newborn intensive care units of eight hospitals in three metropolitan areas. The data were group interviews in which nurses gave narrative accounts of exemplars from their practice; in addition, a sub-sample of 48 nurses were observed in their practice and participated in intensive personal history interviews. Knowing the patient means both knowing the patient's typical pattern of responses and knowing the patient as a person. Knowing the patient is central to skilled clinical judgment, requires involvement, and sets up the possibility for patient advocacy and for learning about patient populations.

This paper presents a novel method for validation of nonrigid medical image registration. This method is based on the simulation of physically plausible, biomechanical tissue deformations using finite-element methods. Applying a range of displacements to finite-element models of different patient anatomies generates model solutions which simulate gold standard deformations. From these solutions, deformed images are generated with a range of deformations typical of those likely to occur in vivo. The registration accuracy with respect to the finite-element simulations is quantified by co-registering the deformed images with the original images and comparing the recovered voxel displacements with the biomechanically simulated ones. The functionality of the validation method is demonstrated for a previously described nonrigid image registration technique based on free-form deformations using B-splines and normalized mutual information as a voxel similarity measure, with an application to contrast-enhanced magnetic resonance mammography image pairs. The exemplar nonrigid registration technique is shown to be of subvoxel accuracy on average for this particular application. The validation method presented here is an important step toward more generic simulations of biomechanically plausible tissue deformations and quantification of tissue motion recovery using nonrigid image registration. It will provide a basis for improving and comparing different nonrigid registration techniques for a diversity of medical applications, such as intrasubject tissue deformation or motion correction in the brain, liver or heart.

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