The construction and evaluation of new educational software for nursing diagnoses: a randomized controlled trial
Introduction
In clinical practice, nurses address the responses of individuals, families, groups and communities to health problems and life processes. These responses are called Nursing Diagnoses (NDx), and they occupy a central position in nursing care and must be focused on the patient and family. A nursing diagnosis can be defined as a judgment based on a comprehensive nursing assessment. Therefore, an accurate nursing diagnosis is essential to ensure more effective results and safer patient care (Herdman, Internet source). There are various classifications of NDx. NANDA-I classification is used internationally and includes 13 domains, 47 classes, and 235 current diagnoses (Herdman and Kamitsuru, 2014).
Clinical reasoning is the basis of a NDx. Clinical reasoning is needed to distinguish normal and abnormal conditions, group related data, recognize missing data, identify data inconsistencies and make inferences (Alfaro-Lefebre, 2004).
Student nurses face many challenges in performing the clinical reasoning to identify NDx. These difficulties include the following: the complexity of clinical reasoning, the difficulty/impossibility of measuring some human responses, the fact that many events are not presented as listed in books or in nursing classifications, and the fact that some diagnoses share symptoms or defining characteristics (DC) (Cruz and Pimenta, 2005).
Clinical reasoning difficulties lead student nurses and nurses to have poor accuracy of diagnosis, which compromises the quality of nursing care and patient outcomes (Herdman and Kamitsuru, 2014). Thus, strategies that contribute to the increased accuracy of nurses' diagnostic inferences are required.
The accuracy of a NANDA-I diagnosis is validated when the nurse is able to clearly identify DC and related or risk factors (RF) found in the patient's evaluation and to connect them to the diagnostic inference (Herdman and Kamitsuru, 2014). In this study, we present new educational software, called Wise Nurse, which was developed to help student nurses improve their clinical reasoning as they prepare for clinical practice. This study's hypothesis is that using Wise Nurse may increase the knowledge of student nurses about NDx and their capacity to correctly identify NDx and its elements.
Section snippets
Software Development
Based on a literature review, we found some computerized educational systems pertaining to NDx. These systems address various topics and stimulate the development of student nurses' diagnostic reasoning through various interfaces and content. Most systems have focused on solving clinical cases in specific clinical scenarios, such as those of neonatal care (Góes et al., 2011), or in a group of specific disorders such as cardiopulmonary diseases (Lopes et al., 2013) and urinary dysfunction (
Study Design
A randomized controlled trial (RCT) was conducted to compare the effectiveness of Wise Nurse versus printed clinical cases in student nurses' performance in identifying NANDA-I diagnoses. The research design was guided by the Consolidated Standards of Reporting Trials (CONSORT) statement. The CONSORT statement was developed to improve the reporting of an RCT, enabling readers to understand a trial's conduct and to assess the validity of its results (Schulz et al., 2010). The RCT involved two
Sample Characteristics
Participants' age ranged from 20 to 26, with a median age of 22 (SD = 1.8). Most participants were female. The two groups were homogeneous in terms of gender, age, course level, and knowledge level, as measured during the pretest. Characteristic variables of the two participant groups are listed in Table 2.
Students' Performances Before, During, and After the Experiment
Students' grades in the pre- and the post-test were measured. As shown in Table 3, no significant difference was found between the two groups in both the pre- and the post-test (p > 0.05 by
Discussion
The present study shows that Wise Nurse has some advantages and limitations. The software has successfully contributed to the improvement of knowledge and the ability of student nurses to infer NANDA-I diagnoses.
The experiment also revealed that the students had more difficulty to correctly identify DC than RF or NDx. Lopes et al. found that 3rd and 4th year student nurses were better at identifying NDx than DC and RF when solving three clinical case studies (Lopes et al., 2013). An accurate
Conclusion
Wise Nurse is an innovative teaching tool that can contribute to improving student nurses' diagnostic ability and facilitate the use of the NANDA-I taxonomy. Although Wise Nurse has some limitations, its use has advantages, such as simplicity, time efficiency, adequate usability perceived by users and content coverage, considering that it covers all NANDA-I domains. The program's limitations need to be addressed in future studies, and a software update is needed to refine the tool.
Acknowledgments
We thank financial support from the Coordination for the Improvement of Higher Education Personnel — CAPES (Grant No. BEX14504/13-8).
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