The aim of the study was to develop a prediction model using deep learning approach to identify breast cancer patients at high risk for chronic pain.
This study was a retrospective, observational study.
We used demographic, diagnosis, and social survey data from the NIH ‘All of Us’ program and used a deep learning approach, specifically a Transformer-based time-series classifier, to develop and evaluate our prediction model.
The final dataset included 1131 patients. We evaluated the deep learning prediction model, which achieved an accuracy of 72.8% and an area under the receiver operating characteristic curve of 82.0%, demonstrating high performance.
Our research represents a significant advancement in predicting chronic pain among breast cancer patients, leveraging deep learning model. Our unique approach integrates both time-series and static data for a more comprehensive understanding of patient outcomes.
Our study could enhance early identification and personalized management of chronic pain in breast cancer patients using a deep learning-based prediction model, reducing pain burden and improving outcomes.
This study examines whether racism exists among Jewish and Arab patients in Israel, as reflected in patient preference for receiving treatment from a nurse with the same ethnic background.
We examine the relationship between racism and the level of trust in a nurse from a different ethnic group than the patient, as well as the preferred level of social distance, in the context of ongoing conflicts between the Jewish majority and the Arab minority in Israel.
A cross-sectional study was conducted using a unique study questionnaire that asked 534 Jewish and 478 Arab respondents to express their preference for an Arab and a Jewish nurse.
Among both the Jews and the Arabs, there is a similar tendency of racism toward nurses of the dissimilar ethnic group. This racism was also prevalent among participants who live in a mixed environment or those who studied or are studying and worked or work in a mixed environment. As the trust in nursing staff members from the other group increases, the level of racism decreases. The greater the social distance the participants felt from the members of the other group, the more racist the attitudes they expressed.
Both Jews and Arabs preferred to be treated by nurses of their own ethnic group. In contrast to the contact hypothesis theory, participants who live in a mixed environment did not express fewer racist preferences. We conclude with some useful practical suggestions aimed at decreasing racism in health care.
Findings imply that prospective patients prefer to receive nursing care from nurses of their own ethnic group and trust these nurses more than they trust nurses of different ethnic group.
Many papers reporting on QI projects are not publishable for a variety of reasons. We compared manuscripts submitted as QI reports between June 2014 and June 2016 (prior to publication of the revised Standards for Quality Improvement Reporting Excellence (SQUIRE 2.0) with papers submitted to the American Journal of Nursing between July 2016 and December 2022). The aim was to evaluate any changes in the quality of manuscripts and identify problems that led to rejection; we also compared the quality of students with non-student submissions.
We conducted a non-randomized descriptive study to evaluate 349 papers submitted as QI project reports between June 2014 and December 2022 using screening templates based on the SQUIRE 2.0 checklist and findings of the INANE Working Group on Student Papers.
Manuscripts designated as QI reports accepted for publication increased from 4% during 2014–2016 (T1) to 14% during 2016–2022 (T2); one student submission was accepted. There was a slight decrease in submissions designated as QI that were not QI: 36% of student submissions during T1 and 31% of student submissions during T2. Among clinician submissions, 44% in T1 designated as QI reports were not QI versus 31% submitted during T2. There was a decrease in student submissions that followed the SQUIRE guidelines (36% during T1 to 24% during T2).
Findings demonstrate that by following the SQUIRE 2.0 guidelines, authors submit more complete manuscripts with fewer missing components. However, there are still misconceptions about what constitutes QI versus research and how to report QI initiatives. After comparing the findings from both periods, it is noteworthy that there is essentially the same level of inaccuracy and lack of acceptable manuscripts.
Sharing findings from QI activities through presentations and publications is a vital way of helping spread the learnings from these projects and improve health care for a wider audience. Clinicians, academicians, and students must understand the elements of the SQUIRE guidelines and ensure that this framework is used for both designing and submitting QI projects for publication.
The purpose of this study was to explore nurses' perspectives on Machine Learning Clinical Decision Support (ML CDS) design, development, implementation, and adoption.
Qualitative descriptive study.
Nurses (n = 17) participated in semi-structured interviews. Data were transcribed, coded, and analyzed using Thematic analysis methods as described by Braun and Clarke.
Four major themes and 14 sub-themes highlight nurses' perspectives on autonomy in decision-making, the influence of prior experience in shaping their preferences for use of novel CDS tools, the need for clarity in why ML CDS is useful in improving practice/outcomes, and their desire to have nursing integrated in design and implementation of these tools.
This study provided insights into nurse perceptions regarding the utility and usability of ML CDS as well as the influence of previous experiences with technology and CDS, change management strategies needed at the time of implementation of ML CDS, the importance of nurse-perceived engagement in the development process, nurse information needs at the time of ML CDS deployment, and the perceived impact of ML CDS on nurse decision making autonomy.
This study contributes to the body of knowledge about the use of AI and machine learning (ML) in nursing practice. Through generation of insights drawn from nurses' perspectives, these findings can inform successful design and adoption of ML Clinical Decision Support.
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