Optimizing pain management in breast cancer care: Utilizing 'All of Us' data and deep learning to identify patients at elevated risk for chronic pain.

PURPOSE: 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. DESIGN: This study was a retrospective, observational study. METHODS: 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. RESULTS: 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. CONCLUSION: 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. CLINICAL RELEVANCE: 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.

Validación de la escala de autoevaluación de competencias de enfermeria en informática en español / Validation of the nursing informatics competence self-assessment scale in Spanish

RESUMEN Objetivo: validar la escala de autoevaluación de competencias de enfermería en informática (SANICS-MX) en profesionales de enfermería mexicanos. Método:diseño descriptivo prospectivo y polietápico. Las etapas fueon: traducción y validación lingüística, validación por jueces, prueba piloto y confiabilidad, prueba final en una muestra representativa y análisis de fiabilidad y factorial. Asi mismo, la muestra fue de 160 profesionales de enfermería de instituciones de salud. El periodo de la realizacion del estudio fue de enero 2022 a febrero 2023. Resultados:se evaluó el Índice de Validez por Ítem (3.0); Criterio de Validez (16.0%) e Índice de Validez de Contenido (8.33) y se obtuvo la segunda versión del instrumento. Se realizó una prueba piloto en 30 profesionales de enfermería para verificar la consistencia, persistencia y comprensión de los ítems, obteniendo una Alpha de Cronbach de 0.83, lo que indica que es un instrumento confiable. Se realizó una prueba final a 160 profesionales de enfermería. Se realizo el análisis factorial y el nivel de fiabilidad de la versión final de la escala SANICS-MX, en el cual se determinó un Alpha de Cronbach de 0.943. Conclusión:la escala SANICS-MX es un instrumento válido y confiable para medir las competencias en informática en enfermería.

ABSTRACT Objetive:to validate the Self-assessment Nursing Informatics Competence Scale (SANICS-MX) in mexican nurses. Method:descriptive prospective and multistage design. The steps were: translation and linguistic validation, validation by judges, pilot test and reliability, final test on a representative sample, and reliability and factorial analysis. Likewise, the sample consisted of 160 nursing professionals from health institutions. The period of the study was from January 2022 to February 2023. Results: the validity index per Item (3.0) was evaluated; validity criterion (16.0%) and content validity index (8.33) and the second version of the instrument was obtained. A pilot test was carried out on 30 nursing professionals to verify the consistency, persistence and comprehension of the items, obtaining a Cronbach's Alpha of 0.83, which indicates that it is a reliable scale. A final test was carried out on 160 nursing professionals. The factorial analysis and the level of reliability of the final version of the SANICS-MX scale was carried out, in which a Cronbach's Alpha of 0.943 was determined.Conclusion:SANICS-MX is valid and reliable instrument to measure computer skills and competencies in nurses.

A Predictive Model for Developing Long Term Opioid Use After Neurosurgery and Orthopedic Surgery.

This study aimed to identify patient characteristics that predict long-term opioid use after an orthopedic or neurosurgery procedure. Long-term opioid use was defined as opioid use for 90 or more days following the surgical procedure. A retrospective analysis was conducted of orthopedic and neurosurgery patients 18 years and older from 01/01/2011 through 12/31/2017 (n = 12,301). Characteristics included age, sex, race, length of hospital stay, body mass index, surgical procedure specialty, presence of opioid use before and after surgery, and opioid use 90 days or more after surgery. A multiple logistic regression model was used to model characteristics predictive of long-term use of opioids. In this cohort, 32.0% of patients had prescriptions for opioids 90 or more days after surgery. Statistically significant risk factors for long-term opioid use were being Caucasian, younger (18-25 years age group) or older than age 45 and being obese. People who were African American or Black, in the 25-45 years age group, underweight, and used opioids before surgery were less likely to use opioids 90 days after surgery. Nurse anesthetist awareness of predictive characteristics of long-term opioid use can lead to alternative options to prevent opioid abuse.

Evaluating Systemized Nomenclature of Medicine Clinical Terms Coverage of Complementary and Integrative Health Therapy Approaches Used Within Integrative Nursing, Health, and Medicine.

The use of complementary and integrative health therapy strategies for a wide variety of health conditions is increasing and is rapidly becoming mainstream. However, little is known about how or if complementary and integrative health therapies are represented in the EHR. Standardized terminologies provide an organizing structure for health information that enable EHR representation and support shareable and comparable data; which may contribute to increased understanding of which therapies are being used for whom and for what purposes. Use of standardized terminologies is recommended for interoperable clinical data to support sharable, comparable data to enable the use of complementary and integrative health therapies and to enable research on outcomes. In this study, complementary and integrative health therapy terms were extracted from multiple sources and organized using the National Center for Complementary and Integrative Health and former National Center for Complementary and Alternative Medicine classification structures. A total of 1209 complementary and integrative health therapy terms were extracted. After removing duplicates, the final term list was generated via expert consensus. The final list included 578 terms, and these terms were mapped to Systemized Nomenclature of Medicine Clinical Terms. Of the 578, approximately half (48.1%) were found within Systemized Nomenclature of Medicine Clinical Terms. Levels of specificity of terms differed between National Center for Complementary and Integrative Health and National Center for Complementary and Alternative Medicine classification structures and Systemized Nomenclature of Medicine Clinical Terms. Future studies should focus on the terms not mapped to Systemized Nomenclature of Medicine Clinical Terms (51.9%), to formally submit terms for inclusion in Systemized Nomenclature of Medicine Clinical Terms, toward leveraging the data generated by use of these terms to determine associations among treatments and outcomes.

Evaluation of neighborhood resources and mental health in American military Veterans using geographic information systems.

Neighborhood-level social determinants are increasingly recognized as factors shaping mental health in adults. Data-driven informatics methods and geographic information systems (GIS) offer innovative approaches for quantifying neighborhood attributes and studying their influence on mental health. Guided by a modification of Andersen's Behavioral Model of Health Service Use framework, this cross-sectional study examined associations of neighborhood resource groups with psychological distress and depressive symptoms in 1,528 U.S. Veterans. Data came from the Veteran Affairs (VA) Health Services Research and Development Proactive Mental Health trial and publicly available sources. Hierarchical clustering based on the proportions of neighborhood resources within walkable distance was used to identify neighborhood resource groups and generalized estimating equations analyzed the association of identified neighborhood resource groups with mental health outcomes. Few resources were found in walkable areas except alcohol and/or tobacco outlets. In clustering analysis, four meaningful neighborhood groups were identified characterized by alcohol and tobacco outlets. Living in an alcohol-permissive and tobacco-restrictive neighborhood was associated with increased psychological distress but not depressive symptoms. Living in urban or rural areas and access to VA care facilities were not associated with either outcome. These findings can be used in developing community-based mental health-promoting interventions and public health policies such as zoning policies to regulate alcohol outlets in neighborhoods. Augmenting community-based services with Veteran-specialized services in neighborhoods where Veterans live provides opportunities for improving their mental health.

Development of a nursing diagnosis educational software program / Desarrollo de un software educativo para diagnosticos de enfermeria / Desenvolvimento de um software educativo de diagnósticos de enfermagem

ABSTRACT Introduction The human-computer interaction is essential in simulated electronic systems associated with teaching-learning activities. Interactive clinical cases reinforce the diagnostic reasoning ability, a stage of the advanced Nursing Process. Purpose To build educational software, based on NANDA International, to improve the accuracy of nursing diagnoses. Method Methodological study in three stages: preparation and validation of case studies; construction; homologation and evaluation. Held between January / 2012 and December / 2013, in a university hospital in southern Brazil. Teachers, nurses, undergraduate students and graduate students in nursing and information technology participated. Approved by the Research Ethics Committee (130035). Results Construction of five case studies and two versions of the software: one simulates the reasoning process for establishing the diagnosis; another, aimed at teachers, makes it possible to edit/create cases. Conclusion The software helps in the teaching-learning process, generating accurate diagnoses, supporting more appropriate interventions.

RESUMEN Introducción Interfaz humano-técnica es esencial en sistemas electrónicos simulados asociados con actividades de enseñanza-aprendizaje. Los casos clínicos interactivos refuerzan la capacidad de razonamiento diagnóstico, una etapa del proceso avanzado de enfermería. Objetivo Construir un software educativo, basado en NANDA International, para mejorar la precisión de los diagnósticos de enfermería. Método Estudio metodológico en tres fases: preparación y validación de estudios de caso; construcción; homologación y evaluación. Celebrada entre enero/2012 y diciembre/2013, en hospital universitario en el sur de Brasil. Participaron docentes, enfermeras, estudiantes universitarios y estudiantes de posgrado en enfermería y tecnología de la información. Aprobado por el Comité de Ética de Investigación (130035). Resultados Construcción de cinco estudios de caso y dos versiones del software: uno simula el proceso de razonamiento para establecer el diagnóstico; otro, dirigido a docentes, permite editar/crear casos. Conclusión El software ayuda en el proceso de enseñanza-aprendizaje, genera diagnósticos precisos y respalda intervenciones más apropiadas.

RESUMO Introdução A interface humano-técnica é essencial em sistemas eletrônicos simulados associados a atividades de ensino-aprendizagem. Casos clínicos interativos reforçam a habilidade de raciocínio diagnóstico, etapa do Processo de Enfermagem avançado. Objetivo Construir um software educativo, baseado na NANDA International, para melhoria da acurácia de diagnósticos de enfermagem. Método Estudo metodológico em três fases: elaboração e validação de estudos de caso; construção; homologação e avaliação. Realizado entre janeiro/2012 e julho/205, em hospital universitário do sul do Brasil. Participaram professores, enfermeiros, alunos de graduação e alunos de pós-graduação de enfermagem e tecnologia da informação. Aprovado pelo Comitê de Ética em Pesquisa (130035). Resultados Construção de cinco estudos de caso e duas versões do software: uma simula o processo de raciocínio para estabelecimento do diagnóstico; outra, destinada a professores, possibilita editar/criar os casos. Conclusão O software auxilia no processo de ensino-aprendizagem gerando diagnósticos acurados, subsidiando intervenções mais adequadas.

An Applied Healthcare Data Science Roadmap for Nursing Leaders: A Workshop Development, Conceptualization, and Application.

Nurse leaders working with large volumes of interdisciplinary healthcare data are in need of advanced guidance for conducting analytics to improve population outcomes. This article reports the development of a roadmap to help nursing leaders use data science principles and tools to inform decision-making, thus supporting research and approaches in clinical practice that improve healthcare for all. A consensus-building and iterative process was utilized based on the Cross-Industry Standard Process for Data Mining approach to big data science. Using the model, a set of components are described that combine and achieve a process for data science projects applicable to healthcare issues with the potential for improving population health outcomes. The roadmap was tested using a workshop format. The workshop was presented to two audiences: nurse leaders and informatics/healthcare leaders. Results were positive and included suggestions for how to further refine and communicate the roadmap.

Nursing outcomes for pain assessment of patients undergoing palliative care.

OBJECTIVE: To select outcomes and indicators of the Nursing Outcomes Classification (NOC), in order to assess patients with cancer under palliative care with Acute and Chronic Pain Nursing Diagnoses; and to construct the conceptual and operational definitions of the indicators. METHOD: Expert opinion study and literature review. The sample consisted of 13 experts. The data collection was in own tool applied in face-to-face meeting and by e-mail. In the analysis of the data, it was considered between 75% and 100% of agreement. RESULTS: Eight outcomes and 19 indicators were selected. The results with higher scores were Pain Level, Pain Control and Client Satisfaction: Pain Management. For all indicators selected, conceptual and operational definitions were constructed. CONCLUSION: The selection of results and priority indicators for the assessment of pain in palliative care, as well as the construction of its definitions, will support clinical practice.

Nursing outcomes for pain assessment of patients undergoing palliative care / Resultados de enfermería para evaluación del dolor de pacientes en cuidado paliativo / Resultados de enfermagem para avaliação da dor de pacientes em cuidado paliativo

ABSTRACT Objective: To select outcomes and indicators of the Nursing Outcomes Classification (NOC), in order to assess patients with cancer under palliative care with Acute and Chronic Pain Nursing Diagnoses; and to construct the conceptual and operational definitions of the indicators. Method: Expert opinion study and literature review. The sample consisted of 13 experts. The data collection was in own tool applied in face-to-face meeting and by e-mail. In the analysis of the data, it was considered between 75% and 100% of agreement. Results: Eight outcomes and 19 indicators were selected. The results with higher scores were Pain Level, Pain Control and Client Satisfaction: Pain Management. For all indicators selected, conceptual and operational definitions were constructed. Conclusion: The selection of results and priority indicators for the assessment of pain in palliative care, as well as the construction of its definitions, will support clinical practice.

RESUMEN Objetivo: Selección de resultados e indicadores de la Nursing Outcomes Classification (NOC) para evaluar pacientes oncológicos en cuidados paliativos con los diagnósticos de enfermería de Dolor Agudo y Crónico; construir las definiciones conceptuales y operativas de los indicadores. Método: Estudio de opinión de expertos y de revisión de literatura. La muestra fue compuesta por 13 especialistas. La recolección de datos fue en instrumento propio aplicado en encuentro presencial y por e-mail. En el análisis de los datos, se consideró entre el 75% y el 100% de concordancia. Resultados: Se seleccionaron ocho resultados y 19 indicadores. Los resultados con mayores puntuaciones fueron Nivel del Dolor, Control del Dolor y Satisfacción del Cliente: Control del Dolor. Para todos los indicadores seleccionados, se construyeron definiciones conceptuales y operativas. Conclusión: La selección de los resultados e indicadores prioritarios a la evaluación del dolor en cuidado paliativo, así como la construcción de sus definiciones, subsidiarán la práctica clínica.

RESUMO Objetivo: Selecionar resultados e indicadores da Nursing Outcomes Classification (NOC) para avaliar pacientes oncológicos em cuidados paliativos com os diagnósticos de enfermagem de Dor Aguda e Crônica; construir as definições conceituais e operacionais dos indicadores. Método: Estudo de opinião de especialistas e de revisão de literatura. A amostra foi composta por 13 especialistas. A coleta de dados foi em instrumento próprio aplicado em encontro presencial e por e-mail. Na análise dos dados, considerou-se entre 75% e 100% de concordância. Resultados: Selecionaram-se oito resultados e 19 indicadores. Os resultados com maiores escores foram Nível da Dor, Controle da Dor e Satisfação do Cliente: Controle da Dor. Para todos os indicadores selecionados, foram construídas definições conceituais e operacionais. Conclusão: A seleção dos resultados e indicadores prioritários à avaliação da dor em cuidado paliativo, bem como a construção de suas definições, subsidiarão a prática clínica.

Quality of Electronic Nursing Records: The Impact of Educational Interventions During a Hospital Accreditation Process.

Hospital accreditation is a strategy for the pursuit of quality of care and safety for patients and professionals. Targeted educational interventions could help support this process. This study aimed to evaluate the quality of electronic nursing records during the hospital accreditation process. A retrospective study comparing 112 nursing records during the hospital accreditation process was conducted. Educational interventions were implemented, and records were evaluated preintervention and postintervention. Mann-Whitney and χ tests were used for data analysis. Results showed that there was a significant improvement in the nursing documentation quality postintervention. When comparing records preintervention and postintervention, results showed a statistically significant difference (P < .001) between the two periods. The comparison between items showed that most scores were significant. Findings indicated that educational interventions performed by nurses led to a positive change that improved nursing documentation and, consequently, better care practices.

Modeling Flowsheet Data to Support Secondary Use.

The purpose of this study was to create information models from flowsheet data using a data-driven consensus-based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same "thing," but the names of these observations often differ, according to who performs documentation or the location of the service (eg, pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use because of the existence of multiple semantically equivalent measures representing the same concepts. Ten information models were created in this study: five related to quality measures (falls, pressure ulcers, venous thromboembolism, genitourinary system including catheter-associated urinary tract infection, and pain management) and five high-volume physiological systems: cardiac, gastrointestinal, musculoskeletal, respiratory, and expanded vital signs/anthropometrics. The value of the information models is that flowsheet data can be extracted and mapped for semantically comparable flowsheet measures from a clinical data repository regardless of the time frame, discipline, or setting in which documentation occurred. The 10 information models simplify the representation of the content in flowsheet data, reducing 1552 source measures to 557 concepts. The amount of representational reduction ranges from 3% for falls to 78% for the respiratory system. The information models provide a foundation for including nursing and interprofessional assessments and interventions in common data models, to support research within and across health systems.