Developing a breast cancer screening decision aid in Spanish for average-risk women: a mixed methods study / Estudios originales Publicado el 14 de marzo de 2024 | http://doi.org/10.5867/medwave.2024.02.2726 Developing a breast cancer screening decision aid in Spanish for average-risk women: a mixed methods study Desarrollo de una herramienta en español para ayudar en la toma de decisiones del cribado de cáncer de mama para mujeres de riesgo promedio: un estudio de métodos mixtos

Introduction We aimed to develop a decision aid to support shared-decision making between physicians and women with average breast cancer risk when deciding whether to participate in breast cancer screening. Methods We included women at average risk of breast cancer and physicians involved in supporting the decision of breast cancer screening from an Academic Hospital in Buenos Aires, Argentina. We followed the International Patient Decision Aid Standards to develop our decision aid. Guided by a steering group and a multidisciplinary consultancy group including a patient advocate, we reviewed the evidence about breast cancer screening and previous decision aids, explored the patients' information needs on this topic from the patients' and physicians' perspective using semi-structured interviews, and we alpha-tested the prototype to determine its usability, comprehensibility and applicability. Results We developed the first prototype of a web-based decision aid to use during the clinical encounter with women aged 40 to 69 with average breast cancer risk. After a meeting with our consultancy group, we developed a second prototype that underwent alpha-testing. Physicians and patients agreed that the tool was clear, useful and applicable during a clinical encounter. We refined our final prototype according to their feedback. Conclusion We developed the first decision aid in our region and language on this topic, developed with end-users' input and informed by the best available evidence. We expect this decision aid to help women and physicians make shared decisions during the clinical encounter when talking about breast cancer screening.

Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?

ABSTRACT Objective: To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. Methods: Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. Results: The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. Conclusion: The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources.

Effect of a brief training program on the knowledge of Filipino primary care providers in a rural and a remote setting: A before and after study

Background and Objective@#Primary care providers are key players in providing quality care to patients and advancing Universal Health Care (UHC). However, effective and quality healthcare delivery may be affected by inadequate knowledge and failure to adhere to evidence-based guidelines among providers. The Philippine Primary Care Studies (PPCS) is a five-year program that pilot tested interventions aimed at strengthening the primary care system in the country. Evidence-based training modules for healthcare providers were administered in Sorsogon and Bataan from the years 2018 to 2021. Module topics were selected based on common health conditions encountered by providers in rural and remote settings. This program aimed to evaluate the effectiveness of training in increasing provider knowledge.@*Methods@#A series of training workshops were conducted among 184 remote- and 210 rural-based primary care providers [nurses, midwives, barangay or village health workers (BHWs)]. They covered four modules: essential intrapartum and newborn care (EINC), integrated management of childhood illness (IMCI), non-communicable diseases (NCD), and geriatrics. A decision support system (UpToDate) was provided as a supplementary resource for all participants. We administered pre-tests and post-tests consisting of multiple-choice questions on common health conditions. Data was analyzed using paired one-tailed t-test, with an alpha of 0.05.@*Results@#The knowledge of nurses, midwives, and BHWs improved after the training workshops were conducted. The largest increase from pre-test to post-test scores were observed among the midwives, with a mean difference (MD) of 32.9% (95% CI 23.9 to 41.9) on the EINC module, MD of 25.0% (95% CI 16.6 to 33.4) in the geriatrics module, and MD of 13.5% (95% CI 6.9 to 20.1) in the NCDs module. The nurses had the greatest improvement in the IMCI module (MD 10.8%, 95% CI 2.5 to 19.1). The knowledge of BHWs improved in all participated modules, with greatest improvement in the NCD module (MD 9.0%, 95% CI 5.77 to 12.14). @*Conclusions@#Primary care workshops, even if conducted as single-sessions and on a short-term basis, are effective in improving short-term knowledge of providers. However, this may not translate to long-term knowledge and application in practice. Furthermore, comparisons across provider categories cannot be made as participant composition for each training workshop varied. Ultimately, this study shows enhancing provider knowledge and competence in primary care will therefore require regular and diverse learning interventions and access to clinical decision support tools.

Validación y Comparación de un Nuevo Método para Predecir el Ancho Mesiodistal de Caninos y Premolares no Erupcionados en una Población Chilena de la Región de Valparaíso, Chile

Uno de los principales problemas durante la dentición mixta es la determinación de la futura discrepancia entre tamaño dentario y el espacio disponible. Para predecir el ancho mesiodistal de los dientes permanentes no erupcionados se han introducido diferentes métodos de análisis. Objetivo: El propósito de este estudio fue comparar el método Tanaka-Johnston con una nueva ecuación de regresión para predecir el ancho mesiodistal de caninos y premolares permanentes no erupcionados en una población de la región de Valparaíso, Chile. Material y método: Este estudio fue realizado en la Facultad de Odontología de la Universidad de Valparaíso, desde octubre de 2022 a junio de 2023 (8 meses), la muestra estuvo compuesta por 202 modelos de estudio del departamento de ortodoncia (91 hombres y 111 mujeres) en el rango de edad de 11 -20 años. Resultados: Se demostró que el método elaborado por Lara-Sandoval presenta mayor fiabilidad respecto a las medidas mesiodistales reales de los pacientes (ICC 0,773 para maxilar y 0,762 para mandíbula), en comparación con el método de Tanaka-Johnston (ICC 0,665 para maxilar y 0,623 para mandíbula). No existen diferencias significativas entre los valores reales y el método de Lara-Sandoval. Conclusión: El método de Lara-Sandoval es mejor que el propuesto por Tanaka-Johnston para determinar el ancho mesiodistal de caninos y premolares para esta muestra. Es necesario validar este método en otras regiones del país para ser utilizado con mayor seguridad que el ya existente como método estándar nacional.

One of the main orthodontic problems in mixed dentition is the determination of future tooth and size arch discrepancy. In order to predict the mesiodistal widths of unerupted permanent teeth different methods of analyses have been introduced. The aim of this study is to compare the Tanaka-Johnston analysis with a new regressive equation to predict the mesiodistal width of unerupted permanent canines and premolars in a Chilean population sample, from Valparaíso region. This study was conducted at the Universidad de Valparaíso Dental Faculty, from october 2022 to june 2023 (8 months), and the sample comprised historical dental casts from 202 patients (91 boys and 111 girls) in the age range of 11-20 from the orthodontics department. All the patients are from the Valparaíso region, Chile. The results show that the predictions of the new regressive equation method are closer to the actual mesiodistal measurements of the patients (ICC 0,773 for maxilla and 0,762 for mandible), compared to the Tanaka- Johnston method (ICC 0,665 for maxilla and 0,623 for mandible). There are no significant differences between the real values and the Lara-Sandoval method. Lara-Sandoval method is better than the one proposed by Tanaka-Johnston to determine the mesiodistal width of canines and premolars in this sample population. It is necessary to validate this method in other regions of the country to be used with greater security than the ones that already exists as a national standard method.

Modelo de inteligencia artificial para la detección temprana de diabetes

Introducción. La diabetes es una enfermedad crónica que se caracteriza por el aumento de la concentración de la glucosa en sangre. Puede generar complicaciones que afectan la calidad de vida y aumentan los costos de la atención en salud. En los últimos años, las tasas de prevalencia y mortalidad han aumentado en todo el mundo. El desarrollo de modelos con gran desempeño predictivo puede ayudar en la identificación temprana de la enfermedad. Objetivo. Desarrollar un modelo basado en la inteligencia artificial para apoyar la toma de decisiones clínicas en la detección temprana de la diabetes. Materiales y métodos. Se llevó a cabo un estudio de corte transversal, utilizando un conjunto de datos que incluía edad, signos y síntomas de pacientes con diabetes y de individuos sanos. Se utilizaron técnicas de preprocesamiento para los datos. Posteriormente, se construyó el modelo basado en mapas cognitivos difusos. El rendimiento se evaluó mediante tres parámetros: exactitud, especificidad y sensibilidad. Resultados. El modelo desarrollado obtuvo un excelente desempeño predictivo, con una exactitud del 95 %. Además, permitió identificar el comportamiento de las variables involucradas usando iteraciones simuladas, lo que proporcionó información valiosa sobre la dinámica de los factores de riesgo asociados con la diabetes. Conclusiones. Los mapas cognitivos difusos demostraron ser de gran valor para la identificación temprana de la enfermedad y en la toma de decisiones clínicas. Los resultados sugieren el potencial de estos enfoques en aplicaciones clínicas relacionadas con la diabetes y respaldan su utilidad en la práctica médica para mejorar los resultados de los pacientes.

Introduction. Diabetes is a chronic disease characterized by a high blood glucose level. It can lead to complications that affect the quality of life and increase the costs of healthcare. In recent years, prevalence and mortality rates have increased worldwide. The development of models with high predictive performance can help in the early identification of the disease. Objective. To develope a model based on artificial intelligence to support clinical decision-making in the early detection of diabetes. Materials and methods. We conducted a cross-sectional study, using a dataset that contained age, signs, and symptoms of patients with diabetes and of healthy individuals. Pre-processing techniques were applied to the data. Subsequently, we built the model based on fuzzy cognitive maps. Performance was evaluated with three metrics: accuracy, specificity, and sensitivity. Results. The developed model obtained an excellent predictive performance with an accuracy of 95%. In addition, it allowed to identify the behavior of the variables involved using simulated iterations, which provided valuable information about the dynamics of the risk factors associated with diabetes. Conclusions. Fuzzy cognitive maps demonstrated a high value for the early identification of the disease and in clinical decision-making. The results suggest the potential of these approaches in clinical applications related to diabetes and support their usefulness in medical practice to improve patient outcomes.

THE NEW ERA OF DENTISTRY: MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

Innovative computer techniques are now being utilized not only in academic research but also in commercial dental practice, revolutionizing various areas of dentistry. This digitalization trend is driven by the increasing demands for treatment and diagnosis in the eld. Accurate diagnosis is crucial in dentistry, whether it be in orthodontics, maxillofacial surgery, periodontics, or prosthetics, as it forms the basis for creating effective treatment plans and restoring patients' oral health. While a specialist's expertise plays a vital role in diagnosis and treatment planning, it is susceptible to the inherent risks of human error, given the multifactorial nature of dental conditions. Consequently, there is growing interest in leveraging multi-parametric pattern recognition methods, including statistics, machine learning, and articial intelligence (AI), to enhance clinical decision-making. The introduction of clinical decision support systems (CDSS) and genetic algorithms (GAs) in dental research and clinical practice holds great promise for both healthcare professionals and patients. Extensive work has been undertaken to develop CDSS in dentistry, and this article reviews the latest advancements in this eld.

Analysis of Watson for oncology and clinicians� treatment recommendations for patients with breast cancer in Korea: A single center experience

Background: Various clinical applications have been attempted using artificial intelligence (AI) clinical decision support system (CDSS), and it has become a starting point for personalized cancer treatment. We aimed to identify the degree of agreement between the AI?CDSS, Watson for Oncology (WFO), and the clinician in treatment recommendations for Korean breast cancer patients and to provide guidelines for future improvement. Methods: One hundred and eighty?three breast cancer patients who underwent treatment at the Pusan National University Hospital between January 1, 2016 and May 31, 2017 were enrolled in this study. The concordance between WFO抯 and clinicians� treatment recommendations were examined according to various factors. Results: WFO gave the same treatment option recommendations as clinicians in 74 (40.4%) of the cases. According to the logistic regression, the difference in recommendation concordance between stage I and stage III was statistically significant (P = 0.004), and there was no difference among other factors. Conclusion: The concordance of treatment recommendations was low overall. However, this is largely attributable to the differences of medical insurance system and healthcare environment between the United States and Korea. In the future, region?specific features should be considered or reflected during the development of AI?CDSS.

Clinical decision support systems for diabetic foot ulcers: a scoping review / Sistemas de apoyo a decisiones clínicas para las úlceras del pie diabético: una revisión de alcance / Sistemas de apoio à decisão clínica em úlceras de pé diabético: revisão de escopo

ABSTRACT Objective: Map the scientific evidence on the use of clinical decision support systems in diabetic foot care. Method: A scoping review based on the JBI Manual for Evidence Synthesis and registered on the Open Science Framework platform. Searches were carried out in primary and secondary sources on prototypes and computerized tools aimed at assisting patients with diabetic foot or at risk of having it, published in any language or period, in eleven databases and grey literature. Results: A total of 710 studies were identified and, following the eligibility criteria, 23 were selected, which portrayed the use of decision support systems in diabetic foot screening, predicting the risk of ulcers and amputations, classifying the stage of severity, deciding on the treatment plan, and evaluating the effectiveness of interventions, by processing data relating to clinical and sociodemographic information. Conclusion: Expert systems stand out for their satisfactory results, with high precision and sensitivity when it comes to guiding and qualifying the decision-making process in diabetic foot prevention and care.

RESUMEN Objetivo: Mapeo de la evidencia científica sobre el uso de sistemas de apoyo a la toma de decisiones clínicas en el cuidado del pie diabético. Método: Revisión de alcance basada en el Manual de Síntesis de la Evidencia del JBI y registrada en la plataforma Open Science Framework. Se realizaron búsquedas en fuentes primarias y secundarias sobre prototipos y herramientas informatizadas dirigidas a la asistencia de pacientes con pie diabético o en riesgo de padecerlo, publicadas en cualquier idioma o periodo, en once bases de datos y literatura gris. Resultados: Se identificaron 710 estudios y, tras cumplir los criterios de elegibilidad, se seleccionaron 23, que retrataban el uso de sistemas de apoyo a la toma de decisiones en el diagnóstico del pie diabético, la predicción del riesgo de úlceras y amputaciones, la clasificación del estadio de gravedad, la decisión sobre el plan de tratamiento y la evaluación de la eficacia de las intervenciones, mediante el procesamiento de datos relativos a la información clínica y sociodemográfica. Conclusión: Los sistemas expertos destacan por sus resultados satisfactorios, con gran precisión y sensibilidad a la hora de orientar y cualificar el proceso de toma de decisiones en la prevención y el cuidado del pie diabético.

RESUMO Objetivo: Mapear as evidências científicas sobre uso de Sistemas de Apoio à Decisão Clínica no pé diabético. Método: Revisão de escopo fundamentada no JBI Manual for Evidence Synthesis e registrada na plataforma Open Science Framework. Realizaram-se buscas, em fontes primárias e secundárias, sobre protótipos e ferramentas informatizadas direcionadas à assistência ao paciente com pé diabético ou em risco de tê-lo, publicados em qualquer idioma ou período, em onze bases de dados e literatura cinza. Resultados: Foram identificados 710 estudos e, após critérios de elegibilidade, foram selecionados 23 que retratam o uso de sistemas de apoio à decisão no rastreio do pé diabético, predição do risco de úlceras e amputações, classificação do estágio de gravidade, decisão quanto ao plano de tratamento e avaliação da efetividade das intervenções, por meio do processamento de dados referentes a informações clínicas e sociodemográficas. Conclusão: Os sistemas especialistas destacam-se por resultados satisfatórios, com alta precisão e sensibilidade no que tange à orientação e qualificação do processo de tomada de decisão na prevenção e no cuidado ao pé diabético.

Software para apoio à decisão logística no processo de captação e transplante de órgãos / Software de apoyo a la decisión logística en el proceso de obtención y trasplante de órganos / Software to support logistic decision in the process of organ acquisition and transplantation

Objetivo: desenvolver e avaliar um software para apoio à tomada de decisão dos profissionais da central de transplantes nas definições logísticas envolvidas no processo de captação e distribuição de órgãos para transplante. Método: estudo de produção tecnológica aplicada, sustentado pelo método Design Science Research Methodology. Participaram da etapa de avaliação da usabilidade dez enfermeiros da Central de Transplantes de Santa Catarina. A coleta de dados ocorreu de 1 a 20 de julho de 2021 por meio do questionário System Usability Scale. Resultados: o software utilizou linguagem JavaScript com ReactJS e PHP com Laravel, para o banco de dados PostgreSQL. A avaliação obteve escore médio de 98,25, sendo sua usabilidade considerada como melhor alcançável. Conclusão: o software demonstrou ser adequado e funcional, com fácil manuseio, reunindo informações integradas e objetivas. Representa um avanço na área, propondo uma inovação tecnológica para a gestão e apoio às decisões logísticas envolvidas no processo de captação e transplante de órgãos.

Objetivo: desenvolver e avaliar um software para apoio à tomada de decisão dos profissionais da central de transplantes nas definições logísticas envolvidas no processo de captação e distribuição de órgãos para transplante. Método: estudo de produção tecnológica aplicada, sustentado pelo método Design Science Research Methodology. Participaram da etapa de avaliação da usabilidade dez enfermeiros da Central de Transplantes de Santa Catarina. A coleta de dados ocorreu de 1 a 20 de julho de 2021 por meio do questionário System Usability Scale. Resultados: o software utilizou linguagem JavaScript com ReactJS e PHP com Laravel, para o banco de dados PostgreSQL. A avaliação obteve escore médio de 98,25, sendo sua usabilidade considerada como melhor alcançável. Conclusión: o software demonstrou ser adequado e funcional, com fácil manuseio, reunindo informações integradas e objetivas. Representa um avanço na área, propondo uma inovação tecnológica para a gestão e apoio às decisões logísticas envolvidas no processo de captação e transplante de órgãos.

Objective: to develop and evaluate a software to support the decision-making of transplant center professionals in the logistic definitions involved in the process of organ procurement and distribution for transplantation. Method: applied technological production study, supported by the Design Science Research Methodology method. Ten nurses from the Transplant Center of Santa Catarina participated in the usability evaluation stage. Data collection took place from 1 to 20 July 2021 through the System Usability Scale questionnaire. Results: the software used JavaScript language with ReactJS and PHP with Laravel, for the PostgreSQL database. The evaluation obtained a mean score of 98.25, and its usability is considered as best achievable. Conclusion: the software proved to be adequate and functional, with easy handling, gathering integrated and objective information. It represents a breakthrough in the area, proposing a technological innovation for the management and support to the logistic decisions involved in the process of organ procurement and transplantation.

Deep learning for diagnosis of malign pleural effusion on computed tomography images

Abstract Background The pleura is a serous membrane that surrounds the lungs. The visceral surface secretes fluid into the serous cavity and the parietal surface ensures a regular absorption of this fluid. If this balance is disturbed, fluid accumulation occurs in the pleural space called "Pleural Effusion". Today, accurate diagnosis of pleural diseases is becoming more critical, as advances in treatment protocols have contributed positively to prognosis. Our aim is to perform computer-aided numerical analysis of Computed Tomography (CT) images from patients showing pleural effusion images on CT and to examine the prediction of malignant/benign distinction using deep learning by comparing with the cytology results. Methods The authors classified 408 CT images from 64 patients whose etiology of pleural effusion was investigated using the deep learning method. 378 of the images were used for the training of the system; 15 malignant and 15 benign CT images, which were not included in the training group, were used as the test. Results Among the 30 test images evaluated in the system; 14 of 15 malignant patients and 13 of 15 benign patients were estimated with correct diagnosis (PPD: 93.3%, NPD: 86.67%, Sensitivity: 87.5%, Specificity: 92.86%). Conclusion Advances in computer-aided diagnostic analysis of CT images and obtaining a pre-diagnosis of pleural fluid may reduce the need for interventional procedures by guiding physicians about which patients may have malignancies. Thus, it is cost and time-saving in patient management, allowing earlier diagnosis and treatment.

Application of Mengchao Liver Disease-Brain System version 2.0 in artificial intelligence-assisted clinical diagnosis and treatment： A preliminary study / 临床肝胆病杂志

ObjectiveTo investigate the application of Mengchao Liver Disease-Brain System version 2.0 in clinical diagnosis and treatment. MethodsThis study was conducted among 160 patients who were admitted to the internal medicine and surgical departments from June 9 to 21， 2021， and their data were automatically captured by the intelligent information system of Southeast Big Data Institute of Hepatobiliary Health， Mengchao Hepatobiliary Hospital of Fujian Medical University. The completeness and accuracy of Mengchao Liver Disease-Brain System version 2.0 were evaluated based on the intelligent diagnostic tools such as auxiliary diagnosis of chronic hepatitis B， interpretation of liver fibrosis， staging model of chronic hepatitis B， auxiliary diagnosis of liver cirrhosis， auxiliary staining of liver cirrhosis， auxiliary diagnosis of primary liver cancer， BCLC stage of primary liver cancer， Chinese staging of primary liver cancer， Child-Pugh score， and APRI score. ResultsAll auxiliary diagnostic tools had a complete rate of 94.17% in terms of the extraction of correct key dimensions within the test period. The artificial intelligence report had a structured accuracy of 97.55% in capturing data and an accuracy rate of 91.61% in text processing. ConclusionMengchao Liver Disease-Brain System version 2.0 provides an innovative mode for the construction of big data platform in medical specialties and has a high accuracy as an auxiliary diagnostic tool in clinical diagnosis and treatment.

Study on safety performance and condition-suggestion accuracy of the symptom assessment mobile applications / 中华全科医师杂志

Objective:To compare the breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of the 8 symptom assessment mobile applications (APPs) available on the Chinese market.Methods:The APPs were assessed using 200 primary care vignettes and were measured against the vignettes′ standard. The primary outcome measures were proportion of conditions covered by an APP, proportion of vignettes with the correct primary diagnosis,and proportion of safe urgency advice.Results:For APPs assessed,condition-coverage was from 29.0%(58/200)to 99.5%(199/200), top-3 suggestion accuracy was from 8.5%(17/200) to 61.5%(123/200), the proportion of safe urgency advice was from 84.8%(167/197) to 99.5% (198/199).Conclusions:The APPs showed a wide range of coverage, safety performance and condition-suggestion accuracy. Symptom assessment APPs with good performance could be used by general practitioners as supporting tools. However, even symptom assessment APPs with excellent performance need to be further assessed in a real clinical environment.

Clinical decision support system based on explainable artificial intelligence?brain of Mengchao liver disease / 中华消化外科杂志

In recent years, the artificial intelligence machine learning and deep learning technology have made leap progress. Using clinical decision support system for auxiliary diagnosis and treatment is the inevitable developing trend of wisdom medical. Clinicians tend to ignore the interpretability of models while pursuing its high accuracy, which leads to the lack of trust of users and hamper the application of clinical decision support system. From the perspective of explainable artificial intelligence, the authors make some preliminary exploration on the construction of clinical decision support system in the field of liver disease. While pursuing high accuracy of the model, the data governance techniques, intrinsic interpretability models, post-hoc visualization of complex models, design of human-computer interactions, providing knowledge map based on clinical guidelines and data sources are used to endow the system with interpretability.

Developing an implementation protocol of clinical decision-support system about pediatric parenteral nutrition administration based on Guideline Implementation with Decision Support Checklist / 中国实用护理杂志

Objective:To develop an implementation protocol of clinical decision-support system about pediatric parenteral nutrition administration based on Guideline Implementation with Decision Support Checklist.Methods:From November to December 2021, using 4 dimensions and 16 items of Guideline Implementation with Decision Support Checklist, an multidisciplinary expert consultation was conducted, based on the previous clinical decision-support system and implementation protocol draft, to identify qualitative suggestions and quantitative assessment, and form the final protocol.Results:According to the Guideline Implementation with Decision Support Checklist, experts evaluated the implementation protocol draft, ranked the scores of 4 dimensions, system, content, context, and implementation, successively. Based on 12 updated suggestions, the final protocol included 4 dimensions and 12 interventions, including CDS updates, preparation before launching, experimental application and promotion, and systematic monitoring.Conclusions:The development of Guideline Implementation with Decision Support Checklist-based implementation protocol of clinical decision-support system about pediatric parenteral nutrition administration facilitated the thorough and structured consideration and agreement of multidisciplinary team, thus to optimize protocol and provide foundation for clinical practice.

Influencing factors of adherence to clinical decision support system which based on clinical practice guideline / 中国实用护理杂志

The clinical decision support system can provide medical staff with targeted patient diagnosis, treatment and care plan according to the recommendations of the guidelines, and assist medical staff to make clinical decisions. However, the adherence to clinical decision support system which based on guidelines was poor in clinical practice. Therefore, this article reviewed the influence factors of adherence to clinical decision support system which based on guideline from four aspects: system factors, individual factors, organizational factors and environmental factors, so as to improve the hindering factors and promote the application of clinical decision support system which based on guideline in future research and clinical practice.

Preliminary evaluation of the practicability of a clinical decision support system for diagnosis and treatment of thyroid cancer (CDSS-TC) / 中华内分泌外科杂志

Objective:To study the practical efficacy of the clinical decision support system for diagnosis and treatment of thyroid cancer (CDSS-TC) in assisting doctors to complete several diagnosis and treatment tasks, and to make a preliminary evaluation of its clinical practicability according to the test results.Methods:From Jan. 2022 to Mar. 2022, 90 patients with thyroid cancer who were admitted to the Head and Neck Surgery Department of Shaw Hospital affiliated to Zhejiang University were prospectively analyzed, and the average time spent in reading the pre-operative B-ultrasound report, as well as the individual fitness of the dose adjustment of eugenol in 70 patients with thyroid cancer after surgery. A retrospective analysis was made of the compliance of the basis of the "recommended scheme" and the deviation of the basis of the doctor’s "final scheme" for the preoperative surgery of 120 patients with thyroid cancer who were treated for the first time in the head and neck surgery of Shaw Hospital affiliated to Zhejiang University from Mar. 2021 to May. 2021. All cases were treated by pure artificial (group A) and CDSS-TC assisted (group B) , and the differences in organization were compared.Results:The average time for disposal of a single B-ultrasound report in Group B was much shorter than that in Group A ( P=5.600E-04) ; The number of patients with excellent grade and the total number of patients with excellent grade and qualified grade recommended by the doctor in group B were significantly higher than those in group A ( P=7.819E-20 and P=1.335E-18) ; The conformity rate of the basis of CDSS-TC "Recommended Scheme" ≥ 98%; The deviation rate of the basis for "final protocol" of doctors in group B was lower than that in group A ( P=0.059 for total resection or not, P=0.075 for lateral neck dissection or not) . Conclusions:CDSS-TC can accurately extract the disease-related source information in all the original examination/laboratory reports, and provide accurate decision-making suggestions through efficient correlation analysis. In view of the accurate and objective conclusions of its analysis, it can provide high-quality and all-link decision support for doctors’ clinical diagnosis and treatment, and is an ideal information work platform.

Uma abordagem de Análise de Decisão Multicritérios (MCDA) para identificar e priorizar critérios de tomada de decisão para tratamentos da osteoporose pós-menopausa no Sistema Único de Saúde do Brasil / A Multicriteria Decision Analysis (MCDA) framework to identify and prioritize criteria for guiding decision making for postmenopausal osteoporosis treatment in the Brazilian Public Healthcare System

Objetivo: Identificar os principais critérios e preferências na tomada de decisão em saúde para osteoporose pós-menopausa, por três grupos de stakeholders (n=3, cada): médicos; representantes de pacientes; gestores de saúde. Métodos: Uma estrutura de Análise de Decisão Multicritério (MCDA) foi realizada para gerar priorização entre tecnologias: uma revisão da literatura formou conjuntos de critérios; um painel online validou os critérios selecionados; o método AHP (Analytic Hierarchy Process) atribuiu pesos de importância para cada critério, por consenso. Resultados: Os critérios avaliados foram: eficácia (fraturas clínicas, vertebrais, não vertebrais e de quadril, densidade mineral óssea), segurança (eventos adversos e tolerabilidade), conveniência (adesão e comodidade posológica) e economia (razão de custo-efetividade incremental ­ RCEI, custo por respondedor, impacto orçamentário e custos indiretos). Fraturas clínicas e de quadril apareceram nas primeiras posições para todos os grupos. Para os médicos, fratura de quadril (26,11%) e eventos adversos (14,64%) foram os principais critérios de priorização; para os representantes dos pacientes, fratura clínica (25,09%) e de quadril (22,84%), enquanto critérios econômicos receberam os menores pesos (1,2% a 0,98%), abaixo da comodidade posológica, por exemplo (4%). Gestores públicos priorizaram RCEI (19,44%) e fratura de quadril (16,21%). Conclusões: Os resultados apresentados têm potencial para auxiliar na tomada de decisão e priorização de tratamentos para osteoporose e estão em linha ao observado em estudos de preferência nesta área terapêutica. Embora os pesos finais tenham variado entre os grupos, os desfechos de eficácia que envolvem fraturas foram os critérios priorizados.

Objective: To identify the main criteria and preferences in healthcare decision-making for postmenopausal osteoporosis according to three stakeholder groups (n=3, each): physicians, patient representatives, and public healthcare managers. Methods: A multi-Criteria Decision Analysis framework was performed to generate prioritization rankings between technologies: a literature review formed sets of criteria; an online panel validated the pre-selected criteria; the Analytic Hierarchy Process (AHP) method assigned importance weights to each criterion by consensus. Results: The final weighted average included: efficacy (clinical fractures, new vertebral, non-vertebral, hip fractures, and bone mineral density), safety (clinically significant adverse events and tolerability), convenience (adherence and dosing convenience), and economics (incremental cost-effectiveness ratio ­ ICER, cost per responder, budget impact and indirect costs). New hip and clinical fractures appeared in the top-five positions for all stakeholder groups. For physicians the main criteria were new hip fracture (26.11%) and adverse events (14.64%); similarly, for patient representatives, clinical fracture (25.09%) and new hip fracture (22.84%) were the most important ones, while economic criteria received the lowest weights (1,2% to 0,98%), below dosing convenience, for example (4%). Public healthcare managers prioritized ICER (19.44%) and new hip fractures (16.21%). Conclusions: The presented results have the potential to assist decision-making and treatment prioritization in postmenopausal osteoporosis. Although final weightings varied among stakeholders, efficacy outcomes involving fractures were the priority criteria for all of them. It is possible to observe similar results in previously published studies of preferences in osteoporosis.

Sistemas de apoio à decisão clínica e desfecho clínico na Atenção Primária à Saúde: uma revisão sistemática / Clinical decision support systems and clinical outcomes in Primary Health Care: a systematic review

Esta revisão sistemática aborda o uso de Sistemas de Suporte à Decisão Clínica (SADC) nos atendimentos realizados na Atenção Primária à Saúde (APS), identificando relações existentes entre o uso dos sistemas e os desfechos clínicos. Foram selecionados trabalhos, estudos em português e inglês, sem restrição ao cenário brasileiro, encontrados em diferentes bases de dados. Os resultados demonstram que os SADC ainda se encontram em estágio de desenvolvimento e refinamento, com aplicação ainda incipiente nas mais diversas patologias e condições clínicas. São raros os ensaios clínicos que tracem os desfechos clínicos primários, levando ao acúmulo de dados apenas sobre desfechos secundários ou compostos, dificultando a avaliação dos sistemas. Há indicativos de relativa eficiência no uso dos SADC para situações de diagnóstico e prevenção, com eficiência limitada na fase de tratamento. Finalmente, não existem dados suficientes para afirmar se os SADC geram desfechos clínicos primários mais favoráveis ou desfavoráveis na APS.

This systematic review addresses the use of Clinical Decision Support Systems (CDSS) in Primary Health Care (PHC), identifying relationships between the use of the Systems and clinical outcomes. The research employed selected studies in Portuguese and English, with no restriction to the Brazilian scenario, found in different databases. Results demonstrate that CDSS are still in the development and refinement stage, and their application is still incipient for the most diverse pathologies and clinical conditions. Clinical trials that trace the primary clinical outcomes are rare, leading to the accumulation of data only on secondary or compound outcomes, making it difficult to evaluate the systems. There are indications of relative efficiency in the use of CDSS for diagnosis and prevention situations, with limited efficiency in the treatment phase. Finally, there is insufficient data to establish whether CDSS generates more favorable or unfavorable primary clinical outcomes in PHC.

Cross-Cultural Translation into Brazilian Portuguese and Validation of the Oral Anticoagulation Knowledge Tool (AKT-Br)

ABSTRACT Introduction: Oral anticoagulants are the treatment of choice for diverse types of coagulation disorders. Warfarin is widely used by the Brazilian population, possibly due to its lower cost than other oral anticoagulants. However, it has a high risk of serious adverse effects if used incorrectly. The Anticoagulation Knowledge Tool (AKT) can assess a patient's knowledge about her/his oral anticoagulant therapy and can assist health professionals in identifying patients with difficulties in adherence. This study aimed to translate, culturally adapt, and validate the AKT into Brazilian Portuguese. Methods: After a standard forward-backward procedure to translate the AKT into Brazilian Portuguese (AKT-Br), a version of the instrument was applied in three groups (patients, pharmacists, and the general population). The reliability of the AKT-Br was tested using an internal consistency measure and test-retest. The validity of the instrument was confirmed with data from the contrasted groups. All statistical analyses were performed with RStudio. Results: The median scores obtained with the AKT-Br were 29.0, 17.0, and 7.5 for pharmacists, patients, and the general population, respectively (maximum score of 35 points). There was moderate internal consistency for the instrument and test-retest reliability was satisfactory. Analysis of variance for validity of the groups revealed a significant relationship between the total score and the evaluated groups. Conclusion: The ATK-Br is a reliable and valid tool to assess knowledge about oral anticoagulants. AKT-Br can be used in clinical practice as an auxiliary tool to improve patient care through personalised educational interventions.

How an electronic prescription tool enables better prescription quality for patients

Objective: Medication-related errors in patients are among the leading causes of preventable health damage and harm worldwide. In the United States, these errors cause at least one death a day and damage approximately 1.3 million people annually. According to the World Health Organization, the global expenditure on medication-related errors is estimated to be U$ 42 billion per year. In Brazil, the rate of potential drug interactions varies between 28% and 63.6% for primary care patients. The prevalence of drug interactions has increased following an aging population, increased chronic conditions, combined use of different drugs, and increased prescription drugs per patient. Methods: The data used for this study were obtained through the database from Nexodata do Brasil S.A a private health technology company with an electronic prescription system and a data intelligence area. Results: 65,867 electronic prescriptions were evaluated during 2019. Of these, 4,828 prescriptions had an average of 2.5 interactions. These interactive prescriptions were generated by 197 different doctors, totaling 24.5 prescriptions with interaction per doctor over 12 months. A total of 12,005 interactions were identified, 15.6% classified as mild, 70.9% as moderate, and 13.5% as severe. Conclusion: By implementing an electronic prescription tool, a reduction of 32.9% in the number of prescriptions with drug interaction was observed.