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INTRODUCTION AND OBJECTIVES: Autoimmune liver diseases (AILDs) are rare and require precise evaluation, which is often challenging for medical providers. Chatbots are innovative solutions to assist healthcare professionals in clinical management. In our study, ten liver specialists systematically evaluated four chatbots to determine their utility as clinical decision support tools in the field of AILDs. MATERIALS AND METHODS: We constructed a 56-question questionnaire focusing on AILD evaluation, diagnosis, and management of Autoimmune Hepatitis (AIH), Primary Biliary Cholangitis (PBC), and Primary Sclerosing Cholangitis (PSC). Four chatbots -ChatGPT 3.5, Claude, Microsoft Copilot, and Google Bard- were presented with the questions in their free tiers in December 2023. Responses underwent critical evaluation by ten liver specialists using a standardized 1 to 10 Likert scale. The analysis included mean scores, the number of highest-rated replies, and the identification of common shortcomings in chatbots performance. RESULTS: Among the assessed chatbots, specialists rated Claude highest with a mean score of 7.37 (SD = 1.91), followed by ChatGPT (7.17, SD = 1.89), Microsoft Copilot (6.63, SD = 2.10), and Google Bard (6.52, SD = 2.27). Claude also excelled with 27 best-rated replies, outperforming ChatGPT (20), while Microsoft Copilot and Google Bard lagged with only 6 and 9, respectively. Common deficiencies included listing details over specific advice, limited dosing options, inaccuracies for pregnant patients, insufficient recent data, over-reliance on CT and MRI imaging, and inadequate discussion regarding off-label use and fibrates in PBC treatment. Notably, internet access for Microsoft Copilot and Google Bard did not enhance precision compared to pre-trained models. CONCLUSIONS: Chatbots hold promise in AILD support, but our study underscores key areas for improvement. Refinement is needed in providing specific advice, accuracy, and focused up-to-date information. Addressing these shortcomings is essential for enhancing the utility of chatbots in AILD management, guiding future development, and ensuring their effectiveness as clinical decision-support tools.

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Primary studies have demonstrated that despite being useful, most of the drug-drug interaction (DDI) alerts generated by clinical decision support systems are overridden by prescribers. To provide more information about this issue, we conducted a systematic review and meta-analysis on the prevalence of DDI alerts generated by CDSS and alert overrides by physicians. The search strategy was implemented by applying the terms and MeSH headings and conducted in the MEDLINE/PubMed, EMBASE, Web of Science, Scopus, LILACS, and Google Scholar databases. Blinded reviewers screened 1873 records and 86 full studies, and 16 articles were included for analysis. The overall prevalence of alert generated by CDSS was 13% (CI95% 5-24%, p-value <0.0001, I^2 = 100%), and the overall prevalence of alert override by physicians was 90% (CI95% 85-95%, p-value <0.0001, I^2 = 100%). This systematic review and meta-analysis presents a high rate of alert overrides, even after CDSS adjustments that significantly reduced the number of alerts. After analyzing the articles included in this review, it was clear that the CDSS alerts physicians about potential DDI should be developed with a focus on the user experience, thus increasing their confidence and satisfaction, which may increase patient clinical safety.

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OBJECTIVE: To present a systematic review of the state of the art regarding clinical applications, main features, and outcomes of artificial intelligence (AI) in orthognathic surgery. METHODS: The PICOS strategy was performed on a systematic review (SR) to answer the following question: "What are the state of the art, characteristics and outcomes of applications with artificial intelligence for orthognathic surgery?" After registering in PROSPERO (CRD42021270789) a systematic search was performed in the databases: PubMed (including MedLine), Scopus, Embase, LILACS, MEDLINE EBSCOHOST and Cochrane Library. 195 studies were selected, after screening titles and abstracts, of which thirteen manuscripts were included in the qualitative analysis and six in the quantitative analysis. The treatment effects were plotted in a Forest-plot. JBI questionnaire for observational studies was used to asses the risk of bias. The quality of the SR evidence was assessed using the GRADE tool. RESULTS: AI studies on 2D cephalometry for orthognathic surgery, the Tau2 = 0.00, Chi2 = 3.78, p = 1.00 and I² of 0 %, indicating low heterogeneity, AI did not differ statistically from control (p = 0.79). AI studies in the diagnosis of the decision of whether or not to perform orthognathic surgery showed heterogeneity, and therefore meta-analysis was not peformed. CONCLUSION: The outcome of AI is similar to the control group, with a low degree of bias, highlighting its potential for use in various applications.

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Drug information tools help avoid medication errors, a common cause of avoidable harm in health care systems. We sought to describe the design, development process and architecture of an electronic drug information tool, as well as its overall use by health professionals. We developed a tool that can be accessed by all health professionals in a tertiary level university hospital. The functionalities of eDrugs are organized into two main parts: Drug Summary sheet, and Prescription Simulator. Most users accessed eDrugs to use the Drug summary sheet. Clinical information and antimicrobial drugs were the most accessed drug information and drug group. The analysis of log data provides insights into the information priorities of health professionals.

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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.

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OBJECTIVES: A substantial portion of the data contained in Electronic Health Records (EHR) is unstructured, often appearing as free text. This format restricts its potential utility in clinical decision-making. Named entity recognition (NER) methods address the challenge of extracting pertinent information from unstructured text. The aim of this study was to outline the current NER methods and trace their evolution from 2011 to 2022. METHODS: We conducted a methodological literature review of NER methods, with a focus on distinguishing the classification models, the types of tagging systems, and the languages employed in various corpora. RESULTS: Several methods have been documented for automatically extracting relevant information from EHRs using natural language processing techniques such as NER and relation extraction (RE). These methods can automatically extract concepts, events, attributes, and other data, as well as the relationships between them. Most NER studies conducted thus far have utilized corpora in English or Chinese. Additionally, the bidirectional encoder representation from transformers using the BIO tagging system architecture is the most frequently reported classification scheme. We discovered a limited number of papers on the implementation of NER or RE tasks in EHRs within a specific clinical domain. CONCLUSIONS: EHRs play a pivotal role in gathering clinical information and could serve as the primary source for automated clinical decision support systems. However, the creation of new corpora from EHRs in specific clinical domains is essential to facilitate the swift development of NER and RE models applied to EHRs for use in clinical practice.

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BACKGROUND: Cardiovascular disease (CVD) imposes a significant burden on the Argentinian population. Management of its leading risk factors can significantly reduce the CVD burden in high-resource settings, but there is insufficient evidence for effective implementation of evidence-based interventions in lower-resource settings like Argentina. METHODS: In this two-arm cluster-randomized trial we seek to compare the effective implementation, of a multicomponent intervention, versus usual care, to improve the management of high CVD risk across the care continuum in three provinces of Argentina. The multicomponent intervention strategy links five primary components of the CVD care continuum to improve its management: (1) a data management system linking a digital mHealth (mobile health) screening tool used by community health workers (CHWs), (2) an electronic appointment scheduler that is integrated with the primary care center electronic appointment system, (3) point of care testing for lipid profiles, (4) a clinical decision support (CDS) system for medication initiation, and (5) a text message (SMS) reminder system to improve treatment adherence and life-style changes. The primary outcome is the mean change in Framingham laboratory-based, 10-year absolute CVD risk score between the study arms from baseline to twelve months after enrollment. CONCLUSIONS: This protocol describes the development of a multicomponent intervention to implement effective management of CVD, developed with partners at the National and provincial Departments of Health in Argentina, with the goal of understanding its effective implementation in a primary health care system strengthened by universal health coverage, provision of free health care services, and provision of free medication.

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OBJECTIVE: To analyze the nursing diagnostic concordance among users of a clinical decision support system (CDSS), The Electronic Documentation System of the Nursing Process of the University of São Paulo (PROCEnf-USP®), structured according to the Nanda International, Nursing Intervention Classification and Nursing Outcome Classification (NNN) Taxonomy. MATERIALS AND METHODS: This pilot, exploratory-descriptive study was conducted from September 2017 to January 2018. Participants were nurses, nurse residents, and nursing undergraduates. Two previously validated written clinical case studies provided participants with comprehensive initial assessment clinical data to be registered in PROCEnf-USP®. After having registered the clinical data in PROCEnf-USP®, participants could either select diagnostic hypotheses offered by the system or add diagnoses not suggested by the system. A list of nursing diagnoses documented by the participants was extracted from the system. The concordance was analyzed by Light's Kappa (K). RESULTS: The research study included 37 participants, which were 14 nurses, 10 nurse residents, and 13 nursing undergraduates. Of the 43 documented nursing diagnoses, there was poor concordance (K = 0.224) for the diagnosis "Ineffective airway clearance" (00031), moderate (K = 0.591) for "Chronic pain" (00133), and elevated (K = 0.655) for "Risk for unstable blood glucose level" (00179). The other nursing diagnoses had poor or no concordance. DISCUSSION: Clinical reasoning skills are essential for the meaningful use of the CDSS. CONCLUSIONS: There was concordance for only 3 nursing diagnoses related to biological needs. The low level of concordance might be related to the clinical judgment skills of the participants, the written cases, and the sample size.

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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 decisionmaking 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.

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.

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Dengue is the most widespread vector-borne disease worldwide. Timely diagnosis and treatment of dengue is the main objective of medical professionals to decrease mortality rates. In this paper, we propose an autonomous cycle that integrates data analysis tasks to support decision-making in the clinical management of dengue. Particularly, the autonomous cycle supports dengue diagnosis and treatment. The proposed system was built using machine learning techniques for classification tasks (artificial neural networks and support vector machines) and evolutionary techniques (a genetic algorithm) for prescription tasks (treatment). The system was quantitatively evaluated using dengue-patient datasets reported by healthcare institutions. Our system was compared with previous works using qualitative criteria. The proposed system has the ability to classify a patient's clinical picture and recommend the best treatment option. In particular, the classification of dengue was done with 98% accuracy and a genetic algorithm recommends treatment options for particular patients. Finally, our system is flexible and easily adaptable, which will allow the addition of new tasks for dengue analysis.

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BACKGROUND: In this work, we developed many machine learning classifiers to assist in diagnosing respiratory changes associated with sarcoidosis, based on results from the Forced Oscillation Technique (FOT), a non-invasive method used to assess pulmonary mechanics. In addition to accurate results, there is a particular interest in their interpretability and explainability, so we used Genetic Programming since the classification is made with intelligible expressions and we also evaluate the feature importance in different experiments to find the more discriminative features. METHODOLOGY/PRINCIPAL FINDINGS: We used genetic programming in its traditional tree form and a grammar-based form. To check if interpretable results are competitive, we compared their performance to K-Nearest Neighbors, Support Vector Machine, AdaBoost, Random Forest, LightGBM, XGBoost, Decision Trees and Logistic Regressor. We also performed experiments with fuzzy features and tested a feature selection technique to bring even more interpretability. The data used to feed the classifiers come from the FOT exams in 72 individuals, of which 25 were healthy, and 47 were diagnosed with sarcoidosis. Among the latter, 24 showed normal conditions by spirometry, and 23 showed respiratory changes. The results achieved high accuracy (AUC > 0.90) in two analyses performed (controls vs. individuals with sarcoidosis and normal spirometry and controls vs. individuals with sarcoidosis and altered spirometry). Genetic Programming and Grammatical Evolution were particularly beneficial because they provide intelligible expressions to make the classification. The observation of which features were selected most frequently also brought explainability to the study of sarcoidosis. CONCLUSIONS: The proposed system may provide decision support for clinicians when they are struggling to give a confirmed clinical diagnosis. Clinicians may reference the prediction results and make better decisions, improving the productivity of pulmonary function services by AI-assisted workflow.

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Dengue is a viral infection widely distributed in tropical and subtropical regions of the world. Dengue is characterized by high fatality rates when the diagnosis is not made promptly and effectively. To aid in the diagnosis of dengue, we propose a clinical decision-support system that classifies the clinical picture based on its severity, and using causal relationships evaluates the behavior of the clinical and laboratory variables that describe the signs and symptoms related to dengue. The system is based on a fuzzy cognitive map that is defined by the signs, symptoms and laboratory tests used in the conventional diagnosis of dengue. The evaluation of the model was performed on datasets of patients diagnosed with dengue to compare the model with other approaches. The developed model showed a good classification performance with 89.4% accuracy and could evaluate the behaviour of clinical and laboratory variables related to dengue severity (it is an explainable method). This model serves as a diagnostic aid for dengue that can be used by medical professionals in clinical settings.

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The incapability to move the facial muscles is known as facial palsy, and it affects various abilities of the patient, for example, performing facial expressions. Recently, automatic approaches aiming to diagnose facial palsy using images and machine learning algorithms have emerged, focusing on providing an objective evaluation of the paralysis severity. This research proposes an approach to analyze and assess the lesion severity as a classification problem with three levels: healthy, slight, and strong palsy. The method explores the use of regional information, meaning that only certain areas of the face are of interest. Experiments carrying on multi-class classification tasks are performed using four different classifiers to validate a set of proposed hand-crafted features. After a set of experiments using this methodology on available image databases, great results are revealed (up to 95.61% of correct detection of palsy patients and 95.58% of correct assessment of the severity level). This perspective leads us to believe that the analysis of facial paralysis is possible with partial occlusions if face detection is accomplished and facial features are obtained adequately. The results also show that our methodology is suited to operate with other databases while attaining high performance, even though the image conditions are different and the participants do not perform equivalent facial expressions.

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The acceptance of artificial intelligence (AI) systems by health professionals is crucial to obtain a positive impact on the diagnosis pathway. We evaluated user satisfaction with an AI system for the automated detection of findings in chest x-rays, after five months of use at the Emergency Department. We collected quantitative and qualitative data to analyze the main aspects of user satisfaction, following the Technology Acceptance Model. We selected the intended users of the system as study participants: radiology residents and emergency physicians. We found that both groups of users shared a high satisfaction with the system's ease of use, while their perception of output quality (i.e., diagnostic performance) differed notably. The perceived usefulness of the application yielded positive evaluations, focusing on its utility to confirm that no findings were omitted, and also presenting distinct patterns across the two groups of users. Our results highlight the importance of clearly differentiating the intended users of AI applications in clinical workflows, to enable the design of specific modifications that better suit their particular needs. This study confirmed that measuring user acceptance and recognizing the perception that professionals have of the AI system after daily use can provide important insights for future implementations.

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The role of nurses in the management of sepsis and clinical deterioration, and the potential use of Clinical Decision Support Systems to identify patients' undesired outcomes are well recognized. In order to verify the association of such a system adoption in the compliance of a hospital sepsis protocol on the nurses' workflow and documentation, a three-stage study was conducted. Main findings show that there is no statistically significant difference between the sepsis protocol compliance data from before and after the system implementation; that nursing caring-related activities are a priority over documentation and time spent using the system; and that it was possible to validate a proposal of nursing documentation for sepsis and clinical deterioration. This contributes to the improvement of nurses' awareness of their engagement in nursing informatics issues in the era of big data.

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The high prevalence of PIMs in elderly is a major healthcare concern and indicates the need for medication monitoring systems. Most PIM CDSS have shown positive effects respecting PIM prescription but these results were more consistently in hospital settings compared with ambulatory care. We describe the post-implementation evaluation of a PIM CDSS for general practitioners (GP) in the ambulatory setting and explore GP interactions with the PIM alerts. The CDSS generated 3218 unique alerts and involved 2863 elderly patients. Benzodiazepines was the drug with the most alerts triggered. Only 129 (4 %) were opened by GP during patient appointments. We need to develop an understanding of how alerts should be designed and display information to support the workflow of general practitioners. Pos-implementation evaluations are the key of CDSS improvements.

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BACKGROUND: Pediatric growth tracking has been identified as a top priority by international health agencies to assess the severity of malnutrition and stunting. However, remote low-resource settings often lack the necessary infrastructure for longitudinal analysis of growth for the purposes of early identification and immediate intervention of stunting. METHODS: To address this gap, we developed a portable field unit (PFU) capable of identifying a child over the course of multiple visits, each time adding new anthropomorphic measurements. We conducted a preliminary field evaluation of the PFU by using the unit on two distinct visits to three schools in the area surrounding a medical clinic in rural San Jose, Honduras. The unit was used to assess children at each school as part of the community outreach. RESULTS: Community outreaches to three schools were conducted by two distinct teams, where they used the device to assess 210 children. Of the 180 children registered during the first visit, 112 were re-identified and assessed on the subsequent visit. Twenty-four instances of moderate-to-severe malnutrition were identified and referred for further evaluation to the central clinic. CONCLUSION: This initial assessment suggests that the PFU could be an effective means of identifying at-risk children.

CONTEXTE: Les organismes internationaux de santé ont identifié le suivi de la croissance des enfants comme une priorité absolue pour évaluer la gravité de la malnutrition et les retards de croissance. Cependant, les zones reculées à faibles ressources n'ont souvent pas les infrastructures nécessaires à l'analyse longitudinale de la croissance à des fins d'identification précoce et d'intervention immédiate de lutte contre les retards de croissance. MÉTHODES: Pour combler ces lacunes, nous avons développé un appareil portatif de terrain (PFU) capable d'identifier un même enfant lors de plusieurs visites et d'ajouter les nouvelles mesures anthropomorphiques de chaque visite. Nous avons réalisé une évaluation de terrain préliminaire du PFU en utilisant l'appareil lors de deux visites différentes dans trois écoles de la zone rurale aux alentours d'une clinique médicale de San Jose, Honduras. L'appareil a été utilisé pour évaluer les enfants de chaque école dans le cadre d'un programme de sensibilisation communautaire. RÉSULTATS: Des programmes de sensibilisation communautaire ont été menés dans trois écoles par deux équipes différentes, qui ont utilisé l'appareil pour évaluer 210 enfants. Sur les 180 enfants enregistrés lors de la première visite, 112 ont été de nouveau identifiés et évalués lors de la visite suivante. Vingt-quatre cas de malnutrition modérée à sévère ont été identifiés et adressés pour examen complémentaire à la clinique centrale. CONCLUSION: Cette évaluation initiale suggère que le PFU pourrait être un moyen efficace d'identification des enfants à risque.

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BACKGROUND: The restrictions imposed by the COVID-19 pandemic reduced health service access by patients with chronic diseases. The discontinuity of care is a cause of great concern, mainly in vulnerable regions. OBJECTIVE: This study aimed to assess the impact of the COVID-19 pandemic on people with hypertension and diabetes mellitus (DM) regarding the frequency of consultations and whether their disease was kept under control. The study also aimed to develop and implement a digital solution to improve monitoring at home. METHODS: This is a multimethodological study. A quasiexperimental evaluation assessed the impact of the pandemic on the frequency of consultations and control of patients with hypertension and DM in 34 primary health care centers in 10 municipalities. Then, an implementation study developed an app with a decision support system (DSS) for community health workers (CHWs) to identify and address at-risk patients with uncontrolled hypertension or DM. An expert panel assessment evaluated feasibility, usability, and utility of the software. RESULTS: Of 5070 patients, 4810 (94.87%) had hypertension, 1371 (27.04%) had DM, and 1111 (21.91%) had both diseases. There was a significant reduction in the weekly number of consultations (107, IQR 60.0-153.0 before vs 20.0, IQR 7.0-29.0 after social restriction; P<.001). Only 15.23% (772/5070) of all patients returned for a consultation during the pandemic. Individuals with hypertension had lower systolic (120.0, IQR 120.0-140.0 mm Hg) and diastolic (80.0, IQR 80.0-80.0 mm Hg) blood pressure than those who did not return (130.0, IQR 120.0-140.0 mm Hg and 80.0, IQR 80.0-90.0 mm Hg, respectively; P<.001). Also, those who returned had a higher proportion of controlled hypertension (64.3% vs 52.8%). For DM, there were no differences in glycohemoglobin levels. Concerning the DSS, the experts agreed that the CHWs can easily incorporate it into their routines and the app can identify patients at risk and improve treatment. CONCLUSIONS: The COVID-19 pandemic caused a significant drop in the number of consultations for patients with hypertension and DM in primary care. A DSS for CHW has proved to be feasible, useful, and easily incorporated into their routines.

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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.

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AIM AND METHOD: To determine the effect on decisional-related and clinical outcomes of decision aids for depression treatment in adults in randomised clinical trials. In January 2019, a systematic search was conducted in five databases. Study selection and data extraction were performed in duplicate. Meta-analyses were performed, and standardised and weighted mean differences were calculated, with corresponding 95% confidence intervals. The certainty of the evidence was evaluated with GRADE methodology. RESULTS: Six randomised clinical trials were included. The pooled estimates showed that decision aids for depression treatment had a beneficial effect on patients' decisional conflict, patient knowledge and information exchange between patient and health professional. However, no statistically significant effect was found for doctor facilitation, treatment adherence or depressive symptoms. The certainty of the evidence was very low for all outcomes. CLINICAL IMPLICATIONS: Using decision aids to choose treatment in patients with depression may have a a beneficial effect on decisional-related outcomes, but it may not translate into an improvement in clinical outcomes.