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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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In recent years, particularly following the definition of the UN Sustainable Development Goals (SDGs) for 2030, Nature-Based Solutions (NBS) have gained considerable attention, capturing the interest of both the scientific community and policymakers committed to addressing urban environmental issues. However, the need for studies to guide decision-makers in identifying suitable locations for NBS implementation within urban stormwater management is evident. To address this gap, the present study employs a methodological approach grounded in multi-criteria analysis integrated with Geographic Information Systems (GIS) to identify areas with potential for NBS implementation. In this process, ten NBS were proposed and tested in the drainage area of a shallow tropical urban lake in Londrina, southern Brazil. Additionally, the study investigates areas hosting lower-income populations, a relevant aspect for public managers given the diverse economic subsidies required to implement NBS. Furthermore, the study incorporates a preliminary analysis that evaluates the potential ecosystem benefits to determine the most suitable NBS for a specific site. The result shows that all the ten analyzed NBS were deemed suitable for the study area. Rain barrels had the highest percentage coverage in the study area (37.1%), followed by tree pits (27.9%), and rain gardens (25.4%). Despite having the highest distribution in the basin area, rain barrels exhibited only moderate ecosystem benefits, prompting the prioritization of other NBS with more significant ecological advantages in the final integrated map. In summary, the methodology proposed showed to be a robust approach to selecting optimal solutions in densely populated urban areas.

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Introduction: Acute Myeloid Leukemia (AML) is one of the most aggressive hematological neoplasms, emphasizing the critical need for early detection and strategic treatment planning. The association between prompt intervention and enhanced patient survival rates underscores the pivotal role of therapy decisions. To determine the treatment protocol, specialists heavily rely on prognostic predictions that consider the response to treatment and clinical outcomes. The existing risk classification system categorizes patients into favorable, intermediate, and adverse groups, forming the basis for personalized therapeutic choices. However, accurately assessing the intermediate-risk group poses significant challenges, potentially resulting in treatment delays and deterioration of patient conditions. Methods: This study introduces a decision support system leveraging cutting-edge machine learning techniques to address these issues. The system automatically recommends tailored oncology therapy protocols based on outcome predictions. Results: The proposed approach achieved a high performance close to 0.9 in F1-Score and AUC. The model generated with gene expression data exhibited superior performance. Discussion: Our system can effectively support specialists in making well-informed decisions regarding the most suitable and safe therapy for individual patients. The proposed decision support system has the potential to not only streamline treatment initiation but also contribute to prolonged survival and improved quality of life for individuals diagnosed with AML. This marks a significant stride toward optimizing therapeutic interventions and patient outcomes.

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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.

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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.

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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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PURPOSE: To describe our experiences implementing and iterating CYP2C19 genotype-guided clopidogrel pharmacogenetic clinical decision support (CDS) tools over time in the setting of a large health system-wide, preemptive pharmacogenomics program. SUMMARY: Clopidogrel-treated patients who are genetically predicted cytochrome P450 isozyme 2C19 (CYP2C19) intermediate or poor metabolizers have an increased risk of atherothrombotic events, some of which can be life-threatening. The Clinical Pharmacogenetics Implementation Consortium provides guidance for the use of clopidogrel based on CYP2C19 genotype in patients with cardiovascular and cerebrovascular diseases. Our multidisciplinary team implemented an automated, interruptive alert that fires when clopidogrel is ordered or refilled for biobank participants with structured CYP2C19 intermediate or poor metabolizer genomic indicators in the electronic health record. The implementation began with a narrow cardiovascular indication and setting and was then scaled in 4 primary dimensions: (1) clinical indication; (2) availability across health-system locations; (3) care venue (e.g., inpatient vs outpatient); and (4) provider groups (eg, cardiology and neurology). We iterated our approach over time based on evolving clinical evidence and proactive strategies to optimize CDS maintenance and sustainability. A key facilitator of expansion was socialization of the broader pharmacogenomics initiative among our academic medical center community, accompanied by clinician acceptance of pharmacogenetic alerts in practice. CONCLUSION: A multidisciplinary collaboration is recommended to facilitate the use of CYP2C19 genotype-guided antiplatelet therapy in patients with cardiovascular and cerebrovascular diseases. Evolving clopidogrel pharmacogenetic evidence necessitates thoughtful iteration of implementation efforts and strategies to optimize long-term maintenance and sustainability.

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An increasing number of countries and regions consider the bioeconomy transition a strategic policy priority. When approached through the lens of a circular economy perspective, investments in bioeconomy have the potential to enhance resource utilisation efficiency, preserve biodiversity and ecosystems, and foster sustainable development with low emissions. At the same time, if requirements and contextual factors of bioeconomy strategies are not formally analysed, bioeconomic investments might lead to unintended negative consequences. This paper proposes a decision support procedure to design, assess, prioritise, and monitor bioeconomy investments and policies. The flexibility and scalability of our decision support procedure is tested in Colombia to foster a regional and local transition to bioeconomy initiatives that consider the local capital assets and the stakeholders' views. The heterogeneous character of the Colombian environment, economy, society and culture represents an ideal condition to test the strength of the decision support procedure to promote bioeconomy in low and middle-income countries. Our empirical results highlight the benefit of adopting a formal assessment framework that includes strategic national indicators, regional features and stakeholders' views. In terms of the Colombian regional bioeconomy ambitions, we highlight the need for expanding knowledge hubs and participatory stakeholder networks and buttressing appropriate financial mechanisms.

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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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ABSTRACT Objective: To assess the agreement among three different online drug-drug interaction (DDI) checkers for the detection of psychotropic drug interactions among dental patients in the state of Minas Gerais, Brazil. Material and Methods: Between January and December 2017, a cross-sectional study was conducted in Minas Gerais with data on pharmaceutical claims of psychotropic drugs prescribed by dental practitioners. Data from the Pharmaceutical Management System provided the drug dispensing history of the patients, allowing the identification of those on concomitant medication use. The occurrence of DDI was determined by entering the name of the drugs taken by each patient into Merative Micromedex®, Medscape®, and DrugBank. The degree of agreement among the three DDI online checkers was analyzed using the Fleiss' kappa test. Results: Overall, 797 dental patients were found to be taking some psychotropic medication with other drugs simultaneously. The number of patients with DDI varied according to Micromedex® (n= 366), Medscape® (n= 473), and DrugBank (n= 736). The agreement between the DDI checkers was poor (Fleiss' kappa: 0.165; p<0.001). Conclusion: The online DDI checkers assessed in this study showed variations in their ability to detect interactions and poor agreement among them.

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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.

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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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Influenza has been a stationary disease in Mexico since 2009, and this causes a high cost for the national public health system, including its detection using RT-qPCR tests, treatments, and absenteeism in the workplace. Despite influenza's relevance, the main clinical features to detect the disease defined by international institutions like the World Health Organization (WHO) and the United States Centers for Disease Control and Prevention (CDC) do not follow the same pattern in all populations. The aim of this work is to find a machine learning method to facilitate decision making in the clinical differentiation between positive and negative influenza patients, based on their symptoms and demographic features. The research sample consisted of 15480 records, including clinical and demographic data of patients with a positive/negative RT-qPCR influenza tests, from 2010 to 2020 in the public healthcare institutions of Mexico City. The performance of the methods for classifying influenza cases were evaluated with indices like accuracy, specificity, sensitivity, precision, the f1-measure and the area under the curve (AUC). Results indicate that random forest and bagging classifiers were the best supervised methods; they showed promise in supporting clinical diagnosis, especially in places where performing molecular tests might be challenging or not feasible.

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BACKGROUND: Temporomandibular disorders (TMD) do not only occur in adults but also in adolescents, with negative impacts on their development. AIM: To propose a predictive model for TMD in adolescents using a decision tree (DT) analysis and to identify groups at high and low risk of developing TMD in the city of Recife, PE, Brazil. DESIGN: This cross-sectional study was conducted in Recife on 1342 schoolchildren of both sexes aged 10-17 years. The analyses were performed using Pearson's chi-squared test and Fisher's exact test, as well as the CHAID algorithm for the construction of the DT. The SPSS statistical program was used. RESULTS: The prevalence of TMD was 33.2%. Statistically significant associations were observed between TMD and sex, depression, self-reported orofacial pain, and orofacial pain on clinical examination. The DT consisted of self-reported orofacial pain, orofacial pain on physical examination, and depression, with an overall predictive power of 73.0%. CONCLUSION: The proposed tree has a good predictive capacity and permits to identify groups at high risk of developing TMD among adolescents, such as those with self-reported orofacial pain or orofacial pain on examination associated with depression.

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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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In this article, we present an agile method based on a cycle of meetings that guides the construction of intelligent decision support systems. This method presents the phases of initiation, analysis and planning, negotiation, control and intelligent decision support. A cycle represents a passage through all the phases of the method, where as the execution of a phase means that all the planned meetings were held. Each meeting lasted 15 min, and input and output were composed of artifacts that supported the evolution of each meeting. In the initial phase, a meeting was held with everyone with the cards for the survey of the requirements and the construction of the 3D graph to represent the size. In IT meetings, artifacts, forms and tables were used to define the first packages. In the analysis and planning phases, the objectives by key results form were used. In the negotiation, we use the structural sets form. In the control phase, we have the configuration artifact and its control graph. Finally, in intelligent decision support, we use the essential questions form. The method serves as a guide for building intelligent decision support systems that can help with problems like determining whether or not to sign a contract.•In the initial phase, cards for requirement gathering together with a complexity graph and Board Requirements by Layers and Key Person supported the organization of development packages.•In the control phase, the input structures enabled the creation of a continuous control artifact. Furthermore, the control chart showed what is in scope and is part of ongoing control.•The intelligent decision support phase guaranteed the refinement of requirements, which brought intelligence criteria to the development packages and gave them their unique characteristics.

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The problems of flexible planning of the design of logistics systems for the collection of food products such as raw milk can result in a decrease in the performance of critical indicators for their performance. This paper proposes a new efficient methodology for robustly designing a first-mile logistics system for storing and refrigerating milk as a perishable product considering decisions related to open facilities and the flow of products, including sustainability indices. The proposed approach is modeled as a bi-objective problem by considering the minimization of greenhouse gas emissions (CO2) produced by milk transportation canteens and the maximization of the system configuration's net present value (NPV). We have analyzed and determined the most robust configuration for the first time and explained the robustness-NPV and robustness-CO2 relationships. The proposed mathematical model is solved by the Epsilon constraints method, and the robustness is calculated considering an extension of the FePIA methodology for multiobjective problems. A novel contribution is a balance in the possible future values generated by the company related to its cash flows and the generation of CO2 emissions when using a motorized transport frequently used in the shipment of raw milk considering a new important aspect such as the volume of product transported and the slope of the path between the production farm and the storage cooling tanks.

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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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