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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.
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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 articial 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.
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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.
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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.
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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.
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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.
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Objective:To explore the effect of the application of nursing decision support information system in the continuing nursing of stroke patients in convalescent period, and to provide guidance for the information-based whole process nursing of stroke patients in convalescent period.Methods:A total of 107 stroke patients in convalescent period admitted to 4 Grade Ⅲ Level A hospitals of Beijing city from March to November 2019 were selected. The patients were divided into control group (53 cases) and test group (54 cases) by coin tossing method. The control group followed uniformly formulated discharge health education manual for post-discharge management and follow-up, while the test group received health guidance and follow-up through the nursing decision support information system. Barthel Index and MOS SF-36 were used to evaluate the activities of daily living and quality of life of the two groups of patients before intervention and 3 and 6 months after the intervention, and the results were compared.Results:There was no significant difference in Barthel Index before the intervention between the two groups( P>0.05). After 3 months of intervention, the Barthel Index ≤ 49, 50-70 and ≥ 71 in the test group were 7, 17 and 27 cases respectively, and 16, 21 and 13 cases in the control group respectively, and the difference between the two groups was statistically significant ( Z=-2.95, P<0.01). After 6 months of intervention, the Barthel Index ≤ 49, 50-70 and ≥ 71 in the test group were 7, 12 and 32 cases respectively, and 10, 15 and 25 cases in the control group respectively,and the difference between the two groups was statistically significant ( Z=-2.21, P<0.05). There was no significant difference in MOS SF-36 before the intervention between the two groups( P>0.05). After 3 and 6 months of intervention, the total score of MOS SF-36 in the test group was (50.51 ± 14.57), (57.06 ± 14.85) respectively, and that in the control group was (42.02 ± 15.48), (45.58 ± 14.97) respectively, and the differences between the two groups were statistically significant ( t=2.84, 3.23, both P<0.05). Conclusions:The application of nursing decision support information system can effectively improve the daily life ability of patients, enhance the quality of life of patients.
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Clinical decision support system based on medical artificial intelligence(AI)is a key link in medical artificial intelligence transformation of Alzheimer's disease.This paper reviews the status of medical AI used for diagnosis and treatment of Alzheimer's disease, proposed the deficiencies existing in the current application process, in order to provide new ideas for the development of a more professional clinical decision support system for Alzheimer's disease that is suitable for China's national conditions.
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Objective:To evaluate the effectiveness of antimicrobial stewardship based on self-developed antibiotic clinical decision support system (aCDSS) in the inpatients at a tertiary hospital for consecutive 6 years, and to provide reference for rational use and antimicrobial stewardship.Methods:aCDSS was self-designed based on information technology and applied in clinical use in our hospital from 2015. Data of inpatient information and antibacterial use from January 2015 to December 2020 were collected from HIS and aCDSS. A retrospective study was conducted in all inpatients on the utilization rate and antibiotic use density.Results:Since 2015, with the comprehensive implementation of antimicrobial stewardship based on the aCDSS,there was a significant decline on the annual rate of antibiotic usage from 44.18% in 2015 to 38.70% in 2020, as well as on the usage rate of extended-spectrum antimicrobial agents including carbapenems, broad-spectrum β-lactam/β-lactamase inhibitors, tigecycline, broad-spectrum cephalosporins, fluoroquinolones, as well as glycopeptide and antifungal drugs. Compared with 2015, the usage of carbapenems, tigecycline and broad-spectrum β-lactam/β-lactamase inhibitors was declined nearly 50% in 2020, and the density of carbapenems and tigecycline were decreased by 29.6% and 7.1%, respectively in 2020. On the other side, the utilization rate and use density of narrow-spectrum cephalosporins continued to increase by year, the use density of narrow-spectrum cephalosporins accounting for 28.2% of all antibiotics in 2020.Conclusions:With the comprehensive implementation of aCDSS, the utilization rate and density of broad-spectrum and high-priced antibacterial drugs in our hospital have decreased continuously to decline in the past 6 years, while the proportion of narrow-spectrum antimicrobials has increased year by year, indicating that the structure of antimicrobial use has been continuously optimized and that antimicrobial stewardship based on the information technology have achieved remarkable results.
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@#Objective To verify the reliability of Anticlot Assistant, a patient self-management system for warfarin therapy assisted by artificial intelligence. Methods It was a single-center, prospective cohort study. The eligible 34 participants were recruited consecutively between November 29, 2017 to September 27, 2018 and managed by warfarin therapy via Anticlot Assistant. The recommendations of Anticlot Assistant were examined and verified by the doctors to ensure the security. Medical records were exported from the the background management system. An univariate analysis compared the outcomes between accepted and overridden records and a logistic regression model was built to determine independent predictors of the outcomes. The research team analyzed 153 medical records, which were from 18 participants and were input by 19 doctors. There were 97 records with doctor accepting the suggestion and 56 records with doctor rejecting the suggestion . Results When the doctors accepted the recommendations, the percentage of the next-test international normalized ratio (INR) in the therapeutic range was higher (64.95% vs. 44.64%, RR=2.298, 95%CI 1.173 to 4.499, P=0.014). The logistic regression analysis revealed that accepting the recommendations was an independent predictor for the next-test INR being in the therapeutic range after controlling potentially confounding factors (OR=2.446, 95%CI 1.103 to 5.423, P=0.028). Conclusion The algorithm of Anticlot Assistant is reasonable and reliable.
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Objective:To analyze the application status and problems of clinical decision support system(CDSS) in medical institutions in China, and put forward corresponding suggestions.Methods:From April to May in 2020, a questionnaire survey was conducted in 1 013 medical institutions in 31 provinces of China. The contents of the questionnaire included the current situation of CDSS installation and deployment, the purpose of establishment, the source of knowledge base, the content to be optimized and the factors hindering the use.Results:199(19.64%) medical institutions had CDSS, among which 123 were used in the whole hospital and 76 in some departments; 426 medical institutions did not use CDSS, but had plans to use it. It was found that the current CDSS system had setbacks, such as big cognitive difference, lack of authority in knowledge, high difficulty in data governance, lack of industry standards and so on.Conclusions:In the future, the standardized use of CDSS in medical institutions could be promoted from the aspects of raising awareness, establishing knowledge authority and establishing standards.
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RESUMO Os elevados custos e a limitação de recursos financeiros tornam essencial a priorização de projetos de saneamento. Os métodos multicritérios, para decisões individuais ou em grupo, são ferramentas modernas para hierarquizar portfólios de projetos. Há muitos métodos disponíveis na literatura técnico-científica. No Brasil, os Comitês das Bacias Hidrográficas constituem-se no ambiente para decisões descentralizadas sobre projetos nos âmbitos das bacias hidrográficas. Este artigo propôs um modelo multicritério de apoio à decisão para a priorização de um portfólio de projetos de saneamento no âmbito de um comitê de bacia. Como estudo de caso, utilizou-se um portfólio de 14 empreendimentos, objeto de um edital para aplicação de recursos financeiros do Fundo Estadual de Recursos Hídricos e/ou arrecadados pela cobrança da água bruta nas bacias dos rios Piracicaba, Capivari e Jundiaí. Empregou-se o método Preference Ranking Organization Method for Enrichment Evaluation II com a extensão gráfica Geometric Analysis for Interactive Aid. O modelo propiciou uma análise detalhada sobre as potencialidades das alternativas. Foi possível visualizar não somente a ordenação, mas também todas as propriedades de superação de cada alternativa pelas matrizes e pelos gráficos apresentados.
ABSTRACT High costs and limited financial resources make it essential to prioritize sanitation projects. Multicriteria methods, for individual or group decisions, constitute modern tools to rank project portfolios. Currently, there are many methods available in the scientific and technical literature. In Brazil, the Hydrographic Basin committees constitute the institution for decentralized decisions to manage projects portfolios. This article proposes a multicriteria model of decision support for prioritizing a portfolio of sanitation projects within the scope of a basin committee. As a case study, a portfolio of 14 projects is used, which is the subject of a public notice for the application of financial resources from FEHIDRO and/or collected by charging raw water in the Piracicaba, Capivari and Jundiaí (PCJ) basins. The method Promethee II was used with the graphic extension GAIA. The model provided a detailed analysis of the potentialities of the alternatives. It was possible to visualize not only the ordering, but also all the overcoming properties of each alternative by the matrices and the graphs presented.
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Introdução: A maioria dos países do mundo está implantando o Registro Eletrônico em Saúde como uma das iniciativas mais importantes em sua política de assistência à saúde, na perspectiva de obter atendimento seguro e de qualidade. No entanto, problemas de usabilidade podem afetar a eficácia, a eficiência e a satisfação do usuário. Objetivo: avaliar a usabilidade de um Sistema de Apoio à Decisão Clínica para documentar o Processo de Enfermagem. Método: estudo quantitativo, desenvolvido em três etapas. A primeira etapa configurou o delineamento quase- experimental, do tipo antes de depois, comparando a qualidade de 81 registros de enfermagem da Versão I do sistema (pré-intervenção) com 58 registros da versão II (pós-intervenção). O instrumento utilizado foi o Quality of Diagnoses, Interventions and Outcomes Q-DIO Versão Brasileira, que possui quatro domínios e escore máximo de 58 pontos. As intervenções consistiram em planejamento e implantação piloto da versão II do sistema, treinamento e acompanhamento dos usuários. A segunda etapa correspondeu à avaliação da eficiência por meio da mensuração do tempo despendido pelo enfermeiro para documentar a Avaliação de Enfermagem e à correlação com os itens da avaliação. Na terceira etapa, foi medida a satisfação dos usuários na utilização da versão II do sistema por meio do questionário Software Usability Measurement Inventory SUMI, cujas escalas possuem escores padronizados com referência a uma média populacional de 50 pontos. Os escores das escalas SUMI foram computados e analisados no software SUMISCO. Os demais dados foram analisados no software R, utilizando estatística descritiva e inferencial. A coleta de dados das três etapas ocorreu entre janeiro de 2019 e janeiro de 2020. Resultados: A média obtida na aplicação do Q-DIO foi 38,24 pontos na versão I e 46,35 pontos na versão II. Houve evidências de diferença estatística entre as médias dos grupos pré e pós-intervenção (valor-p menor que 0,001) e diminuição dos itens não documentados nos quatro domínios avaliados. O tempo médio despendido pelo enfermeiro para documentar a Avaliação de Enfermagem foi de 12,5 minutos, desvio padrão de 11,2 minutos e mediana de 8,9 minutos. A média dos enfermeiros nas escalas SUMI foram: Eficiência 59,58, Satisfação 56,83, Utilidade 55,92, Controle 44,80, Aprendizagem 55,75 e Usabilidade Global 56,00. A média dos técnicos de enfermagem foram: Eficiência 60,42, Satisfação 62,58, Utilidade 60,84, Controle 54,47, Aprendizagem 65,79 e Usabilidade Global 60,68. Conclusões: a qualidade da documentação na versão II do sistema foi superior à versão I. A eficácia do sistema para documentar o Processo de Enfermagem e a efetividade das intervenções foram comprovadas. A avaliação da eficiência identificou o tempo despendido para documentar a Avaliação de Enfermagem. A média na maioria das escalas SUMI ficaram acima do banco de dados de referência internacional, exceto na escala Controle que ficou abaixo do valor médio na avaliação dos enfermeiros. Apontados problemas de usabilidade que podem impactar negativamente na experiência do usuário. Este estudo contribui para a prática clínica, auditoria de qualidade da documentação, visibilidade da enfermagem enquanto ciência do cuidado, desenvolvimento e implementação de sistemas de apoio à decisão clínica funcionais, interativos e amigáveis.
Introduction: most countries in the world have been implementing the Electronic Health Record as one of the most important initiatives of their health care policy, with a view to obtaining safe and quality care. However, usability issues can affect the effectiveness, efficiency and users satisfaction. Objectives: assessing the usability of a Clinical Decision Support System used for Nursing Process documentation purposes. Method: quantitative study developed in three stages. The first stage comprised the adoption of a quasi-experimental design (of the before and after type) comparing the quality of 81 nursing records of Version I of the system (pre-intervention) with 58 records of version II (post-intervention). The instrument used was the Quality of Diagnoses, Interventions and Outcomes - Q-DIO Brazilian Version, which has four domains and a maximum score of 58 points. The interventions consisted of planning and pilot implementing the system version II, users training and monitoring. The second stage comprised efficiency analysis based on the time spent by nurse to document the Nursing Assessment and the correlation with the assessment items. The third stage referred to users satisfaction assessment based on the Software Usability Measurement Inventory - SUMI questionnaire, whose scales have standardized scores with reference to a population average of 50 points. The scores of the SUMI scales were computed and analyzed using the SUMISCO software. The other data were analyzed using R software, and descriptive and inferential statistics. Data collection for the three stages took place between January 2019 and January 2020. Results: The average Q-DIO scores reached 38.24 points (in version I) and 46.35 points (in version II). There was evidence of statistical difference between the means of the pre and post- intervention groups (p-value less than 0.001) and a decrease in the undocumented items in the four domains evaluated. The mean time spent by nurse to document the assessment in the system was 12.5 minutes (standard deviation = 11.2 minutes and median = 8.9 minutes). Nurses recorded the following averages: Global Usability (56.00), Efficiency (59.58), Affect (56.83), Helpfulness (55.92), Control (44.80) and Learnability (55.75). Nursing technicians recorded the following averages: Global Usability (60.68), Efficiency (60.42), Affect (62.58), Helpfulness (60.84), Control (54.47) and Learnability (65.79). Conclusion: the quality of the documentation in version II of the system was superior to version I. The effective of the system to document the Nursing Process and the effectiveness of interventions were proven. The efficiency analysis identified the time spent to document the Nursing Assessment. The average recorded in most SUMI scales was higher than that of the international reference database, except for the Control scale, which recorded below-the-average value in the nurses evaluation. Usability issues pointed out may negatively affect the user experience. This study contributes to clinical practice, documentation quality audit, visibility of nursing as a science of care, development and implementation of functional, interactive and friendly support systems for clinical decision.
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Technology Assessment, Biomedical , Decision Support Systems, Clinical , Nursing Process , Nursing Informatics , Electronic Health Records , Standardized Nursing TerminologyABSTRACT
On May 25, 2019, the World Health Assembly approved the eleventh revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-11), containing a chapter on traditional medicine. This means that the traditional East Asian medicine (TEAM) is now officially recognized as a part of mainstream medical practice. However, the patterns presented in the ICD-11 traditional medicine chapter are only the tip of the iceberg of TEAM clinical practice, and it will be necessary to supplement and upgrade the contents. In order to implement this, objectification and standardization of TEAM must be premised, and grafting with proper modern science and technology is imperative. Pattern Identification and Prescription Expert-11 (PIPE-11), which is a TEAM clinical decision support system, adopts vastly from clinical literature on pattern identification and the prescription. By adopting the rule-based reasoning method, the way of diagnosis and prescription by a TEAM practitioner in actual clinical practice is implemented as it is. PIPE-11 could support to improve both the accuracy of medical diagnosis and the reliability of the medical treatment of TEAM in clinical practices. In the field of research, it might facilitate the usage for reliable reference for symptoms and signs retrieval and patient simulation. In the field of education, it can provide a high level of training for learning pattern identification and prescription, and further be used to reinforce skills of diagnosis and prescription by providing self-simulation methods. Therefore, PIPE-11 as a digital application is expected to support the traditional medicine chapter of ICD-11 to successfully contribute to the improvement of human health.
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Background: In a developing country like India, with a vibrant information technology (IT) sector, employing Artificial Intelligence (AI) should be carefully weighed before its introduction in healthcare with relation to perception of healthcare providers (HCP's/Doctors). Methods: This qualitative study was conducted in medical college and affiliated hospital in India. Initially a pilot study was conducted for reliability and internal consistency of questionnaire. Thereafter, pre-tested questionnaire was distributed to 153 healthcare providers and their responses were analyzed on SPSS version 20.0 (IBM) to identify the demographic and job-related differences in their perception regarding the benefits and challenges of using AI in healthcare.Results: Most of respondent were agreed upon the benefits of using AI in healthcare and most cited benefits were speedy decision making, better resource utilization and improvement in staff satisfaction. Similarly most cited challenges were lack of training on AI enabled machines, lack of skilled technical support, high cost of AI and data privacy issue. Further, Age and Job experience were significantly associated with benefits like timely and speedy decision making, improvement in the patient and staff satisfaction respectively. Furthermore, Age, Department, Job experience, Job profile were significantly associated with challenges like high cost of AI, lack of skilled technical support, lack of training in AI enabled machines and lack of trust in AI among patients.Conclusions: Significant challenges of using AI in healthcare with demographic and job related variable based on the results of this research paper need to be resolved first in order to overcome the initial resistance in employing AI in healthcare.
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Explaining the clinical decision support system and its related concepts, reviewing the application status of the clinical decision support system in the nursing field, summarizing the necessity and challenges of developing the clinical decision system in the nursing field, hoping to provide suggestion for the development of nursing decision support system suitable for China′s clinical status
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Explaining the clinical decision support system and its related concepts, reviewing the application status of the clinical decision support system in the nursing field, summarizing the necessity and challenges of developing the clinical decision system in the nursing field, hoping to provide suggestion for the development of nursing decision support system suitable for China′s clinical status
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Objective Establishing intelligent nursing clinical decision support system to improve the safety, quality and efficiency of clinical nursing work. Methods Guided by the HIMSS EMRAM Analytics stage 7 evaluation standard, the nursing decision support system is continuously improved through the establishment of knowledge bases such as nursing plans and nursing conventions. Results By comparing the writing time of nursing documents, the correct rate of nursing diagnosis, and the incidence of nursing risk events before and after the used of the system, the results showed that after the used of the nursing decision-making support system, whole hospital′s writing time could save 222.5 hours per day, improved accuracy of nursing diagnosis from 68.33％ (205/300)to 90.67％ (272/300), the difference was statistical significace (χ2=45.907, P<0.05). Decreased incidents, hospital-wide, on falls from 0.127‰(80/631702) to 0.071‰(45/638715),and on pressure ulcer form 0.064‰ (41/631702)to 0.028‰(18/638715), the difference was statistical significace (χ2=13.004～15.071, P<0.05). Conclusion Nursing clinical decision support system is the trend of hospital informatization and is worthy of clinical application.
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Vancomycin has been widely prescribed as the first-line antibiotic in the treatment of methicillin-resistant Staphylococcus aureus and other serious Gram-positive infections. Due to its large pharmacokinetic (PK) variability and narrow therapeutic range, it requires optimization of dosage to achieve target exposure. In this study, SmartDose, a decision support system for individualization of vancomycin dosage is developed using the maximum a posterior Bayesian estimation (MAPB) by the open-source language R combined with the population PK characteristics of vancomycin in Chinese patients. It provides initial design and adjustment of dose regimens based on the therapeutic drug monitoring (TDM) results, as well as a user-defined module to facilitate optimal vancomycin therapy. SmartDose has a high computational reliability, which is validated by NONMEM, the golden standard PK software. Meanwhile, SmartDose is established as a web-based application and its operational flexibility makes it an efficient tool for vancomycin dose optimization in routine clinical settings.
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PURPOSE: Patients with a history of radiocontrast media (RCM) hypersensitivity can be overlooked, resulting in repeated reactions. Therefore, a consultation support system for RCM hypersensitivity has been in operation at Seoul National University Bundang Hospital since December 2011. We analyzed the effect of this system on physicians' practice. METHODS: A retrospective study was conducted on patients with previous RCM reactions (December 1, 2010 to November 30, 2012). The control period was December 2010 to November 2011, and the intervention period was December 2011 to November 2012. The primary outcome was the composite outcome of premedication and consultation. Premedication was defined as preventive medication prescribed by the physician who ordered RCM-enhanced computed tomography (CT) at the same time. The secondary outcome was the recurrence rate after using the consultation support system. RESULTS: A total of 189 clinicians prescribed 913 CT scans during the control period and 225 clinicians performed 1,153 examinations during the intervention period. The odds ratio (OR) of achieving the composite outcome increased significantly after use of the consultation support system (OR, 1.54; 95% confidence interval [CI], 1.15–2.05). Clinicians in both medical (OR, 1.48; 95% CI, 1.06–2.07) and surgical (OR, 2.07; 95% CI, 1.24–3.46) departments showed significant changes in their behavior, whereas those in the emergency department did not (OR, 1.07; 95% CI, 0.41–2.78). Professors (OR, 1.47; 95% CI, 1.06–2.04) and trainees (OR, 1.97, 95% CI, 1.22–3.18) showed significant changes in their behavior toward patients with previous RCM reactions. The behavior of 86 clinicians who ordered CT scans during both the control and intervention periods was unchanged. CONCLUSIONS: The consultation support system for those with previous RCM hypersensitivity reactions changed physicians' practice patterns and decreased recurrent RCM hypersensitivity reactions as well.