Computer-based Decision Support System Research Articles

Nurses' experiences of using a computer-based triage decision support system in the emergency department.

Emergency department triage is a vital and complex decision-making process. There is limited knowledge about nurses' experiences with triage decision support systems in emergency departments. This study was conducted to examine nurses' experiences with a computer-based triage decision support system in the emergency department. This is a qualitative and phenomenological study. Data were collected through interviews from 14 triage nurses who used a triage decision support system in the emergency department of a university hospital. The nurses were recruited for semi-structured interviews. All interviews were recorded on a voice recorder and then transcribed. Data were analysed with the inductive content analysis method. The interviewer asked comprehensive questions about the nurses' experiences with the triage decision support system. Three main and 11 sub-themes were elicited as a result of the analysis of the in-depth interviews: (a) the facilitating the triage decision theme, which included help in case of a dilemma, team collaboration, monitoring/supervision, and error reduction sub-themes; (b) the contribution to professionalism theme, which included ease of learning and teaching triage, professional autonomy, creating a database, and evidence-based practice sub-themes; (c) the areas that need improvement theme, which included reducing screen clicks, the effect of the hospital automation system performance, and clinical descriptors not included in the algorithm sub-themes. Triage nurses stated that the decision support system was beneficial and facilitated decision-making. The decision support system enabled triage nurses to make their own decisions using their clinical knowledge and experience, without a restriction on their professional autonomy, and this was perceived positively. It was emphasized that this system could be a support tool in educating nurses new to triage. However, nurses stated that the pace of the decision support system was affected by the performance of the hospital automation system and that the large number of steps used for electing items caused a waste of time. They also added that these were technical areas that needed improvement. The study provides important data that will help healthcare organizations and professionals better understand the emergency department nurse triage decision support system and gain a versatile, comprehensive, and general understanding.

Nursing Diagnosis Accuracy in Nursing Education: Clinical Decision Support System Compared With Paper-Based Documentation-A Before and After Study.

Computer-based technologies have been widely used in nursing education, although the best educational modality to improve documentation and nursing diagnostic accuracy using electronic health records is still under investigation. It is important to address this gap and seek an effective way to address increased accuracy around nursing diagnoses identification. Nursing diagnoses are judgments that represent a synthesis of data collected by the nurse and used to guide interventions and to achieve desirable patients' outcomes. This current investigation is aimed at comparing the nursing diagnostic accuracy, satisfaction, and usability of a computerized system versus a traditional paper-based approach. A total of 66 nursing students solved three validated clinical scenarios using the NANDA-International terminologies traditional paper-based approach and then the computer-based Clinical Decision Support System. Study findings indicated a significantly higher nursing diagnostic accuracy ( P < .001) in solving cancer and stroke clinical scenarios, whereas there was no significant difference in acute myocardial infarction scenario. The use of the electronic system increased the number of correct diagnostic indicators ( P < .05); however, the level of students' satisfaction was similar. The usability scores highlighted the need to make the electronic documentation systems more user-friendly.

A Comparative Study of Breast Mass Detection Using YOLOv8 Deep Learning Model in Various Data Scenarios on Multi-View Digital Mammograms

Breast cancer is one of the most common types of cancer among women worldwide. It typically begins with abnormal cell growth in the breast glands or milk ducts and can spread to other tissues. Many breast cancer cases start with the presence of a mass and should be carefully examined. Masses can be monitored using X-ray-based digital mammography images, including right craniocaudal, left craniocaudal, right mediolateral oblique, and left mediolateral oblique views. In this study, automatic mass detection and localization were performed on mammography images taken from the full-field digital mammography VinDr-Mammo dataset using the YOLOv8 deep learning model. Three different scenarios were tested: raw data, data with preprocessing to crop breast regions, and data with only mass regions cropped to a 1.2x ratio. The data were divided into 80% for training and 10% each for validation and testing. The results were evaluated using performance metrics such as precision, recall, F1-score, mAP, and training graphs. At the end of the study, it is demonstrated that the YOLOv8 deep learning model provides successful results in mass detection and localization, indicating its potential use as a computer-based decision support system.

The effect of computer-based decision support system on emergency department triage: Non-randomised controlled trial

BackgroundDeciding on triage in emergency departments is difficult and requires comprehensive knowledge and experience. PurposeThis study was conducted to evaluate the effect of a “computer-based emergency department triage decision support system (DSS),“ which was designed and integrated into the hospital information management system, on triage decision accuracy and triage duration by using real patient data. MethodsSingle-group, pretest–posttest non-randomised clinical trial. The study was conducted with the real data of patients who had been triaged in the adult emergency department of a university hospital. The pretest was applied between July 16 and September 16, 2019, and the post-test on September 1 and October 31, 2020. In the pre-test and post-test phases of the study, triage decision accuracy rates, and triage duration were evaluated. In the post-test phase, Emergency Triage Decision Support System (ETDSS) was prepared with a rule-based decision trees method using the Emergency Severity Index Version 4 and The Australasian Triage Scale and was integrated into the hospital information management system. The effect of the developed ETDSS was evaluated. The mean, standard deviation, frequency and percentage values were calculated for the descriptive characteristics. Independent samples t-test, analysis of variance, Sidak paired comparison, and Bonferroni tests were applied. ResultsThe effect of the computer-based emergency triage DSS on triage management was tested based on the data of 16,409 patients in the pretest phase and 7,765 patients in the posttest phase. While the accuracy rate of nurses' triage decisions was 57.8% in the pretest, it was found to increase to 64.9% in the posttest. The mean duration of triage was 1.47 ± 0.72 in the pretest and 1.79 ± 0.85 min in the posttest. ConclusionsThe DSS increased triage decision accuracy independently of professional and triage experience and brought the triage duration closer to the time recommended in the literature. Clinically, this is associated with patient safety, quality improvement processes, and professional accountability.

Evaluation of Drinking Water Quality for Salem District Using Weighted Product Method

The quality of drinking water is one of the most important factors affecting human health. However, in many countries, especially developing countries the quality of drinking water is not satisfactory and Poor drinking water quality causes many waterborne diseases. This special issue of Expression and Health focuses on drinking water quality Edited to better understand the implications for public health, thus improving many countries appropriate action may be taken. This editorial introduction, reviewed some recent research, briefly summarizing the main points of each contribution to this issue, then in to increase scientific proposed some research/directions. The articles in this magazine are interesting and this research covers many aspects of the topic, and is meaningful for sustainable drinking water quality protection. Using the correct linear mathematical equation Home selection problems cannot be solved and treated as semi-structured problem a heuristic process is required. Computer-based decision support systems can be used as an alternative to expert decision-making by inexperienced users. This article is about improving decision support systems for the household selection and Presents empirical work. For choosing a house based on weighted product method providing web-based decision support systems with the general objective of the research. As a result of the interview with the respondents, the calculation of choosing this house. In the weighted product method, help people choose the best alternative, Computational results show that the given alternatives can be sorted. The Alternative Parameters is Salem West, Salem South, Yercaud, Attur, Pethanaickenpalayam, Valapady, Sankari, Edapadi, Mettur, Omalur, Kadayampatti. The Evaluation Parameters is PH, TDS, TH, Calcium, Magnesium, Chloride, Sulphate. First rank is Pethanaickenpalayam and first rank is Salem West is lowest rank.

Decision Support System for Choosing the Best General Practitioner with Additive Ratio Assessment (ARAS) Method

A general practitioner is a medical practitioner who focuses on treating sudden or chronic illnesses, and is often considered a first-line doctor. In health services in hospitals, doctors are an important asset. Previously, the selection of the best general practitioner at Bhakti Medan Hospital was done manually, namely through an evaluation of the number of patients cured and the opinions of the nurses who worked with the doctor. However, this method is not effective enough in assessing the professionalism and quality of a doctor. However, determining the best doctor is not easy and requires many criteria, such as communication skills, ability to listen to patients, compliance with the obligations and rights of doctors and patients, rational use of drugs, and adherence to professional ethics. Therefore, a computer-based decision support system is used to assist in selecting the best doctor effectively. Decision support systems are part of a computer-based information system that is used to assist decision making within an organization or company. In this study the authors used the Additive Ratio Assessment (ARAS) method. With the ARAS method the problem of determining the best general practitioner will be easier because the ARAS method in general does a lot of ranking by comparing with other alternatives so as to get the ideal and best results. Based on the results of calculations that have been done previously, in this study it can be concluded that alternative A3 is with a value of 0.1855 on behalf of Dr. Lenny Tampubolon as the best general practitioner at Bhakti General Hospital

SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN PENERIMA BEASISWA MENGGUNAKAN METODE ELECTRE

This study aims to address the problem of determining whether or not students are eligible to receive scholarships at SMP Pembangunan Bagan Batu. Determination of scholarship recipients is done by building a computer-based decision support system that helps make it easier to determine students who are entitled to get the scholarship. Decision support system built using the Electre method. The criteria used in the DSS Electre Method consist of the Average Student Report Card Score, Total Parental Income, Total Dependents of Parents, Total Achievements of the student with a weight of 3,3,2,2 for each criterion. The final result of the DSS Electre method is a recommendation through favorable matrix data which is determined based on the highest number of 1s. The results of this research case study, alternatives with a value of at least 1 can be eliminated. The biggest score was obtained by Alternative Siswa_01 with the highest score of 1.

Making decisions for effective humanitarian actions: a conceptual framework for relief distribution

Responding to a disaster encompasses a myriad of humanitarian actions; the ultimate and crucial is immediate relief distribution. Making effective decisions in chaotic disaster environment is always complex and challenging. Decisions made here are heavily influenced by the decisions made in several related problem areas such as facility locations, relief supply chain, transportation, scheduling, and inventory management. While each of these problem areas has its own set of decision factors, several of these factors are also common in multiple problem areas. These common decision factors offer both an opportunity and a challenge. The challenge is to balance the relative importance of a factor that is common between one or more problem areas—one factor that is considered vital in one area may have a lower priority in another area. The opportunity here is to develop a common framework that can help all problem areas to work together to achieve the main objective of effectively distributing essential relief goods among affected people. While the literature has studied individual problem areas and their decision factors, an integrated view showing the linkages between multiple problem areas is missing. In this paper, we propose such an integrative framework. Based on a systematic review of the literature, we first identified problem areas that are linked to relief distribution and then identified the linkages between these areas. We synthesized the findings into a conceptual framework and validated it through a panel of experienced field experts who work in relief distribution. We framed our refined framework as an information ecosystem of humanitarian actions where relief distribution resides at the core. Such a conceptualization will not only enrich the in-depth understanding of humanitarian domain, but also offer insights for developing computer-based decision support systems for relief distribution.

A Computer-Based Decision Support System for Emergency Department Triage.

Bişkin Çetin, Songül PhD, RN; Cebeci, Fatma PhD, RN; Eray, Oktay MD; Bilge, Uğur; Coşkun, Mustafa MSEditor(s): Alexander, Susan DNP, ANP-BC, ADM-BC Author Information

The Bayesian approach to analysis of financial operational risk

The article provides a short overview of methods for constructing mathematical models in the form of Bayesian Networks for modeling operational risks under conditions of uncertainty. Let’s provide the sequence of actions necessary for creating a model in the form of the network, methods for computing a probabilistic output in BN, and give examples of using the tool to solve practical problems of operational financial risk estimation. The study results can be used by financial institutions as a tool for resolving specific practical issues of risk estimation.&#x0D; The object of research: methods for constructing Bayesian Networks for modeling operational risk in financial institutions.&#x0D; Investigated problem: modeling operational risk under conditions of uncertainty.&#x0D; The main scientific results: overview of methods for constructing Bayesian Networks for modeling operational risk under conditions of uncertainty; the methodology in the form of sequence of actions necessary for creating the model in the form of the network; methods for computing a probabilistic output in BN; examples of applying such approaches to solve practical problems of operational financial risk estimation.&#x0D; The area of practical use of the research results: The research results can be used in the following financial institutions: banking system, insurance and investment companies.&#x0D; Innovative technological product: computer based decision support system, allowing for high quality modeling and estimation of operational risks. &#x0D; Scope of the innovative technological product: the practice of usage the proposed models in financial organizations provides an evidence of their high efficiency in terms of formal description and estimation of operational risk

A Coding Framework for Usability Evaluation of Digital Health Technologies.

Several studies have reported low adherence and high resistance from clinicians to adopt digital health technologies into clinical practice, particularly the use of computer-based clinical decision support systems. Poor usability and lack of integration with the clinical workflow have been identified as primary issues. Few guidelines exist on how to analyze the collected data associated with the usability of digital health technologies. In this study, we aimed to develop a coding framework for the systematic evaluation of users' feedback generated during focus groups and interview sessions with clinicians, underpinned by fundamental usability principles and design components. This codebook also included a coding category to capture the user's clinical role associated with each specific piece of feedback, providing a better understanding of role-specific challenges and perspectives, as well as the level of shared understanding across the multiple clinical roles. Furthermore, a voting system was created to quantitatively inform modifications of the digital system based on usability data. As a use case, we applied this method to an electronic cognitive aid designed to improve coordination and communication in the cardiac operating room, showing that this framework is feasible and useful not only to better understand suboptimal usability aspects, but also to recommend relevant modifications in the design and development of the system from different perspectives, including clinical, technical, and usability teams. The framework described herein may be applied in other highly complex clinical settings, in which digital health systems may play an important role in improving patient care and enhancing patient safety.

Cluster randomised controlled trial to assess a tailored intervention to reduce antibiotic prescribing in rural China: study protocol

IntroductionUp to 80% of patients with respiratory tract infections (RTI) attending healthcare facilities in rural areas of China are prescribed antibiotics, many of which are unnecessary. Since 2009, China has...

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LOKASI USAHA KULINER DI DAERAH BAGAN BATU DENGAN MENGGUNAKAN METODE TOPSIS

This study aims to design a decision support system for choosing a culinary business location typical of the Bagansiapiapi area in the Bagan Batu area. The many variations of the typical culinary of Bagansiapiapi are in great demand by the people in the Bagan Batu area. Therefore, opening a culinary business for regional specialties is one of the businesses that has the potential to advance in Bagan Batu. To help entrepreneurs who want to choose a culinary business location in the Bagan Batu area, a computer-based Decision Support System is designed. The decision support system for selecting the location of this entrepreneur uses the Topsis method. The criteria used in this study consisted of 5 criteria, namely the level of population density, rental fees, access to business locations, convenience of parking lots, cleanliness of business locations. The results of this study are recommendations for business locations in the form of alternative rankings that can be taken into consideration as the chosen location. Of the 5 alternative data processed in this DSS, the best alternative location is location 4 with a weighted value of 0.6566.

Age Estimation from Left-Hand Radiographs with Deep Learning Methods

Bone age is estimated in pediatric medicine for medical and legal purposes. In pediatric medicine, it aids in the growth and development assessment of various diseases affecting children. In forensic medicine, it is required to determine criminal liability by age, refugee age estimation, and child-adult discrimination. In such cases, radiologists or forensic medicine specialists conduct bone age estimation from left hand-wrist radiographs using atlas methods that require time and effort. This study aims to develop a computer-based decision support system using a new modified deep learning approach to accelerate radiologists' workflow for pediatric bone age estimation from wrist radiographs. The KCRD dataset created by us was used to test the proposed method. The performance of the proposed modified IncepitonV3 model compared to IncepitonV3, MobileNetV2, EfficientNetB7 models. Acceptably high results (MAE=4.3, RMSE=5.76, and R2=0.99) were observed with the modified IncepitonV3 transfer deep learning method.

Current status and future prospects in biotic stress management in rice

Insect pests and diseases are the major biotic constraints in rice production. According to the ecosystem, the incidence of insect pests and diseases vary. The degree of loss due to different biotic stresses differ widely depending upon the predominant factors of abundance of these pests in a particular year, season or locality. Though around 800 insect species damaging rice in one way or another, the majority of them do very little damage. In India, about a dozen of insect species are of major pest status. Farmers bear an estimated average of 37% losses in rice crop due to insect pests and diseases every year. This review focuses on status of insect pests and diseases, extent of losses, different tools used in pest monitoring and management in rice. Among the important pest management tools used in rice pest and diseases management are forecasting model for real-time pest-advisory services, light trap, hyper-spectral remote sensing, computer-based decision support system, disruptive technologies (mobile apps).

Evaluation of the efficiency of computerized algorithms to formulate a decision support system for deepbite treatment planning

This study aimed to evaluate the efficiency of a newly constructed computer-based decision support system (DSS) on the basis of artificial intelligence technology and designed to plan treatment for patients with a deep overbite. With the help of information technology, a DSS was developed specifically for treatment planning of deepbite malocclusion. The program inputs were the components and the contributing factors used commonly by the orthodontic clinicians in deepbite diagnosis. The program outputs were the treatment planning options for deepbite treatment. A total of 357 decisions made by the algorithm were evaluated for accuracy by comparing them to the actual treatment changes of 51 patients with a well-treated deepbite. The decisions made by the algorithm were precise, with 94.4% having a very good agreement with actual treatment changes determined using Cohen's kappa coefficient. The constructed DSS was shown to be an efficient tool for planning treatment of deep overbite malocclusion in the permanent dentition; thus, the artificial intelligence could be used to formulate a customized plan for orthodontic clinicians.

Analisis Penutupan Kartu Kredit Menggunakan Metode Analytical Hierarchy Process dan Preference Ranking Organization Methode for Enrichment Evaluation

The rapid development in Indonesia is information technology. Today's computer technology can collaborate with other fields of science. One of them is a Computer Based Decision Support System (DSS). Decision Support System (DSS) is a system that is useful for increasing effectiveness in terms of decision making. A credit card is a payment instrument in the form of a card that has a credit facility to the owner in lieu of cash payment, where at maturity it can be paid with the minimum payment amount and the rest is used as credit. In applying for credit card closure, it can be combined with a decision support system. The decision support system for this case uses the Analytical Hierarchy Process (AHP) method and the Preference Ranking Organization Methods for Enrichment Evaluations (Promethee). The Analytic Hierarchy Process (AHP) method has proven its reliability in weighting the criteria value and the Preference Ranking Organization Methods for Enrichment Evaluations (Promethee) is used to rank the alternatives provided. Establishing this decision support system will produce fast and accurate recommendations to credit card management on this issue.

Duncan Joseph Greenwood. 16 October 1932—13 February 2010

During his 55-year career as a highly distinguished scientist, Duncan Greenwood made a major contribution to the field of soil science and plant nutrition. His early studies on soil aeration revolutionized thinking on the mechanisms by which oxygen influences the activity of microorganisms and their metabolism of organic substances in structured soil. His ideas resulted in the ‘micro-site concept of soil aeration’, which is still widely accepted today. Later he turned his attention to the nutritional requirements of vegetable crops at a time when the horticultural industry was starting to introduce inorganic fertilizers. He developed a novel static model of N, P and K response, which he used to produce the first scientifically based inorganic fertilizer recommendations for 23 different vegetable crops. Not satisfied with this major development, Duncan started to create more sophisticated dynamic models using novel widely applicable relationships that took more account of the effects of weather on nutrient behaviour in the soil and plant. He not only used these to provide important insights into many of the complex interacting processes involved, but also incorporated them into some of the first computer-based decision support systems for practical site-specific fertilizer advice for the vegetable growing industry. The application of these advances had an important effect on the efficiency of crop production and the reduction of associated environmental impacts. Duncan continued to devise new models for nutrient and water use throughout the rest of his career and to explore ways of applying them in different areas of soil and crop science.

A computer-based decision support system for knowledge management in the swine industry

The swine industry contributes to food security around the world. However, the most vulnerable point in the industry occurs at the pig production cycle. This production cycle generates an unbalance...

A computer-based decision support system for knowledge management in the swine industry

A computer-based decision support system for knowledge management in the swine industry