Computer-assisted Decision Support Research Articles

How well do decision support systems help decision makers? An examination of adopting lean manufacturing processes

Choice is crucial for industrial enterprises as their success or failure may depend on it. Consequently, emerging technologies, especially Industry 4.0 (I4.0) are making precision decisions and enabling enriching collaborations with the industry’s computer-assisted decision support systems (DSSs). However, the arrival of Industry 4.0 poses challenges to this emerging application in terms of data variations and interconnectivity. A thorough search across Scopus, Web of Science, and Emerald Science databases yielded 2023 relevant documents. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), the top 250 academic documents were meticulously filtered and analysed in this study. The analysis resulted in the classification of the four main purposes of a DSS as data evaluation, optimisation, scheduling, and selection. The research also investigated the impact of DSSs on the performance of lean manufacturing (LM). Next, this research discussed the controversies with regard to the confidence, prejudice, and discrimination of users, discipline-based DSS application bias, as well as criticisms and suggestions for the future development of DSS, especially in the manufacturing industry. It is believed that, based on its novel findings, this work will pave the way for future research in the same field.

Computer-assisted decision support for the usage of preventive antibacterial therapy in children with febrile pyelonephritis: A preliminary study

Urinary tract infection (UTI) is one of the most common infectious diseases among children, but there is controversy regarding the use of preventive antibiotics for children first diagnosed with febrile pyelonephritis. To the best of our knowledge, no studies have addressed this issue by the deep learning technology. Therefore, in the current study, we conducted a study using Tc99m−DMSA renal static imaging data to investigate the need for preventive antibiotics on children first diagnosed with febrile pyelonephritis under 2 years old. The self-collected dataset comprised 64 children who did not require preventive antibiotic treatments and 112 children who did. Using several classic deep learning models, we verified that it is feasible to screen whether the first diagnosed children with febrile pyelonephritis require preventive antibacterial therapy, achieving a graded diagnosis. With the AlexNet model, we obtained accuracy of 84.05%, sensitivity of 81.71% and specificity of 86.70%, respectively. The experimental results indicate that deep learning technology could provide a new avenue to implement computer-assisted decision support for the diagnosis of the febrile pyelonephritis.

In vitro evaluation of a computer-assisted decision support system for the primary care of polytrauma patients

IntroductionThe management of polytraumatized patients is set in a stressful environment with numerous critical decisions in a brief amount of time. Working along a standardised procedure can improve the outcome for these patients and reduce mortality. To help clinical practitioners, we developed “TraumaFlow”, a workflow management system for the primary care of polytrauma patients based on the current treatment guidelines. This study sought to validate the system and investigate its effect on user performance and perceived workload.MethodsThe computer-assisted decision support system was tested in two scenarios in a trauma room of a level 1 trauma centre by 11 final-year medical students and 3 residents. In simulated polytrauma scenarios, the participants functioned as a trauma leader. The first scenario was performed without decision support and the second with support by “TraumaFlow” via tablet. During each scenario, the performance was evaluated in a standardized assessment. After each scenario, the participants answered a questionnaire on workload [NASA Raw Task Load Index (NASA RTLX)].ResultsIn total, 14 participants (mean 28 ± 4 years, 43% female) managed 28 scenarios. During the first scenario without computer-assisted support, the participants achieved a mean of 6.6 out of 12 points (SD 1.2, range 5 to 9). With the support of TraumaFlow, the mean performance score was significantly higher with 11.6 out of 12 points (SD 0.5, range 11 to 12, p < 0.001). In the 14 scenarios performed without support, there was no run in which no errors were made. In comparison, ten of the 14 scenarios performed with TraumaFlow ran free of relevant errors. The mean improvement in the performance score was 42%. There was a significant decrease in the mean self-reported mental stress level in scenarios with support of TraumaFlow (55, SD 24) as compared to scenarios without support (72, SD 13, p = 0.041).ConclusionIn a simulated environment, computer-assisted decision-making improved the performance of the trauma leader, helped to adhere to clinical guidelines, and reduced stress in a fast-acting environment. In reality, this may improve the treatment outcome for the patient.

A Decision Support System For Detecting Stage In Hodgkin Lymphoma Patients Using Artificial Neural Network and Optimization Algorithms

Hodgkin-type lymphoma is a disease with unique histological, immunophenotypic, and clinical features. This disease occurs in nearly 30% of all lymphomas. Its treatable is high. However, the treatment plan is specified after the stage and risk status are determined. For this reason, it is an important process for doctors to decide on the stage of the disease correctly. Some of the data used for this decision are the patient's history, detailed physical examination, laboratory findings, imaging methods and bone marrow biopsy results. Hybrid FDG-PET is the other method used in the medical world. This method is used in diagnosis, evaluation of response given to treatment, staging and restaging process. However, it is radiation-based. Therefore it has the possibility of producing undesirable results in the future. In this study, an artificial intelligence-based computer-assisted decision support system is done to reduce the number of used medical methods and radiation exposure. Data were obtained from the NCBI-GEO dataset. The evaluation of these data, which contains missing values, is handled in two ways. Firstly, samples with missing values in the initial evaluation are deleted from the dataset. Then, these data are trained with “trainlm” function in artificial neural network architecture. However, reducing the error value of the estimates is important. For this, the artificial neural network architecture is retrained with the artificial bee colony algorithm, particle swarm optimization algorithm and invasive weed algorithm, respectively. Secondly, the same operations are performed again on the dataset containing missing values. As a result of the training, the maximum performance was obtained for invasive weed and particle swarm optimization algorithms with 1,45547E+14 and 1,23103E+14 average error rates, respectively.

Time to evaluate decision support systems for antimicrobial prescribing outside the hospital

Time to evaluate decision support systems for antimicrobial prescribing outside the hospital

Sistem Pendukung Keputusan Pemilihan Atlet Renang Sumatera Utara Untuk Kejuaraan Tingkat Nasional Dengan Metode ARAS Dan ROC

In this study, researchers used the ROC method and the ARAS method while the criteria used in the selection of North Sumatran swimming athletes for the national championship were Physical, Age, Weight, and Distance. A decision support system is a computer-assisted decision support process to assist decision support by using certain data and models to solve some unstructured problems. The calculation results from the application of the ROC method &amp; the ARAS method were declared successful in testing the system in the assessment and ranking of the selection of North Sumatran swimming athletes for the national level championship based on the criteria inputted for alternatives. Then provide a definite assessment answer for swimming athletes who represent North Sumatra for the national level championship based on the final score obtained.

Antimicrobial stewardship for surgical antibiotic prophylaxis and surgical site infections: a systematic review.

Background Surgical site infections account for 14-17% of all healthcare-associated infections. Antimicrobial stewardship (AMS) are complementary strategies developed to optimize the use of antimicrobials. Aim to evaluate the effectiveness of AMS in promoting adherence to surgical antibiotic prophylaxis protocols in hospitalized patients, reducing surgical site infection rate and cost-benefit ratio. Method This systematic review of randomized clinical trials, non-randomized clinical trials and before and after studies was performed using Pubmed, Cochrane, Web of Science, Scopus, Embase, Google Scholar and ClinicalTrials.gov, in addition to reference lists of included studies. The risk of bias of studies was measured by the ROBINS-I checklist and the quality of the evidence synthesis by GRADE. Results Fourteen before and after design studies were included. In 85.7% of the studies, AMS was effective in increasing adherence to surgical antibiotic prophylaxis protocols and in 28.5%, there was reduction in surgical site infection rate. Three studies evaluated cost-benefit ratio and found a favorable impact. Eight (57%) studies were at risk of moderate bias and six had severe bias. The evaluation of the synthesis of evidence showed quality ranging from low to very low. Conclusion AMS, such as audit, feedback, education, implementation of a protocol, and a computer-assisted decision support methodology, appear to be effective in promoting adherence to surgical antibiotic prophylaxis protocols, reducing surgical site infection rate with a positive economic impact. However, more studies, particularly randomized clinical trials, are needed to improve the level of evidence of available information on AMS in order to favor decision-making.

A Computer Assisted Decision Support System for Education Planning

The advances in technology are eliminating the demand for certain occupations and creating new opportunities. Thus, the universities, teachers, and students have to collaboratively work together to restructure their departments, course offerings, and course contents. Failure to realize the aforementioned initiatives may lead to a loss of quality and competitiveness. This study proposes a decision support system capable of maintaining the quality and competitiveness of the departments and the course offerings. The proposed system consists of three stages. The first stage is the data collection stage. At this stage, data are collected from the internet using web scraping methods. In the second stage, the collected data are turned into meaningful and processable information by natural language processing methods. In the third stage, the alternatives are ranked using multi-criteria decision-making methods. The proposed decision support system provides useful information to several educational stakeholders. First, universities are informed on which departments to create or close as well as the relevant course offerings. Second, information are provided to the teachers to create new courses or shape the course contents. Finally, students are better informed on how to go about choosing the universities, departments, courses, or career paths to pursue. The applicability and reliability of the proposed decision support system were experimentally proven through the use of computer engineering-related job postings and course contents of the universities in Turkey.

Artificial intelligence in echocardiography diagnostics – detection of takotsubo syndrome

Abstract Background Machine learning allows classifying diseases based only on raw echocardiographic imaging data and is therefore a landmark in the development of computer-assisted decision support systems in echocardiography. Purpose The present study sought to determine the value of deep (machine) learning systems for automatic discrimination of takotsubo syndrome and acute myocardial infarction. Methods Apical 2- and 4-chamber echocardiographic views of 110 patients with takotsubo syndrome and 110 patients with acute myocardial infarction were used in the development, training and validation of a deep learning approach, i.e. a convolutional autoencoder (CAE) for feature extraction followed by classical machine learning models for classification of the diseases. Results The deep learning model achieved an area under the receiver operating curve (AUC) of 0.801 with an overall accuracy of 74.5% for 5-fold cross validation evaluated on a clinically relevant dataset. In comparison, experienced cardiologists achieved AUCs in the range 0.678–0.740 and an average accuracy of 64.5% on the same dataset. Conclusions A real-time system for fully automated interpretation of echocardiographic videos was established and trained to differentiate takotsubo syndrome from acute myocardial infarction. The framework provides insight into the algorithms' decision process for physicians and yields new and valuable information on the manifestation of disease patterns in echocardiographic data. While our system was superior to cardiologists in echocardiography-based disease classification, further studies should be conducted in a larger patient population to prove its clinical application. Funding Acknowledgement Type of funding source: None

Effective Antibiotic Stewardship Program: Controlling Antibiotic Resistance and Optimizing Drug Use in Ahmadi Hospital

An Antimicrobial Stewardship Program (ASP) is established to promote cost effective and safe use of antimicrobial agents. Basically, this approach directs the management of antimicrobials in terms of selection of appropriate drugs, determination of administration routes and standardization of both medication dosage and treatment duration. The program involves, but is not limited to, Hospital Staff Education, Pharmacodynamics Dose Optimization, Computer-Assisted Decision Support Programs, Pharmacist-Driven Intravenous to Oral Switch Programs, Pharmacy Dosing Programs and Antibiotic Cycling. A study was conducted to determine effectiveness of the ASP in significantly improving patient outcomes (e.g. decrease morbidity and mortality from infection). This was undertaken by collecting restrospective data on patient length of hospital stay, bed turnover rate and antibiotic consumption over a period of 2 years since the ASP was initiated. Periodic data were then compared and interpreted based on its clinical, quality and financial implications in order to measure effectiveness of the program implementation.

Designing a Structured System for Mammography Reporting

Background: Breast cancer is among the top causes of cancer-related mortality among females in both developed and developing countries. Screening for breast cancer plays a crucial role in the prevention of disease burden. Among various imaging modalities, mammography is currently considered the first step for the detection of breast lesions. The mammography data of breast cancer patients encompass a wide array of texts related to specific visual findings. Accordingly, unorganized mammography documents usually impede physicians’ efforts to fully review the previous information in follow-up visits; hence, this not only affects clinical judgment and treatment planning adversely, but also adds to the financial burdens and workload imposed on the healthcare system. This further highlights the great potential of structured mammography reporting in the management of breast cancer patients. Methods: In the first step, an expert panel consisting of two attending radiologists and a health informatician (all affiliated to the Shiraz University of Medical Sciences, Shiraz, Iran) reviewed related guidelines to determine the appropriate items, ontologies, and standard formats for data entry. These included radiology textbooks and the systematized nomenclature of medicine (SNOMED). In addition, 100 mammography reports were completely examined for data extraction and the creation of a template report. In the next step, the structure of the data registry was discussed among a group of radiologists and breast cancer surgeons during several sessions using Delphi technique. Results: Overall, 119 fields were selected for data entry. Our survey showed that all of the contributing physicians believed that a structured reporting system for mammography can help standardize and reduce reporting time and errors. Following the completion of the template, a user interface was developed by the expert panel for integration in the cloud software workflow, which will be deployed and assessed in the next phase of the project. Conclusion: Structured mammography reporting helps radiologists and surgeons to efficiently and confidently track the management course of their patients. In addition, with the advancement of artificial intelligence, especially deep learning for image classification, clean and labeled image databases can be used for designing computer-assisted decision support systems without significant data preprocessing.

Impact of a computer-assisted decision support system (CDSS) on nutrition management in critically ill hematology patients: the NUTCHOCO study (nutritional care in hematology oncologic patients and critical outcome)

BackgroundMortality of critically ill hematology (HM) patients has improved over time. Thus, those patients require an extensive diagnostic workup and the optimal use of available treatments. There are no data regarding nutrition strategy for critically ill HM patients, while nutritional support is crucial for both HM and critically ill patients. We hypothesized that the implementation of a computer-assisted decision support system (CDSS), designed to supervise a nutritional intervention by a multidisciplinary team, would be able to increase guidelines adherence and outcomes.ResultsIn this before/after study, 275 critically ill hematology patients admitted to the ICU over 5-year period were included. Energy and protein intakes were delivered using standard protocol in the 147 patients (53%) of the ‘before group’ and using a CDSS in order to reach every day predefined caloric and protein targets accordingly to the catabolic or anabolic status in the 128 patients (47%) of the ‘after group.’ Using a Poisson regression, we showed that the use of CDSS allowed to reach a relative increase in the rate of days in compliance with caloric (1.57; 95% confidence interval (CI), [1.17–2.10], p = 0.0025) and protein targets (3.86 [2.21–6.73], p < 0.0001) in the ‘after group’ by more than 50% as compared with the ‘before group.’ Interestingly, compliance rates were low and only reached 30% after intervention. Hospital mortality, ICU-acquired infection, and hospital, and ICU length of stay were similar in the two groups of patients. Importantly, exploratory analysis showed that hospital mortality was lower in the ‘after group’ for neutropenic and severely ill patients.ConclusionFor critically ill hematology patients, the use of a nutritional CDSS allowed to increase the days in compliance with caloric and protein targets as compared with no CDSS use. In this context, overall hospital mortality was not affected.

506. The Impact of Bowel Management System (BMS) on the Incidence of Hospital-Onset (HO) Clostridium difficile Infection Laboratory-ID Events Despite Diagnostic Stewardship

Background Clostridium difficile infection (CDI) Laboratory identified events are reportable to CMS through the CDC’s NHSN. Diagnostic stewardship has been shown to decrease the incidence by decreasing false-positive incidence. Bowel management systems (BMS) have been associated with transient loss of tone of anal sphincter muscles that result in diarrhea. These episodes of diarrhea may be misdiagnosed as CDI due to a false-positive test result. The objective of this study was to determine whether the use of BMS has resulted in false-positive CDI Lab ID events.MethodsWe performed a retrospective review of all HO-CDI Lab ID events from October 1, 2016 to December, 31, 2017 in a 1,157-bed tertiary academic medical center. Since 2013, several interventions were implemented to decrease the incidence of CDI Lab ID events. These interventions have included: (i) enhanced environmental cleaning, (ii) CDI testing algorithm, (iii) use of hydrogen peroxide enhanced terminal cleaning of high-risk units, and (iv) computer-assisted decision support diagnostic stewardship. Poisson regression analysis was performed to compare incidence rates. A P-value of ≤0.05 was considered significant.ResultsA sustained low and decreasing HO-CDI incidence was observed from 2013 to 2017 (7.9, 6.0, 7.1, 6.5 and 5.2 CDI/10,000 patient days; P = 0.01). An incremental decrease was observed when comparing the annual incidence in 2016 to the YTD incidence in 2017 (6.5 vs. 5.2 CDI/10,000 patient days; P < 0.001). Comparing the five quarters before diagnostic stewardship was implemented to post implementation, the CDI incidence decreased from 6.7 to 5.2 CDI events/10,000 patient days (P = 0.009). Of the 180 HO-CDI Lab ID events that occurred post-implementation of the diagnostic stewardship, 31 (17%) were cases in which the computer-assisted alerts were overridden and may have been false positives. An additional 12 (6.7%) cases occurred in patients who had BMS in place within 48 hours and 22 (12%) had BMS in place within 1 week.ConclusionDiagnostic stewardship through computer-assisted decision support is an effective method of reducing false-positive CDI Lab ID events. We found that an additional 12% of the HO-CDI are potentially false positives as these were obtained from patients who had BMS in place immediately before the positive test results.Disclosures All authors: No reported disclosures.

AgroDSS: A decision support system for agriculture and farming

Decision support systems, data analysis and data mining have become significant tools for improving business in professional world. The emerging technologies are making the precision agriculture omnipresent and allow potential for enriching it with computer-assisted decision support systems for farm management. In this paper we describe a novel system AgroDSS that bridges the gap between agricultural systems and state-of-the-art decision support methodology. The described system is intended for integration into the existing farm management information systems and provides a cloud-based decision support toolbox, allowing farmers to upload their own data, utilize several data analysis methods and retrieve their outputs. The implemented tools include predictive modeling with explanation, accuracy evaluation, time series clustering and decomposition, and structural change detection. They can help users make predictions for simulated scenarios and better understand the dependencies (interactions) within their domain. We apply the AgroDSS system on a case study of pest population dynamics, illustrating the potential for its use.

Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images

Pulmonary cancer is considered as one of the major causes of death worldwide. For the detection of lung cancer, computer-assisted diagnosis (CADx) systems have been designed. Internet-of-Things (IoT) has enabled ubiquitous internet access to biomedical datasets and techniques; in result, the progress in CADx is significant. Unlike the conventional CADx, deep learning techniques have the basic advantage of an automatic exploitation feature as they have the ability to learn mid and high level image representations. We proposed a Computer-Assisted Decision Support System in Pulmonary Cancer by using the novel deep learning based model and metastasis information obtained from MBAN (Medical Body Area Network). The proposed model, DFCNet, is based on the deep fully convolutional neural network (FCNN) which is used for classification of each detected pulmonary nodule into four lung cancer stages. The performance of proposed work is evaluated on different datasets with varying scan conditions. Comparison of proposed classifier is done with the existing CNN techniques. Overall accuracy of CNN and DFCNet was 77.6% and 84.58%, respectively. Experimental results illustrate the effectiveness of proposed method for the detection and classification of lung cancer nodules. These results demonstrate the potential for the proposed technique in helping the radiologists in improving nodule detection accuracy with efficiency.

Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance

Objective. To develop predictive computational models forecasting the academic performance of students in the didactic-rich portion of a doctor of pharmacy (PharmD) curriculum as admission-assisting tools. Methods. All PharmD candidates over three admission cycles were divided into two groups: those who completed the PharmD program with a GPA ≥ 3; and the remaining candidates. Random Forest machine learning technique was used to develop a binary classification model based on 11 pre-admission parameters. Results. Robust and externally predictive models were developed that had particularly high overall accuracy of 77% for candidates with high or low academic performance. These multivariate models were highly accurate in predicting these groups to those obtained using undergraduate GPA and composite PCAT scores only. Conclusion. The models developed in this study can be used to improve the admission process as preliminary filters and thus quickly identify candidates who are likely to be successful in the PharmD curriculum.

Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance

Computer-Assisted Decision Support for Student Admissions Based on Their Predicted Academic Performance

Co-Design of a Computer-Assisted Medical Decision Support System to Manage Antibiotic Prescription in an ICU Ward.

About 37 thousand people die per year in Europe due to infections by resistant bacteria. Fighting antimicrobial resistances (AR) is a top priority to save lives and reduce costs. AR is triggered mostly by uncritical antibiotic prescription. This paper presents HAITool, a decision-making information system to support antibiotic prescription. The system was co-developed together with health professionals using Design Science Research Methodology, empowered with innovative data visualization techniques to improve AR management. HAITool includes integrated visualizations of patient, microbiology, and pharmacy data, facilitating clinical decision support, antibiotic prescriptions quality and antibiotic-resistant bacteria monitoring. It also includes an alert module that monitors conformance of antibiotic prescriptions with norms and guidelines. HAITool is evaluated using both the Österle principles and interviews with physicians and infection control team from three participant hospitals.

Capsule Commentary on Michaelidis et al., Cost-Effectiveness of Decision Support Strategies in Acute Bronchitis

U sing cost-effectiveness analysis (CEA) to make decisions about health care resources faces significant political and popular opposition in the U.S. The Affordable Care Act (ACA) explicitly prohibits the use of Quality Adjusted Life Years in determining coverage or reimbursement or recommending treatments, and the executive director of the Patient Centered Outcomes Research Institute (PCORI) has distanced the organization from CEA altogether. Michaelidis, et al.’s cost-effectiveness analysis was based on estimates of effect derived from a cluster-randomized trial by Gonzales et al. that compared printed decision support (PDS) and computer-assisted decision support (CDS) with usual care. That article’s point estimates for the odds ratios of antibiotic prescribing were used in the base case (0.57 for the PDS strategy and 0.64 for CDS). However, the 95 % confidence intervals for the two strategies overlapped almost completely, indicating that the adjusted odds of antibiotic use were not different. The authors attempt to address this issue in a secondary sensitivity analysis and conclude that the PDS strategy cost $220 less per patient. Operationally, the only difference between the two interventions was that one had posters, while the other incurred computer programming costs. The cost difference seems to have resulted because the CEA also retained the point estimates from the original trial for rates of return to clinic within 30 days and use of chest x-rays. In both cases, the nominal rates were higher at the CDS sites than at the PDS sites. Yet the original study states BReturn visit rates...increased modestly at all study sites and were not significantly different among study sites.^ With no difference in prescriptions avoided and no difference in associated utilization, the cost difference comes down to the comparison between the direct costs of poster printing vs. computer programming. With Meaningful Use incentives driving health systems to adopt electronic decision support, it is useful to be reminded that more traditional means of physician education can be equally cost-effective. However, some would argue that a methodologically complex analysis like CEA was not required to determine that posters are probably less costly than computer programming time.

Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: a prospective ‘before/after’ cohort study

ObjectivesAntibiotic resistance has risen dramatically over the past years. For individual patients, adequate initial antibiotic therapy is essential for clinical outcome. Computer-assisted decision support systems (CDSSs) are advocated to support...