Decision support systems in the diagnosis of urological diseases

In 819% of patients with atrial fibrillation (AF) with anticoagulant therapy (ACT), hemorrhagic complications occur, including due to excess doses of AC. At the same time, ACT is necessary for patients with AF, since anticoagulants effectively reduces the risk of ischemic stroke. To make a decision on the appointment of ACT, it is necessary to correlate the risks of ischemic stroke and bleeding, this requires knowledge of current clinical using ACT recommendations and instructions. Among patients admitted to hospital, 30% receive ACT, so increasing adherence to clinical recommendations for prescribing AC to patients with AF by doctors of various profiles is an urgent task. To analyze the adherence of physicians to recommendations for prescribing ACT before and after the introduction of decision support system (DSS) in patients with AF in a multi-specialty hospital. A single-center non-randomized study with historical control to assess adherence to recommendations based on the analysis of medical prescriptions and the structure of drug errors in patients with AF in a multi-specialty hospital in Moscow before and after the introduction of DSS. Compliance with the recommendations of physicians was evaluated in the sections indications /contraindications to AC and dosage regimen of AC. The presence of deviations from the clinical recommendations /instructions for medical use of AC was regarded as management of the patient with non-compliance with recommendations. Physicians adherence level to recommendations was calculated as the ratio of cases of compliance with recommendations to the total number of cases. In the control and experimental groups, there was a significant increase in the proportion of POAC at discharge in comparison with admission to hospital: from 54.5 to 76.8% (p=0.0005) and from 63 to 85.7% (p=0.0002), respectively. However, only in the experimental group it was possible to significantly reduce the number of patients without a prescribed ACT (if there are indications) from 7.6 to 1% (p=0.04) in comparison with admission. During the study, it was possible to significantly increase physicians adherence level to the recommendations for the AC dosage regimen in patients with AF from 59% (44 discrepancies for 107 prescriptions) to 84.6% (16 discrepancies for 104 prescriptions); p0.005. Before the introduction of the DSS, the analysis of drug prescriptions revealed 56 drug errors (0.5 errors per patient), after the introduction of the DSS, the number of drug errors significantly decreased to 21 (0.2 errors per patient); p0.05. After the introduction of DSS, the number of sub-therapeutic doses of AC was reduced from 31 (27.7%) to 8 (7.6%); p0.05. The level of adherence to the recommendations for prescribing ACT to patients with AF in the hospital is high. The use of DSS increases the level of adherence to the recommendations on the AC dosage regimen in patients with AF, as well as eliminates errors in calculating the risk of ischemic stroke and systemic thromboembolic complications, and contributes to reducing the frequency of prescribing sub-therapeutic doses of AC.

SABER and MyDispense: Building better pharmacy education through innovation and sharing

Health Professional Involvement in Health Information Technology Development: What Can Your Do to Improve Them?

Cancer is the second most fatal disease worldwide. Management of cancer is a complex process consisting of diagnosis and staging of the disease and planning and execution of treatment followed by post-treatment follow up. The conventional method of treatment often fails in many patients due to the variability of the disease process amongst a heterogeneous patient population. In the past few years, various biomarkers have been developed to identify the subtype of disease which leads to developing personalized treatment in oncology i.e., precision oncology. Medical imaging plays a key role in cancer management at various stages. Imaging modalities are used in diagnosis, staging, planning of treatment and follow up of disease. It is also used in the restaging of disease in case of progression or recurrence. The information stored in medical images is analysed by imaging experts either by qualitatively using visual interpretation or by semi-quantitative methods, which allows sub-optimal use of information stored in medical images. The huge amount of informative quantitative data stored in medical images remains unexplored. After intended use, these medical images are stored in the archival system (PACS) of the hospital. In the last decade, the medical images archived in hospital PACS have been identified for quantitative analysis and development of imaging biomarkers. The quantitative analysis of medical images (radiomics) has led to the data explosion which is the source of BIG data in oncology. Artificial intelligence (AI) algorithms like machine learning (ML) and deep learning (DL) have been applied to imaging Big data to develop decision support systems in precision oncology. Several imaging biomarkers (radiomic features) have been identified as digital phenotypes of the disease. Nevertheless, several radiomic features have shown potential to predict various endpoints in oncology, but the translation of these radiomics based prediction models as decision support systems (DSS) in the clinic will require addressing several key issues. The radiomic community needs to address the key issues related to the implementation of radiomics based DSS: (a) robustness of radiomic features, (b) development and implementation of AI infrastructure in hospitals, (c) multicentre and prospective radiomics studies, (d) creating awareness and faith among doctors and patients. Through this work, we have tried to address most of these issues to facilitate the implementation of radiomics based DSS in clinical practice.

1557 Background: Molecular Tumor Boards (MTB) have become the standard for deriving consensus therapy (Rx) recommendations (rec) for patients (pts) with advanced cancer undergoing comprehensive genomic profiling (CGP). However, finding matched Rx and trial options remains laborious, needing informatics strategies. Methods: We examined the outcomes before and after implementation of a decision support system (DSS) at the MTB of the Australian Molecular Screening & Therapeutics program (MoST, ACTRN12616000908437), enrolling pts with refractory solid tumors and linking CGP results to Rx and trials through rec on clinical reports. The DSS comprises a variant interpretation pipeline, a symbolic AI system integrating the TOPOGRAPH database and an annotated trials registry, and a web platform used by the MTB for producing reports shortlisting matching Rx and therapeutic trials. The primary endpoint is the cumulative participation rate (CPR) in genomic matched trials (excluding MoST-related trials) in pts who received Rx after CGP. The secondary endpoints are the CPR in any trial, and overall survival (OS) from MTB. The Fine-Gray model was used to estimate the subdistribution hazard ratios (sHR) for between-group differences in participation. Kaplan-Meier and Cox regression were used for OS analysis. The time-to-event analyses were adjusted for age, ECOG, cancer type, lines of prior rx, and lead-time bias (OS only). Results: In 2203 of 5186 pts (42.5%) received ≥ 1 Rx after CGP, 971 pts were reviewed at MTB before and 1272 after DSS implementation in Sep 2020. At 3, 6 and 12 months (m) after enrolment, CPR in matched trials were 3.2%, 5.0%, and 6.8% before implementation and 3.6%, 5.8%, and 8.9% afterwards (sHR 1.19, 95% CI 0.90–1.57, p=0.23). Pts who received an MTB rec were more likely to participate in a matched trial (sHR 4.75, 3.13–7.23, p<0.001). Notably, DSS specifically increased the likelihood of trial participation following MTB rec (interaction sHR 2.69, 1.02–7.07, p=0.045), with 12m CPR increasing from 10.0% to 12.2% after implementation of the DSS in pts with a MTB rec, whilst for pts without a MTB rec, the CPR fell from 3.1% to 1.6% after implementation. No association was seen between participation in any trials and MTB recs (sHR 1.12, 0.95–1.32) or implementation (sHR 0.95, 0.81–1.11; CPR pre 21.6% v post 23.7% at 12m). No OS difference was seen comparing before with after implementation of the DSS (median 16.7 v 15.3m, adjusted HR 0.97, 0.87–1.08). However, pts receiving genomic matched Rx had a longer OS (median 20.1 v 15.0m unmatched, HR 0.76, p=0.002). Conclusions: The observed interaction between the implementation of DSS and MTB rec suggests there is effect modification in participation rates of pts in genomic matched trials, warranting exploration into how AI might enhance trial participation among pts with incurable cancers undergoing CGP.

The Intelligent Management Information System (IMIS) has the potential to transform human decision making by combining research in Artificial Intelligence, Information Technology, and Systems Engineering. The field of Intelligent Decision Making (IDM) is expanding rapidly, due in part to advances in artificial intelligence and networkcentric environments that can deliver the technology. Communication and coordination between dispersed systems can deliver just-in-time information, real-time processing, collaborative environments, and globally up-to-date information to the human decision maker. At the same time, artificial intelligence techniques have demonstrated that they have matured sufficiently to provide computational assistance to humans in practical applications. It is the development direction of modern management science and technology. In this chapter, firstly we introduce the introduction and background of IMIS, and briefly, the related design conception. Subsequently, the Intelligent Decision Support System (IDSS) is depicted, which is the most significant technology of IMIS and related activities in the manufacturing process. The applications of IDSS and two cases for industrial manufacturing are then presented, representing the future development direction of manufacturing management. Lastly, a summary of this chapter is given. IMIS researchers and technologists have built and investigated Decision Support Systems (DSS) for more than 35 years. The developments in DSS began with building model-oriented DSS in the late 1960s which were followed by theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-1980s. During the mid-1980s, Intelligent DSS were implemented through combining knowledge systems with DSS. These developments are discussed below, as well as the origins of Executive Information Systems, On-line Analytical Processing (OLAP), Business Intelligence, and the implementation of Web-based DSS in the mid-1990s, which quickly became a topic for active discussion, and whose influence spread widely.

Nursing publications on the use of decision-support systems in clinical practice are just beginning to appear. Unfortunately, this small body of literature already suffers from confusion and lack of clarity because of the various definitions and conceptualizations of decision-support systems. It is characterized by authors who use the same term to refer to different concepts or who use different terms for the same concept. However, there is consensus among authors that decision-support systems should be used to extend the nurse’s decision-making capacity rather than to replace it. The care planning systems now in use are not decision-support systems. Standardized care plans, whether manual or computer-based, only provide care for standardized patients! Standardized care plans do not enhance nursing decision making; on the contrary, their cookbook approach discourages active decision making by nurses.

Nowadays, the modelling of observable problems can be carried out in a combined or algebraic way. Expert systems appeared in the 70s introducing the heuristic research and belonging to the combined trend. The introduction of decision support systems (DSS) based on this modelling brings to the decision maker (DM) a wider range of decisions. The role of the DSS, faced with an expressed problem, is to capture a set of decisions using a combined modelling of this problem. In most of the cases, decisions are expressed by a specific processing. Therefore the evolution of the system behaviour is a consequence of the qualitative or quantitative user's choice. Consequently, the DSS appear to be more adaptable to the problems that are likely to be met in the human sciences. In the particular domain that concerns the authors' research their Multicriteria Choice Decision Aid (MCDA) model deals with qualitative criteria. In the first part of this paper, the authors introduce the model called Satisfaction-Regret and present its rules. These rules represent in fact a method for synthesizing qualitative criteria. The rules use fuzzy logic operators. This method allows the DM or the expert to evaluate abstracted notions (such as the picturesque features of an environment). The different stages of the method called Satisfaction-Regret are described and an example for a better understanding is also proposed. In the second part, the authors give the architecture of the DSS by briefly presenting its components before explaining in the third part how this system works.

BackgroundArtificial intelligence (AI) applications in health care are expected to provide value for health care organizations, professionals, and patients. However, the implementation of such systems should be carefully planned and organized in order to ensure quality, safety, and acceptance. The gathered view of different stakeholders is a great source of information to understand the barriers and enablers for implementation in a specific context.ObjectiveThis study aimed to understand the context and stakeholder perspectives related to the future implementation of a clinical decision support system for predicting readmissions of patients with heart failure. The study was part of a larger project involving model development, interface design, and implementation planning of the system.MethodsInterviews were held with 12 stakeholders from the regional and municipal health care organizations to gather their views on the potential effects implementation of such a decision support system could have as well as barriers and enablers for implementation. Data were analyzed based on the categories defined in the nonadoption, abandonment, scale-up, spread, sustainability (NASSS) framework.ResultsStakeholders had in general a positive attitude and curiosity toward AI-based decision support systems, and mentioned several barriers and enablers based on the experiences of previous implementations of information technology systems. Central aspects to consider for the proposed clinical decision support system were design aspects, access to information throughout the care process, and integration into the clinical workflow. The implementation of such a system could lead to a number of effects related to both clinical outcomes as well as resource allocation, which are all important to address in the planning of implementation. Stakeholders saw, however, value in several aspects of implementing such system, emphasizing the increased quality of life for those patients who can avoid being hospitalized.ConclusionsSeveral ideas were put forward on how the proposed AI system would potentially affect and provide value for patients, professionals, and the organization, and implementation aspects were important parts of that. A successful system can help clinicians to prioritize the need for different types of treatments but also be used for planning purposes within the hospital. However, the system needs not only technological and clinical precision but also a carefully planned implementation process. Such a process should take into consideration the aspects related to all the categories in the NASSS framework. This study further highlighted the importance to study stakeholder needs early in the process of development, design, and implementation of decision support systems, as the data revealed new information on the potential use of the system and the placement of the application in the care process.

The European cherry fruit fly, Rhagoletis cerasi (Diptera: Tephritidae), is a key pest for the cherry production industry in Europe and west Asia that has recently invaded North America. Insecticide applications are frequently employed to control this devastating pest, often without considering its population trends. We developed a novel decision support system (DSS), and field tested it in commercial sweet cherry orchards in central Greece. The DSS includes two algorithms that predict the timing of adult activity in the wild and support pest management decisions, based on R. cerasi population trends and pesticide properties, respectively. Preparatory monitoring of the testing area during 2014, using adult traps, revealed high population densities of R. cerasi in non-managed sweet cherry orchards and low densities in commercial ones. Implementation of the DSS during 2015 resulted in low R. cerasi adult population densities and zero fruit infestation rates in commercial cherry orchards. Similar population and infestation rates were recorded in conventionally treated plots that received on average two insecticide applications compared to the one-half that the DSS treated plots received. Simultaneously, high population densities and fruit infestation rates were recorded in non-managed cherry orchards. Apparently, the implementation of the simple DSS we developed reduces the cost of R. cerasi management and minimizes the chemical footprint on both the harvested fruit and the environment.

Modern agriculture requires technology to give precise measures about relevant parameters such as pest control. Here, we developed a decision support system (DSS) based on semi-automatic pest monitoring for managing the olive fruit fly Bactrocera oleae (Rossi), in Mallorca (Balearic Islands, Spain). The DSS was based on an algorithm that took into account spatial and temporal patterns of olive fruit fly population in an orchard where all trees were georeferenced, thus precise treatments against the pest were conducted through a location aware system (LAS). The olive fruit fly adult population was monitored by using ad hoc off-the-grid autonomous electronic traps.The results were compared with those obtained with conventional methods. For a pilot trial, we selected an olive-producing orchard, where from June to October 2015, three plots using LAS management and three plots under conventional control (NO-LAS plots) were compared. Spray threshold considered both adult population and fruit damage. An additional non-sprayed plot was selected for assessing biological control due to the parasitoid, Psyttalia concolor (Szépligeti). Results showed that the use of DSS reduced by 36.84% the volume of insecticide used in LAS compared to NO-LAS plots. Accordingly, time and distance needed for spraying were also reduced. Adult olive fruit fly population was lower in the LAS plots when compared with the NO-LAS plots; conversely, fruit infestation was higher in LAS compared with NO-LAS. The implementation of LAS and DSS at field level allowed real-time monitoring of adult olive flies, thereby increasing the accuracy and precision of sprays in time and space and decreasing impact on natural enemies.

The purpose of this paper is to review decision support system application trend in manufacturing sector. Following the introduction of decision support system, the paper has discussed the application of decision support system in manufacturing sector and identifies the trend in term of decision support system types and their application types. In year 2011 until 2015, the most preferred decision support system were developed by using the model application. It also been found that, most of the developed decision support system are used to support evaluation activities in manufacturing operations. This review provides research trend on decision support system for the recent five years (2011 -2015) in the context of decision support system application in manufacturing industry.

Introduction: The development of medicine and information technology in recent decades has undoubtedly contributed to improving public health. Artificial intelligence is a technology that has great potential to revolutionize the functioning of health care around the world. Appropriate use of the development of technology can revolutionize many areas of modern medicine, however, it should not be forgotten that this technology should be subjected to appropriate standardization and legal regulation. Objective: The purpose of this study is to review the available scientific literature in order to systematize the current knowledge on the use of artificial intelligence in the process of diagnosis and treatment. Ethical aspects related to the implementation of AI for use in health care are also analyzed. Results: Artificial intelligence uses deep machine learning algorithms. It is a technology that has been known for a long time, but recently the chances of its widespread use have increased significantly, although scientists still do not fully understand the operation of AI algorithms. Currently, there are attempts to use this technology in many medical fields such as cardiology, diagnostic imaging, gastroenterology, pathomorphology, ultrasound. Artificial intelligence can also be used to improve the functioning of patient service in health care. Summary: The development of artificial intelligence algorithms creates a huge opportunity to improve the quality of diagnostic and treatment processes. The current rapid development of the technology is revolutionizing many branches of medicine, improving treatment outcomes. However, the development of this technology requires the creation of an appropriate law governing AI in medicine.

The introduction of Decision Support Systems (DSSs) in weed management poses an attractive option for creating improved and more environmentally friendly control strategies. The aim of the current study was to present key factors affecting decision-making process that need to be taken into account before developing a DSS in terms of weed management. First, attention should be paid to the effects of environmental factors and agronomic practices on weed emergence and the composition of the weed flora in an agricultural field. If weed emergence and timing of weed emergence could be predicted, then a DSS could make accurate suggestions for weed control. Secondly, to develop any weed management program, it is essential to have a deep understanding of weed biology and ecology. The biological traits of weeds, weed growth, the impact of weed competition during crucial growth stages for the crop should be estimated in order to optimize decision-making process. Moreover, a better understanding of seed production and weed seedbank dynamics into the soil would help experts develop DSSs able to provide management strategies also in the long-term period. However, these objectives are quite complex and need to be addressed in the near future. Furthermore, carrying out field surveys, hosting workshops, and group meetings in order to communicate with farmers and help them familiarize with the adoption of DSS methodologies. This is a vital step for persuading farmers to trust the use DSSs for the management of weeds in their fields. Further research and extended experimentation are needed in order to develop effective DSSs in terms of weed management under different soil and climatic conditions, always according to the special needs of each farmer.KeywordsDecision support systemWeed managementDecision-makingSmart farmingAgriculture

Abstract:Chronic Kidney Disease (CKD) poses a significant global health challenge, necessitating early detection and intervention to mitigate progression and associated complications. This study explores the application of Deep Learning (DL) approaches for CKD detection. We review various deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and ensemble methods, which have demonstrated promising results in analyzing medical datasets. By employing datasets that include clinical parameters, laboratory results, and patient demographics. Our investigations/study indicate that DL methods can significantly improve CKD detection rates compared to traditional techniques, paving the way for the development of robust, scalable decision-support systems in clinical practice. This research underscores the potential of Artificial Intelligence (AI) in transforming kidney health management and facilitating timely interventions for patients with CKD. Index Terms -CKD, Art-Risk Patients, DL, Disease, Risk Detection, Medical Datasets