The American Online Journal of Science and Engineering (AOJSE)
The American Online Journal of Science and Engineering (AOJSE) is an international, peer-reviewed, open-access journal publishing original research across computer science, artificial intelligence, data science, and engineering disciplines. The journal provides researchers, engineers, and industry practitioners with a rigorous, transparent venue for disseminating applied and theoretical work. AOJSE publishes quarterly and is committed to open access under a CC BY 4.0 license, with no reader-side subscription barriers. All submissions undergo double-blind peer review before an editorial decision is issued.
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Self-Evolving Distributed AI for Adaptive IoT Ecosystems
Intelligence in Internet of Things (IoT) ecosystems is largely centralized and limited in its capacity to adapt, learn new tasks quickly, and optimize its operation. EdgeMind offers a framework for self-evolving distributed intelligence, enabling a high level of autonomy for intelligent applications. Each application can be decomposed into dedicated components along the edges of the cloud-edge continuum, acting on local data and collaborating with others for coordination and knowledge sharing. A diverse range of learning and adaptation mechanisms is supported to address these tasks and drive continual improvement of intelligent components. More broadly, EdgeMind provides an operational architecture to support intelligence at multiple levels of autonomy; the focus here is on applications that require a significant level of independence. A meta-architectural approach is adopted, allowing independent design and development of components. Novelty is embedded within the module-specific learning loops that enable EdgeMind to meet the intelligence-oriented research directions set forth in recent reports by the UK Government Office for Science, AI Council and Defence and Security Accelerator. The design welcomes adaptation and optimization of operation (including the profile of computation and communication resources) through self-optimization, continual learning and meta-learning. EdgeMind is therefore positioned to go beyond established paradigms such as federated learning by addressing the learning and adaptation needs of applications across the full diversity of the AI/ML lifecycle and operational continuum.
Copyright (c) 2024 Tanja Schultz (Author) https://creativecommons.org/licenses/by/4.0
Generative AI and DevOps for Scalable Insurance Fraud Detection
Novel AI applications transform insurance operations from intelligence surveillance to intelligent services, and a core focus is fraud detection. The hypergrowth of generative AI creates new capabilities that enable organizations to shift from mainly reactive to proactive fraud detection. Generative models support the synthesis of fraud-related data signals to augment existing training datasets, thereby enhancing detection performance. Underlying data patterns should be suitable for the generation of realistic synthetic samples. With demographic, health, and police-report data forming the foundation, convincing anomaly signals can be synthesized for insurance fraud. The integration of such signals into the fraud-detection pipeline is supported by DevOps practices, which cover data engineering, continuous integration/continuous delivery, governance, and lifecycle management. The proposed approach is operational in property and casualty, health, and auto insurance. The generation of data signals aligned with actual fraud has the potential to fill critical training gaps in detection models. The samples introduced through synthesis offer business value not just in detection but also as adversarial test cases and for automated explainability checks. Such comprehensive exploitation can be achieved at scale and overtime without data-ownership friction. The established architecture supports the creation of any form of signal related to any dimension of fraud. Driving connecting veins into the systems of record offers a pragmatic, scalable, and operational approach to doing so. Attention to latency tolerance and data governance ensures that synthesis is feasible, useful, and compliant.
Copyright (c) 2024 Elena Petrova (Author) https://creativecommons.org/licenses/by/4.0
Fusing Generative AI and Agentic Workflows for Fraud Mitigation in InsurTech
In a hybrid generative and agentic artificial intelligence architecture, generative AI generates fraud-detection signals based on historical events while agentic AI provides decision-suggestion commands to the human stakeholder in-the-loop. This synergistic approach overcomes the limitations of pure generative and agentic frameworks in detecting fraud patterns in modern InsurTech platforms. Rapidly growing digitalization and increased dependence on digital channels increase the susceptibility of insurance platforms to behavioral fraud. Advanced technologies such as AI and Machine Learning (ML) have the potential to mitigate the risk of billions of dollars lost through these frauds. To harness the power of both generative and agentic AIs, a novel hybrid system is proposed. Key stakeholders of the system include the InsurTech venture for which the system is being built, customers of the insurance platform, the development team, and the operational team responsible for maintaining and managing the AI pipelines. Data aspects of the architecture encompass novel feature-engineering techniques, a sophisticated data-pipeline approach, and an incremental learning mechanism to prevent performance deterioration due to concept drift. An unsupervised-ML-based segmentation model has been instantiated for the generation of training datasets and enables scalable continuous-signal generation in a real-time production environment. The quality of signals in the fraud-detection domain is not always suitable for direct operationalization. Hence, the operational design leverages the hybrid nature of the architecture by enabling human evaluation of the suggestion signals in a real-time mode. The primary fraud-detection application of the system is claims related, although it has the capability to generate fraud signals for policy underwriting, channel fraud, and behavioral fraud use cases as well.
Copyright (c) 2024 Carlos Mendoza (Author) https://creativecommons.org/licenses/by/4.0
Autonomous Cloud Intelligence for Predictive Healthcare Risk
Intelligent cloud-native ecosystems powered by autonomous learning systems enable predictive healthcare and risk optimization. The approach facilitates responsive switching between predictive analytics and autonomous learning while respecting cloud governance controls. A cloud-native architecture, tailored to the demands of health-related predictive workloads, fundamentally changes the design of predictive analytics and related decision-support solutions. It addresses quality, risk, and bias assessments in the data pipelines, enabling continuous monitoring of the health status of deployed predictive models to inform corrective actions. Preparation of the cloud infrastructure for deploying predictive workloads reduces operating costs while enhancing the reliability of predictive assessments. Technical validation and clinical assessment of prediction models, in collaboration with hospitals, health networks, and third-party cloud service providers, underpin system deployment. The technical, clinical, and economic validation of advanced health management solutions serves as an exemplary use case for the proposed paradigm.
Copyright (c) 2025 Luca Bianchi (Author) https://creativecommons.org/licenses/by/4.0
Real-Time Decisioning Across Health, Business, and Finance
A unified intelligent data fabric enables real-time decisioning across rapidly evolving healthcare, enterprise AI, and financial ecosystems. The growing body of services and products in these domains—often developed in silos but requiring coherent integration—creates onerous operational and administrative overheads. Real-time decisioning capitalizes on investment in a data fabric to provide cohesive responses across data domains with minimal additional cost. Architectural principles that underpin the data fabric also govern cross-domain decisioning: modularity, interoperability, latency awareness, and governance-by-design. These qualities enable transparent integration of shared components into an end-to-end decisioning flow with comprehensive management. Real-time decisioning across these ecosystems poses specific challenges. Healthcare centers on analytics and decision-support tools that empower patients and their families. In the enterprise, latent models powered by data lineage and continuous learning supervise and improve data-driven automation. In the financial domain, low-latency connections verify trades and positions, sustain anti-fraud vigilance, communicate with customers, satisfy compliance requirements, and mitigate risk. Addressing these challenges establishes a foundation for additional domains, such as telecommunications and online gaming.
Copyright (c) 2025 Alejandro Fernández (Author) https://creativecommons.org/licenses/by/4.0
Adaptive Data Integration and Autonomous Learning for Intelligent Healthcare Ecosystems
An intelligent healthcare ecosystem comprises an interconnected and mutually supportive network of healthcare stakeholders and functions aimed at improving healthcare quality and accessibility while reducing costs. Intelligent healthcare ecosystems provide continuous learning by integrating data from heterogeneous sources, such as electronic health records, wearables, and patient-reported outcomes, and feeding the data into automated decision-support systems that learn from real-world evidence. Intelligent healthcare ecosystems can be assembled in an incremental manner, with targeted investments in the development of trustworthy adaptive data integration utilities and autonomous learning systems that can deliver value independently while scaling up and maturing. Research on the development of intelligent healthcare ecosystems is particularly timely as cross-industry stakeholders prepare for the deployment of 5G networks and the Internet of Things. The adaptability and performance of intelligent healthcare ecosystems can be enhanced by focussing on clinical decision-support and clinical risk stratification capabilities that change the conduct and treatment of care, thereby producing clinical outcomes that enhance the day-to-day activities of healthcare caregivers. Enabling a shift in the conduct of care reduces the pressure on healthcare systems and the cost of care delivery. Adaptive data integration utilities and autonomous learning systems designed specifically for healthcare permit a more flexible approach to practical realisation of intelligent healthcare ecosystems, supporting incremental integration effort and enabling an agile response to changing business needs.
Dimitris Plexousakis Dimitris Plexousakis
Copyright (c) 2025 Dimitris Plexousakis (Author) https://creativecommons.org/licenses/by/4.0
AI-Powered Payment Risk Scoring on Cloud-Native Big Data Systems
The growing volumes of payment transactions have placed significant burden on payment service providers or financial institutions to evaluate the risk profiles of customers swiftly. Various risk-related metrics of customers can be obtained from the data related to their payment transactions, and the recent development of machine learning-based modeling approaches allows these metrics to be modeled as a risk score. However, the shoe has on the other foot as providing such services has become a competitive game between players leading to the demand for near-native response time in serving customer requests. The services, therefore, require a cloud-native big data architecture where the data generation, ingestion, processing and modeling operations can be carried out at huge scales and low response and lead times while integrating the required principles of data governance and privacy. Cloud-native IoT systems collect vast volumes of data daily that forms the data lake for processing. Data processing includes both batch and event-driven processing. Batch processing is used for generating risk-related metrics where as event-driven processing is used for carrying out risk scoring. The ML models behind risk metrics are provided as services and lifelong learning is done by using CI/CD pipelines. Firewall and observability adopt a zero-trust policy to promote not just security but also privacy and governance compliance. Robust architecture has enormous readability and scalability features for tackling such diverse services. The entire architecture is illustrated with an example in the domain of payment service providers using a risk scoring system proposed in the previous work.
Copyright (c) 2024 Michael Anderson (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Risk Governance for the AI-Driven Data Center
Automation may cause traditional modes of operations and control assigned to the Governance, Risk, and Compliance (GRC) units of organizations to become limited, virtually dormant, and misunderstood. Organizations worldwide continuously face growing – and often conflicting – pressures to react and implement ever-evolving regulatory requirements and changes. Simultaneously, these organizations face real managing risks and incidents that can affect their operations. Risk intelligence is needed that allows the strategy and operational areas to complete their mandates: make the business successful while achieving an optimal level of risk. Adaptive Artificial Intelligence, combined with more traditional analytical modalities and statistical business-as-usual modeling, can form a new operational paradigm to support the enterprises’ risk-no-risk continuum. The demand is for practical strategies and methods that can help organizations achieve pragmatic risk management across the enterprise, with the GRC areas clearly becoming enablers of that strategy rather than the exclusive means of managing everything related to risk. Compliance requirements become the amplifiers of risk rather than the possible triggers for incidents and breaches. Progress, experience, growth, and technology become the new vectors of support rather than the demand for an ever-expanding reservoir of controls.
Copyright (c) 2025 Olivia Johnson (Author) https://creativecommons.org/licenses/by/4.0
A Theoretical Model for Carbon-Neutral Concrete Mix Design Considering Diverse Curing Conditions
The greening of concrete is a critical research focus in civil engineering, driven by the increasing global demand for aggregates, the worldwide enforcement of decarbonization policies, and the urgent challenge of climate change. As one of the largest energy consumers and CO2 producers, the cement and concrete industry must align structural efficiency with ambitious sustainability targets. This paper develops a conceptual theoretical model within a numerical framework, based on first principles, to analyze the impact of various curing conditions on the environmental-structural performance of carbon-neutral concrete mix designs. The model is built upon three intertwined pillars: (1) an Environmental Pillar focused on reducing Portland cement content via green substitutes like fly ash and slag, using embodied carbon as a key metric; (2) a Mechanical Pillar ensuring engineering performance (strength, durability) through optimized mix proportions; and (3) a Curing Conditions Pillar that accounts for the effects of different curing methods (e.g., wet, steam, CO2) on carbon performance and long-term structural response. This integrated approach allows for the study of complex interactions between environmental, technical, and structural factors. The model provides a foundation for future computational simulations and the development of standardized performance metrics for sustainable construction codes, offering a practical framework for engineers, scholars, and policymakers to advance toward carbon-neutral concrete.
Samer S. Abdulhussein Samer S. Abdulhussein
Copyright (c) 2025 Hussein K. Khalaf, Ali Dhafer Abed, Samer S. Abdulhussein, Ryan C Rampair (Author) https://creativecommons.org/licenses/by/4.0
Harnessing the Power of Total Quality Management to Achieve Unparalleled Excellence in the Saudi Construction Industry
Total Quality Management (TQM) is a crucial approach for enhancing efficiency, reducing costs, and improving customer satisfaction in the construction industry. This study investigates the adoption and implementation of TQM in the Saudi construction sector, focusing on its impact on project performance and its role in achieving Saudi Vision 2030 objectives. Using a quantitative research approach, data were collected from a sample of 200 construction professionals across Saudi Arabia through a structured questionnaire. The analysis, which involved descriptive and inferential statistical methods, revealed that while TQM adoption is relatively high, challenges remain in employee training and interdepartmental collaboration. Leadership commitment, worker safety improvements, and innovation emerged as key factors influencing successful TQM implementation. The study confirmed that TQM positively impacts project efficiency, reduces maintenance and rework costs, and enhances customer satisfaction. Furthermore, it plays a significant role in supporting Saudi Vision 2030 by improving construction quality, attracting foreign investment, and promoting sustainable development. However, barriers such as resistance to change, inadequate training, and limited cross-departmental cooperation hinder its full implementation. Based on these findings, the study recommends enhancing employee training, strengthening leadership engagement, improving collaboration between departments, fostering a culture of continuous improvement, and aligning quality management strategies with national development goals. Addressing these challenges will allow the Saudi construction industry to maximize the benefits of TQM and contribute to long-term economic growth and infrastructure excellence. Future research could explore the impact of digital transformation on TQM practices and examine variations in TQM adoption across different construction sectors.
Prof. Dr. Mohammed Jameel Prof. Dr. Mohammed Jameel
Copyright (c) 2025 Prof. Dr. Mohammed Jameel (Author) https://creativecommons.org/licenses/by/4.0
Employing digitization and spatial analysis tools in GIS to manage educational institutions: The College of Engineering at Mustansiriya University as a case study
Geographic Information Systems (GIS) are one of the most important tools for organizing the development of integrated spatial databases for educational institutions. These databases support infrastructure analysis, resource management, expansion planning, and improve planning and decision-making efficiency. Therefore, this research aims to use corrected satellite imagery to produce an integrated digital map of the study area (College of Engineering/Al-Mustansiriya University) within a GIS environment. This map will then be linked to an integrated database and spatial analysis will be conducted on this data. Field surveys will be used to collect the database for each department within the College of Engineering. This data will be linked to its geographic location on the digital map. Spatial analyses will be conducted to arrive at the final results, which will serve as an integrated database for department management and forecasting future plans for expansion of faculty, students, and staff. This analysis will also produce multiple strategies for decision-making regarding the required number of undergraduate, graduate, and evening students, and linking these numbers to the facilities, laboratories, and infrastructure that will accommodate the acceptable number of students.
Copyright (c) 2025 Ali Dhafer Abed, Rhowena J. Ramdeen, Ryan C. Rampair, Nasir Ahmad, Mir Bintul Huda (Author) https://creativecommons.org/licenses/by/4.0
CICD for Real-Time Analytics on Azure Databricks
An end-to-end Continuous Integration/Continuous Deployment (CI/CD) framework for data engineering is proposed, enabling automation from source control to development, testing, deployment, and monitoring. Supporting advanced development patterns like Infrastructure as Code (IaC), DataOps, and MLOps, these best practices enhance collaboration, streamline provisioning, and improve observability while ensuring consistent quality, security, and compliance. As the underpinning for a Zoomcar production-grade data engineering pipeline on Azure Databricks, the architecture supports a demand-based data engineering approach that favors data availability over access latency for Data Mesh adoption. Cloud-based Data and Analytics platforms support advanced analytics, Artificial Intelligence, and Machine Learning on Near Real-Time Streaming data, enabling data-driven enterprise decision-making. Continuous Integration/Continuous Deployment (CI/CD) with DevSecOps is ubiquitous in Software Development and Application Development. Expanding CI/CD to ETL pipelines supporting MLOps and DataOps brings powerful, tested, and fast-productized code into Production for Cloud-Based Data Engineering Process. Data Availability on Demand supersedes Data Availability in Real-Time. A well-governed Data Lakehouse serving a Data Mesh architecture with a focus on Data Availability, Quality, and Security over Streaming Latency meets the demands of a Data Vision for a Data-Driven Enterprise.
Copyright (c) 2026 Mallesham Goli (Author) https://creativecommons.org/licenses/by/4.0
An abstract encapsulates the essence of a research work. This piece presents an overview of AI-Enabled Payer Intelligence in healthcare. Schmitt and Gamasai explore the processing of payer claims—an activity that defines each health insurance company in the United States—through an automated claims and data pipeline. A data integration strategy, stakeholder data-sourcing choice, supportive enterprise data governance, and the automation of end-to-end operations in a controlled manner allow for greater efficiency and lower costs. Automated management of fraud, waste, and abuse; natural language understanding of adjudication; and predictive modeling applied upstream, upstream, midpoint, and downstream in supplementing human judgment throughout the operations chain are pursued. Automated processing of claims constitutes an essential component of the operation of any North American healthcare payer. Claims-management autoroutes; a natural-language-understanding model dedicated to adjudication; and prompt-creation, natural-language-generation, and predictive-modeling approaches applied throughout the claims lifecycle down a human-in-the-loop pathway support greater efficiency, lower costs, enhanced quality, and improved experiences.
Copyright (c) 2026 Vikram Boga (Author) https://creativecommons.org/licenses/by/4.0
Federated AI for Real-Time Smart System Control
Scalable AI-based systems are outlined that support distributed, real-time monitoring and decision-making in the context of smart automation systems. The architectural principles presented are driven by the following requirements: Distributed sensing and data ingestion; AI methods that support real-time monitoring of large-scale sensor networks; Scalability and performance enhancement; Support for privacy; Governance and data management regulation; Federated capabilities and privacy support preserving data confidentiality. Within this framework, federated adaptive neurons are proposed. These AI nano-scopes provide local information about distributed streams, support anomaly detection and declaration in large sensor networks such as water networks, and can operate in a privacy-preserving environment. Additionally, horizontal and vertical scalability with distributed or high-performance computing is exposed. The proposed principles are supported by experimental cases that illustrate a distributed real-time monitoring capability. Monitoring of the water quality and surveillance of an aquatic environment is investigated as an application area. A Smart City use case within a European initiative is also proposed. Finally, recent advances regarding governance, security, and privacy of data, especially on personal nature, are examined. These aspects are worthy of special consideration, since data governance regulation is evolving rapidly. The analytics to monitor data that may cross data sovereignty boundaries, such as those originating from users, need to respect end-user privacy preferences.
Copyright (c) 2026 The American Online Journal of Science and Engineering (AOJSE)
Intelligent Data Governance for Next-Gen Investment and Insurance
To address the institutional reliance on a combination of traditional data sources and poor quality alternative data, two distinct—but related—problems linked to the Governance of Big Data are analysed. In a Big Data and AI-driven world, the modelling paradigm changes from a linear perspective to a non-linear one, leading to a shift in focus: the analysis, selection and creation of model features becomes the main modelling task and modelling is performed in a segmented way. Data quality and data processing time gain special importance. Moreover, Data Governance impacts any enterprise involved in developing decision-making tools via an AI/ML Data Modeling Paradigm. Deficiencies, inadequacies, ethical concerns and governance checks of data required for the development of risk, pricing and portfolio management models for Investment Solutions, as well as for the data driving the underwriting decision process and risk scoring for Insurance Solutions, are analysed. Finally, the underlying Technical Architecture is proposed. Specific checks, balances and design controls would help Institutions to develop Big Data-based Decision-making Tools with more robust results and safer performance.
Autonomous Finance, Native Cloud, Agentic Core
Conventional automated trading and risk management employ deterministic models within a well-defined decision support paradigm. Emerging DevOps-driven cloud-native architectures, however, enable scalable, resilient, operable, observant, and automated systems where models are continuously updated in response to changing real-world conditions. Recent research suggests extending agent-based reinforcement learning beyond a decision support role toward direct decision-making responsibilities, underpinned by proper control and governance principles. The combination of such agentic models within automated financial systems presents novel challenges, while fulfilling requirements defined by supervisory authorities. A suitably architected cloud-native environment thus has the potential to not only expedite the delivery of financial AI models, but also to support their increasingly frequent deployment and operation with minimum effort and oversight. The specific considerations, from foundational concepts through to development and operational constraints, then govern the underlying implementation. The ultimate goal is a DevOps-driven cloud-native implementation framework that guarantees the reliable delivery of agentic AI models into automated trading and risk management systems, quickly, frequently, and with minimal overhead. In this highly regulated domain, controls must therefore be embedded within the supporting infrastructure and processes rather than bolted on afterwards. The key metrics for measuring success then become the speed, frequency, and simplicity with which models are delivered, continuously improved, and retired, all while remaining compliant with a multitude of ever-present security, risk, governance, and operational requirements. By explicitly specifying these considerations, the intention is to present a coherent synthesis of the architectural rationale and a repeatable implementation approach.
Copyright (c) 2025 The American Online Journal of Science and Engineering (AOJSE)
Latency-Optimized Interoperability Design
The use of Health Level 7's Fast Healthcare Interoperability Resources (FHIR) for facilitating real-time data exchange among healthcare systems in life-critical scenarios enables patients to receive timely and appropriate treatment whenever and wherever they need it. FHIR provides models, rules, and interfaces for data exchange between computerized applications operating in the healthcare domain. Being a web-based standard, it can be easily deployed using an event-based architecture. Furthermore, it allows for online communications and moves away from standard file batch processes. The eventing mechanism facilitates the push of data from an event publisher to interested subscribers and notifies them of updates for resources of interest. The increasing diversity and complexity of healthcare information systems, along with the demand for timely integration of data across these systems, point toward the need for a real-time event-based integration approach using FHIR REST services. Open-source frameworks have been developed for achieving the required integration. However, architectural decisions in designing such frameworks have a significant impact on their ability to support data exchange with low latency. The selection of an appropriate integration architecture is also crucial for meeting requirements such as interaction with external enterprises, protocol translation, security, and compliance with cloud deployment models. Therefore, several architecture patterns commonly considered and deployed for event-based integration using FHIR are discussed. Each architecture pattern is detailed, along with its strengths and weaknesses for supporting real-time data exchange.
Hybrid AI for Automated Clinical Risk Coding
The long-overdue transition of the United States health system toward value-based care has triggered a new demand for healthcare risk adjustment. The chronic medical complexities of at-risk populations are captured and represented through clinical concepts from the International Classification of Diseases (ICD). These codes are prevalent in payment models such as the Medicare Advantage program and the Affordable Care Act marketplace. Medicare Advantage payers receive funding from the Medicare program based on a patient-level risk adjustment prediction model, which is based on Hierarchical Condition Categories (HCC). The accurate and efficient identification of HCC codes in unstructured clinical notes could reduce operational costs while directly benefiting the medical community and patients. However, few solutions address this problem. Generative AI can help address these emerging needs by automating the complex natural language processing (NLP) tasks involved in HCC code identification. This research study proposes a novel coding architecture that employs generative AI for automatic HCC code identification from clinical notes. A hybrid large language model-natural language processing (LLM-NLP) generative AI architecture is designed; it includes a large language model that generates a set of candidate HCC codes, which is filtered based on the restricted natural language processing of classifiers using a new embedding approach. The novel architecture is applicable to domain-specific tasks requiring extracting relevant concepts from a very large number of entity classes.
Multi-Agent RL for Value-Based Population Health Optimization
Agentic artificial intelligence (AI) systems, including those realized through multi-agent reinforcement learning (MARL), are emerging in multiple domains, including information filtering, navigation, gaming, and robotics. These novel approaches may also be relevant to healthcare delivery and policy, as MARL is well suited to complex strategic problems that Richard Thaler characterized as “makeshift solutions to impossibly complex problems.” Population health management is such an arena, particularly in optimizing risk- and performance-based contracts, payment redistribution across agents and cadre types, and performance-related benchmarks for an actor-network consisting of several competing provider-organizations functioning in a shared ecosystem. Health information systems, including such agentic, MARL-based systems, could help realize “better, faster, cheaper, and ‘kinder’ healthcare” by improving risk-adjusted health and cost outcomes across multiple resident cohorts, thereby more effectively realizing the goals of modern econometrics in medicine and public health. Current culture tends to represent healthcare decision-making as a Black Box or an informationally centralized “big brain” where supervision assures correctness and compliance with Thaler’s ideal of omniscience for coordinating activities. Yet actual healthcare organizations are not Black Boxes, much less a single Big Brain, corporate or otherwise. Decision makers are often overpromoted, misinformed, late in their decisions, veto the best ideas of the organization, or are unable to manage rich information effectively. Supporting such overloaded Decision Makers-MD, using training and expertise gained trying to wisely harvest large but incomplete information- is a hard problem. Because the decisions should be seen as Agents making active decisions, it is viewed in terms of Agent-based Systems, or the study of multi-oriented organizations as complex adaptive systems.
Detecting Healthcare Billing Anomalies with Graph Neural Networks
Healthcare payment accuracy directly influences both cost and quality of care, yet high rates of fraud, waste, and abuse continue to persist. These challenges are exacerbated by the inherent complexity and lack of transparency surrounding payment analytics. Addressing these issues, this study proposes an effective, data-driven solution for detecting potentially fraudulent or erroneous billing practices, using unsupervised machine learning to analyze payer-provider relationships. The primary motivation is threefold: first, such unsupervised approaches eliminate the need for a labeled training set; second, they take advantage of the richness and heterogeneity of the data, including subrogation and encounter information; and third, they have no bias toward any specific type of anomaly. Core methodological contributions include the novel application of unsupervised and graph-based methods for billing analysis and anomaly detection, and the integration of external data into traditional billing tables to uncover new patterns. Testing demonstrates that model-enriched Normalized Payment Amount can help identify fraudulent encounters, offering long-term potential for both real-time monitoring and assisting state-level agencies responsible for managing Medicaid-funded healthcare.
Copyright (c) 2024 The American Online Journal of Science and Engineering (AOJSE)
StreamCare: Real-Time Healthcare Analytics at Scale
Healthcare analytics is increasingly expected to operate in real-time. The demand for real-time queries and feedback has propelled the move toward event-driven data streaming architectures, which have been adopted in many industries—commerce, finance, social networking—but have yet to be fully realized in healthcare. Although challenges remain in isolating, modeling, and sharing the right data, there are clear motivations for both clinical care—with the potential for detecting and acting on sudden changes in patient health—and administrative use cases, with the prospect of providing proactive operations dashboards that alert business analysts to deepening problems that they can address before they escalate into costly crises. This paper focuses on two specific use cases: the real-time processing of healthcare claims as they are submitted and a framework for measuring, monitoring, and improving healthcare quality in real-time and at scale. The first addresses data provenance and instrumentation needs for the timely execution of the rules governing claims submission and adjudication. The second defines the requirements—timeliness, accuracy, completeness, and so on—for quality measurement at scale based on New York State Department of Health and Centers for Medicare & Medicaid Services performance indicators and establishes a framework for providing real-time feedback on adherence to these requirements. Although a number of high-level components of a comprehensive real-time analytics architecture have been previously proposed or described, decision support, data , governance, and provisioning are still in their formative stages. Building from previous work outlining an event-driven, micro-batch-enabled architecture for real-time data streaming and analytics at scale, the present discussion focuses on claims adjudication and quality measurement use cases. Other descriptive aspects complete the taxonomy of real-time analytics building blocks. Technical requirements are then explicitly addressed for a claims processing framework that analyzes a claims life by identifying the key events, states, and timing constraints that drive the process, and for a real-time quality measurement system that detects anomalies across key performance indicators and provides closed-loop feedback for quality improvement.
Explainable ML for Clinical Decision Transparency
Artificial intelligence has the potential to augment clinical decision making. By learning patterns of risk and disease directly from empirical data, AI methods offer one solution to the difficulty health care professionals face in considering ever-increasing amounts of information. Clinicians making a medical decision for a patient want not only an accurate estimate of the risks associated with their patient's disease or treatment options but also an understanding of the reasoning behind these risks. This desire for explanation drives the growing interest in explainability in AI, particularly in AI for health care. Explainable Artificial Intelligence (XAI) is defined as methods that generate new AI models for which the behaviour can be understood, directly or indirectly, by humans. The concept of human understanding encompasses three different levels – transparency, interpretability and accountability. The heart of the concern for transparency in AI is the incomprehensibility of the learned representations, the “black box” nature of the complex function learned from the training data.
Reengineering Care Pathways Through Quantitative Process Analytics
Clinical motivation, objectives, data-driven approach, methods, findings, and implications are synthesized. Clinical workflows govern patient management in specialty practices, creating a need for technique deployment to improve operational outcomes and resource utilization. Data-Driven Process Modeling uses systematic data analysis to provide rigorous evidence and formal structure to clinical decision-making. ‡Process mining identifies the real recurrence of patient journeys; machine learning models future event occurrences to replicate desirable flows; simulation of mapped processes quantifies throughput, waits, and cycle times; and hybrid methods combine these techniques. Together, they identify bottlenecks and variations affecting performance. An Emergency Department throughput case shows how analyzed paths reveal long wait times at triage and radiology, together with extended time between physician disposition and patient departure. Measurable improvements result from targeted interventions. In a second application, development of Machine Learning algorithms for segments of an inpatient discharge planning pathway highlights excessive attorney handoffs, leading to simplified processes and faster discharge times. Analysis of quantitative results indicates throughput times, waits, and cycle times are shortened, variance reduced, and changes statistically significant. ‡Qualitative evidence complements numbers by revealing practitioner satisfaction and user acceptance.
Neuro-Behavioral Fusion for Predictive Tax Fraud Detection in Cloud-Based Government Systems
Accurate prediction of tax fraud fosters favorable taxpayer-business relationships, streamlines tax authorities' operations, and optimally directs government investments to enhance public services. Reliable prediction of fraudulent taxpayers, however, requires careful selection of assessed features because of pronounced privacy, ethical, and security concerns associated with government-related data. Furthermore, limited historical records are available because fraud often remains undetected. Consequently, effective machine learning-based predictive models must deploy hybrid architectures capable of learning from different types of features, integrating feature engineering with representation learning, and extracting fraud behavioral patterns during training. Moreover, tax fraud detection is a pattern-discovery problem with critical imbalance between the positive and negative classes, necessitating the use of behaviorally informative indicators that optimize predictive performance, fairness, and robustness. Predictive models should therefore integrate hybrid deep learning representations with behavioral-based tax fraud indicators for risk detection. Cloud-enabled government data ecosystems provide abundant data sources for detecting and predicting all forms of tax fraud, and during operation can be simulated to include as many behaviors that are indicators of tax fraud as possible. Predictive models can thus be trained and tested using a behavioral pattern-discovery approach, forming the foundation for hybrid deep learning-based models that use a dual-tower architecture to employ both behavioral indicators and independently engineered features to optimize detection risk.
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