Aug 2026· European Scientific Journal· Vol 56· 0 citations
TL;DR
This study proposes a comprehensive framework that leverages low-code RPA to automate complex, rule-based processes, including electronic medical record (EMR) updates, insurance claims processing, and compliance reporting, without requiring extensive software development expertise.
Abstract
The healthcare sector is under increasing pressure to optimize operational efficiency, reduce administrative burdens, and enhance patient-centric services; all while maintaining strict regulatory compliance. This study investigates the integration of Robotic Process Automation (RPA) with low-code development platforms as a transformative approach to healthcare automation. The study proposes a comprehensive framework that leverages low-code RPA to automate complex, rule-based processes, including electronic medical record (EMR) updates, insurance claims processing, and compliance reporting, without requiring extensive software development expertise.
Using system architecture modeling, workflow analysis, and proof-of-concept implementation, this research evaluates the technical efficacy, scalability, and security of low-code RPA solutions in healthcare environments. Key contributions include a domain-specific methodology for identifying automation candidates, a modular orchestration model for RPA deployment, and performance metrics demonstrating improvements in task execution time, accuracy, and resource utilization. The study also addresses critical challenges, including interoperability with legacy healthcare information systems, data privacy (e.g., HIPAA compliance), and governance of autonomous processes.
This article presents an analytical synthesis of approaches to de-risking the digital transformation of healthcare organizations based on SAP S/4HANA through automated quality assurance. The study is conducted as a systematic analysis of peer-reviewed scientific sources and focuses on interpreting the quantitative effects of test automation in terms of change controllability rather than solely development efficiency. Particular attention is paid to measurable indicators of release speed, test coverage, system recovery resilience, and behavioral predictability under frequent updates, as well as to their role in forming managed quality control loops. It is shown that the key factor of transformation sustainability is the extent to which critical quality-assurance risk areas—related to test authoring, maintenance, execution, and result formalization—are addressed, rather than the mere presence of automated test scripts. It is established that, in the healthcare context, the applicability of automation is determined by its integration into the computer system validation lifecycle and change-control processes that ensure requirements traceability and test reproducibility. The analysis demonstrates that the highest level of sustainability is achieved in models where test automation is embedded within a formalized quality-management framework and used as a mechanism for preventing risks associated with digital change. The article is of interest to researchers in digital health, enterprise information system architects, quality and validation specialists, and practitioners involved in transforming SAP landscapes in regulated environments.
Pranay Raj Kanakala· Universal Library of Innovat...· 0 citations
Healthcare prescription automation is increasingly dependent on interoperable clinical data exchange, cloud-native service orchestration, secure application programming interfaces, and dependable software delivery pipelines. However, current prescription automation platforms often treat clinical decision support, e-prescribing workflow execution, software reliability engineering, cybersecurity governance, and deployment optimization as separate concerns. This separation creates architectural fragmentation in environments where prescription requests must be clinically valid, auditable, resilient to distributed failure, compliant with privacy obligations, and scalable under fluctuating enterprise workloads. This paper proposes a conceptual AI-driven cloud-native microservices framework for secure healthcare prescription automation, software reliability, and scalable deployment optimization. The framework integrates modular prescription services, AI-assisted clinical and operational intelligence, policy-driven security controls, observability-centered reliability engineering, and container-based deployment automation into a unified architecture. The major contribution of this paper is not an empirical claim of clinical superiority, but a structured reference model for designing prescription automation platforms that can support high-assurance workflows in regulated healthcare environments. The study identifies practical gaps in existing approaches, including a weak linkage between prescription standards and runtime reliability, insufficient integration of defect prediction with development pipelines, limited explainability in AI-assisted prescription decisions, and fragmented governance across APIs, containers, and machine learning components. The paper further presents a comparative methodology, architectural layers, implementation considerations, expected analytical outcomes, risk limitations, and future research directions. The proposed framework can guide healthcare enterprises, cloud architects, software reliability engineers, and AI governance teams in designing secure, scalable, and auditable prescription automation systems without binding the architecture to a single vendor or proprietary platform.
Srikanth Reddy Gudi· International Journal of Eme...· 0 citations
Enterprise and healthcare software systems demand rigorous, compliance-aware testing across both API and user interface layers. Conventional test automation toolchains treat API and UI testing as separate disciplines, forcing organizations to maintain parallel frameworks, duplicate test logic, and reconcile compliance evidence from disparate sources. This fragmentation imposes compounding overhead in regulated environments governed by HIPAA, HL7 FHIR R4, and FDA 21 CFR Part 11. The cost of maintaining disjoint toolchains is particularly acute in healthcare information technology, where test failures may carry patient safety implications and every audit trail gap constitutes a regulatory risk. This paper introduces ClinQA, a large language model orchestrated framework that unifies API and UI test generation through a single compliance-aware pipeline. ClinQA uses a structured prompt schema with five components, which it sends to Claude, an instruction-following language model. It then routes the generated test artifacts to platform-specific adapter modules for Playwright-based UI execution and consumer-driven contract testing for API validation. A four-layer architecture separates intent capture, test generation, execution, and compliance reporting into independently maintainable components. The framework embeds PHI masking, FHIR conformance evidence capture, and 21 CFR Part 11 audit trail emission at the infrastructure level rather than delegating compliance responsibility to individual test authors. Preliminary observations from 12 scenarios spanning Salesforce CRM workflows and FHIR R4 conformance testing indicate a Cross-Layer Coverage Index of 87%, a Test Authoring Effort Reduction of 68% compared to manual authoring, and a Compliance Coverage Score of 94% against active compliance profiles. ClinQA demonstrates that unified LLM-orchestrated test generation is a feasible approach for regulated enterprise and healthcare environments, and it establishes a foundation for controlled empirical validation at production scale.
Pratik Dinkar Rane· International journal of com...· 0 citations
The design of healthcare facilities must comply with applicable regulatory requirements, which significantly influence functional, spatial, and performance aspects of healthcare buildings. However, compliance checking is still predominantly conducted through manual procedures, leading to time-consuming workflows and a high risk of errors. In this context, Computational Compliance Checking (CCC) offers the potential to enhance the efficiency and reliability of regulatory verification processes. The adoption of Building Information Modelling (BIM), supported by the open standard Industry Foundation Classes (IFC), enables the formalisation of Information Delivery Specifications (IDS) as machine-interpretable rules for model validation. Additionally, the buildingSMART Data Dictionary (bSDD) contributes to the semantic structuring and classification of information requirements. This study explores how the IDS standard can enhance CCC processes by supporting the automated verification of regulatory requirements. Complementary solutions based on Python are proposed to address cases where IDS alone proves insufficient. A methodology for the semi-automated compliance checking of healthcare regulatory requirements is developed and applied to a case study based on regional regulations in the Veneto Region (Italy). Tested on the inpatient ward of a hospital BIM model, the procedure processed all 35 accreditation requirements, 91.4% through IDS and 8.6% through complementary Python routines, revealing several design non-conformities.
Giorgia Marcellino, C. Zanchetta, Michele Berlato et al.· Buildings· 0 citations
The rapid adoption of large language models (LLMs) has accelerated the use of conversational agents in digital health. However, in regulated and safety-critical environments, challenges related to trust, governance, and controlled integration with clinical information systems continue to limit their practical deployment. In particular, reliance on model-level behavior alone is insufficient to ensure operational safety and compliance.This paper proposes a modular architectural pattern for governed orchestration of LLM-based conversational agents in healthcare. The approach integrates (i) explicit separation between institutional knowledge, structured clinical data, and session-level context, (ii) a configurable governance layer that decouples prompts, model parameters, and operational constraints from workflow implementation, and (iii) controlled interaction with clinical systems through rule-based, tool-restricted operations.The architecture was implemented using a low-code orchestration platform and evaluated in a test healthcare environment through controlled scenario-based testing. The evaluation included repeated execution of operational scenarios, inspection of workflow logs, and analysis of system behavior. Observations indicate consistent enforcement of architectural constraints, absence of unsupported data generation in tested scenarios, and response latency ranging from 5 to 10 seconds depending on the type of request.The results suggest that embedding governance and control mechanisms at the architectural level can improve transparency, traceability, and operational reliability of conversational agents in healthcare. The proposed approach highlights the importance of system-level design in supporting trustworthy AI beyond model-centric optimization.
José Trajano Mendes, Francisco Milton Mendes, Cláudia Leite Rolim Moreira· International Conference on...· 0 citations
Machine Learning Operations (MLOps) practices have reached a notable level of maturity within general-purpose software engineering. Pipelines are standardized, monitoring is automated and deployment patterns are well rehearsed. Yet when these same practices are transferred into clinical environments, their adequacy becomes less certain. The healthcare domain introduces operational and ethical pressures that conventional MLOps frameworks were not originally designed to address.
Clinical AI systems operate under constraints that extend beyond typical production settings. Stringent data protection regimes including GDPR and the EU AI Act shape how data can be processed and retained. Model drift is not merely a statistical inconvenience; it may alter clinical decisions with tangible consequences for patient outcomes. Regulatory compliance demands comprehensive, tamper-evident audit trails. At the same time, decision-critical contexts impose a clear expectation of human oversight. In other words, technical robustness alone is insufficient; governance and accountability become integral system properties.
Daniel Spadacini· Journal of Advanced Artifici...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
