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Papers by Altrix Technologies

From Silos to Synergy: Designing the Next Frontier of AI-Native Healthcare Systems, 2025

The growing intersection of artificial intelligence and Electronic Health Records (EHRs) signals ... more The growing intersection of artificial intelligence and Electronic Health Records (EHRs) signals a shift from digitizing healthcare data to building intelligent, adaptive systems that augment clinical workflows, administrative processes, and patient engagement. This paper explores the emergence of “AI-native” healthcare operating systems, in which agentic AI architectures, real-time data orchestration, and natural language interfaces replace siloed, add-on solutions. We examine how interoperability frameworks can evolve from rigid, standards-based approaches toward unified platforms that link autonomous documentation, billing, care coordination, and patient-engagement tools. By comparing incremental “horizontal” deployments of specialized AI modules to fully “vertical” integration within a complete suite, the analysis underscores that the most transformative impact emerges when these modules operate cohesively under a shared data backbone. Early adopters of AI scribes, autonomous billing engines, and interoperable data hubs are already demonstrating improvements in clinician efficiency and care delivery; yet scalability requires a single ecosystem where each AI agent continuously enriches the others. Looking ahead, the commoditization of large language models and AI agents will both reduce development costs and heighten demands for interoperability, data security, and ethical oversight. Organizations that harness this momentum – by adopting or building vertically integrated, AI-first EHR frameworks – will likely drive the next leap forward in value-based care, personalized medicine, and predictive analytics.

Ambient AI in Nursing Workflows: From Model Development to Workflow Implementation, 2024

Introduction: Nursing is increasingly burdened by excessive but necessary administrative tasks, r... more Introduction: Nursing is increasingly burdened by excessive but necessary administrative tasks, reducing time available for direct patient care. This white paper explores the potential of ambient AI to alleviate these burdens through technology designed to integrate seamlessly into nursing workflows.

Ambient AI in Healthcare: Ambient AI refers to background technology that helps streamline healthcare processes, such as documentation, without disrupting clinical workflows. Developing a nursing-specific ambient AI involves choosing either an off-the-shelf or proprietary AI model, adding agent capabilities, fine-tuning it using relevant data, and validating its performance through internal testing, prospective validation, and compliance with regulatory standards.

EHR Infrastructure and Interoperability: Integrating ambient AI requires compatibility with electronic health record (EHR) systems. Enhanced interoperability between EHRs using technologies like FHIR APIs allows smoother data exchange, enabling AI to provide insights directly within clinical workflows.

AI Procurement Process: Implementing AI solutions in healthcare involves a structured procurement process, including budget cycle considerations, a bidding process, compliance checks, pilot programs, and contract negotiations to ensure that AI solutions meet clinical, regulatory, and operational needs.

Workflow Implementation: Successful implementation of AI technology in nursing requires prospective validation, careful integration into existing workflows, ongoing monitoring, and continuous updates. This process ensures that the solution is adopted effectively, supports clinical needs, and evolves over time.

Future of Nursing Ambient AI: The future of ambient AI in nursing is promising, focusing on reducing the documentation burden, improving clinical workflows, and advancing patient-centered care. However, only the most effective and well-implemented solutions will be able to meet the evolving challenges of healthcare delivery.

An Observational Study on Nursing Workflows: Task Distribution and Burden Analysis , 2024

Background & Objective: Nurses play an important role in healthcare delivery but face workflow bu... more Background & Objective: Nurses play an important role in healthcare delivery but face workflow burdens that impact job satisfaction and patient care.1,2,3,7 Understanding these burdens is essential for developing interventions to improve nursing efficiency and well-being. The purpose of this study is to analyze the workflow of nurses, identify the most significant burdens they encounter and determine which aspects can be mitigated.

Methods: This observational study was conducted across general-medicine units at large health systems. Eight full-time registered nurses were observed over 56 sessions totaling 210 hours during 12-hour day shifts. A structured observation instrument was used to record time allocation across various activities, frequencies of tasks, and perceived burden ratings. Data were analyzed to identify tasks consuming the most time and those rated as most burdensome by nurses.

Results: Nurses spent a significant portion of their time on documentation, with an average of 186 minutes per 12-hour shift dedicated to electronic health record (EHR) charting and review. Documentation was rated as the most burdensome task by all participating nurses. Delegable tasks, such as vital sign monitoring and patient positioning, accounted for 24 minutes per 12-hour shift and were also rated in burden due to staffing shortages. Indirect medication tasks and frequent communication interruptions further added to workload stress. The study identified key areas where technology could alleviate these burdens, such as streamlining documentation processes, enhancing task delegation, improving medication management systems, and optimizing communication channels.

Conclusions: The study highlights workflow burdens faced by nurses that can be mitigated through targeted technological interventions. Addressing these issues is important for improving nurse satisfaction, enhancing patient care, and ensuring sustainable healthcare delivery.

From Silos to Synergy: Designing the Next Frontier of AI-Native Healthcare Systems, 2025

The growing intersection of artificial intelligence and Electronic Health Records (EHRs) signals ... more The growing intersection of artificial intelligence and Electronic Health Records (EHRs) signals a shift from digitizing healthcare data to building intelligent, adaptive systems that augment clinical workflows, administrative processes, and patient engagement. This paper explores the emergence of “AI-native” healthcare operating systems, in which agentic AI architectures, real-time data orchestration, and natural language interfaces replace siloed, add-on solutions. We examine how interoperability frameworks can evolve from rigid, standards-based approaches toward unified platforms that link autonomous documentation, billing, care coordination, and patient-engagement tools. By comparing incremental “horizontal” deployments of specialized AI modules to fully “vertical” integration within a complete suite, the analysis underscores that the most transformative impact emerges when these modules operate cohesively under a shared data backbone. Early adopters of AI scribes, autonomous billing engines, and interoperable data hubs are already demonstrating improvements in clinician efficiency and care delivery; yet scalability requires a single ecosystem where each AI agent continuously enriches the others. Looking ahead, the commoditization of large language models and AI agents will both reduce development costs and heighten demands for interoperability, data security, and ethical oversight. Organizations that harness this momentum – by adopting or building vertically integrated, AI-first EHR frameworks – will likely drive the next leap forward in value-based care, personalized medicine, and predictive analytics.

Ambient AI in Nursing Workflows: From Model Development to Workflow Implementation, 2024

Introduction: Nursing is increasingly burdened by excessive but necessary administrative tasks, r... more Introduction: Nursing is increasingly burdened by excessive but necessary administrative tasks, reducing time available for direct patient care. This white paper explores the potential of ambient AI to alleviate these burdens through technology designed to integrate seamlessly into nursing workflows.

Ambient AI in Healthcare: Ambient AI refers to background technology that helps streamline healthcare processes, such as documentation, without disrupting clinical workflows. Developing a nursing-specific ambient AI involves choosing either an off-the-shelf or proprietary AI model, adding agent capabilities, fine-tuning it using relevant data, and validating its performance through internal testing, prospective validation, and compliance with regulatory standards.

EHR Infrastructure and Interoperability: Integrating ambient AI requires compatibility with electronic health record (EHR) systems. Enhanced interoperability between EHRs using technologies like FHIR APIs allows smoother data exchange, enabling AI to provide insights directly within clinical workflows.

AI Procurement Process: Implementing AI solutions in healthcare involves a structured procurement process, including budget cycle considerations, a bidding process, compliance checks, pilot programs, and contract negotiations to ensure that AI solutions meet clinical, regulatory, and operational needs.

Workflow Implementation: Successful implementation of AI technology in nursing requires prospective validation, careful integration into existing workflows, ongoing monitoring, and continuous updates. This process ensures that the solution is adopted effectively, supports clinical needs, and evolves over time.

Future of Nursing Ambient AI: The future of ambient AI in nursing is promising, focusing on reducing the documentation burden, improving clinical workflows, and advancing patient-centered care. However, only the most effective and well-implemented solutions will be able to meet the evolving challenges of healthcare delivery.

An Observational Study on Nursing Workflows: Task Distribution and Burden Analysis , 2024

Background & Objective: Nurses play an important role in healthcare delivery but face workflow bu... more Background & Objective: Nurses play an important role in healthcare delivery but face workflow burdens that impact job satisfaction and patient care.1,2,3,7 Understanding these burdens is essential for developing interventions to improve nursing efficiency and well-being. The purpose of this study is to analyze the workflow of nurses, identify the most significant burdens they encounter and determine which aspects can be mitigated.

Methods: This observational study was conducted across general-medicine units at large health systems. Eight full-time registered nurses were observed over 56 sessions totaling 210 hours during 12-hour day shifts. A structured observation instrument was used to record time allocation across various activities, frequencies of tasks, and perceived burden ratings. Data were analyzed to identify tasks consuming the most time and those rated as most burdensome by nurses.

Results: Nurses spent a significant portion of their time on documentation, with an average of 186 minutes per 12-hour shift dedicated to electronic health record (EHR) charting and review. Documentation was rated as the most burdensome task by all participating nurses. Delegable tasks, such as vital sign monitoring and patient positioning, accounted for 24 minutes per 12-hour shift and were also rated in burden due to staffing shortages. Indirect medication tasks and frequent communication interruptions further added to workload stress. The study identified key areas where technology could alleviate these burdens, such as streamlining documentation processes, enhancing task delegation, improving medication management systems, and optimizing communication channels.

Conclusions: The study highlights workflow burdens faced by nurses that can be mitigated through targeted technological interventions. Addressing these issues is important for improving nurse satisfaction, enhancing patient care, and ensuring sustainable healthcare delivery.