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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Real-Time Automated Hazard Detection Framework for Health Information Technology Systems

An increase in the reliability of Health Information Technology (HIT) will facilitate institutional trust and credibility of the systems. Here, we present an end-to-end framework for improving the reliability and performance of HIT systems. Specifically, we describe the system model, present some of the methods that drive the model, and discuss an initial implementation of two of the proposed methods using data from the Veterans Affairs HIT and Corporate Data Warehouse systems. The contributions of this work, thus, include (1) the design of a system model for monitoring and detecting hazards in HIT systems, (2) a data-driven approach for analysing the health care data warehouse, (3) analytical methods for characterising and analysing failures in HIT systems, and (4) a tool architecture for generating and reporting hazards in HIT systems. Our goal is to work towards an automated system that will help identify opportunities for improvements in HIT systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Real-Time Automated Health Information Technology Hazard Detection

The objective of this project is to develop an automated surveillance tool that will help the US Department of Veterans Affairs (VA) to detect potential hazards in health information technology (HIT) systems. To achieve this objective, Oak Ridge National Laboratory (ORNL) researchers have developed computational methods and a surveillance tool that will detect safety concerns and identify opportunities to improve HIT and safety within the VA system using Corporate Data Warehouse (CDW) data. This work is limited to the safe operation and safe use of HIT. It does not address the use of HIT to make health care safer. This project leveraged ORNL expertise in computational sciences and its computing resources accordingly.This report documents the FY 2019 accomplishments on this project. There were three major tasks. The first task developed data extraction approaches, which helped provide a better overall understanding of the important data elements in CDW. The outputs of this task served as inputs into the second task, which is the hazard detection and characterization task. This second task involved development of three methods that can be categorized into probabilistic model, event-based hazards detection, and system-based reliability performance evaluation. The third task dealt with the design, development, and demonstration of the automated surveillance tool. The tool currently resides in ORNL’s Private Health Information enclave at Oak Ridge, Tennessee, and operates on the VA’s CDW data. The developed capabilities and tool have been tested on CDW data from different clinical domains including Consult, Radiology, Laboratory, and Medication.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Advanced Health Information Technology Analytic Framework and Application to Hazard Detection

Health Information Technology (HIT) aims to improve healthcare outcomes by organizing and analyzing various health-related data. With data accumulating at a staggering rate, the importance of real-time analytics has been increasing dramatically, shifting the focus of informatics from batch processing to streaming analytics. HIT is also facing unprecedented challenges in adapting to this new requirement and leveraging advanced IT technologies. This paper introduces a HIT data and compute platform that supports multi-granularity real-time analytics from heterogeneous data sources. The paper first identifies functional requirements and proposes a framework that satisfies the requirements using state-of-the-art big data technologies including Apache Kafka, Spark Structured Streaming Engine, and Delta Lake. To demonstrate its capability to support data analytics in multiple time granularities analytics, a statistical process control-based hazard detection algorithm has been implemented on top of the framework to detect unexpected hazards from order cancellation data of the Department of US Veterans Affairs (VA) in near real-time.

Kumar, Mohit↗
DOE OSTI2020

Feature Engineering and Process Mining to Enable Hazard Detection in Health Information Technology

Motivation for this work originates from a recognized need for developing data-driven approaches through analysis of complex electronic health records. Specifically, we aim to enhance the reliability of health information technology (HIT) systems by detection of plausible HIT hazards in clinical order transactions. In the absence of well-defined event logs in corporate data warehouses, our proposed approach identifies relevant timestamped data fields that could indicate transactions in the clinical order life cycle generating raw event sequences. Subsequently, we adopt state transitions of the OASIS Human Task standard to map the raw event sequences and simplify the complex process that clinical radiology orders go through. We describe how the current approach provides the potential to investigate areas of improvement and potential hazards in HIT systems. The discussion concludes with a use case and opportunities for future applications.

Ozmen, Ozgur↗
DOE OSTI2024

Weather radar utility in hazard detection and response

Publicly accessible weather radar data have significant capabilities for meteorological measurements and predictions and, further, have the potential to measure nonmeteorological events that include smoke, ash, and debris plumes as well as explosions. The ability to identify and track nonmeteorological events can be of assistance in emergency response, hazard mitigation, and related activities in locations where radar coverage both exists and is recorded and accessible to the user. Here, in this study, events from multiple locations in the United States that are reported in news outlets are assessed using a manual inspection process of Level 2 weather radar data to identify anthropogenic and nonbiological returns. Explosive events are also identified, and a large high-altitude debris cloud from the intentional destruction of the SpaceX Starship is tracked across a wide area. Finally, future efforts using a machine learning model are discussed as a means of automating the process and potentially enabling near-real-time nonmeteorological event identification in the same areas where the data are accessible. Using weather radar data can be a valuable new tool for Department of Defense systems to aid in military awareness, and for interagency emergency response and forensic mission experts to consider national weather service data in their mission profiles. Radar data can be effective in detecting several common types of emergencies and inform and aid response personnel.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

ICAPA-HD: Hazard Detection Methods for Improving Overdose Prevention

The Oak Ridge National Laboratory (ORNL) research team in this project has focused on developing methods for improving overdose prevention by addressing three unresolved issues, divided into two groups, that could impact interventions: Understanding events with bad outcomes, and understanding events with better outcomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Hazard Detection Detector Cards

This report presents a comprehensive summary of five advanced anomaly detection tools developed and deployed by Oak Ridge National Laboratory in support of the VA’s Health Information Technology modernization. These detectors—Order Path Tracker, Trend Watcher, Pain Pointer, Performance Monitor, and Patient Record Flag Detector—leverage statistical and machine learning methods to monitor workflow disruptions, detect anomalies in care sequences and volumes, identify bottlenecks, and track system-level performance metrics across VistA and Millennium systems. All detectors have been integrated into the Health Data Analytics Platform (HDAP), with most having completed deployment and testing using live data from targeted stations in cardiology and oncology domains. This work enhances VA’s capacity for proactive system surveillance, promotes patient safety, and informs data-driven operational improvements across the EHR ecosystem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Hazardous Gas Detection Sensor Using Broadband Light-Emitting Diode-Based Absorption Spectroscopy for Space Applications

As space travel matures and extended duration voyages become increasingly common, it will be necessary to include arrays of early fire detection systems aboard spacefaring vessels, space habitats, and in spacesuits. As gasses that are relevant to combustion and pyrolysis have absorption features in the midinfrared range, it is possible to utilize absorption spectroscopy as a means of detecting and quantifying the concentration of these hazardous compounds. Within this work, a sensor for detecting carbon dioxide has been designed and tested autonomously on a high-altitude balloon flight. The sensor utilizes a 4.2-mm lightemitting diode source, amplitude modulation to characterize species concentrations, and frequency modulation to characterize ambient temperature. Future work will include expanding the sensor design to detect other gases, and demonstrating suborbital flight capability.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Detection of hazardous driving using machine learning

An autonomous driving system could create or exacerbate a hazardous driving situation due to incorrect machine learning, algorithm design, sensor limitations, environmental conditions or other factors. This technology presents solutions that use machine learning to detect when the autonomous driving system is in this state e.g., erratic or reckless driving and other behavior, in order to take remedial action to prevent a hazard such as a collision.

Shirvani, Philip↗
DOE OSTI2019

Crowdsourced reliable labeling of safety-rule violations on images of complex construction scenes for advanced vision-based workplace safety

Construction workplace hazard detection requires engineers to analyze scenes manually against many safety rules, which is time-consuming, labor-intensive, and error-prone. Computer vision algorithms are yet to achieve reliable discrimination of anomalous and benign object relations underpinning safety violation detections. Recently developed deep learning-based computer vision algorithms need tens of thousands of images, including labels of the safety rules violated, in order to train deep-learning networks for acquiring spatiotemporal reasoning capacity in complex workplaces. Such training processes need human experts to label images and indicate whether the relationship between the worker, resource, and equipment in the scenes violate spatiotemporal arrangement rules for safe and productive operations. False alarms in those manual labels (labeling no-violation images as having violations) can significantly mislead the machine learning process and result in computer vision models that produce inaccurate hazard detections. Compared with false alarms, another type of mislabels, false negatives (labeling images having violations as “no violations”), seem to have fewer impacts on the reliability of the trained computer vision models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Topic modeling to discern irregular order patterns in unlabeled electronic health records

This paper presents an application of topic modeling on event sequences of Electronic Health Record (EHR) orders. Metaphorically, we approach clinical order event sequences of unlabeled data as if they are documents where words are the events that occurred in the history of an order. We demonstrate the approach leveraging Consult orders data. The details of the data preprocessing and the data structures are described along with the data sources. Latent Dirichlet Allocation (LDA) is leveraged to fit against the limited datasets prepared. Another open source tool – LDAvis is used for exploratory analysis of the LDA results. The preliminary results revealed some order patterns that are qualitatively evaluated as potential irregular transitions. The goal of this analysis is to provide unsupervised learning application to domain experts in the absence of labeled data where they can investigate captured patterns and identify irregular transitions of orders. Ultimately, such efforts will guide formalization of hazard detection algorithms that monitor EHR data to identify health information technology related hazards.

Ozmen, Ozgur↗
DOE OSTI2020

Adaptive Anomaly Detection for Dynamic Clinical Event Sequences

Over the past decade, health information technology (IT) has enabled the amount of digital information stored in electronic health records (EHRs) to expand greatly. However, according to some studies, hazards in health IT can lead to changes in clinical decisions, care processes, and care outcomes, as well as other issues. Thus, the effects of health IT hazards on patient safety have been at the forefront of recent patient safety research. Nonetheless, hazard detection in health IT remains a challenge. In this paper, the authors assume that safety-related issues in health IT would exhibit anomalous characteristics in EHR data. Although all hazards will exhibit some anomalous characteristics, not all anomalies can be regarded as hazards. The authors hypothesize that errors in health IT could lead to interruptions in the sequence of clinical actions. To this end, the problem of detecting anomalous sequences in big EHR data is considered. This paper focuses on dynamic event sequences, which are a series of clinical actions in motion. The authors propose an adaptive anomaly detection approach that uses higher-order network representation to detect anomalous sequences. Furthermore, the authors propose a contiguous subsequence anomaly detection approach that identifies abnormal subsequences in the detected anomalous sequences. The proposed approaches are tested by using synthetic and real-world EHR data. The proposed methods outperform existing state of the art anomaly detection techniques. To reduce the computational complexity associated with the operational implementation of the proposed approaches, the Apache Spark environment was leveraged, and a much shorter run time together with improved performance were achieved, especially for data with more than 60,000 sequences.

Niu, Haoran↗
DOE OSTI2022

Volcano infrasound: progress and future directions

Over the past two decades (2000–2020), volcano infrasound (acoustic waves with frequencies less than 20 Hz propagating in the atmosphere) has evolved from an area of academic research to a useful monitoring tool. As a result, infrasound is routinely used by volcano observatories around the world to detect, locate, and characterize volcanic activity. It is particularly useful in confirming subaerial activity and monitoring remote eruptions, and it has shown promise in forecasting paroxysmal activity at open-vent systems. Fundamental research on volcano infrasound is providing substantial new insights on eruption dynamics and volcanic processes and will continue to do so over the next decade. The increased availability of infrasound sensors will expand observations of varied eruption styles, and the associated increase in data volume will make machine learning workflows more feasible. More sophisticated modeling will be applied to examine infrasound source and propagation effects from local to global distances, leading to improved infrasound-derived estimates of eruption properties. Future work will use infrasound to detect, locate, and characterize moving flows, such as pyroclastic density currents, lahars, rockfalls, lava flows, and avalanches. Infrasound observations will be further integrated with other data streams, such as seismic, ground- and satellite-based thermal and visual imagery, geodetic, lightning, and gas data. The volcano infrasound community should continue efforts to make data and codes accessible and to improve diversity, equity, and inclusion in the field. In summary, the next decade of volcano infrasound research will continue to advance our understanding of complex volcano processes through increased data availability, sensor technologies, enhanced modeling capabilities, and novel data analysis methods that will improve hazard detection and mitigation.

58 GEOSCIENCES↗
DOE OSTI2018

Overall and cause‐specific mortality in the Sentinel system: A power analysis

Abstract Purpose Mortality data within the Sentinel Death Tables remain generally uncharacterized. Assessment of mortality data within Sentinel will help inform its utility for medical product safety studies. Methods To determine if Sentinel contains sufficient all‐cause and cause‐specific mortality events to power postmarketing safety studies. We calculated crude rates of all‐cause mortality and suicide and proportional mortality from suicide from 2004 to 2012 in seven Sentinel data partners. Results were stratified by data partner, sex, age group, and calendar year and compared with national estimates from Centers for Disease Control and Prevention Wide‐ranging ONline Data for Epidemiologic Research. We performed sample size estimations for all‐cause mortality and 10 leading causes of death. Results We observed 479 694 deaths, including 5811 suicides, during 68 million person‐years of follow‐up. Pooled mean death and suicide rates in the data partners were 710 and 8.6 per 100 000 person‐years, respectively (vs 810 and 11.8 nationally). The mean proportional mortality from suicide among the data partners was 1.2%, compared with 1.5% nationally. National trends of decreasing overall mortality and increasing proportional mortality for suicide were reflected within Sentinel. We estimated that detecting hazard ratios of 1.25 and 3 would require 16 442 and 460 exposed patients, respectively, for overall mortality, and 1.3 million and 37 411, respectively, for suicide. Conclusions This was the first study to investigate mortality data in the Sentinel death tables. We found that all‐cause mortality appeared well powered for use as a safety outcome and cause‐specific mortality outcomes may be adequately powered in certain circumstances. Further investigation into the quality of the Sentinel death data is needed.

Swain, Richard S.↗
DOE OSTI2024

Mission planning for photogrammetry-based autonomous 3D Mapping of Dams using a commercial UAV

The application of autonomous unmanned aerial vehicles (UAVs) for conducting inspections of dams represents an innovative approach aimed at enhancing safety, efficiency, and cost-effectiveness. In this context, this paper presents algorithms for UAV mission design in autonomous dam inspections that include the creation of a 3D map based on photogrammetry. The algorithms were systematically developed to incorporate a comprehensive set of parameters that account for the geometric characteristics of the dam and adhere to photogrammetry specifications. To validate the proposed methodology, we utilized a commercial programmable quadrotor, specifically the Parrot Anafi USA Gov drone, which is equipped with high-quality cameras and can be programmed with the help of a software development kit (SDK) provided by the manufacturer. Our results demonstrate the efficacy of our method, highlighting how the generated maps can be used for hazard detection in the downstream slope of dams.

42 ENGINEERING↗
DOE OSTI2026

REFSafE: A RAG-Enabled Framework for Predictive Risk Analysis and Automated Safety Report Generation in Mission-Critical Environments

Operational safety in mission-critical environments requires AI systems that are accurate, interpretable, and resistant to hallucination. We present an agentic Retrieval-Augmented Generation (RAG) framework, REFSafe, for grounded hazard analysis and automated safety report generation. The system integrates Large Language Models (LLMs) with structured operational data, historical incident repositories, policy documents, and external authoritative sources. Through iterative agentic reasoning, the framework retrieves, verifies, and synthesizes evidence prior to generation, enforcing citation-backed outputs with explicit source attribution (documents, links, and prior events) to ensure traceability and trust. To mitigate hallucinations and unsupported claims, all risk assessments and forecasts are constrained to retrieved evidence, with confidence signals derived from retrieval relevance and source consistency. A transparent pipeline enables subject matter experts (SMEs) to validate predictions, and provide structured feedback, forming a continuous performance calibration loop. Preliminary deployment demonstrates improved reliability in hazard detection and safety/vulnerability report generation. This work advances trustworthy, evidence-grounded AI for predictive safety intelligence in mission-critical operations.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗
DOE OSTI2025

Remote Sensing Improves Multi‐Hazard Flooding and Extreme Heat Detection by Fivefold Over Current Estimates

The co‐occurrence of multiple hazards is of growing concern globally as the frequency and magnitude of extreme climate events increases. Despite studies examining the spatial distribution of such events, there has been little work in examining if all relevant life threatening and damaging hazards are captured in existing hazard databases and by common hazard metrics. For example, local/regional flash flooding events are seldom captured by optical satellite instruments and are subsequently excluded from global hazard databases. Similarly, the heat hazard definitions most frequently used in multi‐hazard studies inherently fail to capture events that are life‐threatening but climatologically within an expected range. Our goal is to determine the potential for increasing multi‐hazard event detection capabilities by inferring additional hazard footprints from widely accessible satellite data. We use daily precipitation and temperature satellite data to develop an open‐source framework that infers additional hazard footprints that are not included in traditional methods. With the state of Texas as our study area, we detected 2.5 times as many flood hazards, equivalent to $320 million in property and crop damages. Furthermore, our expanded heat hazard definition increases the impacted area by 56.6%, equivalent to 91.5 million km 2 over an 18 year period. Increasing hazard detection capabilities and expanding existing definitions of hazards using daily satellite data increases the temporal and spatial resolutions at which multi‐hazard events are detected. Having more complete data sets of all relevant hazard extents improves our ability to track global trends and more accurately determine the magnitude of hazard exposure inequities.

equity↗