A new methodological framework for hazard detection models in health information technology systems
Not Available
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
SEARCH · Search NASA
Results for “information systems and technology”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid
The convergence of Information Technologies and Operational Technology systems in industrial networks presents many challenges related to availability, integrity, and confidentiality. In this paper, we evaluate the various cybersecurity risks in industrial control systems and how they may affect these areas of concern, with a particular focus on energy-sector Operational Technology systems. There are multiple threats and countermeasures that Operational Technology and Information Technology systems share. Since Information Technology cybersecurity is a relatively mature field, this paper emphasizes on threats with particular applicability to Operational Technology and their respective countermeasures. We identify regulations, standards, frameworks and typical system architectures associated with this domain. We review relevant challenges, threats, and countermeasures, as well as critical differences in priorities between Information and Operational Technology cybersecurity efforts and implications. These results are then examined against the recommended National Institute of Standards and Technology framework for gap analysis to provide a complete approach to energy sector cybersecurity. We provide analysis of countermeasure implementation to align with the continuous functions recommended for a sound cybersecurity framework.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Comparative Assessment of Data-driven Process Models in Health Information Technology
Process mining for conformance analysis consists of comparing a reference process model against a data-driven process model generated via log files from information technology systems. However, in the absence of a complete reference process model, we found no suggested approaches in the literature to address the need for evaluating process conformance among different healthcare facilities to assess standardization of care. Our goal is to find similarities and dissimilarities in data-driven process models among US Veterans Health Administration (VHA) facilities that can be indicative of patient safety issues. Our hypothesis was that the analysis would not produce statistically significant differences in outcome. We present a unique implementation of conformance analysis in process mining that consists of combining process mining, process mapping and statistical metrics. We illustrate our approach by applying it to the analysis of two clinical radiology order process models generated from healthcare data provided by two similar facilities in the VHA. The comparative assessment showed that about 70% of the orders completed successfully and 30% were not completed due to policy and duplications. Our analysis found a good statistical correlation between both facilities, as the Spearman’s correlation coefficient between facilities for the frequency of cases per total hours was 0.87879, for the frequency of cases by state transition was 0.79702 and for the throughput time per state transition was 0.63582. Additional statistical analyses using the Mann-Whitney U test and the root mean square error both produced values that were not significant. The foregoing approach validated our hypothesis by demonstrating a good statistical correlation of data describing the flow of clinical radiology orders absent a credible reference model. Finding good agreement between both facilities was important in confirming that the clinical orders flow in a similar manner, suggesting standardization of care.
97 MATHEMATICS AND COMPUTING↗
Procedure for locating oil and gas wells in the Appalachian Basin
Locating undocumented (or poorly documented) oil and gas wells for environmental assessment is often difficult. Remnant features that confirm the presence of a well (intact casing/wellhead, well bore, etc.) are typically less than a meter in size and often are obscured from direct observation on the ground or from the air (by dense vegetation, for example). To efficiently find such features, it is useful to first systematically compile publicly available digital data at progressively smaller scales prior to embarking on field campaigns. Further, the information presented here describes the procedure developed and used by the U.S. Department of Energy's National Energy Technology Laboratory to locate potential oil and gas well sites for follow-up field verification and characterization. Digital data are first compiled from national and state resources such as well location/production databases, historical topographic maps, historical aerial photographs, and LiDAR data. Although each data set is likely to be incomplete or inaccurate to some extent, combining the data resources using geographic information system technology can generate potential well site targets with a higher degree of confidence, which improves the efficiency of fieldwork activities. This workflow was developed in the Appalachian Basin region, and although certain aspects may be unique, the general process would be applicable to locating undocumented wells in other regions.
54 ENVIRONMENTAL SCIENCES↗
Evaluation of Data Catalog Software for Hanford Site Environmental Datasets
Environmental information and data underpin achievement of the U.S. Department of Energy (DOE) Office of Environmental Management (EM) mission at the Hanford Site. The Hanford Environmental Data Management (HEDM) Program is the DOE Richland Operations Office (RL) approach to develop and implement a formal program for managing environmental data and the associated records, materials, and systems at the Hanford Site. The current project, contract, organization, and contractor-specific efforts at managing environmental data sets are insufficient to provide orderly, long-term, site-wide access. A vital element to be created within the HEDM program plan is a catalog of data sources, called the Hanford Environmental Information and Data Index (HEIDI), that will enable long-term access and retrievability for the multiple independent sources of data that might otherwise be difficult to discover. This report compares leading open source and commercial data catalog platforms using criteria to assess the functionality needed to develop the HEIDI catalog of Hanford data sources that connects and exchanges data with established Hanford Local Area Network (HLAN) enterprise information technology systems. Proprietary platforms evaluated included ArcGIS Enterprise Sites, Junar, OpenDataSoft, and Socrata, and non-proprietary platforms included Energy Data eXchange (EDX), Comprehensive Knowledge Archive Network (CKAN), and DKAN (a Drupal-based open data portal based on CKAN). Capabilities supporting data discoverability, retrieval, and archival, as well as metadata standard requirements and integration into the HLAN were rated as either failing to meet requirements (F), meeting requirements (M), or exceeding requirements by delivering additional desired features (E). The lowest rating for any capability area was assigned as the overall rating for the platform. These findings enable DOE-RL and the contractors implementing the HEDM plan to focus on candidate tools likely to meet the requirements for implementing HEIDI. All of the platforms receiving an overall rating of ‘F’ were unable to be deployed on Hanford infrastructure or within dedicated cloud resources. A propriety software-as-a-service (SaaS) model of delivering a data catalog (e.g., found in software such as Junar and OpenDataSoft) favors consistency across customers at the expense of customization and configurable roles that are needed for Hanford work. Hosting data on a shared commercial platform places limits on dataset size (maximum of 240 Mb for OpenDataSoft), a significant limitation for HEIDI implementation. EDX, a government data catalog based on CKAN, received the ‘F’ rating due to an inability to incorporate authentication from HLAN into the system. Among platforms rated ‘M’ or ‘E’, only the Socrata platform had a SaaS delivery model. In contrast to other SaaS platforms, Socrata provided custom roles and gateways that allow local datasets to be incorporated into an online catalog. Socrata also complies with the Federal Risk and Authorization Management Program, a significant benefit for cloud-based management of Hanford data. The other platforms rated ‘M’ or ‘E’, ArcGIS Enterprise Sites, CKAN, and DKAN, provide fully self-hosted options, allowing for greater control and flexibility with the HEIDI catalog. These widely used tools have supportive communities of practice, extensive customization options, and demonstrated deployments that provide evidence that they can meet requirements, often deliver additional desired features, and work well with federal government systems. Completely customized alternatives built on a collection of applications were not evaluated because achieving similar performance to CKAN or DKAN requires substantial resources, especially in the absence of the active communities that have grown to support these tools. ArcGIS Enterprise Sites, Socrata, CKAN, and DKAN were evaluated as strong candidates for successful implementation with HEIDI.
54 ENVIRONMENTAL SCIENCES↗
Towards a Comparative Assessment of Data-Driven Process Models in Health Information Technology
Process mining for conformance analysis focuses on comparing a reference process model against a data-driven process model that is generated via log files from information technology systems. While this approach is helpful when there is an existing process model in an organization, it leaves the question of what to do in the absence of a complete reference process model unanswered. In this paper, we present a comparative assessment approach that combines process mining, process mapping for dimensionality reduction, and statistical analysis. Our goal is to find similarities and dissimilarities in data-driven process models among U.S. Veterans Health Administration (VHA) facilities to assess process conformance among different healthcare facilities, which can help assess the standardization of care. We illustrate our approach by applying it to two clinical radiology order process models generated by two similar facilities. Our results demonstrate statistical similarities in the standardization of care among those two facilities.
Klasky, Hilda↗
New Industry Partnerships: Solar Energy Technologies Office Support for Early Projects in the Energy Systems Integration Facility: An Agreement Closeout Summary Report
The U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (EERE) Solar Energy Technologies Office (SETO)-funded New Industry Partnerships (NIPs) agreement established multiple new cooperative research projects with industry partners. Each project demonstrated the use of the National Renewable Energy Laboratory's Energy Systems Integration Facility (ESIF) as a national asset for research and development, testing, and validation of new technologies to support high penetrations of solar energy on the electric grid. The agreement was launched in 2013 and represented the SETO portion of the DOE EERE-wide Integrated Network Testbed for Energy Grid Research and Technology Experimentation (INTEGRATE) program. The focus of INTEGRATE was on multi-energy system testing and on testing the interactions of energy systems with information technology, communications, and telecommunications (ICT) systems. The NIPs agreement was a bit different from traditional research projects because it was specifically structured to enable partnerships for ESIF testing and as such required at least a 1:1 funds-in cost share from industry partners. In the end, the project engaged six different industry partnerships and resulted in testing a large number of innovative technologies. These included tiny inverters; power-to-gas technologies; and advanced simulation, analysis, and power-hardware-in-the-loop testing techniques. The agreement also provided significant impact for both research and industrial communities. This summary report provides a brief, high-level overview of these projects and their highlights along with lists of citations and other impacts. Readers are referred to the corresponding project reports for more in-depth information.
14 SOLAR ENERGY↗
Augmenting LLM-Based Agents for Improved Performance in Pentesting and Commissioning Operational Technology in Critical Infrastructure
Artificial intelligence (AI), and more specifically large language models (LLMs) have the potential for use in penetration testing (“pentesting”) against devices, networks, and computer systems in information technology (IT). We explore the possibility of extending pentesting from IT systems to operational technology (OT) systems, which are more obscure than IT systems in their protocols and design. A challenge therefore exists when applying pretrained LLMs to OT systems as corpora are likely to underrepresent OT systems in comparison to other more prevalent systems. We evaluate augmentations of LLMs with various methods, especially retrieval augmented generation (RAG), to improve performance of the LLMs in the OT domain. In addition to pentesting, some of the testing of these OT devices may include commissioning to ensure that the newly installed devices work correctly. Our framework may also be applied in such cases.
97 MATHEMATICS AND COMPUTING↗
Impacts of Modern IT on LCLS-II Experiment Control Systems
Modern information technology (IT) is a broad term with vague and somewhat arbitrary boundaries which can depend on any given novice or subject matter expert’s notional understanding. For the purposes of this article, IT can be understood as standards in communication, computational power, and storage. Additionally, impacts of modern IT extend beyond its products of hardware and software to include associated work practices and engineering processes.
97 MATHEMATICS AND COMPUTING↗
Quantum for Energy Systems and Technologies
Quantum Information Science (QIS) is expected to profoundly change the practice of science and engineering in the coming decades. QIS technology exploits quantum phenomena for performing tasks that are impossible to do today and is a rapidly progressing field, fueled by large investments from the private sector and governments. Its importance to the U.S. economy and national security is underscored by the National Quantum Initiative Act (NQIA) passed in December 2018, which creates a coordinated multiagency program to support research and training in QIS. After the NIQA signed into law, NETL has launched an initiative to apply QIS to problems encountered in energy technology development. In Jan. 2020, an Quantum for Energy Systems & Technologies (QUEST) working group was formed to establish a workforce capable of developing, reviewing, managing, and advising on QIS-related technologies for NETL and FECM. Since then, the QUEST team have been working on developing quantum sensing technology and performing quantum computing to solve energy-related problems. This poster summarized the QUEST team’s activities & accomplishments on QIS targeting energy-related applications.
Paudel, Hari P.↗
Quantum for Energy Systems and Technologies
Quantum information science (QIS) is creating a transformative opportunity to improve processes for energy production, distribution, and consumption. In this presentation, we will show the works that are currently pursued into two major directions, (i) quantum sensing and (ii) quantum computing is undergoing in order to find applications in the areas of carbon captures, critical minerals and gas sensing applications.
Paudel, Hari P.↗
ORNL Package Testing Program Software Quality Assurance Plan
The Oak Ridge National Laboratory (ORNL) Package Testing Program (PTP) uses commercial off-the-shelf (COTS) software in performing data collection of thermal test results for package designs that contain radioactive materials. Specifically, this software is used to collect temperature data from the furnace, packages, and ambient air to prepare and execute the thermal test specified in 10 CFR 71.73, “Thermal Test.” This software quality assurance (SQA) plan sets forth the guidelines, standards, and procedures that shall be used to provide SQA for PTP software applications. This is a living document that will be maintained for the lifecycle of the PTP program. The SQA plan follows the requirements set forth in ORNL Standards Based Management System (SBMS): Information Technology; Subject Area: Software Quality Assurance. When applicable to the requirements as described in ORNL SBMS, Software Quality Assurance, the software shall be listed in the ORNL Software Registration System (SRS). Exemptions to this SBMS are COTS and firmware that are not modified; spreadsheet applications and personal productivity tools that do not have a utility or safety application, research applications, legacy software, system software, vendor-supplied software used to interface with the vendor’s services, software used within the organization to facilitate processing or management of information, and software developed for applications not specific to the US Department of Energy (DOE).
97 MATHEMATICS AND COMPUTING↗
Evolution and Trends of Industrial Control System Cyber Incidents since 2017
The industrial control systems (ICSs) that manage our critical infrastructure are increasingly converging with corporate networks and the Internet as technology and businesses prioritize digital connectivity. These connections make them more vulnerable and available to malicious cyber actors who traditionally targeted the companies’ more public-facing information technology (IT) networks. This paper will review select publicly reported cyber incidents to highlight the continued and growing threat to ICS devices and operational technology (OT) environments. It will summarize the incident and when available, will provide information on the cyber actors, the vulnerabilities they exploited, and any publications the U.S. Government (USG) provided in response. Data belonging to the Department of Homeland Security (DHS) will be used to highlight quantitative trends concerning ICS incidents. This paper builds on “History of Industrial Control System Cyber Incidents” (Hemsley & Fisher 2018), a paper that highlighted select noteworthy threats and incidents to ICS systems up to 2017. This paper will similarly review select incidents occurring after the last previously reviewed incident, Triton/HatMan, December 2017, and will note ICS incident trends including IT/OT convergence and advances in cyber-threat actors’ capabilities in observed in the examined incidents.
99 GENERAL AND MISCELLANEOUS↗
Anomaly Detection in Electronic Health Records Across Hospital Networks: Integrating Machine Learning With Graph Algorithms
In a large hospital system, a network of hospitals relies on electronic health records (EHRs) to make informed decisions regarding their patients in various clinical domains. Consequently, the dependability of the health information technology (HIT) systems responsible for collecting EHR data is of utmost importance for patient safety. Recently, novel methods and tools aimed at identifying anomalies in EHR data to bolster the reliability of HIT systems have been introduced. However, these existing methods and tools primarily concentrate on individual hospitals, which limits our understanding of system-wide anomalous events and their potential impact on patient safety across multiple hospitals. In this article, we introduce a new approach to detecting anomalies in EHR data within a network of hospitals. This is achieved by combining advanced machine learning techniques with graph algorithms to create a tool capable of swiftly identifying and responding to deviations. Our proposed approach employs a combination of five machine learning models, harnessing the unique strengths of each model to provide a more robust detection system. The detected anomalies are then represented as graphs, allowing us to recognize patterns across the hospital network. This aids in identifying anomalies that span multiple medical facilities, potentially indicating broader system-level risks. Extensive real-world testing of our approach demonstrated its ability to offer actionable insights compared to existing methods. Additionally, its scalable design ensures seamless integration into existing HIT infrastructures.
Niu, Haoran [Oak Ridge National Laboratory (ORNL),↗
Cyber-Physical Tabletop Exercise for Small Modular Reactor Facilities
U.S. nuclear power facilities face increasing challenges in meeting dynamic security requirements caused by evolving and expanding threats while keeping costs reasonable to make nuclear energy competitive. This evolving threat landscape includes adversaries having offensive cyber capabilities to attack information technology (IT) systems and operation technology (OT) systems. These adversaries may have the ability to attack the physical protection system (PPS) networks with potential consequential impacts that could degrade the effectiveness of the PPS. These cyber attacks may also be used to attack the safety and operational systems used to operate and ensure the safety of the reactor. Additionally, adversaries may gain access to unmanned aerial systems (UAS) that may be used to provide reconnaissance and surveillance of the facility, provide information to the adversaries, and be equipped with kinetic capabilities such as explosives or weapons that can be used to directly attack the facility. The Department of Energy’s Office of Nuclear Energy’s Advanced Reactor Safeguards and Security (ARSS) program funded Sandia National Laboratories (SNL) and Idaho National Laboratory (INL) to develop a cyber-physical tabletop exercise (TTX). This exercise was conducted on a hypothetical small modular reactor (SMR) facility, and only considered a potential adversary cyber attack on the PPS to a physical attack on the hypothetical facility to achieve a radiological release. This cyber-physical TTX is meant to provide lessons learned to integrate the cyber security system design and the physical protection system (PPS) design to decrease design, operation, and maintenance costs as well as increase effectiveness for defending against design basis threat attacks at the facility. This TTX will also provide a framework and method for SMR and microreactor vendors to conduct their own cyber-physical TTX and gain impactful insights to improving the cyber and physical protection system design for their SMR or microreactor facility design.
42 ENGINEERING↗
Detecting anomalous sequences in electronic health records using higher-order tensor networks
Detecting anomalous sequences is an integral part of building and protecting modern large-scale health information technology (HIT) systems. These HIT systems generate a large volume of records of patients’ state and significant events, which provide a valuable resource to help improve clinical decisions, patient care processes, and other issues. However, detecting anomalous sequences in electronic health records (EHR) remains a challenge in healthcare applications for several reasons, including imbalances in the data, complexity of relationships between events in the sequence, and the curse of dimensionality. Conventional anomaly detection methods use the finite sequence of events to discriminate sequences. They fail to incorporate salient event details under variable higher-order dependencies (e.g., duration between events) that can provide better discrimination of sequences in their models. To address this problem, we propose event sequence and subsequence anomaly detection algorithms that (1) use network-based representations of interactions in the data, (2) account for variable higher-order dependencies in the data, and (3) incorporate events duration for adequate discrimination of the data. The proposed approach identifies anomalies by monitoring the change in the graph after the test sequence is removed from the network. The change is quantified using graph distance metrics so that dramatic changes in the network can be attributed to the removed sequence. Furthermore, the proposed subsequence algorithm recommends plausible paths and salient information for the detected anomalous subsequences. Our results show that the proposed event sequence anomaly detection algorithm outperforms the baseline methods for both synthetic data and real-world EHR data.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
Use of Event-Time Embeddings via RNN to Discern Novel Event Sequences in EHRs
In highly configurable health information technology (HIT) systems, such as VistA of the Veterans Health Administration, the variations in how the system is used among different healthcare facilities and how the data are recorded can be significant. Despite the successful standardization of care efforts, some of these variations can be indicative of HIT hazards and demand further investigation. In this work, we implemented a recurrent neural network (RNN) architecture to learn clinical provider order sequences and their temporal dynamics while predicting the orders' terminal state. We demonstrate model performance and provide a use case for the model discerning novel event sequences. This model is proposed to find novel event sequences in an operational environment.
Ozmen, Ozgur↗
Interactive Web Application for Traffic Simulation Data Management and Visualization
As traffic simulation software becomes more effective for realistically simulating and analyzing traffic dynamics and vehicle interactions on the mesoscopic and microscopic level, the management, dissemination, and collaborative visualization of traffic simulation results produced by individual transportation planners presents a significant challenge. Existing online content management systems have a very limited capability in allowing users to query specific traffic simulation scenarios and geospatially visualize simulation results through shareable and interactive web interfaces. This paper presents a web-based application for promoting the archiving, sharing, and visualization of large-scale traffic simulation outputs. The application is developed to enhance cyber-physical controls, communications, and public education for collaborative transportation planning. Unique features of the web application include: (a) allowing users to upload their new traffic simulation scenarios (parameters and outputs), as well as search existing scenarios using easily accessible interfaces; (b) optimizing simulation output files with heterogeneous data formats and projected coordinate systems for web-based storage and management using a scalable and searchable data/metadata standard; (c) standardizing user-uploaded simulation outputs using web interfaces and data processing libraries with parallel computing capacity; and (d) providing shareable web visual interfaces for visualizing the traffic flow and signal information stored in simulation outputs (e.g., regional traffic patterns and individual vehicle interactions) and visually comparing multiple simulation outputs both spatially and temporally. Furthermore, the paper presents the conceptual design and implementation of this application, and demonstrates the application’s performance for sharing, comparing, and visualizing simulation outputs from VISSIM and SUMO, two commonly used traffic simulation software programs.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗