Managing macromolecular crystallographic data with a laboratory information management system.

Protein crystallography is an established method to study the atomic structures of macromolecules and their complexes. A prerequisite for successful structure determination is diffraction-quality crystals, which may require extensive optimization of both the protein and the conditions, and hence projects can stretch over an extended period, with multiple users being involved. The workflow from crystallization and crystal treatment to deposition and publication is well defined, and therefore an electronic laboratory information management system (LIMS) is well suited to management of the data. Completion of the project requires key information on all the steps being available and this information should also be made available according to the FAIR principles. As crystallized samples are typically shipped between facilities, a key feature to be captured in the LIMS is the exchange of metadata between the crystallization facility of the home laboratory and, for example, synchrotron facilities. On completion, structures are deposited in the Protein Data Bank (PDB) and the LIMS can include the PDB code in its database, completing the chain of custody from crystallization to structure deposition and publication. A LIMS designed for macromolecular crystallography, IceBear, is available as a standalone installation and as a hosted service, and the implementation of key features for the capture of metadata in IceBear is discussed as an example.

Smart Buildings: A Comprehensive Systematic Literature Review on Data-Driven Building Management Systems.

Buildings are complex structures composed of heterogeneous elements; these require building management systems (BMSs) to dynamically adapt them to occupants' needs and leverage building resources. The fast growth of information and communication technologies (ICTs) has transformed the BMS field into a multidisciplinary one. Consequently, this has caused several research papers on data-driven solutions to require examination and classification. This paper provides a broad overview of BMS by conducting a systematic literature review (SLR) summarizing current trends in this field. Unlike similar reviews, this SLR provides a rigorous methodology to review current research from a computer science perspective. Therefore, our goal is four-fold: (i) Identify the main topics in the field of building; (ii) Identify the recent data-driven methods; (iii) Understand the BMS's underlying computing architecture (iv) Understand the features of BMS that contribute to the smartization of buildings. The result synthesizes our findings and provides research directions for further research.

[Artificial intelligence in diagnostic radiology for dose management : Advances and perspectives using the example of computed tomography]. / Künstliche Intelligenz im Dosismanagement der diagnostischen Radiologie : Entwicklungen und Perspektiven am Beispiel der Computertomographie.

CLINICAL-METHODOLOGICAL PROBLEM: Imaging procedures employing ionizing radiation require compliance with European directives and national regulations in order to protect patients. Each exposure must be indicated, individually adapted, and documented. Unacceptable dose exceedances must be detected and reported. These tasks are time-consuming and require meticulous diligence. STANDARD RADIOLOGICAL METHODS: Computed tomography (CT) is the most important contributor to medical radiation exposure. Optimizing the patient's dose is therefore mandatory. Use of modern technology and reconstruction algorithms already reduces exposure. Checking the indication, planning, and performing the examination are further important process steps with regard to radiation protection. Patient exposure is usually monitored by dose management systems (DMS). In special cases, a risk assessment is required by calculating the organ doses. METHODOLOGICAL INNOVATIONS: Artificial intelligence (AI)-assisted techniques are increasingly used in various steps of the process: they support examination planning, improve patient positioning, and enable automated scan length adjustments. They also provide real-time estimates of individual organ doses. EVALUATION: The integration of AI into medical imaging is proving successful in terms of dose optimization in various areas of the radiological workflow, from reconstruction to examination planning and performing exams. However, the use of AI in conjunction with DMS has not yet been considered on a large scale. PRACTICAL RECOMMENDATION: AI processes offer promising tools to support dose management. However, their implementation in the clinical setting requires further research, extensive validation, and continuous monitoring.

Environmental reporting in Italian thermal power plants: insights from a comprehensive analysis of EMAS environmental statements.

The global energy sector heavily relies on fossil fuels, significantly contributing to climate change. The ambitious European emissions' reduction targets require sustainable processes and alternatives. This study presents a comprehensive analysis of 73 Italian thermal power plants registered to the European Eco-Management and Audit Scheme (EMAS) aimed at assessing EMAS effectiveness in addressing and quantifying the environmental impacts of this relevant industrial sector. The analysis was based on EMAS environmental statements, publicly disclosing verified and certified data, with the secondary objective of evaluating if EMAS could be an efficient tool to improve the plants' environmental performances. An inventory of technical and environmental aspects, adopted indicators, and allocated budgets was based on 2023 data. A strong correlation was found between the significance of the environmental aspects and the number of adopted indicators. Gaps were observed in describing aspects like "biodiversity" and "local issues". Improvement objectives and budget allocation showed discrepancies and lacked correlation with the significance of the related environmental aspects. "Energy production" accounted for 68% of the total allocated budget; "environmental risks", "emissions to air", "electricity consumption", and "local issues" were also key focus areas. Insufficient information on emission control technologies and progress tracking of improvement objectives was detected. This study highlights the need for thermal power installations to improve the selection of appropriate indicators and to better relate allocated budget to improvement objectives when implementing EMAS. Such measures would facilitate the quantification of the effective environmental impacts of the energy production sector, supporting future research on this topic, allowing stakeholders a better comparison among plants, and driving industry-wide improvements.

Dynamic soil properties for soil health (DSP4SH) database 1.0 - Phase 1 and 2 datasets.

The dynamic soil properties for soil health (DSP4SH) is a Science of Soil Health Initiative that was designed to collect, process, and publicize scientifically rigorous datasets that inform sound indicators and interpretations. The Soil and Plant Science Division of the United States Department of Agriculture - Natural Resources Conservation Service (USDA-NRCS) and university cooperators collected a suite of standardized soil health metrics across eight states (Oregon, Washington, Kansas, Minnesota, Illinois, Connecticut, North Carolina, and Texas) within five soil survey regions (Northwest, North Central, Northeast, Southeast, and South Central). The DSP4SH database provides a substantial dataset of soil health metrics assessed. The dataset is composed of dynamic soil properties (DSP) data collected from each management system or ecological state represented by one to three independent plot replicates. Each plot has a minimum of three pedons. Nine groups from the DSP4SH monitoring network provided datasets used in developing the database. The submitted data includes 37 laboratory measured parameters, 60 variables of layer/horizon descriptions, 41 variables for laboratory analysis conducted at the Kellogg Soil Survey laboratory, and 12 variables for the management systems. An additional 31 variables were developed for site or plot description. Additional variables were developed to normalize the dataset. In preparation for DSP assessment, all tables (except for dataset from KSSL lab) were categorized by management system or ecological state. The categories were business as usual (BAU), the reference condition (Ref) and the soil health management (SHM). The overarching goal of DSP4SH phase 1 and 2 dataset publication is to promote increased accessibility, further analysis of the data, and overall understanding of the benefits of surveying dynamic soil properties for soil health.

Taipei Medical University Clinical Research Database: a collaborative hospital EHR database aligned with international common data standards.

OBJECTIVE: The objective of this paper is to provide a comprehensive overview of the development and features of the Taipei Medical University Clinical Research Database (TMUCRD), a repository of real-world data (RWD) derived from electronic health records (EHRs) and other sources. METHODS: TMUCRD was developed by integrating EHRs from three affiliated hospitals, including Taipei Medical University Hospital, Wan-Fang Hospital and Shuang-Ho Hospital. The data cover over 15 years and include diverse patient care information. The database was converted to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) for standardisation. RESULTS: TMUCRD comprises 89 tables (eg, 29 tables for each hospital and 2 linked tables), including demographics, diagnoses, medications, procedures and measurements, among others. It encompasses data from more than 4.15 million patients with various medical records, spanning from the year 2004 to 2021. The dataset offers insights into disease prevalence, medication usage, laboratory tests and patient characteristics. DISCUSSION: TMUCRD stands out due to its unique advantages, including diverse data types, comprehensive patient information, linked mortality and cancer registry data, regular updates and a swift application process. Its compatibility with the OMOP CDM enhances its usability and interoperability. CONCLUSION: TMUCRD serves as a valuable resource for researchers and scholars interested in leveraging RWD for clinical research. Its availability and integration of diverse healthcare data contribute to a collaborative and data-driven approach to advancing medical knowledge and practice.

Leveraging Decentralized Clinical Trial Management Systems (dCTMS) to Advance Science: Exploring Challenges Related to the Diffusion of Innovation and Its Execution.

Decentralized clinical trials (DCTs) recently gained attention in research necessary for drug development. While the COVID-19 pandemic proved to be a challenging time in this arena, drug development was a critical area of emphasis in the rapid advancement of vaccines. The DCTs were necessary to allow research activities to occur across many locations. The use of DCTs can profoundly impact reshaping healthcare by enabling participants to partake in clinical trials remotely; however, implementation challenges must be considered as technology expands. A working group of participants was assembled during an interactive learning exercise at the Conv2X conference (2023) to explore challenges related to the diffusion of innovation among key stakeholders. Pain points experienced with using and implementing technologies were identified, and an innovative solution using a blockchain-anchored option was presented. Participants were divided into three stakeholder groups: patients, payers, and pharmaceutical sponsors. After a time of discussion, the groups reconvened for review. Several themes that can be supported by blockchain technology emerged. These include enhanced efficiencies, patient experience, and demographic diversity, as well as data integrity, privacy, security, and cost-effectiveness. Future research might focus on strategies to facilitate the adoption of the idea across key stakeholder groups.

Identifying and costing common gaps in Central and West Africa pharmaceutical regulation.

Background: Regulatory systems strengthening is crucial for catalyzing access to safe and effective medical products and health technologies (MPHT) for all. Identifying and addressing common regulatory gaps through regional approaches could be instrumental for the newly incepted African Medicine Agency. Aims: This original study sheds light on common gaps among 10 national regulatory authorities (NRAs) and ways to address them regionally. Objectives: The study used NRA self-assessment outcomes to identify common gaps in four critical regulatory pillars and estimate the cost of addressing them from regional perspectives that aimed at raising the maturity level of regulatory institutions. Methods: A cross-sectional study, using the WHO Global Benchmarking Tool (GBT), was conducted between 2020 and 2021 with five NRAs from ECCAS and ECOWAS member states that use French and Spanish as lingua franca. Results: The 10 NRAs operated in a non-formal-to-reactive approach (ML1-2), which hinders their ability to ensure the quality of MPHT and respond appropriately to public health emergencies. Common gaps were identified in four critical regulatory pillars-good regulatory practices, preparedness for public health emergencies, quality management systems, and substandard and falsified medical products-with overall cost to address gaps estimated at US$3.3 million. Contribution: We elaborated a reproducible method to strengthen regulatory systems at a regional level to improve equitable access to assured-quality MPHT. Our bottom-up approach could be utilized by RECs to address common gaps through common efforts.

Managing Risk and Quality of AI in Healthcare: Are Hospitals Ready for Implementation?

Artificial intelligence (AI) provides a unique opportunity to help meet the demands of the future healthcare system. However, hospitals may not be well equipped to handle safe and effective development and/or procurement of AI systems. Furthermore, upcoming regulations such as the EU AI Act may enforce the need to establish new management systems, quality assurance and control mechanisms, novel to healthcare organizations. This paper discusses challenges in AI implementation, particularly potential gaps in current management systems (MS), by reviewing the harmonized standard for AI MS, ISO 42001, as part of a gap analysis of a tertiary acute hospital with ongoing AI activities. Examination of the industry agnostic ISO 42001 reveals a technical debt within healthcare, aligning with previous research on digitalization and AI implementation. To successfully implement AI with quality assurance in mind, emphasis should be put on the foundation and structure of the healthcare organizations, including both workforce and data infrastructure.

Optimizing home energy management: Robust and efficient solutions powered by attention networks.

This paper explores the integration of attention networks in the realm of home energy management systems (HEMS) to enhance the robustness and efficiency of energy consumption optimization. With the growing demand for smart grid technologies, the need to achieve demand side response becomes paramount. The proposed solution leverages attention networks to dynamically allocate significance to various aspects of energy consumption patterns, considering the diverse load types and dynamic loading scenarios present in households. In this investigation, we focus on the AMpds2 dataset, characterized by intricate loading patterns, and assess its performance across various time series forecasting methodologies, including (RNN), (LSTM), (TCN), and transformers. Multiple methodologies undergo performance evaluation using diverse hyperparameter combinations. Evaluation metrics, specifically (RMSE) and (MAE), are employed. Advanced optimizers such as (Adam) and (Adamax) are applied, and activation functions, including sigmoid, linear, tanh, and ReLU, are implemented. A comprehensive performance analysis involves 16 hyperparameter combinations across four distinct time series models. Through meticulous scrutiny, it is determined that the utilization of transformers in forecasting energy and load patterns results in a 4% increase in accuracy, as elucidated in the results section. The implementation of this study is carried out on the Python 3.2 platform, and the matplotlib library is employed to visualize the comparison between actual and predicted data.

If you build it, will they come? Exploring the success factors of knowledge management systems in the Malaysian public sector.

The current study investigates the factors that influence the success of knowledge management systems in the public sector. This study integrates the DeLone and McLean Model with critical organizational factors. The model has been tested on the data collected from 158 employees in the public sector in Malaysia, the study found that knowledge content quality has a higher significant impact on the use of knowledge management systems than system quality. Perceived usefulness also has a greater impact than user satisfaction in determining the system's overall success. Among the organizational factors, leadership is the most significant determinant of success. However, the culture of sharing, perceived trust, and incentives do not significantly influence the use of knowledge management systems. The findings suggest that public sector organizations should focus on both system and organizational factors to implement successful knowledge management systems.

Improving Anesthesia Start Time Documentation Through a Departmental Education Initiative at Yale New Haven Hospital, New Haven, United States.

Background Reimbursement for anesthetic services in the United States utilizes a formula that incorporates procedural and patient factors with total anesthesia time. According to the Centers for Medicare & Medicaid Services and the American Society of Anesthesiologists, the period of billable time starts when the anesthesia practitioner assumes care of the patient and may include transport to the operating room from the preoperative holding area. In this report on a quality improvement effort, we implemented a departmental education initiative aimed at improving the accuracy of anesthesia start-time documentation. Methods Utilizing de-identified, internal data on surgical procedures at Yale New Haven Hospital (YNHH), New Haven, United States, the difference between documented anesthesia start and patient in-room time was determined for all cases. Those with a difference between 0-1 minute were assumed "likely underbilled," and the total revenue lost for these cases was estimated using a weighted average of institutional reimbursement per unit of time. A monthly, department-wide educational email was then introduced to inform practitioners about the guidelines around start-time documentation, and the percentage of "likely underbilled" cases and lost revenue estimates trended over a one-year period. Results Baseline data in December 2020 showed that of the 6,877 total surgical cases requiring anesthesia at YNHH, 55.1% (N=3,790) had an anesthesia start to in-room time of 0-1 minute, which were considered "likely underbilled." The average start-to-in-room time for properly recorded cases (44.9%, N=3,087) was 4.42 minutes. The baseline revenue lost in December 2020 for underbilled cases was estimated at $52,302. Over the one-year quality improvement initiative, the proportion of underbilled cases showed a downward trend, decreasing to 29.2% of total cases by November 2021. The estimate of revenue lost due to underbilling also showed a downward trend, decreasing to $29,300 in November 2021. Conclusion This quality improvement study demonstrated that a relatively simple, department-wide educational email sent monthly correlated with an improvement in anesthesia start-time documentation accuracy and a reduction in estimated revenue lost to underbilling over a one-year period.

Improving Patient Safety: Learning from Reported Hospital-Acquired Pressure Ulcers.

Introduction: A hospital-acquired pressure ulcer (HAPU) is a localized lesion or injury to the underlying tissue (wound) while the patient is on admission. It occurs when standardized nursing care is not correctly followed in the presence of friction and shear, leading to skin or underlying tissue breakdown. Unfortunately, inadequate knowledge of nurses to assess and provide standardized care for pressure ulcers or manage HAPUs results in patient harm. We aim to share lessons from a reported HAPU incident and address the knowledge gap in patient safety risk assessment, identification, and wound management at Nyaho Medical Centre (Accra, Ghana). Methods: A review of HAPU incidents was conducted using quality improvement tools such as cause-and-effect analyses to identify contributing factors and root causes. Subsequently, plan-do-study-act (PDSA) cycles were used to test interventions to improve pressure ulcer assessments and wound management. A run chart was used to analyze and evaluate the interventions over 12 weeks (Aug-Oct 2021). Results: Development of policies and a standard operating procedure for pressure ulcers and wounds improved accuracy in identifying pressure ulcer risks and management of wounds. Eighty-three patients were assessed with the pressure ulcer assessment tool. Complete (100%) adherence to the pressure ulcer and wound policy and standard operating procedure (SOP) was achieved, and the number of HAPUs decreased from five to one during the study period. Conclusion: This study demonstrated that the combined use of quality methods and tools can be suitable for improving processes and outcomes for patients at risk for HAPUs.

Advancing public health informatics during the COVID-19 pandemic: Lessons learned from a public-private partnership with pharmacies.

To support efforts to vaccinate millions of Americans across the United States (US) against COVID-19, the US federal government (USG) launched the Pharmacy Partnership for Long-Term Care Program (PPP) in December 2020 and the Federal Retail Pharmacy Program (FRPP) in February 2021. These programs consisted of a collaborative partnership with the USG and 21 pharmacy organizations, including large retail chains, coordinating pharmacy services administrative organizations (PSAOs) representing independent retail and long-term care pharmacies, and pharmacy network administrators. These pharmacy organizations represented over 46,000 providers and created a robust channel for far-reaching COVID-19 vaccination across 56 state and local jurisdictions. PPP reported more than 8 million COVID-19 doses administered to residents and staff in long-term care facilities (LTCFs) as of June 2021. In addition, FRPP was responsible for administering more than 304 million doses, accounting for approximately 49% of all COVID-19 doses administered as of June 2023. This unprecedented public-private partnership allowed USG to rapidly adapt, expand, and aim to provide equitable access to vaccines for adults and eligible-aged children during the COVID-19 pandemic. As the largest federal COVID-19 vaccination program, the FRPP exemplifies how public-private partnerships can expand access to immunizations during a public health emergency. End-to-end informatics support helped pharmacies meet critical national public health goals and served as convenient access points for sustained health services. This manuscript describes lessons learned regarding informatics coordination with participating pharmacy partners to support the rapid and safe administration of COVID-19 vaccines across the US. The processes of onboarding to CDC's complex data network, establishing connections to state and local immunization information systems (IIS), and monitoring the quality of data pharmacy partners submitted to the CDC Data Clearinghouse (DCH) in alignment with the COVID-19 Vaccine Reporting Specifications (CVRS) are highlighted.

Medical library systems and services: Bangladesh scenario.

This study highlights the present status of medical libraries in Bangladesh. It mainly focuses on the four key areas of medical libraries: Medical Library Services, Research4Life from Bangladesh perspective, Institutional Repository (IR), and Library Automation status. The research study also explores the various key challenges faced by the medical libraries in Bangladesh and finally provides necessary recommendations like a sufficient library budget, implementation of  an Integrated Library System (ILS), skilled manpower, more participation in information networking and resource sharing, sound ICT environment, online collection development for the overall development of medical libraries in Bangladesh.

Does integration with national registers improve the data completeness of local COVID-19 contact tracing tools? A register-based study in Norway, May 2020 - September 2021.

BACKGROUND: During the COVID-19 response in Norway, many municipalities used the Fiks contact tracing tool (FiksCT) to register positive individuals and follow-up contacts. This tool is based on DHIS2, an open source, web-based platform. In this study we examined if data completeness in FiksCT improved after integration with national registers between May 2020 and September 2021. METHODS: Data from municipalities using FiksCT was extracted from the Norwegian Emergency Preparedness Register for COVID-19 (Beredt C19). We linked FiksCT data to the Norwegian Surveillance System for Communicable Diseases (MSIS), the National Population Register (FREG), and the Norwegian Vaccine Registry (SYSVAK) using unique identification numbers (ID). Completeness for each variable linked with a national register was calculated before and after integration with these registers. RESULTS: Of the 125 municipalities using FiksCT, 87 (69.6%) agreed to share and upload their data to Beredt C19. Data completeness for positive individuals improved after integration with national registers. After integration with FREG, the proportion of missing values decreased from 12.5 to 1.6% for ID, from 4.5 to 0.9% for sex, and from 1.2 to 0.4% for date of birth. Missing values for vaccine type decreased from 63.0 to 15.2% and 39.3-36.7% for first and second dose, respectively. In addition, direct reporting from FiksCT to MSIS increased the proportion of complete records in MSIS (on the selected variables) from 68.6% before to 77.0% after integration. CONCLUSION: The completeness of local contact tracing data can be improved by enabling integration with established national registers. In addition, providing the option to submit local data to the national registers could ease workload and reduce the need to collect duplicate data.

Current practice and recommendations for managing transgender patient data in clinical laboratories in the United Kingdom and Republic of Ireland.

BACKGROUND: Transgender people may avoid seeking medical care due to previous negative experiences and fear of discrimination. Clinical laboratories can contribute to a poor patient experience and clinical outcome when the design and functionality of laboratory information management systems (LIMS) do not consider the needs of transgender patients. This survey aimed to capture current practices in United Kingdom and Republic of Ireland clinical laboratories concerning how transgender patient data and test requests are managed throughout the total testing process. METHODS: An anonymous survey was distributed to clinical laboratory professionals in November 2021. Thirty-three questions covered how gender variables are recorded for transgender patients and used to inform gender-specific calculations, test access, and reference intervals (RIs). RESULTS: Of the 66 respondents, 70% were based in laboratories in England, with a majority of laboratories having ISO 15189 accreditation and processing 1000-10,000 blood samples daily. Eighty-five percent stated that their LIMS had a single field recording sex or gender information. Forty-three percent did not limit test access based on gender, but 68% did not append RIs for patients with unknown or indeterminate gender. CONCLUSIONS: This survey was the first to quantify how clinical laboratories manage sex and gender information and report results for transgender and non-binary patients, and details several key recommendations based on the survey responses.

[Functional requirements of patient data management systems in intensive care medicine]. / Funktionelle Anforderungen an Patientendatenmanagementsysteme in der Intensivmedizin.

BACKGROUND: As part of the German government's digitization initiative, the paper-based documentation that is still present in many intensive care units is to be replaced by digital patient data management systems (PDMS). In order to simplify the implementation of such systems, standards for basic functionalities that should be part of basic configurations of PDMS would be of great value. PURPOSE: This paper describes functional requirements for PDMS in several categories. METHODS: Criteria for standardized data documentation were defined by the authors and derived functional requirements were classified into two priority categories. RESULTS: Overall, general technical requirements, functionalities for intensive care patient care, and additional functionalities for PDMS were defined and prioritized. DISCUSSION: Using this paper as a starting point for a discussion about basic functionalities of PDMS, it is planned to develop and obtain consensus on definitive standards with representatives from medical societies, medical informatics and PDMS manufacture.

Technical specifications of dose management systems: An international atomic energy agency survey.

PURPOSE: Dose management systems (DMS) have been introduced in radiological services to facilitate patient radiation dose management and optimization in medical imaging. The purpose of this study was to gather as much information as possible on the technical characteristics of DMS currently available, regarding features that may be considered essential for simply ensuring regulatory compliance or desirable to fully utilize the potential role of DMS in optimization of many aspects of radiological examinations. METHODS: A technical survey was carried out and all DMS developers currently available (both commercial and open source) were contacted and were asked to participate. An extensive questionnaire was prepared and uploaded in the IAEA International Research Integration System (IRIS) online platform which was used for data collection process. Most of the questions (93%) required a "Yes/No" answer, to facilitate an objective analysis of the survey results. Some free text questions and comments' slots were also included, to allow participants to give additional information and clarifications where necessary. Depending on the answer, they were considered either as "Yes" or "No." RESULTS: Given the way that the questions were posed, every positive response indicated that a feature was offered. Thus, the percentage of positive responses was used as a measure of adherence. The percentages of positive answers per section (and sub-section) are presented in graphs and limitations of this type of analysis are discussed in detail. CONCLUSIONS: The results of this survey clearly exhibit that large differences exist between the various DMS developers. Consequently, potential end users of a DMS should carefully determine which of the features available are essential for their needs, prioritize desirable features, but also consider their infrastructure, the level of support required and the budget available before selecting a DMS.