Machine learning modelling for predicting the utilization of invasive and non-invasive ventilation throughout the ICU duration.

The goal of this work is to develop a Machine Learning model to predict the need for both invasive and non-invasive mechanical ventilation in intensive care unit (ICU) patients. Using the Philips eICU Research Institute (ERI) database, 2.6 million ICU patient data from 2010 to 2019 were analyzed. This data was randomly split into training (63%), validation (27%), and test (10%) sets. Additionally, an external test set from a single hospital from the ERI database was employed to assess the model's generalizability. Model performance was determined by comparing the model probability predictions with the actual incidence of ventilation use, either invasive or non-invasive. The model demonstrated a prediction performance with an AUC of 0.921 for overall ventilation, 0.937 for invasive, and 0.827 for non-invasive. Factors such as high Glasgow Coma Scores, younger age, lower BMI, and lower PaCO2 were highlighted as indicators of a lower likelihood for the need for ventilation. The model can serve as a retrospective benchmarking tool for hospitals to assess ICU performance concerning mechanical ventilation necessity. It also enables analysis of ventilation strategy trends and risk-adjusted comparisons, with potential for future testing as a clinical decision tool for optimizing ICU ventilation management.

New directions for malaria vector control using geography and geospatial analysis.

As we strive towards the ambitious goal of malaria elimination, we must embrace integrated strategies and interventions. Like many diseases, malaria is heterogeneously distributed. This inherent spatial component means that geography and geospatial data is likely to have an important role in malaria control strategies. For instance, focussing interventions in areas where malaria risk is highest is likely to provide more cost-effective malaria control programmes. Equally, many malaria vector control strategies, particularly interventions like larval source management, would benefit from accurate maps of malaria vector habitats - sources of water that are used for malarial mosquito oviposition and larval development. In many landscapes, particularly in rural areas, the formation and persistence of these habitats is controlled by geographical factors, notably those related to hydrology. This is especially true for malaria vector species like Anopheles funestsus that show a preference for more permanent, often naturally occurring water sources like small rivers and spring-fed ponds. Previous work has embraced geographical concepts, techniques, and geospatial data for studying malaria risk and vector habitats. But there is much to be learnt if we are to fully exploit what the broader geographical discipline can offer in terms of operational malaria control, particularly in the face of a changing climate. This chapter outlines potential new directions related to several geographical concepts, data sources and analytical approaches, including terrain analysis, satellite imagery, drone technology and field-based observations. These directions are discussed within the context of designing new protocols and procedures that could be readily deployed within malaria control programmes, particularly those within sub-Saharan Africa, with a particular focus on experiences in the Kilombero Valley and the Zanzibar Archipelago, United Republic of Tanzania.

Surgeon perspectives on preference cards and environmental stewardship.

BACKGROUND: Refinement of surgical preference cards may reduce waste from surgery. This study aimed to characterize surgeon perceptions and practices regarding preference card maintenance, identify barriers to updating preference cards, and explore whether opinions on environmental stewardship relate to preference card maintenance. METHODS: This was a mixed methods survey performed at a single tertiary academic medical center. Surgeons completed questions on accuracy, frequency of updates, and perceived environmental impact of their preference cards. Responses were compared between early career and mid-to late-career surgeons using Kruskal-Wallis, chi-squared, and Fisher's exact tests. RESULTS: The response rate was 46.4% (n = 89/192). Among respondents, 46.1% (n = 41/89) rarely or never updated preference cards. Nearly all (98.9%, n = 87/88) said some of their cases had unused items on their cards. Most (87.6%, n = 78/89) made updates via verbal requests. Unfamiliar processes (83.7%, n = 72/86) and effort required (64.0%, n = 55/86) were viewed as barriers to card maintenance. Most agreed that more frequent updates would reduce waste (80.5%, n = 70/87), but respondents did not feel knowledgeable about the environmental impact of items on their cards (62.1%, n = 54/87). Mid-to late-career surgeons were less likely to update their cards annually or more often compared to early career surgeons (18.9%, n = 7/37 vs. 57.1%, n = 24/42, p < 0.001). No other responses varied significantly between early career and mid-to late-career surgeons. CONCLUSIONS: Surgeons acknowledged the utility of preference card maintenance in environmental stewardship, but unfamiliar systems and perceived effort hindered preference card review. Greater attention to preference card maintenance would promote environmentally sustainable practices in surgery.

Benefits and challenges of geographic information systems (GIS) for data-driven outreach in black communities experiencing overdose disparities: results of a stakeholder focus group.

BACKGROUND: Black individuals in the U.S. face increasing racial disparities in drug overdose related to social determinants of health, including place-based features. Mobile outreach efforts work to mitigate social determinants by servicing geographic areas with low drug treatment and overdose prevention access but are often limited by convenience-based targets. Geographic information systems (GIS) are often used to characterize and visualize the overdose crisis and could be translated to community to guide mobile outreach services. The current study examines the initial acceptability and appropriateness of GIS to facilitate data-driven outreach for reducing overdose inequities facing Black individuals. METHODS: We convened a focus group of stakeholders (N = 8) in leadership roles at organizations conducting mobile outreach in predominantly Black neighborhoods of St. Louis, MO. Organizations represented provided adult mental health and substance use treatment or harm reduction services. Participants were prompted to discuss current outreach strategies and provided feedback on preliminary GIS-derived maps displaying regional overdose epidemiology. A reflexive approach to thematic analysis was used to extract themes. RESULTS: Four themes were identified that contextualize the acceptability and utility of an overdose visualization tool to mobile service providers in Black communities. They were: 1) importance of considering broader community context; 2) potential for awareness, engagement, and community collaboration; 3) ensuring data relevance to the affected community; and 4) data manipulation and validity concerns. CONCLUSIONS: There are several perceived benefits of using GIS to map overdose among mobile providers serving Black communities that are overburdened by the overdose crisis but under resourced. Perceived potential benefits included informing location-based targets for services as well as improving awareness of the overdose crisis and facilitating collaboration, advocacy, and resource allocation. However, as GIS-enabled visualization of drug overdose grows in science, public health, and community settings, stakeholders must consider concerns undermining community trust and benefits, particularly for Black communities facing historical inequities and ongoing disparities.

Spatial dependence of non-traumatic out-of-hospital cardiac arrest in a Swiss region: A retrospective analysis.

Background: Out-of-hospital cardiac arrest (OHCA) incidence and survival often vary within regions according to patient-related and contextual factors. This study aims to establish the overall spatial dependence of incidence, bystander cardiopulmonary resuscitation (BCPR) and 48-h survival of OHCA with their associated demographic and socioeconomic characteristics in a Swiss region. Methods: We conducted a retrospective study using data of all OHCAs recorded between 2007 and 2019 in the canton of Vaud and, more specifically, in the Lausanne area. Provision of BCPR and 48-h survival were analysed using Getis-Ord Gi statistics and OHCA incidence by local Moran's I with empirical Bayes standardised rates. Demographic and socioeconomic characteristics were compared between incidence clusters generated by local Moran's I method. Results: Significant spatial variations of OHCA incidence, BCPR and 48-h mortality were observed. Although BCPR was statistically more likely in rural areas, 48-h survival was improved in a few main cities. At the cantonal level, postcode areas with a higher incidence of OHCAs were less densely inhabited with lower salary levels, more Swiss citizens, and an older population. At city level, small area variations were detected within urban neighbourhoods. The more affected hectares with more OHCAs were less inhabited, with a better median salary, more Swiss citizens, and off-centre. Conclusions: Spatial variations associated with demographic and socioeconomic factors were observed for OHCA incidence and survival, with sparsely populated areas particularly at risk. These data suggest an unmet need for targeted prevention interventions and structural modifications of the existing prehospital system at the cantonal level.

Distributed management of patient data-sharing informed consents for clinical research.

BACKGROUND: The consent protocol is now a critical part in the overall orchestration of clinical research. We aimed to demonstrate the feasibility of an Ethereum-based informed consent system, which includes an immutable and automated channel of consent matching, to simultaneously assure patient privacy and increase the efficiency of researchers' data access. METHOD: We simulated a multi-site scenario, each assigned 10000 consent records. A consent record contained one patient's data-sharing preference with regards to seven data categories. We developed a blockchain-based infrastructure with a smart contract to record consents on-chain, and to query consenting patients corresponding to specific criteria. We measured our system's recording efficiency against a baseline design and verified accuracy by testing an exhaustive list of possible queries. RESULTS: Our method achieved ∼3-4% lead with an average insertion speed of ∼2 s per record per node on either a 3-, 4- or 5-node network, and 100 % accuracy. It also outperformed other solutions in external validation. DISCUSSION: The speed we achieved is reasonable in a real-world system under the realistic assumption that patients may not change their minds too frequently, with the added benefit of immutability. Furthermore, the per-insertion time did improve slightly as the number of network nodes increased, attesting to the benefit of node parallelism as it suggests no attrition of insertion efficiency due to scale of nodes. CONCLUSIONS: Our work confirms the technical feasibility of a blockchain-based consent mechanism, assuring patients with an immutable audit trail, and providing researchers with an efficient way to reach their cohorts.

Relationship Between Primary Care Physician Capacity and Usual Source of Care.

BACKGROUND: The NASEM Primary Care Report and Primary Care scorecard highlighted the importance of primary care physician (PCP) capacity and having a usual source of care (USC). However, research has found that PCP capacity and USC do not always correlate. This exploratory study compares geographic patterns and the characteristics of counties with similar rates of PCP capacity but varying rates of USC. METHODS: Our county-level, cross-sectional approach includes estimates from the Robert Graham Center and data from the Robert Wood Johnson County Health Rankings (CHR). We utilized conditional mapping methods to first identify US counties with the highest rates of social deprivation (SDI). Next, counties were stratified based on primary care physician (PCP) capacity and usual source of care (USC) terciles, allowing us to identify 4 types of counties: (1) High-Low (high PCP capacity, low USC); (2) High-High (high PCP capacity, high USC); (3) Low-High (low PCP capacity, high USC); and (4) Low-Low (low PCP capacity, low USC). We use t test to explore differences in the characteristics of counties with similar rates of primary care capacity. RESULTS: The results show clear geographic patterns: High-High counties are located primarily in the northern and northeastern US; High-Low counties are located primarily in the southwestern and southern US. Low-High counties are concentrated in the Appalachian and Great Lakes regions; Low-Low counties are concentrated in the southeastern US and Texas. Descriptive results reveal that rates of racial and ethnic minorities, the uninsured, and social deprivation are highest in counties with low rates of USC for both high PCP and low PCP areas. CONCLUSIONS: Recognizing PCP shortages and improving rates of USC are key strategies for increasing access to high-quality, primary care. Targeting strategies by geographic region will allow for tailored models to improve access to and continuity of primary care. For example, we found that many of the counties with the lowest rates of USC are found in non-Medicaid expansion states (Texas, Georgia, and Florida) with high rates of uninsured populations, suggesting that expanding Medicaid and improving access to health insurance are key strategies for increasing USC in these states.

Pediatric Sedation Assessment and Management System (PSAMS) for Pediatric Sedation in China: Development and Implementation Report.

Background: Recently, the growing demand for pediatric sedation services outside the operating room has imposed a heavy burden on pediatric centers in China. There is an urgent need to develop a novel system for improved sedation services. Objective: This study aimed to develop and implement a computerized system, the Pediatric Sedation Assessment and Management System (PSAMS), to streamline pediatric sedation services at a major children's hospital in Southwest China. Methods: PSAMS was designed to reflect the actual workflow of pediatric sedation. It consists of 3 main components: server-hosted software; client applications on tablets and computers; and specialized devices like gun-type scanners, desktop label printers, and pulse oximeters. With the participation of a multidisciplinary team, PSAMS was developed and refined during its application in the sedation process. This study analyzed data from the first 2 years after the system's deployment. Unlabelled: From January 2020 to December 2021, a total of 127,325 sedations were performed on 85,281 patients using the PSAMS database. Besides basic variables imported from Hospital Information Systems (HIS), the PSAMS database currently contains 33 additional variables that capture comprehensive information from presedation assessment to postprocedural recovery. The recorded data from PSAMS indicates a one-time sedation success rate of 97.1% (50,752/52,282) in 2020 and 97.5% (73,184/75,043) in 2021. The observed adverse events rate was 3.5% (95% CI 3.4%-3.7%) in 2020 and 2.8% (95% CI 2.7%-2.9%) in 2021. Conclusions: PSAMS streamlined the entire sedation workflow, reduced the burden of data collection, and laid a foundation for future cooperation of multiple pediatric health care centers.

An integration of expectation confirmation model and information systems success model to explore the factors affecting the continuous intention to utilise virtual classrooms.

Virtual classrooms have recently gained significant consideration in educational institutes and universities due to their potential to encourage and support students' learning activities. Although recent research has focused extensively on online learning, virtual classrooms and the factors affecting their continuous use have garnered little attention, especially in Arab Gulf countries such as Saudi Arabia. Thus, this study integrates the expectation confirmation model and the information systems success model to assess the factors affecting students' continuous intention to utilise virtual classrooms in higher education. We examined the effects of information quality, service quality, system quality, confirmation, perceived usefulness, and satisfaction on the continuous intention to utilise virtual classrooms. Data were collected from 441 students and analysed using structural equation modelling "SEM". SEM is a powerful multivariate approach used increasingly in empirical investigation for evaluating and testing casual relationships. The results revealed that the proposed model demonstrated high explanatory power in explaining students' continuous intention to utilise virtual classrooms (R2 = 0. 86). Additionally, information quality had a significant effect on confirmation and an insignificant effect on perceived usefulness. System quality affected perceived usefulness and confirmation. Contrary to our expectations, service quality had a significant negative effect on perceived usefulness and confirmation. Additionally, perceived usefulness and confirmation affected students' satisfaction with using virtual classrooms, and satisfaction affected students' continuous intention to utilise virtual classrooms. This study contributes to the literature by offering a holistic integrated model that increases the understanding of the factors influencing students' continuous intention to utilise virtual classrooms, hence aiding in increasing their utilisation. Furthermore, it provides practical implications for enhancing students' continuous intention to utilise virtual classrooms. Virtual classroom developers must focus on improving the system quality of virtual classrooms. According to our results, higher system quality led the students to perceive virtual classrooms as useful and confirmed their favourable experiences with virtual classrooms. Additionally, providing students with high information quality in virtual classrooms would enhance their confirmation experiences, leading to the continuous intention to utilise virtual classrooms.

Enhancing Patient Understanding of Laboratory Test Results: Systematic Review of Presentation Formats and Their Impact on Perception, Decision, Action, and Memory.

BACKGROUND: Direct access of patients to their web-based patient portal, including laboratory test results, has become increasingly common. Numeric laboratory results can be challenging to interpret for patients, which may lead to anxiety, confusion, and unnecessary doctor consultations. Laboratory results can be presented in different formats, but there is limited evidence regarding how these presentation formats impact patients' processing of the information. OBJECTIVE: This study aims to synthesize the evidence on effective formats for presenting numeric laboratory test results with a focus on outcomes related to patients' information processing, including affective perception, perceived magnitude, cognitive perception, perception of communication, decision, action, and memory. METHODS: The search was conducted in 3 databases (PubMed, Web of Science, and Embase) from inception until May 31, 2023. We included quantitative, qualitative, and mixed methods articles describing or comparing formats for presenting diagnostic laboratory test results to patients. Two reviewers independently extracted and synthesized the characteristics of the articles and presentation formats used. The quality of the included articles was assessed by 2 independent reviewers using the Mixed Methods Appraisal Tool. RESULTS: A total of 18 studies were included, which were heterogeneous in terms of study design and primary outcomes used. The quality of the articles ranged from poor to excellent. Most studies (n=16, 89%) used mock test results. The most frequently used presentation formats were numerical values with reference ranges (n=12), horizontal line bars with colored blocks (n=12), or a combination of horizontal line bars with numerical values (n=8). All studies examined perception as an outcome, while action and memory were studied in 1 and 3 articles, respectively. In general, participants' satisfaction and usability were the highest when test results were presented using horizontal line bars with colored blocks. Adding reference ranges or personalized information (eg, goal ranges) further increased participants' perception. Additionally, horizontal line bars significantly decreased participants' tendency to search for information or to contact their physician, compared with numerical values with reference ranges. CONCLUSIONS: In this review, we synthesized available evidence on effective presentation formats for laboratory test results. The use of horizontal line bars with reference ranges or personalized goal ranges increased participants' cognitive perception and perception of communication while decreasing participants' tendency to contact their physicians. Action and memory were less frequently studied, so no conclusion could be drawn about a single preferred format regarding these outcomes. Therefore, the use of horizontal line bars with reference ranges or personalized goal ranges is recommended to enhance patients' information processing of laboratory test results. Further research should focus on real-life settings and diverse presentation formats in combination with outcomes related to patients' information processing.

Predicted distribution of Metaparasitylenchus hypothenemi (Tylenchida: Allantonematidae), parasite of the coffee berry borer.

Metaparasitylenchus hypothenemi is an endoparasitic nematode of the coffee berry borer Hypothenemus hampei. The nematode has only been recorded across a limited geographical range in coffee-growing areas of southeastern Mexico. Because of its confined geographical distribution, the effect of altitude, temperature, and mean annual precipitation on M. hypothenemi's presence/absence in the Soconusco region of Mexico was investigated. The geographical distribution of this parasite was predicted based on current data, using geographical information systems (GIS), the MaxEnt algorithm, and historical data to improve the prediction accuracy for other Neotropical regions. In Soconusco, the presence of this parasite is directly related to annual precipitation, especially in the areas with the highest annual rainfall (4000 - 4700 mm/year). Four species distribution models were generated for the Neotropical region with environmental variables for sites with parasite presence data, predicting a range of possible distribution with a high probability of occurrence in southeastern Mexico and southwestern Guatemala and a low probability in areas of Central and South America. Characterization of the abiotic habitat conditions suitable for M. hypothenemi development allows us to predict its distribution in the Neotropics and contributes to our understanding of its ecological relationship with environmental variables.

Using Spatial Scan Statistics and Geographic Information Systems to Detect Monthly Human Mobility Clusters and Analyze Cluster Area Characteristics.

Introduction: This study evaluated the detection of monthly human mobility clusters and characteristics of cluster areas before the coronavirus disease 2019 (COVID-19) outbreak using spatial epidemiological methods, namely, spatial scan statistics and geographic information systems (GIS). Methods: The research area covers approximately 10.3 km2, with a population of about 350,000 people. Analysis was conducted using open data, with the exception of one dataset. Human mobility and population data were used on a 1-km mesh scale, and business location data were used to examine the area characteristics. Data from January to December 2019 were utilized to detect human mobility clusters before the COVID-19 pandemic. Spatial scan statistics were performed using SaTScan to calculate relative risk (RR). The detected clusters and other data were visualized in QGIS to explore the features of the cluster areas. Results: Spatial scan statistics identified 33 clusters. The detailed analysis focused on clusters with an RR exceeding 1.5. Meshes with an RR over 1.5 included one with clusters for 1 year which is identified in all months of the year, one with clusters for 9 months, three with clusters for 6 months, three with clusters for 3 months, and four with clusters for 1 month. September had the highest number of clusters (eight), followed by April and November (seven each). The remaining months had five or six clusters. Characteristically, the cluster areas included the vicinity of railway stations, densely populated business areas, ball game fields, and large-scale construction sites. Conclusions: Statistical analysis of human mobility clusters using open data and open-source tools is crucial for the advancement of evidence-based policymaking based on scientific facts, not only for novel infectious diseases but also for existing ones, such as influenza.

A novel tool using social and environmental determinants of health to assess pediatric asthma in the emergency department.

Asthma, the most common chronic disease in children, affects more than 4 million children in the United States, disproportionately affecting those who are economically disadvantaged and racial and ethnic minorities. Studies have shown that the racial and ethnic disparities in asthma outcomes can be largely explained by environmental, socioeconomic and other social determinants of health (SDoH). Utilizing new approaches to stratify disease severity and risk, which focus on the underlying SDoH that lead to asthma disparity, provides an opportunity to disentangle race and ethnicity from its confounding social determinants. In particular, with the growing use of geospatial information systems, geocoded data can enable researchers and clinicians to quantify social and environmental impacts of structural racism. When these data are systematically collected and tabulated, researchers, and ultimately clinicians at the bedside, can evaluate patients' neighborhood context and create targeted interventions toward those factors most associated with asthma morbidity. To do this, we have designed a view (mPage in the Cerner electronic health record) that centralizes key clinical information and displays it alongside SDoH variables shown to be linked to asthma incidence and severity. Once refined and validated, which is the next step in our project, our goal is for emergency medicine clinicians to use these data in real time while caring for patients with asthma. Our multidisciplinary, patient-centered approach that leverages modern informatics tools will create opportunities to better triage patients with asthma exacerbations, choose the best interventions, and target underlying determinants of disease.

Innovative BIM technology application in the construction management of highway.

Within the global architecture, engineering, and construction industry, the use of Building Information Modeling (BIM) technology has significantly expanded. However, given the unique characteristics of road infrastructure, the application of BIM technology is still being explored. This article focuses on the Yuanchen Expressway, exploring innovative applications of BIM technology in comprehensive construction management. The project employs advanced technologies, including BIM, Geographic Information Systems (GIS), and the Internet of Things (IoT), to precisely identify critical nodes and breakthroughs. Supported by a detailed BIM model and a multi-level, diversified digital management platform, the project effectively addresses construction challenges in multiple tunnels, bridges, and complex interchanges, achieving intelligent construction innovation throughout the Yuanchen Expressway with BIM technology. By guiding construction through BIM models, utilizing a BIM+GIS-based management cloud platform system, and employing VR safety briefings, the project effectively reduces the difficulty of communication and coordination in project management, shortens the project measurement cycle, improves on-site work efficiency, and ensures comprehensive control and safety management. This article provides an exemplary case for the application of full-line construction management using BIM technology in the highway sector both in China and globally, offering new perspectives and strategies for highway construction management.

Fine-Tuned Large Language Model for Extracting Patients on Pretreatment for Lung Cancer from a Picture Archiving and Communication System Based on Radiological Reports.

This study aimed to investigate the performance of a fine-tuned large language model (LLM) in extracting patients on pretreatment for lung cancer from picture archiving and communication systems (PACS) and comparing it with that of radiologists. Patients whose radiological reports contained the term lung cancer (3111 for training, 124 for validation, and 288 for test) were included in this retrospective study. Based on clinical indication and diagnosis sections of the radiological report (used as input data), they were classified into four groups (used as reference data): group 0 (no lung cancer), group 1 (pretreatment lung cancer present), group 2 (after treatment for lung cancer), and group 3 (planning radiation therapy). Using the training and validation datasets, fine-tuning of the pretrained LLM was conducted ten times. Due to group imbalance, group 2 data were undersampled in the training. The performance of the best-performing model in the validation dataset was assessed in the independent test dataset. For testing purposes, two other radiologists (readers 1 and 2) were also involved in classifying radiological reports. The overall accuracy of the fine-tuned LLM, reader 1, and reader 2 was 0.983, 0.969, and 0.969, respectively. The sensitivity for differentiating group 0/1/2/3 by LLM, reader 1, and reader 2 was 1.000/0.948/0.991/1.000, 0.750/0.879/0.996/1.000, and 1.000/0.931/0.978/1.000, respectively. The time required for classification by LLM, reader 1, and reader 2 was 46s/2539s/1538s, respectively. Fine-tuned LLM effectively extracted patients on pretreatment for lung cancer from PACS with comparable performance to radiologists in a shorter time.

Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study.

Background: Over the last few decades, diabetes-related mortality risks (DRMR) have increased in Florida. Although there is evidence of geographic disparities in pre-diabetes and diabetes prevalence, little is known about disparities of DRMR in Florida. Understanding these disparities is important for guiding control programs and allocating health resources to communities most at need. Therefore, the objective of this study was to investigate geographic disparities and temporal changes of DRMR in Florida. Methods: Retrospective mortality data for deaths that occurred from 2010 to 2019 were obtained from the Florida Department of Health. Tenth International Classification of Disease codes E10-E14 were used to identify diabetes-related deaths. County-level mortality risks were computed and presented as number of deaths per 100,000 persons. Spatial Empirical Bayesian (SEB) smoothing was performed to adjust for spatial autocorrelation and the small number problem. High-risk spatial clusters of DRMR were identified using Tango's flexible spatial scan statistics. Geographic distribution and high-risk mortality clusters were displayed using ArcGIS, whereas seasonal patterns were visually represented in Excel. Results: A total of 54,684 deaths were reported during the study period. There was an increasing temporal trend as well as seasonal patterns in diabetes mortality risks with high risks occurring during the winter. The highest mortality risk (8.1 per 100,000 persons) was recorded during the winter of 2018, while the lowest (6.1 per 100,000 persons) was in the fall of 2010. County-level SEB smoothed mortality risks varied by geographic location, ranging from 12.6 to 81.1 deaths per 100,000 persons. Counties in the northern and central parts of the state tended to have high mortality risks, whereas southern counties consistently showed low mortality risks. Similar to the geographic distribution of DRMR, significant high-risk spatial clusters were also identified in the central and northern parts of Florida. Conclusion: Geographic disparities of DRMR exist in Florida, with high-risk spatial clusters being observed in rural central and northern areas of the state. There is also evidence of both increasing temporal trends and Winter peaks of DRMR. These findings are helpful for guiding allocation of resources to control the disease, reduce disparities, and improve population health.

The Impact of Real-Time Documentation of In-Hospital Medication Changes on Preventing Undocumented Discrepancies at Discharge and Improving Physician-Pharmacist Communication: A Retrospective Cohort Study and Survey.

Background: Transitional medication safety is crucial, as miscommunication about medication changes can lead to significant risks. Unclear or incomplete documentation during care transitions can result in outdated or incorrect medication lists at discharge, potentially causing medication errors, adverse drug events, and inadequate patient education. These issues are exacerbated by extended hospital stays and multiple care events, making accurate medication recall challenging at discharge. Objective: Thus, we aimed to investigate how real-time documentation of in-hospital medication changes prevents undocumented medication changes at discharge and improves physician-pharmacist communication. Methods: We conducted a retrospective cohort study in a tertiary hospital. Two pharmacists reviewed medical records of patients admitted to the acute medical unit from April to June 2020. In-hospital medication discrepancies were determined by comparing preadmission and hospitalization medication lists and it was verified whether the physician's intent of medication changes was clarified by documentation. By a documentation rate of medication changes of 100% and <100%, respectively, fully documented (FD) and partially documented (PD) groups were defined. Any undocumented medication changes at discharge were considered a "documentation error at discharge". Pharmacists' survey was conducted to assess the impact of appropriate documentation on the pharmacists. Results: After reviewing 400 medication records, patients were categorized into FD (61.3%) and PD (38.8%) groups. Documentation errors at discharge were significantly higher in the PD than in the FD group. Factors associated with documentation errors at discharge included belonging to the PD group, discharge from a non-hospitalist-managed ward, and having three or more intentional discrepancies. Pharmacists showed favorable attitudes towards physician's documentation. Conclusion: Appropriate documentation of in-hospital medication changes, facilitated by free-text communication, significantly decreased documentation errors at discharge. This analysis underlines the importance of communication between pharmacists and hospitalists in improving patient safety during transitions of care.

During transitions of care, communication failures among healthcare professionals can lead to medication errors. Therefore, effective sharing of information is essential, especially when intentional changes in prescription orders are made. Documenting medication changes facilitates real-time communication, potentially improving medication reconciliation and reducing discrepancies. However, inadequate documentation of medication changes is common in clinical practice. This retrospective cohort study underlines the importance of real-time documentation of in-hospital medication changes. There was a significant reduction in documentation errors at discharge in fully documented group, where real-time documentation of medication changes was more prevalent. Pharmacists showed favorable attitudes toward the physician's real-time documenting of medication changes because it provided valuable information on understanding the physician's intent and improving communication and also saved time for pharmacists. This study concludes that physicians' documentation on medication changes may reduce documentation errors at discharge, meaning that proper documentation of medication changes could enhance patient safety through effective communication.

Transforming experimental radiology: Design and implementation of an innovative ePACS image storage system for AI imaging research environments.

INTRODUCTION AND PURPOSE: We present the needs, design, development, implementation, and accessibility of a crafted experimental PACS (ePACS) system to securely store images, ensuring efficiency and ease of use for AI processing, specifically tailored for research scenarios, including phantoms, animal and human studies and quality assurance (QA) exams. The ePACS system plays a crucial role in any medical imaging departments that handle non-care profile studies, such as protocol adjustments and dummy runs. By effectively segregating non-care profile studies from the healthcare assistance, the ePACS usefully prevents errors both in clinical practice and storage security. METHODS AND RESULTS: The developed ePACS system considers the best practices for management, maintenance, access, long-term storage and backups, regulatory audits, and economic aspects. Moreover, key aspects of the ePACS system include the design of data flows with a focus on incorporating data security and privacy, access control and levels based on user profiles, internal data management policies, standardized architecture, infrastructure and application monitorization and traceability, and periodic backup policies. A new tool called DicomStudiesQA has been developed to standardize the analysis of DICOM studies. The tool automatically identifies, extracts, and renames series using a consistent nomenclature. It also detects corrupted images and merges separated dynamic series that were initially split, allowing for streamlined post-processing. DISCUSSION AND CONCLUSIONS: The developed ePACS system encompasses a successful implementation, both in hospital and research environments, showcasing its transformative nature and the challenging yet crucial transfer of knowledge to industry. This underscores the practicality and real-world applicability of our innovative approach, highlighting the significant impact it has on the field of experimental radiology.

Acceptance of AI in Health Care for Short- and Long-Term Treatments: Pilot Development Study of an Integrated Theoretical Model.

BACKGROUND: As digital technologies and especially artificial intelligence (AI) become increasingly important in health care, it is essential to determine whether and why potential users intend to use related health information systems (HIS). Several theories exist, but they focus mainly on aspects of health care or information systems, in addition to general psychological theories, and hence provide a small number of variables to explain future behavior. Thus, research that provides a larger number of variables by combining several theories from health care, information systems, and psychology is necessary. OBJECTIVE: This study aims to investigate the intention to use new HIS for decisions concerning short- and long-term medical treatments using an integrated approach with several variables to explain future behavior. METHODS: We developed an integrated theoretical model based on theories from health care, information systems, and psychology that allowed us to analyze the duality approach of adaptive and nonadaptive appraisals and their influence on the intention to use HIS. We applied the integrated theoretical model to the short-term treatment using AI-based HIS for surgery and the long-term treatment of diabetes tracking using survey data with structured equation modeling. To differentiate between certain levels of AI involvement, we used several scenarios that include treatments by physicians only, physicians with AI support, and AI only to understand how individuals perceive the influence of AI. RESULTS: Our results showed that for short- and long-term treatments, the variables perceived threats, fear (disease), perceived efficacy, attitude (HIS), and perceived norms are important to consider when determining the intention to use AI-based HIS. Furthermore, the results revealed that perceived efficacy and attitude (HIS) are the most important variables to determine intention to use for all treatments and scenarios. In contrast, abilities (HIS) were important for short-term treatments only. For our 9 scenarios, adaptive and nonadaptive appraisals were both important to determine intention to use, depending on whether the treatment is known. Furthermore, we determined R² values that varied between 57.9% and 81.7% for our scenarios, which showed that the explanation power of our model is medium to good. CONCLUSIONS: We contribute to HIS literature by highlighting the importance of integrating disease- and technology-related factors and by providing an integrated theoretical model. As such, we show how adaptive and nonadaptive appraisals should be arranged to report on medical decisions in the future, especially in the short and long terms. Physicians and HIS developers can use our insights to identify promising rationale for HIS adoption concerning short- and long-term treatments and adapt and develop HIS accordingly. Specifically, HIS developers should ensure that future HIS act in terms of HIS functions, as our study shows that efficient HIS lead to a positive attitude toward the HIS and ultimately to a higher intention to use.

Development and Evaluation of a Software Designed by a Nursing and Technology Team to Assess the Health Status of Adults over 65 Years of Age.

Objective: This work sought to develop the Actuasalud platform as a useful tool for nursing that permits assessing health, in term of frailty, in population over 65 years of age. Methods: For the design and development of Actuasalud, two working groups were formed: one from nursing with different profiles, to identify the scientific content and a computer science group responsible for the software programming and development. Both teams adapted the scientific content to the technology so that the tool would allow for population screening with detection of health problems and frailty states. Results: The software was developed in three large blocks that include all the dimensions of frailty: a: sociodemographic variables, b: comorbidities, and c: assessment tools of autonomy-related needs that evaluate the dimensions of frailty. At the end of the evaluation, a detailed report is displayed through bar diagram with the diagnosis of each of the dimensions assessed. The assessment in the participating elderly showed that 44.7% (n = 38) of the population was considered not frail, and 55.3%; (n = 47) as frail. Regarding associated pathologies, high blood pressure (67.1%; n = 57), osteoarthritis and/or arthritis (55.3%; n = 47), diabetes (48.2%; n = 41) and falls during the last year (35.3%; n = 30) were highlighted. Conclusion: Actuasalud is an application that allows nursing professionals to evaluate frailty and issue a quick diagnosis with ordered sequence, which helps to provide individualized care to elderly individuals according to the problems detected during the evaluation.