Improving Naloxone Co-prescribing Through Clinical Decision Support.

BACKGROUND: Despite national guidelines recommending naloxone co-prescription with high-risk medications, rates remain low nationally. This was reflected at our institution with remarkably low naloxone prescribing rates. We sought to determine if a clinical decision support (CDS) tool could increase rates of naloxone co-prescribing with high-risk prescriptions. METHODS:  An alert in the electronic health record was triggered upon signing an order for a high-risk opioid medication without a naloxone co-prescription. We examined all opioid prescriptions written by family and general internal medicine practitioners at the University of Iowa Hospitals and Clinics in outpatient encounters between November 30, 2020, and February 28, 2022. Once triggered by a high-risk prescription, the CDS tool had the option to choose an order set with an automatically selected co-prescription for naloxone along with patient instructions automatically added to the patient's after-visit summary (AVS). We examined the monthly percentage of patients receiving Schedule II opioid prescriptions ≥90 morphine milliequivalents (MME)/day who received concurrent naloxone prescriptions in the 12 months before the CDS went live and the three months following go-live. RESULTS:  Concurrent naloxone prescriptions increased from 1.1% in the 12 months prior to implementation in November 2021 to 9.4% (p<0.001) during the post-intervention period across eight family medicine and internal medicine clinics. DISCUSSION:  This single-center quality improvement project with retrospective analysis demonstrates the potential efficacy of a single CDS tool in increasing the rate of naloxone prescription. The impact of such prescribing on overall mortality requires further research. CONCLUSIONS: The CDS tool was easy to implement and improved rates of appropriate naloxone co-prescribing.

Electronic Glycemic Management System Improved Glycemic Control and Reduced Complications in Patients With Diabetes Undergoing Coronary Artery Bypass Surgery: A Randomized Controlled Trial.

BACKGROUND: In-hospital hyperglycemia poses significant risks for patients with diabetes mellitus undergoing coronary artery bypass graft (CABG) surgery. Electronic glycemic management systems (eGMSs) like InsulinAPP offer promise in standardizing and improving glycemic control (GC) in these settings. This study evaluated the efficacy of the InsulinAPP protocol in optimizing GC and reducing adverse outcomes post-CABG. METHODS: This prospective, randomized, open-label study was conducted with 100 adult type 2 diabetes mellitus (T2DM) patients post-CABG surgery, who were randomized into two groups: conventional care (gCONV) and eGMS protocol (gAPP). The gAPP used InsulinAPP for insulin therapy management, whereas the gCONV received standard clinical care. The primary outcome was a composite of hospital-acquired infections, renal function deterioration, and symptomatic atrial arrhythmia. Secondary outcomes included GC, hypoglycemia incidence, hospital stay length, and costs. RESULTS: The gAPP achieved lower mean glucose levels (167.2 ± 42.5 mg/dL vs 188.7 ± 54.4 mg/dL; P = .040) and fewer patients-day with BG above 180 mg/dL (51.3% vs 74.8%, P = .011). The gAPP received an insulin regimen that included more prandial bolus and correction insulin (either bolus-correction or basal-bolus regimens) than the gCONV (90.3% vs 16.7%). The primary composite outcome occurred in 16% of gAPP patients compared with 58% in gCONV (P < .010). Hypoglycemia incidence was lower in the gAPP (4% vs 16%, P = .046). The gAPP protocol also resulted in shorter hospital stays and reduced costs. CONCLUSIONS: The InsulinAPP protocol effectively optimizes GC and reduces adverse outcomes in T2DM patients' post-CABG surgery, offering a cost-effective solution for inpatient diabetes management.

Implementing AI in Hospitals to Achieve a Learning Health System: Systematic Review of Current Enablers and Barriers.

BACKGROUND: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows. OBJECTIVE: The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS. METHODS: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics. RESULTS: Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework. CONCLUSIONS: Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.

Universal Clinical Decision Support Tool for Thromboprophylaxis in Hospitalized COVID-19 Patients: Post-Hoc Analysis of the IMPROVE-DD Cluster Randomized Trial.

BACKGROUND: Inpatient and extended post-discharge thromboprophylaxis of COVID-19 patients remain suboptimal despite antithrombotic guidelines. OBJECTIVES: To determine whether a novel electronic health record (EHR)-agnostic clinical decision support (CDS) tool incorporating IMPROVE-DD VTE scores increases appropriate inpatient and extended post-discharge thromboprophylaxis and improves outcomes in COVID-19 inpatients. METHODS: This post-hoc analysis of the IMPROVE-DD cluster randomized trial evaluated thromboprophylaxis CDS among COVID-19 inpatients at four New York hospitals between December 21, 2020, and January 21, 2022. Hospitals were randomized 1:1 to CDS (intervention, N=2), versus no CDS (usual care, N=2). The primary outcome was rate of appropriate thromboprophylaxis. Secondary outcomes included rates of major thromboembolism, all-cause and VTE-related readmissions and death, major bleeding (MB), and all-cause mortality 30 days post-discharge. RESULTS: 2,452 COVID-19 inpatients were analyzed (1,355 CDS; 1,097 no CDS). Mean age was 73.7 ± 9.37 years; 50.1% of participants were male. CDS adoption was 96.8% (intervention group). CDS was associated with increased appropriate at-discharge extended thromboprophylaxis (42.6% versus 28.8%, odds ratio [OR] 1.83, 95% Confidence Interval [CI] 1.39 - 2.41, p<0.001). CDS was associated with reduced VTE (OR 0.54, 95% CI 0.39-0.75, p<0.001), arterial thromboembolism (OR 0.10, 95% CI 0.01-0.81, p=0.01), total TE (OR 0.50, 95% CI 0.36-0.69, p<0.001), and 30-day all-cause readmission/death (OR 0.78, 95% CI 0.62-0.99, p=0.04). There were no differences in MB, VTE-related readmissions/death, or all-cause mortality. CONCLUSION: EHR-agnostic CDS incorporating IMPROVE-DD VTE scores had high adoption, was associated with increased appropriate at-discharge extended thromboprophylaxis, and reduced TE and all-cause readmission/death without increasing MB in COVID-19 inpatients.

Capability of multimodal large language models to interpret pediatric radiological images.

BACKGROUND: There is a dearth of artificial intelligence (AI) development and research dedicated to pediatric radiology. The newest iterations of large language models (LLMs) like ChatGPT can process image and video input in addition to text. They are thus theoretically capable of providing impressions of input radiological images. OBJECTIVE: To assess the ability of multimodal LLMs to interpret pediatric radiological images. MATERIALS AND METHODS: Thirty medically significant cases were collected and submitted to GPT-4 (OpenAI, San Francisco, CA), Gemini 1.5 Pro (Google, Mountain View, CA), and Claude 3 Opus (Anthropic, San Francisco, CA) with a short history for a total of 90 images. AI responses were recorded and independently assessed for accuracy by a resident and attending physician. 95% confidence intervals were determined using the adjusted Wald method. RESULTS: Overall, the models correctly diagnosed 27.8% (25/90) of images (95% CI=19.5-37.8%), were partially correct for 13.3% (12/90) of images (95% CI=2.7-26.4%), and were incorrect for 58.9% (53/90) of images (95% CI=48.6-68.5%). CONCLUSION: Multimodal LLMs are not yet capable of interpreting pediatric radiological images.

Twenty-Five Years of Progress-Lessons Learned From JMIR Publications to Address Gender Parity in Digital Health Authorships: Bibliometric Analysis.

BACKGROUND: Digital health research plays a vital role in advancing equitable health care. The diversity of research teams is thereby instrumental in capturing societal challenges, increasing productivity, and reducing bias in algorithms. Despite its importance, the gender distribution within digital health authorship remains largely unexplored. OBJECTIVE: This study aimed to investigate the gender distribution among first and last authors in digital health research, thereby identifying predicting factors of female authorship. METHODS: This bibliometric analysis examined the gender distribution across 59,980 publications from 1999 to 2023, spanning 42 digital health journals indexed in the Web of Science. To identify strategies ensuring equality in research, a detailed comparison of gender representation in JMIR journals was conducted within the field, as well as against a matched sample. Two-tailed Welch 2-sample t tests, Wilcoxon rank sum tests, and chi-square tests were used to assess differences. In addition, odds ratios were calculated to identify predictors of female authorship. RESULTS: The analysis revealed that 37% of first authors and 30% of last authors in digital health were female. JMIR journals demonstrated a higher representation, with 49% of first authors and 38% of last authors being female, yielding odds ratios of 1.96 (95% CI 1.90-2.03; P<.001) and 1.78 (95% CI 1.71-1.84; P<.001), respectively. Since 2008, JMIR journals have consistently featured a greater proportion of female first authors than male counterparts. Other factors that predicted female authorship included having female authors in other relevant positions and gender discordance, given the higher rate of male last authors in the field. CONCLUSIONS: There was an evident shift toward gender parity across publications in digital health, particularly from the publisher JMIR Publications. The specialized focus of its sister journals, equitable editorial policies, and transparency in the review process might contribute to these achievements. Further research is imperative to establish causality, enabling the replication of these successful strategies across other scientific fields to bridge the gender gap in digital health effectively.

Integrated energy informatics technology on microalgae-based wastewater treatment to bioenergy production: A review.

The production of renewable biofuel through microalgae and green technology can be a promising solution to meet future energy demands whilst reducing greenhouse gases (GHG) emissions and recovering energy for a carbon-neutral bio-economy and environmental sustainability. Recently, the integration of Energy Informatics (EI) technology as an emerging approach has ensured the feasibility and enhancement of microalgal biotechnology and bioenergy applications. Integrating EI technology such as artificial intelligence (AI), predictive modelling systems and life cycle analysis (LCA) in microalgae field applications can improve cost, efficiency, productivity and sustainability. With the approach of EI technology, data-driven insights and decision-making, resource optimization and a better understanding of the environmental impact of microalgae cultivation could be achieved, making it a crucial step in advancing this field and its applications. This review presents the conventional technologies in the microalgae-based system for wastewater treatment and bioenergy production. Furthermore, the recent integration of EI in microalgal technology from the AI application to the modelling and optimization using predictive control systems has been discussed. The LCA and techno-economic assessment (TEA) in the environmental sustainability and economic point of view are also presented. Future challenges and perspectives in the microalgae-based wastewater treatment to bioenergy production integrated with the EI approach, are also discussed in relation to the development of microalgae as the future energy source.

Community perspectives on the use of electronic health data to support reflective practice by health professionals.

BACKGROUND: Electronic health records and other clinical information systems have crucial roles in health service delivery and are often utilised for patient care as well as health promotion and research. Government agencies and healthcare bodies are gradually shifting the focus on how these data systems can be harnessed for secondary uses such as reflective practice, professional learning and continuing professional development. Whilst there has been a presence in research around the attitudes of health professionals in employing clinical information systems to support their reflective practice, there has been very little research into consumer attitudes towards these data systems and how they would like to interact with such structures. The study described in this article aimed to address this gap in the literature by exploring community perspectives on the secondary use of Electronic Health Data for health professional learning and practice reflection. METHODS: A qualitative methodology was used, with data being collected via semi-structured interviews. Interviews were conducted via phone and audio recordings, before being transcribed into text for analysis. Reflective thematic analysis was undertaken to analyse the data. RESULTS: Fifteen Australians consented to participate in an interview. Analysis of interview data generated five themes: (1) Knowledge about health professional registration and professional learning; (2) Secondary uses of Electronic Health Data; (3) Factors that enable the use of Electronic Health Data for health professional learning; (4) Challenges using Electronic Health Data for health professional learning and (5) Expectations around consent to use Electronic Health Data for health professional learning. CONCLUSIONS: Australians are generally supportive of health professionals using Electronic Health Data to support reflective practice and learning but identify several challenges for data being used in this way.

The informatics of ADNI.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) has revolutionized the landscape of Alzheimer's research through its Informatics Core, which has facilitated unprecedented data standardization and sharing. Over 20 years, ADNI established a robust informatics framework, enabling the validation of biomarkers and supporting global research efforts. The Informatics Core, centered at the Laboratory of Neuro Imaging (LONI), provides a comprehensive data hub that ensures data quality, accessibility, and security, fostering over 5600 publications and significant scientific advancements. By embracing open data sharing principles, ADNI set a gold standard in data transparency, allowing over 26,000 investigators from 169 countries to access and download a wealth of multimodal data. This collaborative approach not only accelerated biomarker discovery and drug development and advanced our understanding of Alzheimer's disease but also has served as a model for other research initiatives, demonstrating the transformative potential of carefully designed informatics models and shared data in driving global scientific progress. HIGHLIGHTS: Accelerating biomarker discovery and drug development for Alzheimer's disease. Alzheimer's Disease Neuroimaging Initiative's (ADNI's) open data sharing drives scientific progress. Data exploration and coupled analytics to data archives.

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.

The potential for drug incompatibility and its drivers - A hospital wide retrospective descriptive study.

OBJECTIVE: Drug incompatibility, a significant subset of medication errors, threaten patient safety during the medication administration phase. Despite the undeniably high prevalence of drug incompatibility, it is currently poorly understood because previous studies are focused predominantly on intensive care unit (ICU) settings. To enhance patient safety, it is crucial to expand our understanding of this issue from a comprehensive viewpoint. This study aims to investigate the prevalence and mechanism of drug incompatibility by analysing hospital-wide prescription and administration data. METHODS: This retrospective cross-sectional study, conducted at a tertiary academic hospital, included data extracted from the clinical data warehouse of the study institution on patients admitted between January 1, 2021, and May 31, 2021. Potential contacts in drug pairs (PCs) were identified using the study site clinical workflow. Drug incompatibility for each PC was determined by using a commercial drug incompatibility database, the Trissel's™ 2 Clinical Pharmaceutics Database (Trissel's 2 database). Drivers of drug incompatibility were identified, based on a descriptive analysis, after which, multivariate logistic regression was conducted to assess the risk factors for experiencing one or more drug incompatibilities during admission. RESULTS: Among 30,359 patients (representing 40,061 hospitalisations), 24,270 patients (32,912 hospitalisations) with 764,501 drug prescriptions (1,001,685 IV administrations) were analysed, after checking for eligibility. Based on the rule for determining PCs, 5,813,794 cases of PCs were identified. Among these, 25,108 (0.4 %) cases were incompatible PCs: 391 (1.6 %) PCs occurred during the prescription process and 24,717 (98.4 %) PCs during the administration process. By classifying these results, we identified the following drivers contributing to drug incompatibility: incorrect order factor; incorrect administration factor; and lack of related research. In multivariate analysis, the risk of encountering incompatible PCs was higher for patients who were male, older, with longer lengths of stay, with higher comorbidity, and admitted to medical ICUs. CONCLUSIONS: We comprehensively described the current state of drug incompatibility by analysing hospital-wide drug prescription and administration data. The results showed that drug incompatibility frequently occurs in clinical settings.

A pragmatic, stepped-wedge, hybrid type II trial of interoperable clinical decision support to improve venous thromboembolism prophylaxis for patients with traumatic brain injury.

BACKGROUND: Venous thromboembolism (VTE) is a preventable medical condition which has substantial impact on patient morbidity, mortality, and disability. Unfortunately, adherence to the published best practices for VTE prevention, based on patient centered outcomes research (PCOR), is highly variable across U.S. hospitals, which represents a gap between current evidence and clinical practice leading to adverse patient outcomes. This gap is especially large in the case of traumatic brain injury (TBI), where reluctance to initiate VTE prevention due to concerns for potentially increasing the rates of intracranial bleeding drives poor rates of VTE prophylaxis. This is despite research which has shown early initiation of VTE prophylaxis to be safe in TBI without increased risk of delayed neurosurgical intervention or death. Clinical decision support (CDS) is an indispensable solution to close this practice gap; however, design and implementation barriers hinder CDS adoption and successful scaling across health systems. Clinical practice guidelines (CPGs) informed by PCOR evidence can be deployed using CDS systems to improve the evidence to practice gap. In the Scaling AcceptabLE cDs (SCALED) study, we will implement a VTE prevention CPG within an interoperable CDS system and evaluate both CPG effectiveness (improved clinical outcomes) and CDS implementation. METHODS: The SCALED trial is a hybrid type 2 randomized stepped wedge effectiveness-implementation trial to scale the CDS across 4 heterogeneous healthcare systems. Trial outcomes will be assessed using the RE2-AIM planning and evaluation framework. Efforts will be made to ensure implementation consistency. Nonetheless, it is expected that CDS adoption will vary across each site. To assess these differences, we will evaluate implementation processes across trial sites using the Exploration, Preparation, Implementation, and Sustainment (EPIS) implementation framework (a determinant framework) using mixed-methods. Finally, it is critical that PCOR CPGs are maintained as evidence evolves. To date, an accepted process for evidence maintenance does not exist. We will pilot a "Living Guideline" process model for the VTE prevention CDS system. DISCUSSION: The stepped wedge hybrid type 2 trial will provide evidence regarding the effectiveness of CDS based on the Berne-Norwood criteria for VTE prevention in patients with TBI. Additionally, it will provide evidence regarding a successful strategy to scale interoperable CDS systems across U.S. healthcare systems, advancing both the fields of implementation science and health informatics. TRIAL REGISTRATION: Clinicaltrials.gov - NCT05628207. Prospectively registered 11/28/2022, https://classic. CLINICALTRIALS: gov/ct2/show/NCT05628207 .

User's Search for Information: A Multi-Language Cross-Sectional Assessment of Websites about Healthcare-Associated Infections.

Introduction: Healthcare-associated infections have a significant impact on public health, and many patients and their next-of-kin are seeking information on the internet. The study aimed to assess the quality of online written content about healthcare-associated infections available in English, Romanian, and Hungarian languages. Materials and methods: The study sample included 75 websites, 25 for each language subgroup. The assessment involved examining the general characteristics, adherence to established credibility criteria, and the completeness and accuracy of informational content. The evaluation was conducted using a topic-specific, evidence-based benchmark. Two evaluators independently graded completeness and accuracy; scores were recorded on a scale from 0 to 10. A comparative analysis of websites was performed, considering pertinent characteristics, and potential factors influencing information quality were subjected to testing. The statistical significance was set at 0.05. Results: For the overall study sample, the average credibility, completeness, and accuracy scores were 5.1 (SD 1.7), 2.4 (SD 1.5), and 5.9 (SD 1.0), respectively. Pairwise comparison tests revealed that English websites rated significantly higher than Romanian and Hungarian websites on all three quality measures (P<0.05). Website specialization, ownership, and main goal were not associated with credibility or content ratings. However, conventional medicine websites consistently scored higher than alternative medicine and other websites across all three information quality measures (P<0.05). Credibility scores were positively but weakly correlated with completeness (rho=0.273; P=0.0176) and accuracy scores (rho=0.365; P=0.0016). Conclusions: The overall quality ratings of information about healthcare-associated infections on English, Romanian, and Hungarian websites ranged from intermediate to low. The description of information regarding the symptoms and prevention of healthcare-associated infections was notably unsatisfactory. The study identified website characteristics possibly associated with higher-quality online sources about healthcare-associated infections, but additional research is needed to establish robust evidence.

Resilience Informatics: Role of Informatics in Enabling and Promoting Public Health Resilience to Pandemics, Climate Change, and Other Stressors.

Climate change, local epidemics, future pandemics, and forced displacements pose significant public health threats worldwide. To cope successfully, people and communities are faced with the challenging task of developing resilience to these stressors. Our viewpoint is that the powerful capabilities of modern informatics technologies including artificial intelligence, biomedical and environmental sensors, augmented or virtual reality, data science, and other digital hardware or software, have great potential to promote, sustain, and support resilience in people and communities. However, there is no "one size fits all" solution for resilience. Solutions must match the specific effects of the stressor, cultural dimensions, social determinants of health, technology infrastructure, and many other factors.

Engagement With Conversational Agent-Enabled Interventions in Cardiometabolic Disease Management: Protocol for a Systematic Review.

BACKGROUND: Cardiometabolic diseases (CMDs) are a group of interrelated conditions, including heart failure and diabetes, that increase the risk of cardiovascular and metabolic complications. The rising number of Australians with CMDs has necessitated new strategies for those managing these conditions, such as digital health interventions. The effectiveness of digital health interventions in supporting people with CMDs is dependent on the extent to which users engage with the tools. Augmenting digital health interventions with conversational agents, technologies that interact with people using natural language, may enhance engagement because of their human-like attributes. To date, no systematic review has compiled evidence on how design features influence the engagement of conversational agent-enabled interventions supporting people with CMDs. This review seeks to address this gap, thereby guiding developers in creating more engaging and effective tools for CMD management. OBJECTIVE: The aim of this systematic review is to synthesize evidence pertaining to conversational agent-enabled intervention design features and their impacts on the engagement of people managing CMD. METHODS: The review is conducted in accordance with the Cochrane Handbook for Systematic Reviews of Interventions and reported in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Searches will be conducted in the Ovid (Medline), Web of Science, and Scopus databases, which will be run again prior to manuscript submission. Inclusion criteria will consist of primary research studies reporting on conversational agent-enabled interventions, including measures of engagement, in adults with CMD. Data extraction will seek to capture the perspectives of people with CMD on the use of conversational agent-enabled interventions. Joanna Briggs Institute critical appraisal tools will be used to evaluate the overall quality of evidence collected. RESULTS: This review was initiated in May 2023 and was registered with the International Prospective Register of Systematic Reviews (PROSPERO) in June 2023, prior to title and abstract screening. Full-text screening of articles was completed in July 2023 and data extraction began August 2023. Final searches were conducted in April 2024 prior to finalizing the review and the manuscript was submitted for peer review in July 2024. CONCLUSIONS: This review will synthesize diverse observations pertaining to conversational agent-enabled intervention design features and their impacts on engagement among people with CMDs. These observations can be used to guide the development of more engaging conversational agent-enabled interventions, thereby increasing the likelihood of regular intervention use and improved CMD health outcomes. Additionally, this review will identify gaps in the literature in terms of how engagement is reported, thereby highlighting areas for future exploration and supporting researchers in advancing the understanding of conversational agent-enabled interventions. TRIAL REGISTRATION: PROSPERO CRD42023431579; https://tinyurl.com/55cxkm26. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/52973.

The Role of Informatics in Advancing Emergency Medicine: A Comprehensive Review.

Emergency Medicine Informatics (EMI) is a rapidly advancing field that utilizes information technology to enhance the delivery of emergency medical services. This comprehensive literature review explores the key components, benefits, challenges, and future directions of EMI. By integrating Electronic Health Records, Clinical Decision Support Systems, telemedicine, data analytics, interoperability, and patient monitoring systems, EMI has the potential to significantly improve patient outcomes and operational efficiency in emergency departments. However, the implementation of these technologies faces several obstacles, including interoperability issues, data security concerns, usability challenges, and high costs. This review highlights how these technologies are transforming emergency care, discusses the barriers to their implementation, and provides perspectives on potential solutions and future progress in the field.

A Quarter-Century of Online Informatics Education: Learners Served and Lessons Learned.

The value and methods of online learning have changed tremendously over the last 25 years. The goal of this paper is to review a quarter-century of experience with online learning by the author in the field of biomedical and health informatics, describing the learners served and the lessons learned. The author details the history of the decision to pursue online education in informatics, describing the approaches taken as educational technology evolved over time. A large number of learners have been served, and the online learning approach has been well-received, with many lessons learned to optimize the educational experience. Online education in biomedical and health informatics has provided a scalable and exemplary approach to learning in this field.

Parenteral Beta-Lactam/Beta-Lactamase Inhibitor Ordering in Hospitals That Provide Care for Pediatric Patients.

OBJECTIVES: The purpose of this study was to define current practices related to beta-lactam/beta-lactamase inhibitor (BL/BLI) dose descriptions in hospitals that provide care for pediatric patients and to identify perceived implications of standardizing BL/BLI dose communication and ordering to a total drug-based strategy. METHODS: A 27-item electronic survey was distributed via 4 pediatric pharmacy and infectious diseases listservs. Survey questions pertained to hospital demographics, dosing communication practices, BL/BLI ordering and labeling practices, obstacles to safe BL/BLI use, and the effects of potential standardization to a total drug communication strategy. SPSS was used for quantitative analysis and MAXQDA was used for qualitative analysis. RESULTS: A total of 140 unique survey responses were analyzed after exclusion of incomplete responses and reconciliation of multiple responses from the same institution. Overall, 56.2% of institutions order BL/BLIs by BL component for pediatric patients, and 22% of institutions order by BL component for adult patients. Approximately half (51.8%) of respondents felt that standardizing to total drug would have a negative effect at their institution; perception of potential effect varied based on the institution's ordering strategy. CONCLUSION: Communication and ordering of BL/BLIs is inconsistent across institutions and between pediatric and adult patients. In the short term, the perception is that standardization would compound institutional challenges.

Evaluation of the Quality and Readability of Web-Based Information Regarding Foreign Bodies of the Ear, Nose, and Throat: Qualitative Content Analysis.

BACKGROUND: Foreign body (FB) inhalation, ingestion, and insertion account for 11% of emergency admissions for ear, nose, and throat conditions. Children are disproportionately affected, and urgent intervention may be needed to maintain airway patency and prevent blood vessel occlusion. High-quality, readable online information could help reduce poor outcomes from FBs. OBJECTIVE: We aim to evaluate the quality and readability of available online health information relating to FBs. METHODS: In total, 6 search phrases were queried using the Google search engine. For each search term, the first 30 results were captured. Websites in the English language and displaying health information were included. The provider and country of origin were recorded. The modified 36-item Ensuring Quality Information for Patients tool was used to assess information quality. Readability was assessed using a combination of tools: Flesch Reading Ease score, Flesch-Kincaid Grade Level, Gunning-Fog Index, and Simple Measure of Gobbledygook. RESULTS: After the removal of duplicates, 73 websites were assessed, with the majority originating from the United States (n=46, 63%). Overall, the quality of the content was of moderate quality, with a median Ensuring Quality Information for Patients score of 21 (IQR 18-25, maximum 29) out of a maximum possible score of 36. Precautionary measures were not mentioned on 41% (n=30) of websites and 30% (n=22) did not identify disk batteries as a risky FB. Red flags necessitating urgent care were identified on 95% (n=69) of websites, with 89% (n=65) advising patients to seek medical attention and 38% (n=28) advising on safe FB removal. Readability scores (Flesch Reading Ease score=12.4, Flesch-Kincaid Grade Level=6.2, Gunning-Fog Index=6.5, and Simple Measure of Gobbledygook=5.9 years) showed most websites (56%) were below the recommended sixth-grade level. CONCLUSIONS: The current quality and readability of information regarding FBs is inadequate. More than half of the websites were above the recommended sixth-grade reading level, and important information regarding high-risk FBs such as disk batteries and magnets was frequently excluded. Strategies should be developed to improve access to high-quality information that informs patients and parents about risks and when to seek medical help. Strategies to promote high-quality websites in search results also have the potential to improve outcomes.

An exploratory deep learning approach to investigate tuberculosis pathogenesis in nonhuman primate model: Combining automated radiological analysis with clinical and biomarkers data.

BACKGROUND: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis, combined with clinical, microbiological, and immunological data, by machine learning (ML), can help achieve it. METHODS: Six rhesus macaques were experimentally inoculated with pathogenic Mycobacterium tuberculosis in the lung. Data, including Computed Tomography (CT), were collected at 0, 2, 4, 8, 12, 16, and 20 weeks. RESULTS: Our ML-based CT analysis (TB-Net) efficiently and accurately analyzed disease progression, performing better than standard deep learning model (LLM OpenAI's CLIP Vi4). TB-Net based results were more consistent than, and confirmed independently by, blinded manual disease scoring by two radiologists and exhibited strong correlations with blood biomarkers, TB-lesion volumes, and disease-signs during disease pathogenesis. CONCLUSION: The proposed approach is valuable in early disease detection, monitoring efficacy of therapy, and clinical decision making.