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Effective communication in nursing, particularly with older patients, is critical to providing high-quality care. The purpose of this research is to fill key gaps in the existing literature by emphasizing the importance of therapeutic communication in the setting of mental nursing care for elderly patients in Saudi Arabia. Building on the study's foundation, which recognizes the various issues faced by cultural, religious, and linguistic diversity, this research adopted a rigorous research methodology incorporating a broad group of senior healthcare professionals as experts. We analyze various therapeutic communication approaches used by mental health nurses using extensive surveys and observations. This empirical study's findings are likely to make a significant addition to the field by throwing light on the most efficient methods for improving nurse–elderly-patient communication. The study identifies Simulation-Based Training as the most viable technique, with potentially far-reaching implications for improving care for older patients in Saudi Arabia. This study paves the way for significant advances in healthcare practices, with a focus on mental health nursing, ultimately helping both nurses and elderly patients by developing trust, understanding, and increased communication. [ABSTRACT FROM AUTHOR]

Clinical notes contain valuable patient information. These notes are written by health care providers with various scientific levels and writing styles. It might be helpful for clinicians and researchers to understand what information is essential when dealing with extensive electronic medical records. Entities recognizing them and mapping them to standard terminologies is crucial to reducing ambiguity in processing clinical notes. Although named entity recognition and entity linking are critical steps in clinical natural language processing, they can produce repetitive and low-value concepts. On the other hand, all parts of a clinical text do not share the same importance or content in predicting the patient's condition. As a result, it is necessary to identify the section in which each content item is recorded and critical concepts to extract meaning from clinical texts. In this study, these challenges have been addressed by using clinical natural language processing techniques. In addition, a set of unsupervised essential phrase extraction methods has been verified and evaluated to identify key concepts. Considering that most clinical concepts are in the form of multi-word expressions and their accurate identification requires the user to specify an n-gram range, we have proposed a shortcut method to preserve the structure of the term based on TF-IDF (Term Frequency Inverse Document Frequency). To evaluate, we have designed two types of downstream tasks (multiple and binary classification) using the capabilities of transformer-based models. The results show the proposed method's superiority in combination with the SciBERT model. Also, they offer an insight into the efficacy of general methods for extracting essential phrases from clinical notes. [ABSTRACT FROM AUTHOR]

Background: Due to the increased price of foods in recent years and the diminished food security in Iran, nutrition recommender systems can suggest the most suitable and affordable foods and diets to users based on their health status and food preferences. Objective: The present study aimed to design and evaluate a recommender system to suggest healthy and affordable meals and provide a tele-nutrition consulting service. Methods: This applied three-phase study was conducted in 2020. In the first stage, the food items' daily prices were extracted from credible sources, and accordingly, meals were placed in three price categories. After conducting a systematic review of similar systems, the requirements and data elements were specified and confirmed by 10 nutritionists and 10 health information management and medical informatics experts. In the second phase, the software was designed and developed based on the findings. In the third phase, system usability was evaluated by four experts based on Nielsen's heuristic evaluation. Results: Initially, 72 meals complying with nutritional principles were placed in three price categories. Following a literature review and expert survey, 31 data elements were specified for the system, and the experts confirmed system requirements. Based on the information collected in the previous stage, the Web-based software TanSa in the Persian language was designed, developed, and presented on a unique domain. During the evaluation, the mean severity of the problems associated with Nielsen's 10 principles was 1.2, which is regarded as minor. Conclusion: To promote food security, the designed system recommends healthy, nutritional, and affordable meals to individuals and households based on user characteristics. [ABSTRACT FROM AUTHOR]

BACKGROUND: Diabetes Mellitus (DM) is a significant risk, mostly causing blindness, kidney failure, heart attack, stroke, and lower limb amputation. A Clinical Decision Support System (CDSS) can assist healthcare practitioners in their daily effort and can improve the quality of healthcare provided to DM patients and save time. OBJECTIVE: In this study, a CDSS that can predict DM risk at an early stage has been developed for use by health professionals, general practitioners, hospital clinicians, health educators, and other primary care clinicians. The CDSS infers a set of personalized and suitable supportive treatment suggestions for patients. METHODS: Demographic data (e.g., age, gender, habits), body measurements (e.g., weight, height, waist circumference), comorbid conditions (e.g., autoimmune disease, heart failure), and laboratory data (e.g., IFG, IGT, OGTT, HbA1c) were collected from patients during clinical examinations and used to deduce a DM risk score and a set of personalized and suitable suggestions for the patients with the ontology reasoning ability of the tool. In this study, OWL ontology language, SWRL rule language, Java programming, Protégé ontology editor, SWRL API and OWL API tools, which are well known Semantic Web and ontology engineering tools, are used to develop the ontology reasoning module that provides to deduce a set of appropriate suggestions for a patient evaluated. RESULTS: After our first-round of tests, the consistency of the tool was obtained as 96.5%. At the end of our second-round of tests, the performance was obtained as 100.0% after some necessary rule changes and ontology revisions were done. While the developed semantic medical rules can predict only Type 1 and Type 2 DM in adults, the rules do not yet make DM risk assessments and deduce suggestions for pediatric patients. CONCLUSION: The results obtained are promising in demonstrating the applicability, effectiveness, and efficiency of the tool. It can ensure that necessary precautions are taken in advance by raising awareness of society against the DM risk. [ABSTRACT FROM AUTHOR]

Background: Medical errors occur frequently, yet they are often underreported and strategies to increase the reporting of medical errors are lacking. In this work, we detail how a Level 1 trauma center used a secure messaging application to track medical errors and enhance its quality improvement initiatives. Methods: We describe the formulation, implementation, evolution, and evaluation of a chatroom integrated into a secure texting system to identify performance improvement and patient safety (PIPS) concerns. For evaluation, we used descriptive statistics to examine PIPS reporting by the reporting method over time, the incidence of mortality and unplanned ICU readmissions tracked in the hospital trauma registry over the same, and time-to-loop closure over the study period to quantify the impact of the processes instituted by the PIPS team. We also categorized themes of reported events. Results: With the implementation of a PIPS chatroom, the number of events reported each month increased and texting became the predominant way for users to report trauma PIPS events. This increase in PIPS reporting did not appear to be accompanied by an increase in mortality and unplanned ICU readmissions. The PIPS team also improved the tracking and timely resolution of PIPS events and observed a decrease in time-to-loop closure with the implementation of the PIPS chatroom. Conclusions: The adoption of clinical texting as a way to report PIPS events was associated with increased reporting of such events and more timely resolution of concerns regarding patient safety and healthcare quality. [ABSTRACT FROM AUTHOR]

Aim: Reducing re-applications with the same diagnosis within 24 hours and decreasing the density in emergency departments where many patients apply, as in the pandemic period. Materials and Methods: A comparison was made between quarterly and monthly increases in 2021, 2020 and 2019 during the pandemic period. The data was obtained from the Basic Health Statistics Module and 2 data sets followed by the Health Quality Standards Emergency Department. As a statistical method, data on emergency department admissions and re-admissions for the January-June periods of 2019, 2020 and 2021 were compared with Kruskal Wallis and Mann Whitney tests. Results: Kruskal Wallis test shows that there is a statistically significant difference in terms of re- applications between 2019, 2020 and 2021 (p = 0.006). According to Mann Whitney test results, there are statistically significant differences in terms of re-applications between 2019 and 2020 (p = 0.004) and between 2020 and 2021 (p = 0.016). However, this difference is not significant between 2019 and 2021 (p = 0.336). Re-application Rates: The Kruskal Wallis test shows that there is a statistically significant difference in re-application rates between 2019, 2020 and 2021 (p = 0.034). According to Mann Whitney test results, there is a statistically significant difference in re-application rates between 2020 and 2021 (p = 0.025). However, this difference is not significant between 2019 and 2020 (p = 0.078) and 2019 and 2021 (p = 0.150). Conclusion: Re-application rates to emergency services within 24 hours returned to their pre- pandemic level. Differences in physician-based approaches in diagnosis and treatment have been eliminated and standardization has been achieved. It is aimed to make working easier and more effective in situations that lead to mass applications to emergency hospital services. The same situation has become an example of similar practices in hospital services. [ABSTRACT FROM AUTHOR]

Optical Coherence Tomography (OCT) is an imperative symptomatic tool empowering the diagnosis of retinal diseases and anomalies. The manual decision towards those anomalies by specialists is the norm, but its labor-intensive nature calls for more proficient strategies. Consequently, the study recommends employing a Convolutional Neural Network (CNN) for the classification of OCT images derived from the OCT dataset into distinct categories, including Choroidal NeoVascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal. The average k-fold (k = 10) training accuracy, test accuracy, validation accuracy, training loss, test loss, and validation loss values of the proposed model are 96.33%, 94.29%, 94.12%, 0.1073, 0.2002, and 0.1927, respectively. Fast Gradient Sign Method (FGSM) is employed to introduce non-random noise aligned with the cost function's data gradient, with varying epsilon values scaling the noise, and the model correctly handles all noise levels below 0.1 epsilon. Explainable AI algorithms: Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) are utilized to provide human interpretable explanations approximating the behaviour of the model within the region of a particular retinal image. Additionally, two supplementary datasets, namely, COVID-19 and Kidney Stone, are assimilated to enhance the model's robustness and versatility, resulting in a level of precision comparable to state-of-the-art methodologies. Incorporating a lightweight CNN model with 983,716 parameters, 2.37 × 10 8 floating point operations per second (FLOPs) and leveraging explainable AI strategies, this study contributes to efficient OCT-based diagnosis, underscores its potential in advancing medical diagnostics, and offers assistance in the Internet-of-Medical-Things. [ABSTRACT FROM AUTHOR]

Objective This article reports on the alignment between the foundational domains and the delineation of practice (DoP) for health informatics, both developed by the American Medical Informatics Association (AMIA). Whereas the foundational domains guide graduate-level curriculum development and accreditation assessment, providing an educational pathway to the minimum competencies needed as a health informatician, the DoP defines the domains, tasks, knowledge, and skills that a professional needs to competently perform in the discipline of health informatics. The purpose of this article is to determine whether the foundational domains need modification to better reflect applied practice. Materials and Methods Using an iterative process and through individual and collective approaches, the foundational domains and the DoP statements were analyzed for alignment and eventual harmonization. Tables and Sankey plot diagrams were used to detail and illustrate the resulting alignment. Results We were able to map all the individual DoP knowledge statements and tasks to the AMIA foundational domains, but the statements within a single DoP domain did not all map to the same foundational domain. Even though the AMIA foundational domains and DoP domains are not in perfect alignment, the DoP provides good examples of specific health informatics competencies for most of the foundational domains. There are, however, limited DoP knowledge statements and tasks mapping to foundational domain 6—Social and Behavioral Aspects of Health. Discussion Both the foundational domains and the DoP were developed independently, several years apart, and for different purposes. The mapping analyses reveal similarities and differences between the practice experience and the curricular needs of health informaticians. Conclusions The overall alignment of both domains may be explained by the fact that both describe the current and/or future health informatics professional. One can think of the foundational domains as representing the broad foci for educational programs for health informaticians and, hence, they are appropriately the focus of organizations that accredit these programs. [ABSTRACT FROM AUTHOR]

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Background: In response to citizens' concerns about elevated cancer incidence in their locales, US CDC proposed publishing cancer incidence at sub-county scales. At these scales, confidence in patients' residential geolocation becomes a key constraint of geospatial analysis. To support monitoring cancer incidence in sub-county areas, we presented summary metrics to numerically delimit confidence in residential geolocation. Results: We defined a concept of Residential Address Discriminant Power (RADP) as theoretically perfect within all residential addresses and its practical application, i.e., using Emergency Dispatch (ED) Address Point Candidates of Equivalent Likelihood (CEL) to quantify Residential Geolocation Discriminant Power (RGDP) to approximate RADP. Leveraging different productivity of probabilistic, deterministic, and interactive geocoding record linkage, we simultaneously detected CEL for 5,807 cancer cases reported to North Carolina Central Cancer Registry (NC CCR)- in January 2022. Batch-match probabilistic and deterministic algorithms matched 86.0% cases to their unique ED address point candidates or a CEL, 4.4% to parcel site address, and 1.4% to street centerline. Interactively geocoded cases were 8.2%. To demonstrate differences in residential geolocation confidence between enumeration areas, we calculated sRGDP for cancer cases by county and assessed the existing uncertainty within the ED data, i.e., identified duplicate addresses (as CEL) for each ED address point in the 2014 version of the NC ED data and calculated ED_sRGDP by county. Both summary RGDP (sRGDP) (0.62–1.00) and ED_sRGDP (0.36–1.00) varied across counties and were lower in rural counties (p < 0.05); sRGDP correlated with ED_sRGDP (r = 0.42, p < 0.001). The discussion covered multiple conceptual and economic issues attendant to quantifying confidence in residential geolocation and presented a set of organizing principles for future work. Conclusions: Our methodology produces simple metrics – sRGDP – to capture confidence in residential geolocation via leveraging ED address points as CEL. Two facts demonstrate the usefulness of sRGDP as area-based summary metrics: sRGDP variability between counties and the overall lower quality of residential geolocation in rural vs. urban counties. Low sRGDP for the cancer cases within the area of interest helps manage expectations for the uncertainty in cancer incidence data. By supplementing cancer incidence data with sRGDP and ED_sRGDP, CCRs can demonstrate transparency in geocoding success, which may help win citizen trust. Highlights: Confidence in patients' residential geolocation becomes a key constraint of geospatial analysis for small areas. The presented novel methodology uses a concept of Residential Address Discriminant Power (RADP) as theoretically perfect within all residential addresses and its practical application, i.e., Emergency Dispatch (ED) Address Point Candidates of Equivalent Likelihood (CEL) to quantify Residential Geolocation Discriminant Power (RGDP) to approximate RADP. RGDP can be summarized for cases across a specific area (sRGDP) and serve as a threshold of confidence in residential geolocation. The analysis of data from the NC Cancer Registry showed wide variability of sRGDP between NC counties. The proposed methods for sRGDP apply to countries besides the US, specifically, those with a system of ED address points. [ABSTRACT FROM AUTHOR]

Background: The digitalization studies in public hospitals in Türkiye started with the Health Transformation Program in 2003. As digitalization was accomplished, the policymakers needed to measure hospitals' electronic health record (EHR) usage and adoptions. The ministry of health has been measuring the dissemination of meaningful usage and adoption of EHR since 2013 using Electronic Medical Record Adoption Model (EMRAM). The first published study about this analysis covered the surveys applied between 2013 and 2017. The results showed that 63.1% of all hospitals in Türkiye had at least basic EHR functions, and 36% had comprehensive EHR functions. Measuring the countrywide EHR adoption level is becoming popular in the world. This study aims to measure adoption levels of EHR in public hospitals in Türkiye, indicate the change to the previous study, and make a benchmark with other countries measuring national EHR adoption levels. The research question of this study is to reveal whether there has been a change in the adoption level of EHR in the three years since 2018 in Türkiye. Also, make a benchmark with other countries such as the US, Japan, and China in country-wide EHR adoption in 2021. Methods: In 2021, 717 public hospitals actively operating in Türkiye completed the EMRAM survey. The survey results, deals with five topics (General Stage Status, Information Technology Security, Electronic Health Record/Clinical Data Repository, Clinical Documentation, Closed-Loop Management), was reviewed by the authors. Survey data were compared according to hospital type (Specialty Hospitals, General Hospitals, Teaching and Research Hospitals) in terms of general stage status. The data obtained from the survey results were analyzed with QlikView Personal Edition. The availability and prevalence of medical information systems and EHR functions and their use were measured. Results: We found that 33.7% of public hospitals in Türkiye have only basic EHR functions, and 66.3% have extensive EHR functions, which yields that all hospitals (100%) have at least basic EHR functions. That means remarkable progress from the previous study covering 2013 and 2017. This level also indicates that Türkiye has slightly better adoption from the US (96%) and much better than China (85.3%) and Korea (58.1%). Conclusions: Although there has been outstanding (50%) progress since 2017 in Turkish public hospitals, it seems there is still a long way to disseminate comprehensive EHR functions, such as closed-loop medication administration, clinical decision support systems, patient engagement, etc. Measuring the stage of EHR adoption at regular intervals and on analytical scales is an effective management tool for policymakers. The bottom-up adoption approach established for adopting and managing EHR functions in the US has also yielded successful results in Türkiye. [ABSTRACT FROM AUTHOR]

Background: Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care. Design: Systematic review and meta-analyses. Participants: Older adults (≥ 65 years) in any setting. Intervention: Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months. Outcome measures: Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric. Results: Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 – 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 – 0.86) for mortality over 6 months, signifying good discriminatory power. Conclusion: The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes. [ABSTRACT FROM AUTHOR]

Background: This study investigated the acceptance and use of massive open online courses (MOOCs) among health informatics educators and students in Saudi Arabian academic institutions. A theoretical model based on the unified theory of acceptance and use of technology (UTAUT), self-determination theory (SDT), and channel expansion theory (CET) was used to identify factors that affect MOOC adoption in health informatics education. Methods: A survey research design was employed, and cross-sectional data were collected from health informatics instructors and students in academic institutions in Saudi Arabia. A total of 145 completed responses were used in the final analysis of the data. Results: The findings indicated that performance and effort expectancy were important factors that could predict the acceptance and use of MOOCs among health informatics instructors and students. Additionally, perceived media richness affected the actual use of health informatics MOOCs among students and instructors in Saudi Arabian academic institutions. The results of this study show that autonomy, relatedness, and competence must be considered in the design of health informatics MOOCs. Conclusions: A combination of these models can effectively explain the adoption and use of MOOCs in emerging fields such as health informatics. [ABSTRACT FROM AUTHOR]

Background: At least 163 countries use a form of home-based record, a document to record health information kept at home. These are predominantly paper-based, although some countries are digitalizing home-based records for improved access and use. This scoping review aimed to identify efforts already undertaken for the digitalization of home-based records for maternal, newborn, and child health (MNCH) and lessons learned moving forward, by mapping the available peer-reviewed and grey literature. Methods: The scoping review was guided by Arskey and O'Malley's framework. A literature search of references published from 2000 until 2021 was conducted in Medline, Embase, CINAHL, EBM reviews, Google Scholar, IEEE Xplore as well as a grey literature search. Title and abstract and full texts were screened in Covidence. A final data extraction sheet was generated in Excel. Results: The scoping review includes 107 references that cover 120 unique digital interventions. Most of the included references are peer-reviewed articles in English language published after 2015. Of the 120 unique digital interventions, 80 (66.7%) are used in 31 different countries and 40 (33.3%) are globally available pregnancy applications. Out of the 80 digitalization efforts from countries, most are concentrated in high-income countries (n=68, 85%). Maternal health (n=73; 61%) and child health (n=60; 50%) are the main health domains covered; the main users are pregnant women (n=57; 48%) and parents/caregivers (n=43; 36%). Conclusions: Most digital home-based records for MNCH are centered in high-income countries and revolve around pregnancy applications or portals for home access to health records covering MNCH. Lessons learned indicate that the success of digital home-based records correlates with the usability of the intervention, digital literacy, language skills, ownership of required digital devices, and reliable electricity and internet access. The digitalization of home-based records needs to be considered together with digitizing patient health records. [ABSTRACT FROM AUTHOR]

Background: Family violence homicide (FVH) is a major public health and social problem in Australia. FVH trend rates are key outcomes that determine the effectiveness of current management practices and policy directions. Data source–related methodological problems affect FVH research and policy and the reliable measurement of homicide trends. Objective: This study aimed to determine data reliability and temporal trends of Victorian FVH rates and sex and relationship patterns. Method: FVH rates per 100,000 persons in Victoria were compared between the National Coronial Information System (NCIS), Coroners Court of Victoria (CCoV) Homicide Register, and the National Homicide Monitoring Program (NHMP). Trends for 2001–2017 were analysed using Joinpoint regression. Crude rates were determined by sex and relationship categories using annual frequencies and Australian Bureau of Statistics population estimates. Results: NCIS closed FVH cases totalled 360, and an apparent downward trend in the FVH rate was identified. However, CCoV and NHMP rates trended upwards. While NCIS and CCoV were case-based, NHMP was incident-based, contributing to rate variations. The NCIS-derived trend was particularly impacted by unavailable case data, potential coding errors and entry backlog. Neither CCoV nor NHMP provided victim-age in their public domain data to enable age-adjusted rate comparison. Conclusion: Current datasets have limitations for FVH trend determination; most notably lag times for NCIS data. Implications: This study identified an indicative upward trend in FVH rates in Victoria, suggesting insufficiency of current management and policy settings for its prevention and control. [ABSTRACT FROM AUTHOR]

Introduction. Polypharmacy is associated with potentially inappropriate medicine prescribing and avoidable medicine-related harm. Polypharmacy should not be perceived as inherently harmful. Instead, priority should be placed on reducing inappropriate prescribing. Aim. The study aimed to develop and validate PolyScan, a primary care information technology tool, to triage older adults with polypharmacy who are prescribed potentially inappropriate medicines. Methods. Twenty-one indicators from a New Zealand criteria of potentially inappropriate medicines to correct for older adults with polypharmacy were developed into a set of implementable definitions. The definitions were applied as algorithmic logic statements used to interrogate hospital and emergency department records and pharmaceutical collection data to classify whether each indicator was present at an individual patient level, and then triage individuals based on the number of indicators met. Validity was evaluated by comparing PolyScan’s accuracy against a manual review of healthcare records for 300 older adults. Results. PolyScan was successfully implemented as a tool that can be used to identify potentially inappropriate prescribing in older adults with polypharmacy at different levels of aggregation. The tool has utility for individual practitioners delivering patient care, primary care organisations undertaking quality improvement programmes, and policymakers considering system-level interventions for medicines-related safety. During the validity assessment, PolyScan identified nine individuals (3%) with polypharmacy and indicators of potentially inappropriate medicine. Five unique indicators were detected. PolyScan achieved 100% sensitivity, specificity, and positive and negative predictive values. Discussion. PolyScan can support clinicians, clinics, and policymakers with allocation of resources, rational medicine campaigns, and identifying individuals prescribed potentially inappropriate medicines for review. [ABSTRACT FROM AUTHOR]

Relying on user-generated content narrating individual experiences and personalized contextualization of location-specific realities, this study introduced a novel methodological approach and analysis tool that can aid health informatics in understanding the social reality of people with a substance-use disorder in Skid Row, Los Angeles. The study also highlighted analysis possibilities for big unstructured interview text corpus using InfraNodus, a text network analysis tool. InfraNodus, which is a text graph analysis tool, identifies pathways for meaning circulation within unstructured interview data and has the potential to classify topical clusters and generate contextualized analysis results for big narrative textual datasets. Using InfraNodus, we analyzed a 1,103,528-word unstructured interview transcript from 315 interview sessions with people with a substance-use disorder, who narrated their respective social realities. Challenging the overgeneralization of onlookers, the conceptualization process identified topical clusters and pathways for meaning circulation within the narrative data, generating unbiased contextualized meaning for the collective social reality. Our endeavors in this research, along with our methodological setting and selection, might contribute to the methodological efforts of health informatics or the conceptualization and visualization needs of any big text corpus. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has once again highlighted the ubiquity and persistence of health inequities along with our inability to respond to them in a timely and effective manner. There is an opportunity to address the limitations of our current approaches through new models of informatics-enabled research and clinical practice that shift the norm from small- to large-scale patient engagement. We propose augmenting our approach to address health inequities through informatics-enabled citizen science, challenging the types of questions being asked, prioritized, and acted upon. We envision this democratization of informatics that builds upon the inclusive tradition of community-based participatory research (CBPR) as a logical and transformative step toward improving individual, community, and population health in a way that deeply reflects the needs of historically marginalized populations.

The narrative of the digital phenotype as a transformative vector in healthcare is nearly identical to the concept of "data drivenness" in other fields such as law enforcement. We examine the role of a prescription drug monitoring program (PDMP) in California-a computerized law enforcement surveillance program enabled by a landmark Supreme Court case that upheld "broad police powers"-in the interprofessional conflict between physicians and law enforcement over the jurisdiction of drug use. We bring together interview passages, clinical artifacts, and academic and gray literature to investigate the power relations between police, physicians, and patients to show that prescribing data appear to the physician as evidence of problematic patient behavior by the patients, and to law enforcement as evidence of physician misconduct. In turn, physicians have adopted a disciplinary approach to patients, using quasi-legalistic documents to litigate patient behavior. We conclude that police powers have been used to pave data infrastructure through a contested jurisdiction, and law enforcement have used that infrastructure to enroll physicians into the work of disciplining patients.

A healthy, active woman in her 70s reported intermittent exertional dyspnoea for 2 months, notable during frequent open-water swimming. Symptoms were similar to an episode of travel-provoked pulmonary embolism 3 years prior. She denied chest pain, cough, fever, extremity complaints and symptoms at rest. Due to the COVID-19 pandemic, her healthcare system was using secure telemedicine to evaluate non-critical complaints. During the initial video visit, she appeared well, conversing normally without laboured breathing. An elevated serum D-dimer prompted CT pulmonary angiography, which identified acute lobar pulmonary embolism. After haematology consultation and telephone conversation with the patient, her physician prescribed rivaroxaban. Her symptoms rapidly improved. She had an uneventful course and is continuing anticoagulation indefinitely. The pandemic has increased the application of telemedicine for acute care complaints. This case illustrates its safe and effective use for comprehensive management of acute pulmonary embolism in the primary care setting.

ObjectivesObstructive sleep apnoea (OSA) is a risk factor for hypertension (HTN), but the clinical progression of OSA to HTN is unclear. There are also sex differences in prevalence, screening and symptoms of OSA. Our objective was to estimate the time from OSA to HTN diagnoses in females and males.DesignRetrospective analysis of electronic health records (EHR) over 10 years (2006-2015 inclusive).SettingUniversity of California Los Angeles (UCLA) Health System in Los Angeles, California, USA.Participants4848 patients: females n=2086, mean (SD) age=52.8 (13.2) years; males n=2762, age=53.8 (13.5) years. These patients were selected from 1.6 million with diagnoses in the EHR who met these criteria: diagnoses of OSA and HTN; in long-term care defined by ambulatory visits at least 1 year prior and 1 year subsequent to the first OSA diagnosis; no diagnosis of OSA or HTN at intake; and a sleep study performed at UCLA.Primary and secondary outcome measuresThe primary outcome measure in each patient was time from the first diagnosis of OSA to the first diagnosis of HTN (OSA to HTN days). Since HTN and OSA are progressive disorders, a secondary measure was the relationship between OSA to HTN time and age (OSA to HTN=β1×Age+β0).ResultsThe median (lower and upper quartiles) days from OSA to HTN were: all -532 (-1439, -3); females -610 (-1579, -42); and males -451 (-1358, 0). Older age in both sexes was associated with less time to a subsequent HTN diagnosis or more time from a prior HTN diagnosis (β1 days/year: all -16.9, females -18.3, males -15.9).ConclusionsHTN was on average diagnosed years prior to OSA, with a longer separation in females. Our findings are consistent with underscreening of OSA, more so in females than males. Undiagnosed OSA may delay treatment for the sleep disorder and perhaps affect the development and progression of HTN.

ObjectiveInnovative approaches are needed for assessing treatment preferences of individuals with schizophrenia. Conjoint analysis methods may help to identify preferences, but the usability and validity of these methods for individuals with schizophrenia remain unclear. This study examined computerized conjoint analysis for persons with schizophrenia and whether preferences for weight management programs predict service use.MethodsA computerized, patient-facing conjoint analysis system was developed through iterative consultation with 35 individuals with schizophrenia enrolled at a community mental health clinic. An additional 35 overweight participants with schizophrenia then used the system to choose among psychosocial weight management programs varying in four attributes: location (community or clinic), delivery mode (Internet or in person), leader (clinician or layperson), and training mode (individual or group). A multilevel logit model with partial preference data determined contributions of each attribute to groupwide preferences. Associations were studied between preferences and use of a psychosocial weight management group.ResultsConjoint analysis system usability was rated highly. Groupwide preferences were significantly influenced by location (p

[Purpose/Significance] With the development of politics and economy, the public's attention to health information has significantly increased. The public's behavior of searching, sharing, avoiding, and collaborating on health information has attracted widespread attention. In the ubiquitous information environment, the impact of information behavior interactivity is also gradually increasing. The conflict and control of interpersonal information behavior are gradually receiving attention. Among various types of interpersonal relationships, conflicts and controls in family health information behavior are more prominent, with intergenerational conflicts and controls being the most typical. Therefore, taking the intergenerational relationship between parents and children as the starting point, this paper explores the causes of intergenerational conflict health information behavior and its mechanism in the implementation/response process of social control. An analysis of intergenerational conflict health information behavior can provide assistance for the public to better handle intergenerational relationships, improve the efficiency of parent-child health information communication, and provide certain reference for research on interpersonal health information behavior based on interactivity. [Method/Process] On the basis of literature review, centering on the interactive characteristics of intergenerational health information behavior, from the perspective of information conflict and social control, and taking the whole process of health information behavior interaction of different subjects as the underlying logic, this paper puts forward five core issues. A questionnaire was designed based on the core questions. From the perspective of parents as implementers of control behavior, we investigated the attitudes and reactions of children towards family conflict health information behavior. From the perspective of children as implementers of control behavior, we investigated parents' attitudes and responses to conflict health information behavior in their families. [Results/Conclusions] It is found that there are differences among family members in the information acquisition situation, information source selection and judgment in the preparatory process of implementing/responding to social control. In the process of implementing/responding to social control, family members will choose different ways to implement/respond to different social control, such as information sharing, information evaluation, information avoidance, and information utilization. After implementing/coping with social control, compared with children, parents' sharing behavior and control behavior are more likely to be affected by children's attitudes. Intergenerational conflict health information behavior plays an important role in the implementation/response process of social control. Based on the analysis, the analysis model of intergenerational conflict health information behavior was constructed, and the causes of intergenerational conflict health information behavior and its mechanism in the implementation of social control were explained to some extent. Interpersonal communication is an important means to alleviate intergenerational health information conflict. In the future, qualitative research methods can be used to supplement and improve the model, and more in-depth research and exploration can be conducted around issues such as intergenerational conflict health information behavior and intergenerational information communication.

Abstract Background A large number of information and communication technology (ICT) based interventions exist for suicide prevention. However, not much is known about which of these ICTs are implemented in clinical settings and their implementation characteristics. In response, this scoping review aimed to systematically explore the breadth of evidence on ICT-based interventions for suicide prevention implemented in clinical settings and then to identify and characterize implementation barriers and facilitators, as well as evaluation outcomes, and measures. Methods We conducted this review following the Joanna Briggs Institute methodology for scoping reviews. A search strategy was applied to the following six databases between August 17–20, 2021: MEDLINE, Embase, CINAHL, PsycINFO, Web of Science, and Library, Information Science and Technology Abstracts. We also supplemented our search with Google searches and hand-searching reference lists of relevant reviews. To be included in this review, studies must include ICT-based interventions for any spectrum of suicide-related thoughts and behaviours including non-suicidal self-injury. Additionally, these ICTs must be implemented in clinical settings, such as emergency department and in-patient units. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist to prepare this full report. Results This review included a total of 75 citations, describing 70 studies and 66 ICT-based interventions for suicide prevention implemented in clinical settings. The majority of ICTs were computerized interventions and/or applications (n = 55). These ICTs were commonly used as indicated strategies (n = 49) targeting patients who were actively presenting with suicide risk. The three most common suicide prevention intervention categories identified were post-discharge follow-up (n = 27), screening and/or assessment (n = 22), and safety planning (n = 20). A paucity of reported information was identified related to implementation strategies, barriers and facilitators. The most reported implementation strategies included training, education, and collaborative initiatives. Barriers and facilitators of implementation included the need for resource supports, knowledge, skills, motivation as well as engagement with clinicians with research teams. Studies included outcomes at patient, clinician, and health system levels, and implementation outcomes included acceptability, feasibility, fidelity, and penetration. Conclusion This review presents several trends of the ICT-based interventions for suicide prevention implemented in clinical settings and identifies a need for future research to strengthen the evidence base for improving implementation. More effort is required to better understand and support the implementation and sustainability of ICTs in clinical settings. The findings can also serve as a future resource for researchers seeking to evaluate the impact and implementation of ICTs.

BackgroundWith the expansion of online patient portals linked to electronic health records in safety-net health care settings, we need more data on the use of these websites by patients with limited English proficiency (LEP) in order to guide their continued design, implementation, and evaluation as portals for the underserved.MethodsCross-sectional portal data for the Los Angeles County Department of Health Services, the second largest safety-net system in the nation. We examined differences in portal use across language (English vs. non-English/LEP), covering four years since implementation.ResultsOf 425,281 patients assigned to primary care as of March 2019, 55,190 (13%) unique portal enrollments were registered. Among 54,981 portal users, LEP users had lower adjusted odds of using an active portal function (e.g., medication refill) vs. English-speakers.ConclusionsEven among those registered to access portals, these websites are underused, particularly by LEP patients. All systems must facilitate use for these populations, especially for time-saving active functions, which can improve outcomes. Health systems must prioritize design/usability as a factor to counter LEP underuse.

ObjectivesRapid detection and surveillance of COVID-19 is essential to reducing spread of the virus. Inadequate screening capacity has hampered COVID-19 detection, while traditional infectious disease response has been delayed due to significant demands for healthcare resources, time and personnel. This study investigated whether an online health decision-support tool could supplement COVID-19 surveillance and detection in China and the USA.SettingDaily website traffic to Thermia was collected from China and the USA, and cross-correlation analyses were used to assess the designated lag time between the daily time series of Thermia sessions and COVID-19 case counts from 22 January to 23 April 2020.ParticipantsThermia is a validated health decision-support tool that was modified to include content aimed at educating users about Centers for Disease Control and Prevention recommendations on COVID-19 symptoms. An advertising campaign was released on Microsoft Advertising to refer searches for COVID-19 symptoms to Thermia.ResultsThe lead times observed for Thermia sessions to COVID-19 case reports was 3 days in China and 19 days in the USA. We found negative cross-correlation between the number of Thermia sessions and rates of influenza A and B, possibly due to the decreasing prevalence of influenza and the lack of specificity of the system for identification of COVID-19.ConclusionThis study suggests that early deployment of an online campaign and modified health decision-support tool may support identification of emerging infectious diseases like COVID-19. Researchers and public health officials should deploy web campaigns as early as possible in an epidemic to detect, identify and engage those potentially at risk to help prevent transmission of the disease.

BackgroundTo improve mental health care access, the Veterans Health Administration (VA) implemented Primary Care-Mental Health Integration (PC-MHI) in clinics nationally. Primary care clinical leader satisfaction can inform model implementation and may be facilitated by collaborative care managers and technology supporting cross-specialty collaboration.Objective(1) To determine primary care clinical leaders' overall satisfaction with care from embedded mental health providers for a range of conditions and (2) to examine the association between overall satisfaction and two program features (care managers, technology).DesignCross-sectional organizational survey in one VA region (Southern California, Arizona, and New Mexico), 2018.ParticipantsSixty-nine physicians or other designated clinical leaders in each VA primary care clinic (94% response rate).Main measuresWe assessed primary care clinical leader satisfaction with embedded mental health care on four groups of conditions: target, non-target mental health, behavioral health, suicide risk management. They additionally responded about the availability of mental health care managers and the sufficiency of information technology (telemental health, e-consult, instant messaging). We examined relationships between satisfaction and the two program features using χ2 tests and multivariable regressions.Key resultsMost primary care clinical leaders were "very satisfied" with care for targeted anxiety (71%) and depression (69%), but not for other common conditions (37% alcohol misuse, 19% pain). Care manager availability was significantly associated with "very satisfied" responses for depression (p = .02) and anxiety care by embedded mental health providers (p = .02). Highly rated sufficiency of communication technology (only 19%) was associated with "very satisfied" responses to suicide risk management (p = .002).ConclusionsCare from embedded mental health providers for depression and anxiety was highly satisfactory, which may guide improvement among less satisfactory conditions (alcohol misuse, pain). Observed associations between overall satisfaction and collaborative care features may inform clinics on how to optimize staffing and technology based on priority conditions.

A significant increase in the demand for quality healthcare has resulted from people becoming more aware of health issues. With blockchain, healthcare providers may safely share patient information electronically, which is especially important given the sensitive nature of the data contained inside them. However, flaws in the current blockchain design have surfaced since the dawn of quantum computing systems. The study proposes a novel quantum-inspired blockchain system (Qchain) and constructs a unique entangled quantum medical record (EQMR) system with an emphasis on privacy and security. This Qchain relies on entangled states to connect its blocks. The automated production of the chronology indicator reduces storage capacity requirements by connecting entangled BloQ (blocks with quantum properties) to controlled activities. We use one qubit to store the hash value of each block. A lot of information regarding the quantum internet is included in the protocol for the entangled quantum medical record (EQMR). The EQMR can be accessed in Medical Internet of Things (M-IoT) systems that are kept private and secure, and their whereabouts can be monitored in the event of an emergency. The protocol also uses quantum authentication in place of more conventional methods like encryption and digital signatures. Mathematical research shows that the quantum converged blockchain (QCB) is highly safe against attacks such as external attacks, intercept measure -repeat attacks, and entanglement measure attacks. We present the reliability and auditability evaluations of the entangled BloQ, along with the quantum circuit design for computing the hash value. There is also a comparison between the suggested approach and several other quantum blockchain designs.

Social media has become more popular in the last few years. It has been used in public health development and healthcare settings to promote healthier lifestyles. Given its important role in today’s culture, it is necessary to understand its current trends and future directions in public health. This review aims to describe and summarize how public health professionals have been using social media to improve population outcomes. This review highlights the substantial influence of social media in advancing public health objectives. The key themes explored encompass the utilization of social media to advance health initiatives, monitor diseases, track behaviors, and interact with communities. Additionally, it discusses potential future directions on how social media can be used to improve population health. The findings show how social media has been used as a tool for research, implementing health campaigns, and health promotion. Social media integration with artificial intelligence (AI) and Generative Pre-Trained Transformers (GPTs) can impact and offer an innovative approach to tackle the problems and difficulties in health informatics. The research shows how social media will keep growing and evolving and, if used effectively, has the potential to help close public health gaps across different cultures and improve population health.

The advancement of machine learning in healthcare offers significant potential for enhancing disease prediction and management. This study harnesses the PyCaret library—a Python-based machine learning toolkit—to construct and refine predictive models for diagnosing diabetes mellitus and forecasting hospital readmission rates. By analyzing a rich dataset featuring a variety of clinical and demographic variables, we endeavored to identify patients at heightened risk for diabetes complications leading to readmissions. Our methodology incorporates an evaluation of numerous machine learning algorithms, emphasizing their predictive accuracy and generalizability to improve patient care. We scrutinized the predictive strength of each model concerning crucial metrics like accuracy, precision, recall, and the area under the curve, underlining the imperative to eliminate false diagnostics in the field. Special attention is given to the use of the light gradient boosting machine classifier among other advanced modeling techniques, which emerge as particularly effective in terms of the Kappa statistic and Matthews correlation coefficient, suggesting robustness in prediction. The paper discusses the implications of diabetes management, underscoring interventions like lifestyle changes and pharmacological treatments to avert long-term complications. Through exploring the intersection of machine learning and health informatics, the study reveals pivotal insights into algorithmic predictions of diabetes readmission. It also emphasizes the necessity for further research and development to fully incorporate machine learning into modern diabetes care to prompt timely interventions and achieve better overall health outcomes. The outcome of this research is a testament to the transformative impact of automated machine learning in the realm of healthcare analytics.

Abstract Objectives Surveillance of infant and fetal deaths is of paramount importance in thinking about government strategies to reduce these rates, provide greater visibility of these mortality figures in the country, enable the adoption of prevention measures, as well as contribute to a better record of deaths. Data description The dataset comprises fetal, neonatal, early neonatal, late neonatal, and perinatal Mortality Rates of Brazilian municipalities with their respective information, between 2010 to 2020, aggregated by epidemiological week.

This document is an editorial published in the journal Frontiers in Digital Health. It discusses the potential of ubiquitous and learning health systems to revolutionize healthcare by promoting sustainable behavior change and empowering individuals in their health journeys. The editorial highlights the integration of digital health technologies into everyday life and the opportunities they offer for preventive and rehabilitative healthcare. It also emphasizes the challenges and considerations related to data security, privacy, and equitable distribution of healthcare benefits. The authors emphasize the need for personalized and adaptive healthcare interventions, as well as further research to understand the complex interplay of variables in improving individual and population health. [Extracted from the article]

BackgroundIn November 2019, an infectious agent that caused a severe acute respiratory illness was first detected in China. Its rapid spread resulted in a global lockdown with negative economic impacts. In this regard, we expose the solutions proposed by a multinational financial institution that maintained their workers on premises, so this methodology can be applied to possible future health crisis.ObjectivesTo ensure a secure workplace for the personnel on premises employing biomedical prevention measures and computational tools.MethodsProfessionals were subjected to recurrent COVID-19 diagnostic tests during the pandemic. The sanitary team implemented an individual following to all personnel and introduced the information in databases. The data collected were used for clustering algorithms, decision trees, and networking diagrams to predict outbreaks in the workplace. Individualized control panels assisted the decision-making process to increase, maintain, or relax restrictive measures.Results55,789 diagnostic tests were performed. A positive correlation was observed between the cumulative incidence reported by Madrid’s Ministry of Health and the headcount. No correlation was observed for occupational infections, representing 1.9% of the total positives. An overall 1.7% of the cases continued testing positive for COVID-19 after 14 days of quarantine.ConclusionBased on a combined approach of medical and computational science tools, we propose a management model that can be extended to other industries that can be applied to possible future health crises. This work shows that this model resulted in a safe workplace with a low probability of infection among workers during the pandemic.

Summary: Accurate pathological classification and grading of gliomas is crucial in clinical diagnosis and treatment. The application of deep learning techniques holds promise for automated histological pathology diagnosis. In this study, we collected 733 whole slide images from four medical centers, of which 456 were used for model training, 150 for internal validation, and 127 for multi-center testing. The study includes 5 types of common gliomas.A subtask-guided multi-instance learning image-to-label training pipeline was employed. The pipeline leveraged “patch prompting” for the model to converge with reasonable computational cost. Experiments showed that an overall accuracy of 0.79 in the internal validation dataset. The performance on the multi-center testing dataset showed an overall accuracy to 0.73. The findings suggest a minor yet acceptable performance decrease in multi-center data, demonstrating the model’s strong generalizability and establishing a robust foundation for future clinical applications.

Summary: All-solid-state batteries have been developed to increase energy density by replacing the lithiated graphite negative electrode by a lithium metal foil and to increase safety by removing the organic compounds. However, the safety issues of these batteries have received little attention up to now.The behavior of a reassembled all-solid-state battery under thermal stress was recorded by X-ray radiography and a high-speed camera. The thermal runaway (TR) lasted about 5 ms, thus extremely fast reaction kinetics. In comparison, the TR of a lithium-ion battery is about 500 ms. Furthermore, a 188-mbar aerial overpressure was measured using a piezoelectric sensor. Although this cell is not an explosive, 2.7 g TNT equivalent was calculated for it.This atypical behavior could have an impact on the casing or the battery pack. Therefore, it must be studied in greater detail.

The educational recommendations aim is to provide a tool for academic program evaluators to compare and accredit the quality of delivered programs; support educators in developing BMHI (Biomedical and Health Informatics) curricula at various educational levels; identify essential skills and competencies for healthcare professionals and those working in the field of BMHI certification; and encourage institutions, organizations, and health authorities to recognize the need for establishing and further developing BMHI educational programs. A brief presentation outlining the most recent updated version of the recommendations—which are offered for courses and course tracks in BMHI as part of educational programs in biomedical and health sciences, health information management, and informatics/computer science, as well as for dedicated programs in BMHI (leading to bachelor's, master's, or doctoral degrees)—follows a brief history of the IMIA Educational activities and its support in developing the recommendations. The six domain areas of BMHI fundamental principles, health sciences and services, computer, data, and information sciences, social and behavioral sciences, management science, and BMHI specialization are used to characterize the educational requirements for the roles of BMHI user, BMHI generalist, and BMHI specialist. Additionally, suggestions are made for doctorate, master's, and bachelor's degree-level BMHI-focused educational programs. These are the BMHI's main academic offerings. Furthermore, suggestions for protocols related to certification, accreditation, and continuing education are given. The IMIA recommendations center on the educational needs for the healthcare workforce, computer scientists, and decision makers to acquire BMHI knowledge and skills at various levels. These recommendations reflect societal changes related to globalization, digitalization, and digital transformation in general and in healthcare specifically. Finally, a brief explanation of the accrediting quality control procedure will be provided.

The rapid onset of the COVID-19 pandemic elicited a swift response to control the virus ubiquitous within the United States. Expanded telehealth and health informatics became critical components of the pandemic response. The aim of this study was to assess the utilization of the COVID-19 New York (NY) Alert App and identify the perceived benefits and limitations of the App. A cross-sectional design was employed to collect data by using questionnaires with closed-ended questions. The survey was developed and administered during March through April 2021. The study found that the highest rated benefit from using the COVID-19 NY Alert App was receiving alerts about being in close proximity to individuals diagnosed with COVID-19. Results showed that ineffective (insufficient and inappropriate) usage was the highest rated potential challenge for using the App. Study subjects were likely to download this Alert App when they perceived more benefits and less barriers to using the App. Findings from this study can help improve utilization of the App and inform development of similar tele-education tools. The study illuminated considerations for health information applications in scaling-up traditional COVID-19 tracing efforts and may facilitate the design of similar emergency preparedness health technology.

IntroductionHealth informatics (HI) has the potential to address health disparities and improve healthcare outcomes; however, lack of diversity in the workforce, especially at advanced levels, feeds disparities, and creates inequities. Increasing participation from underrepresented minorities requires exposure and connection to the field and career opportunities including advanced degrees. Morehouse School of Medicine (MSM) created the no-cost, 5-week intensive online Bridge to Health Informatics (B2HI) program in 2021 to connect participants from backgrounds underrepresented in HI with industry-relevant skills, professional certification, business experience, and graduate training opportunities.MethodsThe curriculum included intensive training in knowledge and current trends, business propositions, and professional certifications. Successful completion of the B2HI provided scholars with guaranteed admissions into the Master of Science in Health Informatics (MSHI) degree program, which premiered at MSM in August 2021. We deployed several strategies to attract individuals interested in advancing their careers in HI. To assess the impact of the recruitment strategy and program, we analyzed applicant demographics, the interests of those accepted into the program, and feedback from participants via anonymous online surveys.ResultsIn 2021, our recruitment efforts attracted 65 applications, over whom 90% self-identified as African American and the majority were working adults. Among the 46 applicants accepted and enrolled, 38 (83%) completed the program. Thirteen B2HI graduates enrolled in the first offerings (Fall 2021 and Spring 2022) of the online MSHI degree program, the highest number for the first sessions of our online master’s degree programs, indicating that B2HI connects scholars with advanced training. Participants indicated immense overall satisfaction with the program and had strong positive responses for increased interest and confidence to do advanced work in the subject.DiscussionThe data indicate that B2HI addresses key factors that hinder diversity in healthcare and informatics-related fields, including awareness, access, and social support, and connect students with the next stages of training toward careers that require an advanced degree. By providing opportunity, resources, and networks that help participants raise their potential to become leaders and decision-makers in the healthcare industry, programs like B2HI enable the collective effort to decrease health disparities.

Summary: ChatGPT is an artificial intelligence product developed by OpenAI. This study aims to investigate whether ChatGPT can respond in accordance with evidence-based medicine in neurosurgery. We generated 50 neurosurgical questions covering neurosurgical diseases. Each question was posed three times to GPT-3.5 and GPT-4.0. We also recruited three neurosurgeons with high, middle, and low seniority to respond to questions. The results were analyzed regarding ChatGPT’s overall performance score, mean scores by the items’ specialty classification, and question type. In conclusion, GPT-3.5’s ability to respond in accordance with evidence-based medicine was comparable to that of neurosurgeons with low seniority, and GPT-4.0’s ability was comparable to that of neurosurgeons with high seniority. Although ChatGPT is yet to be comparable to a neurosurgeon with high seniority, future upgrades could enhance its performance and abilities.

Rapid advancements in information technology have laid the basis for the move to information management job positions and health informatics education. In fact, information technology becomes an essential component of different healthcare educational programs worldwide. However, scholarly research about health informatics, particularly, in developing countries is still limited. This study is the first investigation to assess and compare health informatics (HI) educational programs across different Arab countries. Websites of all universities and colleges in the Arab world were surveyed and examined. 35 programs in 9 countries have been identified. To systematically collect and evaluate data, a categorization of different variables to assess and compare HI programs has been developed. HI programs in Saudi Arabia and Egypt represent the largest proportion of programs. Most programs are offered by public institutions and belong to medical faculties. 69% of HI educational offerings are undergraduate programs while only 31% are postgraduate programs. HI programs vary in terms of their titles. However, these programs have used the titles 'health informatics', and ' health information management' more frequently. There has also been an uneven distribution in terms of the total hours of programs. The structure of the study plan of 51% of HI programs are built on compulsory courses only. The study has presented a framework that indicates strengths, weaknesses, and emerging trends of HI programs and highlights potential challenges, opportunities, and recommendations to improve such programs.

Summary: Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment (TME), which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the TME. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.

Summary: The increased amount of tertiary lymphoid structures (TLSs) is associated with a favorable prognosis in patients with lung adenocarcinoma (LUAD). However, evaluating TLSs manually is an experience-dependent and time-consuming process, which limits its clinical application. In this multi-center study, we developed an automated computational workflow for quantifying the TLS density in the tumor region of routine hematoxylin and eosin (H&E)-stained whole-slide images (WSIs). The association between the computerized TLS density and disease-free survival (DFS) was further explored in 802 patients with resectable LUAD of three cohorts. Additionally, a Cox proportional hazard regression model, incorporating clinicopathological variables and the TLS density, was established to assess its prognostic ability. The computerized TLS density was an independent prognostic biomarker in patients with resectable LUAD. The integration of the TLS density with clinicopathological variables could support individualized clinical decision-making by improving prognostic stratification.

Summary: Adenosquamous carcinoma (ASC) is frequently misdiagnosed or overlooked in clinical practice due to its dual histological components and potential transformation from either adenocarcinoma (ADC) or squamous cell carcinoma (SCC). Our study aimed to differentiate ASC from ADC and SCC by incorporating features of enhanced CTs and clinical characteristics to build radiomics and deep learning models. The classification models were trained in Xiangya Hospital and validated in two other independent hospitals. The areas under the receiver operating characteristic curves (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to estimate the performance. The optimal three-class classification model achieved a maximum AUC of 0.89 and accuracy of 0.81 in external validation sets, AUC of 0.99 and accuracy of 0.99 in the internal test set. These findings highlight the efficacy of our models in differentiating ASC, providing a non-invasive, timely, and accurate diagnostic approach before and during the treatment.