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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.

Aim: The aim of the study is to reveal the level of cooperation in health informatics practices. Method: In order to reach the aim of the study by determining the level of cooperation between sectors in health informatics studies, the extent of research on the theme of ‘‘health informatics'' in health quality and performance congresses was examined. As a data source, academic studies published at various congresses held between 2009 and 2021 were analyzed. In accordance with the main purpose of the study, document analysis, which is a qualitative research method was carried out in the congress proceedings booklets. Results: According to the findings, it was seen that 66.1% of the academic studies in the field of health informatics were carried out only by university employees, while the rate of studies conducted in university-public cooperation was 16.1%. In addition, it was concluded that the work carried out by the employees of the Ministry of Health has a rate of 10.7%. While only 1.80% of the studies were carried out by private sector employees, it was observed that similarly, studies created through university-private sector cooperation constitute 1.80% of all studies. It has been seen that publications prepared in cooperation with the public-private sector constitute 0.90% of all publications, while publications prepared in cooperation with the university-other public sector constitute 0.90% of all publications at the same rate. Conclusion: The results of the study show that the level of cooperation is not sufficient, considering the importance of cooperation and the benefits that can be obtained from work done in cooperation. In this context, the study emphasizes the need to increase the encouragement of cooperation between health service providers in the field of health informatics and offers suggestions in this regard.

The paper examines the prognostic potential of the Google Trends resource as one of the infodemiological tools that allows collecting and analyzing the frequency of search queries on the Internet. The aim of the research is to analyze the Cyrillic search queries on Google to study the dynamics of the development of COVID-19 in Ukraine in 2020-2022. The time interval of the study from 15.03.2020 to 23.02.2022 was determined by available official information on the incidence of COVID-19 in Ukraine. The data obtained from Google Trends, normalized relative to the country of study and time interval, was downloaded in *.csv format. Correlation between quantitative indicators was assessed using the Spearman rank correlation coefficient. The authors proposed a new direction to study the dynamics of the development of COVID-19, which relies on the analysis of the search for symptoms and names of medications to predict the course of the disease. It has been shown that Google Trends is an effective tool for the rapid collection of information on the state of morbidity in the country. The use of keyword searches not only allows us to predict the development of the disease but can also be an effective tool of pharmacoeconomics. The revealed regularities can be used in international epidemiological studies, taking into account national characteristics, the geographical location of the country, the impact of preventive restrictions, etc.

Introduction: Identifying similar patients is effective in designing many secondary applications to improve the quality of treatments and research services. The similarity of the final diagnoses is one of the aspects of similar patient groups. In order to measure similarity between patients, it is crucial to convert their information into a comparable format. There are different types of data in electronic health records (EHR). An important part of patient EHR are clinical notes, which face challenges to process. Therefore, the present study aims to design a clinical language processing model to identify definitive diagnoses. Research method: In this study, the clinical notes of more than 26,000 patients from the MIMIC-III database were represented as vectors using modern language models, and these vectors were used as input for the diagnostic prediction model.Results: According to the results of the experiments, the BIO-BERT model with 0.715 and then the SciBERT model with 0.713 the best result between the biomedical language models. The results also show that using unique concepts extracted from clinical notes resulted in an increase in model accuracy. Conclusion: Representation models trained with specific biomedical data can be used to map latent clinical note information to embedding vectors and provide the ability to use notes in machine learning algorithms, including prediction of the final diagnostic group.

[Purpose/Significance] China has entered the aging society since 2000 and China's elderly population is increasing rapidly., The degree of aging is growing severely, and the pressure on social security and public services is increasing. During the "14th Five-Year Plan" period, the degree of aging in China will be from mild to moderate, the population structural contradiction centered on population aging is becoming increasingly prominent, and the demand structure of the elderly is changing from the survival-type to the development-type. In order to implement the national strategy and relevant national policies and regulations to actively respond to the aging population and fulfill the mission of public libraries, public libraries should further study how to better serve the elderly. [Method/Process] The paper conducts literature review of the current studies on public library services to the elderly. The main research methods in this field include questionnaire survey and case analysis. The research topics include study of the elderly, service, service system and service facility to the elderly. Among them, the research of service to elderly includes theme space and theme branch library construction, outreach service, health information service, reference service, user education and training, digital service, information literacy service, volunteer service and intergenerational service project. [Results/Conclusions] There are some problems in the existing research, such as a lack of standardization, the need to strengthen the scientific data collection method and a lack of in-depth research. In the future, efforts should be made to improve the norm of research and improve the data collection methods, such as paying attention to the rationality of sampling methods and the compatibility between the research methods and research questions, using in-depth interviews and other research methods, increasing in-depth case analysis, and comprehensively applying a variety of research methods. Efforts are needed to study the elderly group to effectively promote the match between the elderly service supply and user demand in public libraries. In order to adapt to the national conditions of aging in China and the characteristics of the elderly, efforts are needed to innovate service models from the aspects of service publicity and promotion, helping the elderly with digital intelligence, outreach service, intergenerational services, and social integration. There is also a need to evaluate the effectiveness of services to the elderly and develop the elderly service standards to provide guidelines for services for the elderly, and promote service experience. The construction of information resources adapted to the characteristics of the elderly population should be carried out to improve the level of service guarantee for the elderly.

Sarah Musa,1 Ismail Dergaa,1 Rawia Al Shekh Yasin,2 Rajvir Singh3 1Department of Preventative Health, Primary Health Care Corporation (PHCC), Doha, Qatar; 2Department of Quality & Patient Safety, Primary Health Care Corporation (PHCC), Doha, Qatar; 3Department of Adult Cardiology, Heart Hospital, Hamad Medical Corporation (HMC), Doha, QatarCorrespondence: Sarah Musa, Email smusa@phcc.gov.qaBackground: The transition to electronic health records (EHR) has improved the quality of health-care delivery and patient safety. However, poor usability and incongruent workflow may impose a significant burden on documentation and time management, resulting in staff burnout. We aimed to (i) evaluate the effectiveness of personalized EHR training on wellness providers’ knowledge and practical competencies, and (ii) assess staff satisfaction regarding the EHR usage post-training.Methodology: An interventional study was conducted between July 15, 2021, and March 1, 2022, among 14 wellness staff (age: 38 ± 3.9 years; 7 males, 7 females) in the Wellness Center-Rawdat Al-Khail Health Center. Six months of blended training was delivered. The impact of training was assessed using a pre-post survey on the knowledge and practical competencies related to EHR usage. Staff satisfaction was assessed post-training.Results: Majority of respondents had improvement in identifying the advantages of EHR: improve confidentiality of care (pre = 35.7% vs post = 100%, p = 0.001), reduce medical errors (pre = 35.7% vs post = 85.7%, p = 0.02), improve quality of health care (pre = 35.7% vs post = 100%, p = 0.001), and reduce wait time (pre = 42.9% vs post = 85.7%, p = 0.03). Time performing these tasks by massage therapists/receptionists was reduced: viewing/editing ambulatory organizer (pre = 20± 0 s vs post = 10± 0 s), access PM office (pre = 155± 136 s vs post = 10± 0 s), selection/access patient chart (pre = 75± 30 s vs post = 30± 20 s), check-in/out (pre = 120± 0 s vs post = 60± 0 s), and view/edit massage form (pre = 135± 75.5 s vs post = 60± 0 s). For gym instructors, time to access ambulatory organizer (pre = 30± 0 s vs post = 10± 0 s), view/edit the gym form (pre = 101± 57 s vs post = 71± 36 s), view patients’ clinical data (pre = 60± 70 s vs post = 10± 3 s), and place referral orders (pre = 197± 144 vs post = 82± 23 s) was reduced. A mean percentage score of 65.4± 38.7 indicated very good staff satisfaction.Conclusion: This tailored, hands-on training has been well received and effectively improved wellness staff knowledge, competencies, and satisfaction relative to EHR functionalities.Keywords: Electronic health record, health care, health care providers, health care quality, health care safety, health informatics, health information technology

Abstract Background The importance of the health information system faces multiple challenges such as supply, acceptance, and pressure from other professions in Ethiopia. Work-related challenges might cause low professional satisfaction and hinder service provision. There is a paucity of evidence for policy decisions to improve these challenges. Therefore, this study aims to assess Health Informatics professional satisfaction in the Ethiopian health system and associated factors to provide evidence for future improvements. Methods We conducted an institutions-based cross-sectional study on health informatics professionals in three zones in Southern Ethiopia in 2020. We used a simple random sampling technique to select 215 participants. The local health officials were contacted regarding the research questions, and letters of permission were collected for data collection. Results Out of 211(98%) Health Informatics professionals who accepted the interview, 50.8% (95%CI: 47.74%-53.86%) were satisfied. Age (AOR = 0.57; 95% CI: 0.53, 0.95), experience (AOR = 5; 95% CI: 1.50, 19.30), working time (AOR = 1.35; 95% CI: 1.10, 1.70), working as HMIS officers (AOR 2.30; 95% CI: 3.80, 13), single marital status (AOR = 9.60; 95% CI: 2.88, 32), and urban residence (AOR = 8.10; 95% CI: 2.95, 22) were some of the associated factors. Conclusions We found low satisfaction among health informatics professionals compared to other studies. It was suggested that the responsible bodies must keep experienced professionals and reduce pressure from other professions through panel discussions. Work departments and working hours need consideration, as they are the determinants of satisfaction. Improving educational opportunities and career structure is the potential implication area.

Abstract Background In the field of health informatics (HI), there is a crucial gap between employers’ needs and the output of academic programmes. Although industrial organisations and government agencies recognise the importance of training and education in the development and operation of health-information systems, advancements in educational programmes have been comparatively slow in terms of investment in healthcare information technology. This study aims to determine the gap between employer demands and academic programmes in HI in Saudi Arabia. Methods This mixed-methods study collected both qualitative and quantitative data. A qualitative content analysis was performed to identify the role of advertised HI jobs using two sources: Google and LinkedIn. In addition, university websites were searched to determine job opportunities for graduates with a bachelor’s degree in HI. Next, a quantitative, cross-sectional self-report questionnaire was administered to validate the findings of the qualitative data. Data obtained were analysed using SPSS, N-Vivo, and Microsoft Excel. Results The study’s data were obtained from four sources: Google search engine, LinkedIn, five Saudi university websites, and 127 HI experts. The results show a discrepancy between academic programmes’ outputs and employer recruitment needs. In addition, the results reveal a preference for post-graduate degrees, either a master’s or PhD degree, with a bachelor’s degree in a health or medical discipline. Conclusions Employers tend to prefer applicants with a bachelor’s degree in computer science or information technology over those with a degree in HI. Academic programmes should incorporate more practical applications and provide students with a thorough understanding of the healthcare industry to better equip them as efficient future HI professionals.

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

Capturing patients’ medical histories significantly influences clinical decisions. Errors in this process lead to clinical errors, which increase costs and dissatisfaction among physicians and patients. Physicians in developing countries are overloaded with patients and cannot always follow the proper history-taking procedure. The challenges have been acknowledged; however, a comprehensive understanding of the status and the remedies has remained unexplored. This paper aims to investigate the workload, history-taking challenges, and the willingness of the physicians to accept digital solutions. A cross-sectional online survey was conducted on 104 physicians across Bangladesh, featuring 22 questions regarding their professional environment, workload, digitization status of health records, challenges in history taking, and attitudes toward adopting digital solutions for managing patient histories; 92.67% of the physicians face high workloads, 88.46% struggle in medical history taking, and only 4.81% use digital medical records. About 70% struggle to complete the necessary history-taking steps, emphasizing the urgent need for solutions. A novel visualization system, the Smart Health Gantt Chart (SHGC), has been introduced for their instant feedback. A total of 93.27% of physicians expressed their willingness to use such a system. The proposed SHGC has the potential to enhance healthcare efficiency in developing nations, benefit physicians, and improve patient-centered care.

The COVID-19 pandemic continues to constitute a public health emergency of international importance, although the state of emergency declaration has indeed been terminated worldwide, many people continue to be infected and present different symptoms associated with the illness. Undoubtedly, solutions based on divergent technologies such as machine learning have made great contributions to the understanding, identification, and treatment of the disease. Due to the sudden appearance of this virus, many works have been carried out by the scientific community to support the detection and treatment processes, which has generated numerous publications, making it difficult to identify the status of current research and future contributions that can continue to be generated around this problem that is still valid among us. To address this problem, this article shows the result of a scientometric analysis, which allows the identification of the various contributions that have been generated from the line of automatic learning for the monitoring and treatment of symptoms associated with this pathology. The methodology for the development of this analysis was carried out through the implementation of two phases: in the first phase, a scientometric analysis was carried out, where the countries, authors, and magazines with the greatest production associated with this subject can be identified, later in the second phase, the contributions based on the use of the Tree of Knowledge metaphor are identified. The main concepts identified in this review are related to symptoms, implemented algorithms, and the impact of applications. These results provide relevant information for researchers in the field in the search for new solutions or the application of existing ones for the treatment of still-existing symptoms of COVID-19.

Diabetes is a metabolic disorder that affects more than 420 million of people worldwide, and it is caused by the presence of a high level of sugar in blood for a long period. Diabetes can have serious long-term health consequences, such as cardiovascular diseases, strokes, chronic kidney diseases, foot ulcers, retinopathy, and others. Even if common, this disease is uneasy to spot, because it often comes with no symptoms. Especially for diabetes type 2, that happens mainly in the adults, knowing how long the diabetes has been present for a patient can have a strong impact on the treatment they can receive. This information, although pivotal, might be absent: for some patients, in fact, the year when they received the diabetes diagnosis might be well-known, but the year of the disease unset might be unknown. In this context, machine learning applied to electronic health records can be an effective tool to predict the past duration of diabetes for a patient. In this study, we applied a regression analysis based on several computational intelligence methods to a dataset of electronic health records of 73 patients with diabetes type 1 with 20 variables and another dataset of records of 400 patients of diabetes type 2 with 49 variables. Among the algorithms applied, Random Forests was able to outperform the other ones and to efficiently predict diabetes duration for both the cohorts, with the regression performances measured through the coefficient of determination R2. Afterwards, we applied the same method for feature ranking, and we detected the most relevant factors of the clinical records correlated with past diabetes duration: age, insulin intake, and body-mass index. Our study discoveries can have profound impact on clinical practice: when the information about the duration of diabetes of patient is missing, medical doctors can use our tool and focus on age, insulin intake, and body-mass index to infer this important aspect. Regarding limitations, unfortunately we were unable to find additional dataset of EHRs of patients with diabetes having the same variables of the two analyzed here, so we could not verify our findings on a validation cohort.

Summary: The biological rhythms governed by negative feedback loops have undergone extensive investigation. However, developing reliable and versatile warning signals to predict periodic fluctuations in physiological processes and behaviors associated with these rhythms remains a challenge. Here, we monitored the heart rate and tracked ovulation dates of 91 fertile women. The finding strongly links the velocity (derivative) of heart rate with ovulation in menstrual cycles, providing a predictive warning signal. Similarly, an analysis of calcium signaling in the suprachiasmatic nucleus (SCN) of mice reveals that the maximum velocity of rising calcium signal aligns with locomotor activity offsets. To demonstrate the generality of derivative-transitions link, numerical simulations using a negative feedback loop model were conducted. Statistical analysis indicated that over 90% of the oscillations exhibited a correlation between maximum velocity and transition points. Consequently, the maximum velocity derived from oscillatory curves holds significant potential as an early warning signal for critical transitions.

Summary: Colorectal cancer (CRC) intricacies, involving dysregulated cellular processes and programmed cell death (PCD), are explored in the context of N6-methyladenosine (m6A) RNA modification. Utilizing the TCGA-COADREAD/CRC cohort, 854 m6A-related PCD genes are identified, forming the basis for a robust 10-gene risk model (CDRS) established through LASSO Cox regression. qPCR experiments using CRC cell lines and fresh tissues was performed for validation. The CDRS served as an independent risk factor for CRC and showed significant associations with clinical features, molecular subtypes, and overall survival in multiple datasets. Moreover, CDRS surpasses other predictors, unveiling distinct genomic profiles, pathway activations, and associations with the tumor microenvironment. Notably, CDRS exhibits predictive potential for drug sensitivity, presenting a novel paradigm for CRC risk stratification and personalized treatment avenues.

This document is an editorial from the journal "Frontiers in Research Metrics & Analytics" titled "Text mining-based mental health research." It discusses the use of text mining and data analytics in the field of mental health research. The editorial highlights the importance of analyzing scientific literature datasets, social media user-generated content datasets, and the Bipolar Reddit community to gain insights into mental health conditions. The authors emphasize the need for interdisciplinary collaboration and technological advancements to address mental health issues effectively. They also acknowledge the potential challenges and risks involved in analyzing sentiments and topics related to mental health on social media platforms. [Extracted from the article]

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.

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]

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]

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: 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]

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]

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.

As evidence of the associations between social factors and health outcomes continues to mount, capturing and acting on social determinants of health (SDOH) in clinical settings has never been more relevant. Many professional medical organizations have endorsed screening for SDOH, and the U.S. Office of the National Coordinator for Health Information Technology has recommended increased capacity of health information technology to integrate and support use of SDOH data in clinical settings. As these efforts begin their translation to practice, a new subfield of health informatics is emerging, focused on the application of information technologies to capture and apply social data in conjunction with health data to advance individual and population health. Developing this dedicated subfield of informatics-which we term social informatics-is important to drive research that informs how to approach the unique data, interoperability, execution, and ethical challenges involved in integrating social and medical care.

IntroductionDepression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target these diseases separately. Increasing physical activity might be effective to simultaneously lower depressive symptoms and improve glycaemic control. Self-management apps are a cost-effective, scalable and easy access treatment to increase physical activity. However, cutting-edge technological applications often do not reach vulnerable populations and are not tailored to an individual's behaviour and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention.Methods and analysisIn a three-arm randomised controlled trial, we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen from different messaging banks by a reinforcement learning algorithm. The uniform random intervention group receives the same messages, but chosen from the messaging banks with equal probabilities. The control group receives a weekly mood message. We aim to recruit 276 adults from primary care clinics aged 18-75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (Patient Health Questionnaire depression scale-8 (PHQ-8) >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent haemoglobin A1c from medical records at baseline and at intervention completion at 6-month follow-up.Ethics and disseminationThe Institutional Review Board at the University of California San Francisco approved this study (IRB: 17-22608). We plan to submit manuscripts describing our user-designed methods and testing of the adaptive learning algorithm and will submit the results of the trial for publication in peer-reviewed journals and presentations at (inter)-national scientific meetings.Trial registration numberNCT03490253; pre-results.

[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.