Supporting the decision to perform molecular profiling for cancer patients based on routinely collected data through the use of machine learning.

BACKGROUND: Personalized medicine offers targeted therapy options for cancer treatment. However, the decision whether to include a patient into next-generation sequencing (NGS) testing is not standardized. This may result in some patients receiving unnecessary testing while others who could benefit from it are not tested. Typically, patients who have exhausted conventional treatment options are of interest for consideration in molecularly targeted therapy. To assist clinicians in decision-making, we developed a decision support tool using routine data from a precision oncology program. METHODS: We trained a machine learning model on clinical data to determine whether molecular profiling should be performed for a patient. To validate the model, the model's predictions were compared with decisions made by a molecular tumor board (MTB) using multiple patient case vignettes with their characteristics. RESULTS: The prediction model included 440 patients with molecular profiling and 13,587 patients without testing. High area under the curve (AUC) scores indicated the importance of engineered features in deciding on molecular profiling. Patient age, physical condition, tumor type, metastases, and previous therapies were the most important features. During the validation MTB experts made the same decision of recommending a patient for molecular profiling only in 10 out of 15 of their previous cases but there was agreement between the experts and the model in 9 out of 15 cases. CONCLUSION: Based on a historical cohort, our predictive model has the potential to assist clinicians in deciding whether to perform molecular profiling.

Inference of drug off-target effects on cellular signaling using interactome-based deep learning.

Many diseases emerge from dysregulated cellular signaling, and drugs are often designed to target specific signaling proteins. Off-target effects are, however, common and may ultimately result in failed clinical trials. Here we develop a computer model of the cell's transcriptional response to drugs for improved understanding of their mechanisms of action. The model is based on ensembles of artificial neural networks and simultaneously infers drug-target interactions and their downstream effects on intracellular signaling. With this, it predicts transcription factors' activities, while recovering known drug-target interactions and inferring many new ones, which we validate with an independent dataset. As a case study, we analyze the effects of the drug Lestaurtinib on downstream signaling. Alongside its intended target, FLT3, the model predicts an inhibition of CDK2 that enhances the downregulation of the cell cycle-critical transcription factor FOXM1. Our approach can therefore enhance our understanding of drug signaling for therapeutic design.

Representation of child and youth participation within the Unified Medical Language System (UMLS).

PURPOSE: To examine (1) how much participation is represented in the benchmark Unified Medical Language System (UMLS) resource, and (2) to what extent that representation reflects the definition of child and youth participation and/or its related constructs per the family of Participation-Related Constructs framework. MATERIALS AND METHODS: We searched and analysed UMLS concepts related to the term "participation." Identified UMLS concepts were rated according to their representation of participation (i.e., attendance, involvement, both) as well as participation-related constructs using deductive content analysis. RESULTS: 363 UMLS concepts were identified. Of those, 68 had at least one English definition, resulting in 81 definitions that were further analysed. Results revealed 2 definitions (2/81; 3%; 2/68 UMLS concepts) representing participation "attendance" and 18 definitions (18/81; 22%; 14/68 UMLS concepts) representing participation "involvement." No UMLS concept definition represented both attendance and involvement (i.e., participation). Most of the definitions (11/20; 55%; 9/16 UMLS concepts) representing attendance or involvement also represent a participation-related construct. CONCLUSION(S): The representation of participation within the UMLS is limited and poorly aligned with the contemporary definition of child and youth participation. Expanding ontological resources to represent child and youth participation is needed to enable better data analytics that reflect contemporary paediatric rehabilitation practice.

The representation of participation within the Unified Medical Language System (UMLS) is limited and poorly aligned with the contemporary definition of child and youth participation.From a contemporary paediatric rehabilitation perspective, using the current UMLS concepts for data analytics might result in misrepresentation of child and youth participation.There is need to expand ontological resources within the UMLS to fully and exclusively represent participation dimensions (attendance and involvement) in daily life activities to enable better data analytics that reflect contemporary paediatric rehabilitation practice.

Qualitative study exploring the design of a patient-reported symptom-based risk stratification system for suspected head and neck cancer referrals: protocol for work packages 1 and 2 within the EVEREST-HN programme.

INTRODUCTION: Between 2009/2010 and 2019/2020, England witnessed an increase in suspected head and neck cancer (sHNC) referrals from 140 to 404 patients per 100 000 population. 1 in 10 patients are not seen within the 2-week target, contributing to patient anxiety. We will develop a pathway for sHNC referrals, based on the Head and Neck Cancer Risk Calculator. The evolution of a patient-reported symptom-based risk stratification system to redesign the sHNC referral pathway (EVEREST-HN) Programme comprises six work packages (WPs). This protocol describes WP1 and WP2. WP1 will obtain an understanding of language to optimise the SYmptom iNput Clinical (SYNC) system patient-reported symptom questionnaire for sHNC referrals and outline requirements for the SYNC system. WP2 will codesign key elements of the SYNC system, including the SYNC Questionnaire, and accompanying behaviour change materials. METHODS AND ANALYSIS: WP1 will be conducted at three acute National Health Service (NHS) trusts with variation in service delivery models and ensuring a broad mixture of social, economic and cultural backgrounds of participants. Up to 150 patients with sHNC (n=50 per site) and 15 clinicians (n=5 per site) will be recruited. WP1 will use qualitative methods including interviews, observation and recordings of consultations. Rapid qualitative analysis and inductive thematic analysis will be used to analyse the data. WP2 will recruit lay patient representatives to participate in online focus groups (n=8 per focus group), think-aloud technique and experience-based codesign and will be analysed using qualitative and quantitative approaches. ETHICS AND DISSEMINATION: The committee for clinical research at The Royal Marsden, a research ethics committee and the Health Research Authority approved this protocol. All participants will give informed consent. Ethical issues of working with patients on an urgent cancer diagnostic pathway have been considered. Findings will be disseminated via journal publications, conference presentations and public engagement activities.

Predicting pressure injury risk in hospitalised patients using machine learning with electronic health records: a US multilevel cohort study.

OBJECTIVE: To predict the risk of hospital-acquired pressure injury using machine learning compared with standard care. DESIGN: We obtained electronic health records (EHRs) to structure a multilevel cohort of hospitalised patients at risk for pressure injury and then calibrate a machine learning model to predict future pressure injury risk. Optimisation methods combined with multilevel logistic regression were used to develop a predictive algorithm of patient-specific shifts in risk over time. Machine learning methods were tested, including random forests, to identify predictive features for the algorithm. We reported the results of the regression approach as well as the area under the receiver operating characteristics (ROC) curve for predictive models. SETTING: Hospitalised inpatients. PARTICIPANTS: EHRs of 35 001 hospitalisations over 5 years across 2 academic hospitals. MAIN OUTCOME MEASURE: Longitudinal shifts in pressure injury risk. RESULTS: The predictive algorithm with features generated by machine learning achieved significantly improved prediction of pressure injury risk (p<0.001) with an area under the ROC curve of 0.72; whereas standard care only achieved an area under the ROC curve of 0.52. At a specificity of 0.50, the predictive algorithm achieved a sensitivity of 0.75. CONCLUSIONS: These data could help hospitals conserve resources within a critical period of patient vulnerability of hospital-acquired pressure injury which is not reimbursed by US Medicare; thus, conserving between 30 000 and 90 000 labour-hours per year in an average 500-bed hospital. Hospitals can use this predictive algorithm to initiate a quality improvement programme for pressure injury prevention and further customise the algorithm to patient-specific variation by facility.

Development of an enhanced scoring system to predict ICU readmission or in-hospital death within 24 hours using routine patient data from two NHS Foundation Trusts.

RATIONALE: Intensive care units (ICUs) admit the most severely ill patients. Once these patients are discharged from the ICU to a step-down ward, they continue to have their vital signs monitored by nursing staff, with Early Warning Score (EWS) systems being used to identify those at risk of deterioration. OBJECTIVES: We report the development and validation of an enhanced continuous scoring system for predicting adverse events, which combines vital signs measured routinely on acute care wards (as used by most EWS systems) with a risk score of a future adverse event calculated on discharge from the ICU. DESIGN: A modified Delphi process identified candidate variables commonly available in electronic records as the basis for a 'static' score of the patient's condition immediately after discharge from the ICU. L1-regularised logistic regression was used to estimate the in-hospital risk of future adverse event. We then constructed a model of physiological normality using vital sign data from the day of hospital discharge. This is combined with the static score and used continuously to quantify and update the patient's risk of deterioration throughout their hospital stay. SETTING: Data from two National Health Service Foundation Trusts (UK) were used to develop and (externally) validate the model. PARTICIPANTS: A total of 12 394 vital sign measurements were acquired from 273 patients after ICU discharge for the development set, and 4831 from 136 patients in the validation cohort. RESULTS: Outcome validation of our model yielded an area under the receiver operating characteristic curve of 0.724 for predicting ICU readmission or in-hospital death within 24 hours. It showed an improved performance with respect to other competitive risk scoring systems, including the National EWS (0.653). CONCLUSIONS: We showed that a scoring system incorporating data from a patient's stay in the ICU has better performance than commonly used EWS systems based on vital signs alone. TRIAL REGISTRATION NUMBER: ISRCTN32008295.

Modelling of physicians' clinical information-seeking behaviour in Iran: a grounded theory study.

OBJECTIVES: Exploring clinical information-seeking behaviour (CISB) and its associated factors contributes to its theoretical advancement and offers a valuable framework for addressing physicians' information needs. This study delved into the dimensions, interactions, strategies and determinants of CISB among physicians at the point of care. DESIGN: A grounded theory study was developed based on Strauss and Corbin's approach. Data were collected by semistructured interviews and then analysed through open, axial and selective coding. SETTING: The study was conducted at academic centres affiliated with Isfahan University of Medical Sciences. PARTICIPANTS: This investigation involved recruiting 21 specialists and subspecialists from the academic centres. RESULTS: The findings revealed that physicians' CISB encompassed multiple dimensions when addressing clinical inquiries. Seven principal themes emerged from the analysis: 'clinical information needs', 'clinical question characteristics', 'clinical information resources', 'information usability', 'factors influencing information seeking', 'action/interaction encountering clinical questions' and 'consequences of CISB'. The core category identified in this study was 'focused attention'. CONCLUSIONS: The theoretical explanation demonstrated that the CISB process was interactive and dynamic. Various stimuli, including causal, contextual and intervening conditions, guide physicians in adopting information-seeking strategies and focusing on resolving clinical challenges. However, insufficient stimuli may hinder physicians' engagement in CISB. Understanding CISB helps managers, policy-makers, clinical librarians and information system designers optimally implement several interventions, such as suitable training methods, reviewing monitoring and evaluating information systems, improving clinical decision support systems, electronic medical records and electronic health records, as well as monitoring and evaluating these systems. Such measures facilitate focused attention on clinical issues and promote CISB among physicians.

Missed nursing care and its associated factors in public hospitals of Bahir Dar City, Northwest Ethiopia: a cross-sectional study.

OBJECTIVES: The aim of this study was to investigate the prevalence of missed nursing care and its associated factors among public hospitals in Bahir Dar City, Northwest Ethiopia. DESIGN: An institution-based cross-sectional study was conducted among 369 randomly selected nurses. SETTING: The study was conducted in primary and secondary-level public hospitals in Bahir Dar City. PARTICIPANTS: Nurses who had worked in hospitals in Bahir Dar City were included. INTERVENTION: No intervention was needed in this study. PRIMARY AND SECONDARY OUTCOME MEASURES: A binary logistic regression model was used for statistical analysis. Statistical significance of the association between outcome variables and independent variables was declared at a p value of <0.05 with a 95% CI. RESULTS: The prevalence of missed nursing care in this study was 46.3% (95% CI: 41.7% to 50.9%). The activities most frequently missed were physical examination (56.4%), patient discharge planning and teaching (50.9%), providing emotional support to the patient and family (50.8%), monitoring input and output (50.2%), assisting with patient ambulation (48.5%) and documentation (48%). Factors associated with missed nursing care include: male professionals (adjusted OR (AOR): 2.9, 95% CI: 1.8 to 4.8), those who had not received on-the-job training (AOR: 2.2, 95% CI: 1.4 to 3.6), those who worked full 24-hour shifts (AOR: 3.7, 95% CI: 2.0 to 6.5), those who were dissatisfied with the level of teamwork (AOR: 4.6, 95% CI: 2.8 to 7.6) and those who had an intention to leave the nursing profession (AOR: 1.8, 95% CI: 1.1 to 2.9). These factors were statistically associated with missed nursing care. CONCLUSION: A significant proportion of nurses missed essential nursing care activities. Efforts should be made to enhance training, improve teamwork among nurses, provide stability and adjust work shifts to mitigate this issue.

Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach.

Background: Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM. Objectives: We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators. Methods: In this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms. Patients: Regarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling. Results: The indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185. Conclusion: This work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients.

Predictive value of anthropometric and biochemical indices in non-alcoholic fatty pancreas disease: a cross-sectional study.

OBJECTIVES: Triglyceride (TG), triglyceride-glucose index (TyG), body mass index (BMI), TyG-BMI and triglyceride to high-density lipoprotein ratio (TG/HDL) have been reported to be reliable predictors of non-alcoholic fatty liver disease. However, there are few studies on potential predictors of non-alcoholic fatty pancreas disease (NAFPD). Our aim was to evaluate these and other parameters for predicting NAFPD. DESIGN: Cross-sectional study design. SETTING: Physical examination centre of a tertiary hospital in China. PARTICIPANTS: This study involved 1774 subjects who underwent physical examinations from January 2016 to September 2016. PRIMARY AND SECONDARY OUTCOME MEASURES: From each subject, data were collected for 13 basic physical examination and blood biochemical parameters: age, weight, height, BMI, TyG, TyG-BMI, high-density lipoprotein (HDL), low-density lipoprotein, total cholesterol, TG, fasting plasma glucose, TG/HDL and uric acid. NAFPD was diagnosed by abdominal ultrasonography. A logistic regression model with a restricted cubic spline was used to evaluate the relationship between each parameter and NAFPD. The receiver operating characteristic (ROC) curve was used to calculate the area under the curve for each parameter. RESULTS: HDL was negatively correlated with NAFPD, height was almost uncorrelated with NAFPD and the remaining 11 parameters were positively correlated with NAFPD. ROC curve showed that weight-related parameters (weight, BMI and TyG-BMI) and TG-related parameters (TyG, TG and TG/HDL) had high predictive values for the identification of NAFPD. The combinations of multiple parameters had a better prediction effect than a single parameter. All the predictive effects did not differ by sex. CONCLUSIONS: Weight-related and TG-related parameters are good predictors of NAFPD in all populations. BMI showed the greatest predictive potential. Multiparameter combinations appear to be a good way to predict NAFPD.

Design considerations for the migration from paper to screen-based media in current health education for older adults: a scoping review.

OBJECTIVES: To map the current use of paper-based and/or screen-based media for health education aimed at older people. DESIGN: A scoping review was reported following the Preferred Reporting Items of Systematic Reviews and Meta-analyses for Scoping Reviews checklist. DATA SOURCES: The search was carried out in seven databases (Scopus, Web of Science, Embase, Medline, CINAHL, ACM Guide to Computing Literature, PsycINFO), with studies available from 2012 to the date of the search in 2022, in English, Portuguese, Italian or Spanish. In addition, Google Scholar was searched to check the grey literature. The terms used in the search strategy were older adults, health education, paper and screen-based media, preferences, intervention and other related terms. ELIGIBILITY CRITERIA: Studies included were those that carried out health education interventions for older individuals using paper and/or screen-based media and that described barriers and/or facilitators to using these media. DATA EXTRACTION AND SYNTHESIS: The selection of studies was carried out by two reviewers. A data extraction form was developed with the aim of extracting and recording the main information from the studies. Data were analysed descriptively using Bardin's content analysis. RESULTS: The review included 21 studies that carried out health education interventions with different purposes, the main ones being promotion of physical activity, hypertension prevention and psychological health. All 21 interventions involved screen-based media on computers, tablets, smartphones and laptops, while only 4 involved paper-based media such as booklets, brochures, diaries, flyers and drawings. This appears to reflect a transition from paper to screen-based media for health education for the older population, in research if not in practice. However, analysis of facilitators and barriers to using both media revealed 10 design factors that could improve or reduce their use, and complementarity in their application to each media type. For example, screen-based media could have multimedia content, additional functionality and interactivity through good interaction design, but have low accessibility and require additional learning due to complex interface design. Conversely, paper-based media had static content and low functionality but high accessibility and availability and a low learning cost. CONCLUSIONS: We recommend having improved screen-based media design, continued use of paper-based media and the possible combination of both media through the new augmented paper technology. REGISTRATION NUMBER: Open Science Framework (DOI: 10.17605/OSF.IO/GKEAH).

Reporting form and content of research priorities identified in knee osteoarthritis clinical practice guidelines: a methodological literature analysis.

OBJECTIVES: Clinical practice guideline (CPG) developers conduct systematic summaries of research evidence, providing them great capacity and ability to identify research priorities. We systematically analysed the reporting form and content of research priorities in CPGs related to knee osteoarthritis (KOA) to provide a valuable reference for guideline developers and clinicians. DESIGN: A methodological literature analysis was done and the characteristics of the reporting form and the content of the research priorities identified in KOA CPGs were summarised. DATA SOURCES: Six databases (PubMed, Embase, China National Knowledge Infrastructure, VIP Database for Chinese Technical Periodicals, Wanfang and Chinese Biomedical Literature Database) were searched for CPGs published from 1 January 2017 to 4 December 2022. The official websites of 40 authoritative orthopaedic societies, rheumatology societies and guideline development organisations were additionally searched. ELIGIBILITY CRITERIA: We included all KOA CPGs published in English or Chinese from 1 January 2017 that included at least one recommendation for KOA. We excluded duplicate publications, older versions of CPGs as well as guidance documents for guideline development. DATA EXTRACTION AND SYNTHESIS: Reviewers worked in pairs and independently screened and extracted the data. Descriptive statistics were used, and absolute frequencies and proportions of related items were calculated. RESULTS: 187 research priorities reported in 41 KOA CPGs were identified. 24 CPGs reported research priorities, of which 17 (41.5%) presented overall research priorities for the entire guideline rather than for specific recommendations. 110 (58.8%) research priorities were put forward due to lack of evidence. Meanwhile, more than 70% of the research priorities reflected the P (population) and I (intervention) structural elements, with 135 (72.2%) and 146 (78.1%), respectively. More than half of the research priorities (118, 63.8%) revolved around evaluating the efficacy of interventions. Research priorities primarily focused on physical activity (32, 17.3%), physical therapy (30, 16.2%), surgical therapy (27, 14.6%) and pharmacological treatment (26, 14.1%). CONCLUSIONS: Research priorities reported in KOA CPGs mainly focused on evaluating non-pharmacological interventions. There exists considerable room for improvement for a comprehensive and standardised generation and reporting of research priorities in KOA CPGs.

Discovering the importance of health informatics education competencies in healthcare practice. A focus group interview.

BACKGROUND: As healthcare and especially health technology evolve rapidly, new challenges require healthcare professionals to take on new roles. Consequently, the demand for health informatics competencies is increasing, and achieving these competencies using frameworks, such as Technology Informatics Guiding Reform (TIGER), is crucial for future healthcare. AIM: The study examines essential health informatics and educational competencies and health informatics challenges based on TIGER Core Competency Areas. Rather than examine each country independently, the focus is on uncovering commonalities and shared experiences across diverse contexts. METHODS: Six focus group interviews were conducted with twenty-one respondents from three different countries (Germany (n = 7), Portugal (n = 6), and Finland (n = 8)). These interviews took place online in respondents' native languages. All interviews were transcribed and then summarized by each country. Braun and Clarke's thematic analysis framework was applied, which included familiarization with the data, generating initial subcategories, identifying, and refining themes, and conducting a final analysis to uncover patterns within the data. RESULTS: Agreed upon by all three countries, competencies in project management, communication, application in direct patient care, digital literacy, ethics in health IT, education, and information and knowledge management were identified as challenges in healthcare. Competencies such as communication, information and communication technology, project management, and education were identified as crucial for inclusion in educational programs, emphasizing their critical role in healthcare education. CONCLUSIONS: Despite working with digital tools daily, there is an urgent need to include health informatics competencies in the education of healthcare professionals. Competencies related to application in direct patient care, IT-background knowledge, IT-supported and IT-related management are critical in educational and professional settings are seen as challenging but critical in healthcare.

Online health information behaviour and its association with statin adherence in patients with high cardiovascular risk: A prospective cohort study.

Objective: Statins are effective for preventing cardiovascular disease. However, many patients decide not to take statins because of negative influences, such as online misinformation. Online health information may affect decisions on medication adherence, but measuring it is challenging. This study aimed to examine the associations between online health information behaviour and statin adherence in patients with high cardiovascular risk. Methods: A prospective cohort study involving 233 patients with high cardiovascular risk was conducted at a primary care clinic in Malaysia. Participants used a digital information diary tool to record online health information they encountered for 2 months and completed a questionnaire about statin necessity, concerns and adherence at the end of the observation period. Data were analysed using structural equation modelling. Results: The results showed that 55.8% (130 of 233 patients) encountered online health information. Patients who actively sought online health information (91 of 233 patients) had higher concerns about statin use (ß = 0.323, p = 0.023). Participants with higher concern about statin use were also more likely to be non-adherent (ß = -0.337, p < 0.001). Patients who actively sought online health information were more likely to have lower statin adherence, mediated by higher concerns about statin use (indirect effect, ß = -0.109, p = 0.048). Conclusions: Our results suggest that patients with higher levels of concern about statins may be actively seeking online information about statins, and their concerns might influence how they search, what they find, and the potential to encounter misinformation. Our study highlights the importance of addressing patients' concerns about medications to improve adherence.

Electronic Health Records for Population Health Management: Comparison of Electronic Health Record-Derived Hypertension Prevalence Measures Against Established Survey Data.

BACKGROUND: Hypertension is the most prevalent risk factor for mortality globally. Uncontrolled hypertension is associated with excess morbidity and mortality, and nearly one-half of individuals with hypertension do not have the condition under control. Data from electronic health record (EHR) systems may be useful for community hypertension surveillance, filling a gap in local public health departments' community health assessments and supporting the public health data modernization initiatives currently underway. To identify patients with hypertension, computable phenotypes are required. These phenotypes leverage available data elements-such as vitals measurements and medications-to identify patients diagnosed with hypertension. However, there are multiple methodologies for creating a phenotype, and the identification of which method most accurately reflects real-world prevalence rates is needed to support data modernization initiatives. OBJECTIVE: This study sought to assess the comparability of 6 different EHR-based hypertension prevalence estimates with estimates from a national survey. Each of the prevalence estimates was created using a different computable phenotype. The overarching goal is to identify which phenotypes most closely align with nationally accepted estimations. METHODS: Using the 6 different EHR-based computable phenotypes, we calculated hypertension prevalence estimates for Marion County, Indiana, for the period from 2014 to 2015. We extracted hypertension rates from the Behavioral Risk Factor Surveillance System (BRFSS) for the same period. We used the two 1-sided t test (TOST) to test equivalence between BRFSS- and EHR-based prevalence estimates. The TOST was performed at the overall level as well as stratified by age, gender, and race. RESULTS: Using both 80% and 90% CIs, the TOST analysis resulted in 2 computable phenotypes demonstrating rough equivalence to BRFSS estimates. Variation in performance was noted across phenotypes as well as demographics. TOST with 80% CIs demonstrated that the phenotypes had less variance compared to BRFSS estimates within subpopulations, particularly those related to racial categories. Overall, less variance occurred on phenotypes that included vitals measurements. CONCLUSIONS: This study demonstrates that certain EHR-derived prevalence estimates may serve as rough substitutes for population-based survey estimates. These outcomes demonstrate the importance of critically assessing which data elements to include in EHR-based computer phenotypes. Using comprehensive data sources, containing complete clinical data as well as data representative of the population, are crucial to producing robust estimates of chronic disease. As public health departments look toward data modernization activities, the EHR may serve to assist in more timely, locally representative estimates for chronic disease prevalence.

Identified PAH V230A and PAH V230I mutations in a family with diverse clinical presentations.

Phenylketonuria (PKU) is a hereditary disorder caused by phenylalanine hydroxylase enzyme (PAH) defects that might cause severe brain damage. The current main treatment, dietary management, can prevent the symptoms if commenced early. However, it has side effects if used for a long time. Additionally, some patients with mild hyperphenylalaninemia (mHPA), who has serum phenylalanine levels <360 µmol/L, do not require treatment. Since the correlation between genotype and metabolic phenotype has been demonstrated earlier, genotype-based detection of patients who do not need treatment might help with genetic counseling and choosing the most appropriate treatment option. In this study, we report an asymptomatic adult with mHPA who had never taken any medical intervention to control or lower her serum phenylalanine level (Phe). She had 179 µmol/L serum phenylalanine level and carried p.[V230A];[V230I] genotype. Her child was affected with phenylketonuria and had p.[V230A];[V230A] genotype. Both pathogenic variants detected in the asymptomatic adult with mHPA were computationally analyzed to assess their pathogenicity and the p.V230I pathogenic variant was demonstrated to be responsible for the mHPA phenotype in the asymptomatic adult detected in this study. The findings in this study could contribute to genetic counseling and treatment for families and individuals with p.[V2030I];[V230A] genotype.

Perceptions of students and faculty on NCAAA-accredited health informatics programs in Saudi Arabia: an evaluative study.

BACKGROUND: As the healthcare sector becomes increasingly reliant on technology, it is crucial for universities to offer bachelor's degrees in health informatics (HI). HI professionals bridge the gap between IT and healthcare, ensuring that technology complements patient care and clinical workflows; they promote enhanced patient outcomes, support clinical research, and uphold data security and privacy standards. This study aims to evaluate accredited HI academic programs in Saudi Arabia. METHODS: This study employed a quantitative, descriptive, cross-sectional design utilising a self-reported electronic questionnaire consisting of predetermined items and response alternatives. Probability-stratified random sampling was also performed. RESULT: The responses rates were 39% (n = 241) for students and 62% (n = 53) for faculty members. While the participants expressed different opinions regarding the eight variables being examined, the faculty members and students generally exhibited a strong level of consensus on many variables. A notable association was observed between facilities and various other characteristics, including student engagement, research activities, admission processes, and curriculum. Similarly, a notable correlation exists between student engagement and the curriculum in connection to research, attrition, the function of faculty members, and academic outcomes. CONCLUSION: While faculty members and students hold similar views about the institution and its offerings, certain areas of divergence highlight the distinct perspectives and priorities of each group. The perception disparity between students and faculty in areas such as admission, faculty roles, and internships sheds light on areas of improvement and alignment for universities.

Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review.

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space.

Effectiveness of mHealth interventions to promote physical activity and reduce sedentary behaviours on work-related productivity and performance: a systematic review protocol.

INTRODUCTION: Technologies such as health and fitness applications (apps) and wearable activity trackers have recently gained popularity and may play a key role in promoting physical activity and reducing sedentary behaviours. Although several systematic reviews have investigated their efficacy in physical activity and sedentary behaviours, few studies have focused on their impact on work-related outcomes among workers. Here, to explore the effects of mHealth interventions designed to encourage physical activity and decrease sedentary behaviours on work-related outcomes, including absenteeism, presenteeism, productivity, work performance and workability among workers, we will conduct a systematic review based on recent articles and an extensive literature search. METHODS AND ANALYSIS: The literature search will be performed using PubMed, Web of Science, the Cochrane Library and the Japan Medical Abstracts Society from inception to 23 September 2023. We will select studies that (1) investigated the impact of mHealth interventions to promote physical activity and reduce sedentary behaviours on work-related outcomes such as absenteeism, presenteeism, productivity, work performance and workability; (2) were designed as a randomised controlled trial (RCT) or non-randomised study of interventions (NRSI); (3) were conducted among workers and (4) were published as full-text original articles in Japanese or English. We will assess the review quality with the AMSTAR 2 tool. The risk of bias will be assessed with the RoB tool 2.0 and ROBINS-I. ETHICS AND DISSEMINATION: Ethical approval is unnecessary as the study will rely solely on previously published articles. The research results will be submitted for publication in a peer-reviewed scientific journal. TRIAL REGISTRATION NUMBER: The study protocol has been registered with the UMIN Clinical Trials Registry (ID=UMIN000052290).