UNASSIGNED: Integrating health information into university information systems holds significant potential for enhancing student support and well-being. Despite the growing body of research highlighting issues faced by university students, including stress, depression, and disability, little has been done in the informatics field to incorporate health technologies at the institutional level.
UNASSIGNED: This study aims to investigate the current state of health information integration within university systems and provide design recommendations to address existing gaps and opportunities.
UNASSIGNED: We used a user-centered approach to conduct interviews and focus group sessions with stakeholders to gather comprehensive insights and requirements for the system. The methodology involved data collection, analysis, and the development of a suggested workflow.
UNASSIGNED: The findings of this study revealed the shortcomings in the current process of handling health and disability data within university information systems. In our results, we discuss some requirements identified for integrating health-related information into student information systems, such as privacy and confidentiality, timely communication, task automation, and disability resources. We propose a workflow that separates the process into 2 distinct components: a health and disability system and measures of quality of life and wellness. The proposed workflow highlights the vital role of academic advisors in facilitating support and enhancing coordination among stakeholders.
UNASSIGNED: To streamline the workflow, it is vital to have effective coordination among stakeholders and redesign the university information system. However, implementing the new system will require significant capital and resources. We strongly emphasize the importance of increased standardization and regulation to support the information system requirements for health and disability. Through the adoption of standardized practices and regulations, we can ensure the smooth and effective implementation of the required support system.

In recent years, the use of electronic health records (EHRs) has become increasingly prevalent in healthcare settings, including emergency departments (EDs). EHRs offer numerous advantages, such as improved documentation, streamlined communication, and enhanced patient care. Additionally, EHRs contain vital information about patient care and treatment outcomes, which opens up exciting research opportunities. The objective of this study was to present a database comprising information regarding patients admitted to the emergency department of a large hospital. In this study, we are introducing an open-access database sourced from the electronic health records of a general university hospital in Isfahan, Iran. The data were collected from patients admitted to the emergency department between March 2017 and March 2022, resulting in a database containing 143,582 ED stays. The database includes triage information, ED admission patients, and services. To ensure patient privacy, all patient-specific information has been removed from the records.

UNASSIGNED: According to previous reports, very high percentages of individuals in Saudi Arabia are undiagnosed for type 2 diabetes mellitus (T2DM). Despite conducting several screening and awareness campaigns, these efforts lacked full accessibility and consumed extensive human and material resources. Thus, developing machine learning (ML) models could enhance the population-based screening process. The study aims to compare a newly developed ML model\'s outcomes with the validated American Diabetes Association\'s (ADA) risk assessment regarding predicting people with high risk for T2DM.
UNASSIGNED: Patients\' age, gender, and risk factors that were obtained from the National Health Information Center\'s dataset were used to build and train the ML model. To evaluate the developed ML model, an external validation study was conducted in three primary health care centers. A random sample (N = 3400) was selected from the non-diabetic individuals.
UNASSIGNED: The results showed the plotted data of sensitivity/100-specificity represented in the Receiver Operating Characteristic (ROC) curve with an AROC value of 0.803, 95% CI: 0.779-0.826.
UNASSIGNED: The current study reveals a new ML model proposed for population-level classification that can be an adequate tool for identifying those at high risk of T2DM or who already have T2DM but have not been diagnosed.

OBJECTIVE: The use of digital technology in surgery is increasing rapidly, with a wide array of new applications from presurgical planning to postsurgical performance assessment. Understanding the clinical and economic value of these technologies is vital for making appropriate health policy and purchasing decisions. We explore the potential value of digital technologies in surgery and produce expert consensus on how to assess this value.
METHODS: A modified Delphi and consensus conference approach was adopted. Delphi rounds were used to generate priority topics and consensus statements for discussion.
METHODS: An international panel of 14 experts was assembled, representing relevant stakeholder groups: clinicians, health economists, health technology assessment experts, policy-makers and industry.
METHODS: A scoping questionnaire was used to generate research questions to be answered. A second questionnaire was used to rate the importance of these research questions. A final questionnaire was used to generate statements for discussion during three consensus conferences. After discussion, the panel voted on their level of agreement from 1 to 9; where 1=strongly disagree and 9=strongly agree. Consensus was defined as a mean level of agreement of >7.
RESULTS: Four priority topics were identified: (1) how data are used in digital surgery, (2) the existing evidence base for digital surgical technologies, (3) how digital technologies may assist surgical training and education and (4) methods for the assessment of these technologies. Seven consensus statements were generated and refined, with the final level of consensus ranging from 7.1 to 8.6.
CONCLUSIONS: Potential benefits of digital technologies in surgery include reducing unwarranted variation in surgical practice, increasing access to surgery and reducing health inequalities. Assessments to consider the value of the entire surgical ecosystem holistically are critical, especially as many digital technologies are likely to interact simultaneously in the operating theatre.

UNASSIGNED: To investigate long COVID (LC) symptoms self-reported via a digital application. Explore associations between various demographic factors and intensity of LC symptoms.
UNASSIGNED: A retrospective case series study. We analysed self-reported symptoms from 1008 individuals with LC between November 30, 2020, and March 23, 2022.
UNASSIGNED: England and Wales.
UNASSIGNED: Individuals with LC using the healthcare application in 31 post-COVID-19 clinics and self-reporting LC symptoms.
UNASSIGNED: Highest reported LC symptoms, associations with demographic factors and intensity of symptoms.
UNASSIGNED: 109 symptom categories were identified, with pain (26.5%), neuropsychological issues (18.4%), fatigue (14.3%) and dyspnoea (7.4%) the most prevalent. The intensity of reported symptoms increased by 3.3% per month since registration. Age groups 68-77 and 78-87 experienced higher symptom intensity (32.8% and 86% higher, respectively) compared to the 18-27 age group. Women reported 9.2% more intense symptoms than men, and non-white individuals with LC reported 23.5% more intense symptoms than white individuals with LC. Higher education levels (national vocational qualification (NVQ) 3 to NVQ 5) were associated with less symptom intensity (27.7%, 62.8% and 44.7% less, respectively) compared to the least educated (NVQ 1-2). People in less deprived areas had less intense symptoms than those in the most deprived area. No significant association was found between index of multiple deprivation (IMD) decile and number of symptoms.
UNASSIGNED: Treatment plans must prioritise addressing prevalent LC symptoms; we recommend sustained support for LC clinics. Demographic factors significantly influence symptom severity, underlining the need for targeted interventions. These findings can inform healthcare policies to better manage LC.

BACKGROUND: The integration of artificial intelligence and chatbot technology in health care has attracted significant attention due to its potential to improve patient care and streamline history-taking. As artificial intelligence-driven conversational agents, chatbots offer the opportunity to revolutionize history-taking, necessitating a comprehensive examination of their impact on medical practice.
OBJECTIVE: This systematic review aims to assess the role, effectiveness, usability, and patient acceptance of chatbots in medical history-taking. It also examines potential challenges and future opportunities for integration into clinical practice.
METHODS: A systematic search included PubMed, Embase, MEDLINE (via Ovid), CENTRAL, Scopus, and Open Science and covered studies through July 2024. The inclusion and exclusion criteria for the studies reviewed were based on the PICOS (participants, interventions, comparators, outcomes, and study design) framework. The population included individuals using health care chatbots for medical history-taking. Interventions focused on chatbots designed to facilitate medical history-taking. The outcomes of interest were the feasibility, acceptance, and usability of chatbot-based medical history-taking. Studies not reporting on these outcomes were excluded. All study designs except conference papers were eligible for inclusion. Only English-language studies were considered. There were no specific restrictions on study duration. Key search terms included \"chatbot*,\" \"conversational agent*,\" \"virtual assistant,\" \"artificial intelligence chatbot,\" \"medical history,\" and \"history-taking.\" The quality of observational studies was classified using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria (eg, sample size, design, data collection, and follow-up). The RoB 2 (Risk of Bias) tool assessed areas and the levels of bias in randomized controlled trials (RCTs).
RESULTS: The review included 15 observational studies and 3 RCTs and synthesized evidence from different medical fields and populations. Chatbots systematically collect information through targeted queries and data retrieval, improving patient engagement and satisfaction. The results show that chatbots have great potential for history-taking and that the efficiency and accessibility of the health care system can be improved by 24/7 automated data collection. Bias assessments revealed that of the 15 observational studies, 5 (33%) studies were of high quality, 5 (33%) studies were of moderate quality, and 5 (33%) studies were of low quality. Of the RCTs, 2 had a low risk of bias, while 1 had a high risk.
CONCLUSIONS: This systematic review provides critical insights into the potential benefits and challenges of using chatbots for medical history-taking. The included studies showed that chatbots can increase patient engagement, streamline data collection, and improve health care decision-making. For effective integration into clinical practice, it is crucial to design user-friendly interfaces, ensure robust data security, and maintain empathetic patient-physician interactions. Future research should focus on refining chatbot algorithms, improving their emotional intelligence, and extending their application to different health care settings to realize their full potential in modern medicine.
BACKGROUND: PROSPERO CRD42023410312; www.crd.york.ac.uk/prospero.

BACKGROUND: Predicting hospitalization from nurse triage notes has the potential to augment care. However, there needs to be careful considerations for which models to choose for this goal. Specifically, health systems will have varying degrees of computational infrastructure available and budget constraints.
OBJECTIVE: To this end, we compared the performance of the deep learning, Bidirectional Encoder Representations from Transformers (BERT)-based model, Bio-Clinical-BERT, with a bag-of-words (BOW) logistic regression (LR) model incorporating term frequency-inverse document frequency (TF-IDF). These choices represent different levels of computational requirements.
METHODS: A retrospective analysis was conducted using data from 1,391,988 patients who visited emergency departments in the Mount Sinai Health System spanning from 2017 to 2022. The models were trained on 4 hospitals\' data and externally validated on a fifth hospital\'s data.
RESULTS: The Bio-Clinical-BERT model achieved higher areas under the receiver operating characteristic curve (0.82, 0.84, and 0.85) compared to the BOW-LR-TF-IDF model (0.81, 0.83, and 0.84) across training sets of 10,000; 100,000; and ~1,000,000 patients, respectively. Notably, both models proved effective at using triage notes for prediction, despite the modest performance gap.
CONCLUSIONS: Our findings suggest that simpler machine learning models such as BOW-LR-TF-IDF could serve adequately in resource-limited settings. Given the potential implications for patient care and hospital resource management, further exploration of alternative models and techniques is warranted to enhance predictive performance in this critical domain.
UNASSIGNED: RR2-10.1101/2023.08.07.23293699.

OBJECTIVE: In 2021, Solent NHS Trust advertised for a fully remote consultant psychiatrist to meet increasing clinical demand. This pilot scheme was evaluated to determine its success. The job applications underwent content analysis, recruitment and support staff were interviewed, and in-depth rolling interviews were conducted with the three now-employed virtual psychiatrists.
RESULTS: We have gained an objective understanding of this new and innovative way of working and, overall, shown that fully remote working in the National Health Service (NHS) is feasible.
CONCLUSIONS: The findings were used to create a step-by-step guide for the remote hiring process, which outlines the necessary steps for conducting it in a safe, swift and successful way. This guide could help other NHS organisations to advertise, recruit and manage fully remote employees.

BACKGROUND: Historically, diseases and non-battle injuries (DNBI) typically stand for 70%‒95% of all medical events during military missions. There is, however, no comprehensive compilation of medical statistics for Swedish soldiers during deployment.
METHODS: During United Nations Multidimensional Integrated Stabilization Mission in Mali, climate data and medical outpatient health surveillance data were compiled for Swedish soldiers deployed to Timbuctoo, between 2015 and 2019. Correlations between climate data and medical outpatient health surveillance data were analysed.
RESULTS: Battle injuries accounted for 0.4% of the visits to healthcare, while diseases accounted for 53.6%, and non-battle injuries for 46%, the majority being musculoskeletal injuries. The combination of high temperature, humidity, sun radiation and good visibility, during summer rotation weeks, caused more events of injuries and heat stress than any other period.
CONCLUSIONS: Musculoskeletal injuries were the major cause for visits to the Swedish camp hospital. Injuries and heat stress increased during periods of high temperature, humidity, sun radiation and good visibility. Lack of medical data, i.e. unknown number of unique patients seeking healthcare, cause codes not always connected to a primary diagnosis, and revisits not being connected to a diagnose, complicated interpretation of health risk factors.

Proteins control individual patient\'s response to pharmaceutical medication, be they receptors, transporters or enzymes. These proteins are under the control of genes. The study of these genes and the interplay between multiple genes is pharmacogenomics, with individual genes being termed pharmacogenes. The greatest understanding of pharmacogenetics is of the drug metabolising enzymes, the cytochrome P450s. Almost the entire UK population is likely to have at least one genetic variant that controls these P450s and thus the phenotype for metabolic competence. This means two patients receiving the same medication and dose may have very different responses, from adverse reaction to being ineffective. An individual military person\'s response to medications can be predicted from their pharmacogenetics, as an example; the response to the commonly prescribed \'pain killers\', codeine, tramadol, hydrocodone or oxycodone. These opioids are metabolised into their active forms by the cytochrome 2D6. Four phenotypes classify an individual\'s metabolic competency: ultra-rapid, extensive, intermediate or poor. A poor metaboliser is at risk of ineffective pain relief from one of the opioids listed, whereas an ultra-rapid metaboliser is at risk of overexposure and subsequent dependency or abuse. In white European populations, the prevalence of the phenotypes is well known and may be used to guide prescribing; however, in other populations such as Nepalese or Pacific Islander the distribution of these phenotypes is unknown. Genotyping provides a framework for the precise treatment of patients and cost-effective use of medication for the UK Armed Forces, as well as potentially providing equity for minority groups.