A central challenge for digital health interventions aimed at improving habitual behaviors is deciding when to deliver an intervention prompt. For many daily habits, such as tooth brushing or eating, individuals tend to act around a usual time of day, but this timing is not fixed and can shift as routines evolve. When intervention timing is selected in advance and held constant throughout a study, it can gradually become misaligned with behavior, causing interventions to potentially arrive after the behavior has already occurred or too early to be effective. In this work, we address this habitual timing misalignment in digital health interventions by proposing an online decision-making framework that continuously adapts intervention timing as individual behavior patterns change. Rather than treating intervention timing as a static design choice, our framework adapts it over time and integrates it into a sequential process that determines both when and whether to deliver an intervention. Using data from a deployed oral health intervention trial as a case study, we evaluate our approach using both observed data and simulated settings to assess how well different intervention timing s
Due to a lack of medical resources or oral health awareness, oral diseases are often left unexamined and untreated, affecting a large population worldwide. With the advent of low-cost, sensor-equipped smartphones, mobile apps offer a promising possibility for promoting oral health. However, to the best of our knowledge, no mobile health (mHealth) solutions can directly support a user to self-examine their oral health condition. This paper presents OralCam, the first interactive app that enables end-users' self-examination of five common oral conditions (diseases or early disease signals) by taking smartphone photos of one's oral cavity. OralCam allows a user to annotate additional information (e.g. living habits, pain, and bleeding) to augment the input image, and presents the output hierarchically, probabilistically and with visual explanations to help a laymen user understand examination results. Developed on our in-house dataset that consists of 3,182 oral photos annotated by dental experts, our deep learning based framework achieved an average detection sensitivity of 0.787 over five conditions with high localization accuracy. In a week-long in-the-wild user study (N=18), most
Most machine learning models for predicting clinical outcomes are developed using historical data. Yet, even if these models are deployed in the near future, dataset shift over time may result in less than ideal performance. To capture this phenomenon, we consider a task--that is, an outcome to be predicted at a particular time point--to be non-stationary if a historical model is no longer optimal for predicting that outcome. We build an algorithm to test for temporal shift either at the population level or within a discovered sub-population. Then, we construct a meta-algorithm to perform a retrospective scan for temporal shift on a large collection of tasks. Our algorithms enable us to perform the first comprehensive evaluation of temporal shift in healthcare to our knowledge. We create 1,010 tasks by evaluating 242 healthcare outcomes for temporal shift from 2015 to 2020 on a health insurance claims dataset. 9.7% of the tasks show temporal shifts at the population level, and 93.0% have some sub-population affected by shifts. We dive into case studies to understand the clinical implications. Our analysis highlights the widespread prevalence of temporal shifts in healthcare.
Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health related tasks. In this paper, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs' application to mental health and encourage the adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. It is especially critical to ensure that mental health LLMs are fine-tuned for mental health, enhance mental health equity, and adhere to ethical standards and that people, including those with lived experience with mental health concerns, are involved in all stages from development through
Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications-focused on supply chain, patient management, and capacity building, among other use cases-can improve the health system and public health performance. We present an Artificial Intelligence and Reinforcement Learning platform that allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices and send personalized recommendations based on past data and predictions can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be more decisive, is discussed specifically. This framework is, however, similarly applicable to improving efficiency in health systems where scarcity is not an issue.
The rapid spread of health misinformation on online social networks (OSNs) during global crises such as the COVID-19 pandemic poses challenges to public health, social stability, and institutional trust. Centrality metrics have long been pivotal in understanding the dynamics of information flow, particularly in the context of health misinformation. However, the increasing complexity and dynamism of online networks, especially during crises, highlight the limitations of these traditional approaches. This study introduces and compares three novel centrality metrics: dynamic influence centrality (DIC), health misinformation vulnerability centrality (MVC), and propagation centrality (PC). These metrics incorporate temporal dynamics, susceptibility, and multilayered network interactions. Using the FibVID dataset, we compared traditional and novel metrics to identify influential nodes, propagation pathways, and misinformation influencers. Traditional metrics identified 29 influential nodes, while the new metrics uncovered 24 unique nodes, resulting in 42 combined nodes, an increase of 44.83%. Baseline interventions reduced health misinformation by 50%, while incorporating the new metrics
Selecting the right monitoring level in Remote Patient Monitoring (RPM) systems for e-healthcare is crucial for balancing patient outcomes, various resources, and patient's quality of life. A prior work has used one-dimensional health representations, but patient health is inherently multidimensional and typically consists of many measurable physiological factors. In this paper, we introduce a multidimensional health state model within the RPM framework and use dynamic programming to study optimal monitoring strategies. Our analysis reveals that the optimal control is characterized by switching curves (for two-dimensional health states) or switching hyper-surfaces (in general): patients switch to intensive monitoring when health measurements cross a specific multidimensional surface. We further study how the optimal switching curve varies for different medical conditions and model parameters. This finding of the optimal control structure provides actionable insights for clinicians and aids in resource planning. The tunable modeling framework enhances the applicability and effectiveness of RPM services across various medical conditions.
Objectives: To assess self-reported population oral health conditions amid COVID-19 pandemic using user reports on Twitter. Method and Material: We collected oral health-related tweets during the COVID-19 pandemic from 9,104 Twitter users across 26 states (with sufficient samples) in the United States between November 12, 2020 and June 14, 2021. We inferred user demographics by leveraging the visual information from the user profile images. Other characteristics including income, population density, poverty rate, health insurance coverage rate, community water fluoridation rate, and relative change in the number of daily confirmed COVID-19 cases were acquired or inferred based on retrieved information from user profiles. We performed logistic regression to examine whether discussions vary across user characteristics. Results: Overall, 26.70% of the Twitter users discuss wisdom tooth pain/jaw hurt, 23.86% tweet about dental service/cavity, 18.97% discuss chipped tooth/tooth break, 16.23% talk about dental pain, and the rest are about tooth decay/gum bleeding. Women and younger adults (19-29) are more likely to talk about oral health problems. Health insurance coverage rate is the mo
Electronic Health Record (EHR) has become an essential tool in the healthcare ecosystem, providing authorized clinicians with patients' health-related information for better treatment. While most developed countries are taking advantage of EHRs to improve their healthcare system, it remains challenging in developing countries to support clinical decision-making and public health using a computerized patient healthcare information system. This paper proposes a novel EHR architecture suitable for developing countries--an architecture that fosters inclusion and provides solutions tailored to all social classes and socioeconomic statuses. Our architecture foresees an internet-free (offline) solution to allow medical transactions between healthcare organizations, and the storage of EHRs in geographically underserved and rural areas. Moreover, we discuss how artificial intelligence can leverage anonymous health-related information to enable better public health policy and surveillance.
The growing demand for home healthcare calls for tools that can support care delivery. In this study, we explore automatic health assessment from voice using real-world home care visit data, leveraging the diverse patient information it contains. First, we utilize Large Language Models (LLMs) to integrate Subjective, Objective, Assessment, and Plan (SOAP) notes derived from unstructured audio transcripts and structured vital signs into a holistic illness score that reflects a patient's overall health. This compact representation facilitates cross-visit health status comparisons and downstream analysis. Next, we design a multi-stage preprocessing pipeline to extract short speech segments from target speakers in home care recordings for acoustic analysis. We then employ an Audio Language Model (ALM) to produce plain-language descriptions of vocal biomarkers and examine their association with individuals' health status. Our experimental results benchmark both commercial and open-source LLMs in estimating illness scores, demonstrating their alignment with actual clinical outcomes, and revealing that SOAP notes are substantially more informative than vital signs. Building on the illness
Wearable movement data is collected by nearly all commercially available smartwatches and is a valuable resource for mental health research, reflecting fine-grained temporal behavioral trends. Despite its promise, the development of foundation models for health wearable modeling remains limited when compared to clinical image and text analysis. We designed transformers with patch embeddings and used self-supervised masked autoencoder pretraining on minute-level week-long actigraphy (physical activity intensity measurement) sequences to develop and evaluate the Pretrained Actigraphy Transformer (PAT). PAT is an open-source foundation model for wearable movement time series that combines week-long temporal modeling, psychiatric outcome evaluation, and reproducibility on public data. Pretrained on data from 21,538 U.S. participants in a nationally representative cohort from the National Health and Nutrition Examination Survey (NHANES), PAT consistently outperformed non-foundation-model baselines across mental health prediction tasks-including benzodiazepine and SSRI use, depression, and sleep abnormalities. During the benzodiazepine medication usage prediction task, PAT demonstrated t
The Oregon Health Insurance Experiment (OHIE) offers a unique opportunity to examine the causal relationship between Medicaid coverage and happiness among low-income adults, using an experimental design. This study leverages data from comprehensive surveys conducted at 0 and 12 months post-treatment. Previous studies based on OHIE have shown that individuals receiving Medicaid exhibited a significant improvement in mental health compared to those who did not receive coverage. The primary objective is to explore how Medicaid coverage impacts happiness, specifically analyzing in which direction variations in healthcare spending significantly improve mental health: higher spending or lower spending after Medicaid. Utilizing instrumental variable (IV) regression, I conducted six separate regressions across subgroups categorized by expenditure levels and happiness ratings, and the results reveal distinct patterns. Enrolling in OHP has significantly decreased the probability of experiencing unhappiness, regardless of whether individuals had high or low medical spending. Additionally, it decreased the probability of being pretty happy and having high medical expenses, while increasing the
Background: Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has the potential to aid in the diagnosis by providing a quick and informative analysis of the bitewing images. Methods: A dataset of 13,887 bitewings from the HUNT4 Oral Health Study were annotated individually by six different experts, and used to train three different object detection deep-learning architectures: RetinaNet (ResNet50), YOLOv5 (M size), and EfficientDet (D0 and D1 sizes). A consensus dataset of 197 images, annotated jointly by the same six dentist, was used for evaluation. A five-fold cross validation scheme was used to evaluate the performance of the AI models. Results: he trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians. When compared against the dental clinicians, the YOLOv5 model shows the largest improvement, reporting 0.647 mean average precision, 0.548 mean F1-score, and 0.149 mean fal
Objective: To enhance health literacy and accessibility of health information for a diverse patient population by developing a patient-centered artificial intelligence (AI) solution using large language models (LLMs) and Fast Healthcare Interoperability Resources (FHIR) application programming interfaces (APIs). Materials and Methods: The research involved developing LLM on FHIR, an open-source mobile application allowing users to interact with their health records using LLMs. The app is built on Stanford's Spezi ecosystem and uses OpenAI's GPT-4. A pilot study was conducted with the SyntheticMass patient dataset and evaluated by medical experts to assess the app's effectiveness in increasing health literacy. The evaluation focused on the accuracy, relevance, and understandability of the LLM's responses to common patient questions. Results: LLM on FHIR demonstrated varying but generally high degrees of accuracy and relevance in providing understandable health information to patients. The app effectively translated medical data into patient-friendly language and was able to adapt its responses to different patient profiles. However, challenges included variability in LLM responses a
This research paper presents a meta-analysis of the multifaceted role of technology in mental health. The pervasive influence of technology on daily lives necessitates a deep understanding of its impact on mental health services. This study synthesizes literature covering Behavioral Intervention Technologies (BITs), digital mental health interventions during COVID-19, young men's attitudes toward mental health technologies, technology-based interventions for university students, and the applicability of mobile health technologies for individuals with serious mental illnesses. BITs are recognized for their potential to provide evidence-based interventions for mental health conditions, especially anxiety disorders. The COVID-19 pandemic acted as a catalyst for the adoption of digital mental health services, underscoring their crucial role in providing accessible and quality care; however, their efficacy needs to be reinforced by workforce training, high-quality evidence, and digital equity. A nuanced understanding of young men's attitudes toward mental health is imperative for devising effective online services. Technology-based interventions for university students are promising, al
In a qualitative study, Gregory, Gibson and Robinson proposed a framework of items grouped in seven dimensions reflecting oral health related perceptions, attitudes and behavior to encompass what is relevant when patients assess their own oral health related quality of life (OHRQOL). The aim of this study is to quantify the dimensions of Gregory relevance framework and confirm, or otherwise, the influence on the change in the OHIP-14, an instrument widely used to measure OHRQOL. The study was observational and longitudinal with the OHIP-14 measured before a tooth extraction, and two and four weeks thereafter. Statistical methods of analysis consisted of generalised estimating equations. Responsiveness (or sensitivity to change) of the OHIP-14 was established in our patient population. The dimensions of Gregory relevance framework featured significantly in the models. Patients trust in dental products, perception of own oral health as normal in relation to the average person, preference for natural teeth, character bias in judgement and control by adherence to dentists instructions, were all found to be significant factors in the longitudinal change of the OHIP-14. Borderline signif
Research has shown that the general health and oral health of an individual are closely related. Accordingly, current practice of isolating the information base of medical and oral health domains can be dangerous and detrimental to the health of the individual. However, technical issues such as heterogeneous data collection and storage formats, limited sharing of patient information and lack of decision support over the shared information are the principal reasons for the current state of affairs. To address these issues, the following research investigates the development and application of a cross-domain ontology and rules to build an evidence-based and reusable knowledge base consisting of the inter-dependent conditions from the two domains. Through example implementation of the knowledge base in Protege, we demonstrate the effectiveness of our approach in reasoning over and providing decision support for cross-domain patient information.
YouTube has rapidly emerged as a predominant platform for content consumption, effectively displacing conventional media such as television and news outlets. A part of the enormous video stream uploaded to this platform includes health-related content, both from official public health organizations, and from any individual or group that can make an account. The quality of information available on YouTube is a critical point of public health safety, especially when concerning major interventions, such as vaccination. This study differentiates itself from previous efforts of auditing YouTube videos on this topic by conducting a systematic daily collection of posted videos mentioning vaccination for the duration of 3 months. We show that the competition for the public's attention is between public health messaging by institutions and individual educators on one side, and commentators on society and politics on the other, the latest contributing the most to the videos expressing stances against vaccination. Videos opposing vaccination are more likely to mention politicians and publication media such as podcasts, reports, and news analysis, on the other hand, videos in favor are more li
Abundant evidence has tracked the labour market and health assimilation of immigrants, including static analyses of differences in how foreign-born and native-born residents consume health care services. However, we know much less about how migrants' patterns of health care usage evolve with time of residence, especially in countries providing universal or quasi-universal coverage. We investigate this process in Spain by combining all the available waves of the local health survey, which allows us to separately identify period, cohort, and assimilation effects. We find that the evidence of health assimilation is limited and solely applies to migrant females' visits to general practitioners. Nevertheless, the differential effects of ageing on health care use between foreign-born and native-born populations contributes to the convergence of utilisation patterns in most health services after 20 years in Spain. Substantial heterogeneity over time and by region of origin both suggest that studies modelling future welfare state finances would benefit from a more thorough assessment of migration.
Mobile health apps are revolutionizing the healthcare ecosystem by improving communication, efficiency, and quality of service. In low- and middle-income countries, they also play a unique role as a source of information about health outcomes and behaviors of patients and healthcare workers, while providing a suitable channel to deliver both personalized and collective policy interventions. We propose a framework to study user engagement with mobile health, focusing on healthcare workers and digital health apps designed to support them in resource-poor settings. The behavioral logs produced by these apps can be transformed into daily time series characterizing each user's activity. We use probabilistic and survival analysis to build multiple personalized measures of meaningful engagement, which could serve to tailor content and digital interventions suiting each health worker's specific needs. Special attention is given to the problem of detecting churn, understood as a marker of complete disengagement. We discuss the application of our methods to the Indian and Ethiopian users of the Safe Delivery App, a capacity-building tool for skilled birth attendants. This work represents an