AMIA ... Annual Symposium proceedings. AMIA Symposium最新文献

This study investigates the accessibility of open-source electronic health record (EHR) systems for individuals who are visually impaired or blind. Ensuring the accessibility of EHRs to visually impaired users is critical for the diversity, equity, and inclusion of all users. The study used a combination of automated and manual accessibility testing with screen readers to evaluate the accessibility of three widely used open-source EHR systems. We used three popular screen readers - JAWS (Windows), NVDA (Windows), and Apple VoiceOver (OSX) to evaluate accessibility. The evaluation revealed that although each of the three EHR systems was partially accessible, there is room for improvement, particularly regarding keyboard navigation and screen reader compatibility. The study concludes with recommendations for making EHR systems more inclusive for all users and more accessible.

Determining clinically relevant physiological states from multivariate time-series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.

Emergency medical services (EMS) providers often face significant challenges in their work, including collecting, integrating, and making sense of a variety of information. Despite their criticality, EMS work is one of the very few medical domains with limited technical support. To design and implement effective decision support, it is essential to examine and gain a holistic understanding of the fine-grained process of sensemaking in the field. To that end, we reviewed 25 video recordings of EMS simulations to understand the nuances of EMS sensemaking work, including 1) the types of information and situation that are collected and made sense of in the field; 2) the work practices and temporal patterns of EMS sensemaking work; and 3) the challenges in EMS sensemaking and decision-making process. Based on the results, we discuss implications for technology opportunities to support rapid information acquisition and sensemaking in time-critical, high-risk medical settings such as EMS.

The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Region Contrastive Learning (LRCLR), a flexible fine-tuning framework that adds layers for significant image region selection as well as cross-modality interaction. Our results on an external validation set of chest x-rays suggest that LRCLR identifies significant local image regions and provides meaningful interpretation against radiology text while improving zero-shot performance on several chest x-ray medical findings.

Mobile health apps hold great potential for promoting children's health and wellbeing. However, there is limited understanding of how these technologies are currently designed to support children with their health concerns or wellness goals. To gain insight into the current landscape of mobile apps designed for children's health, we retrieved and reviewed 43 apps from IOS and Google Play store that are specifically marketed for children. Our qualitative analysis identified the dominant health focuses and goals of children's mobile health apps. We analyzed the primary users and their expectations as well as the methods of engagement and involvement adopted. Based on our findings, we discussed the opportunities to support children with chronic illnesses through mobile apps, design for dual use, and design for age appropriateness and digital health safety. This study provides insights and recommendations for app designers, health researchers, and policymakers on strategies for engaging children and parents while also promoting children's health and wellbeing through mobile technology.

Abortion is a controversial topic that has long been debated in the US. With the recent Supreme Court decision to overturn Roe v. Wade, access to safe and legal reproductive care is once again in the national spotlight. A key issue central to this debate is patient privacy, as in the post-HITECH Act era it has become easier for medical records to be electronically accessed and shared. This study analyzed a large Twitter dataset from May to December 2022 to examine the public's reactions to Roe v. Wade's overruling and its implications for privacy. Using a mixed-methods approach consisting of computational and qualitative content analysis, we found a wide range of concerns voiced from the confidentiality of patient-physician information exchange to medical records being shared without patient consent. These findings may inform policy making and healthcare industry practices concerning medical privacy related to reproductive rights and women's health.

While prior work has investigated the benefits of online health communities and general-purpose social media used for health-related purposes, little work examines the use of TikTok, an emerging social media platform with a substantial user base. The platform's multimodal capabilities foster creative self-expression, while the content-driven network allows users to reach new audiences beyond their personal connections. To investigate users' challenges and motivations, we analyzed 160 TikTok videos that center on users' firsthand experiences living with chronic illness. We found that users struggled with a loss of normalcy and stigmatization in daily life. To contend with these challenges, they publicly shared their experiences to raise awareness, seek support from peers, and normalize chronic illness experiences. Based on our findings, we discuss the modalities of TikTok that facilitate self-expression around stigmatized topics and provide implications for the design of online health communities that better support adolescents and young adults.

Amyotrophic lateral sclerosis (ALS) is a rare and devastating neurodegenerative disorder that is highly heterogeneous and invariably fatal. Due to the unpredictable nature of its progression, accurate tools and algorithms are needed to predict disease progression and improve patient care. To address this need, we developed and compared an extensive set of screener-learner machine learning models to accurately predict the ALS Function-Rating-Scale (ALSFRS) score reduction between 3 and 12 months, by paring 5 state-of-arts feature selection algorithms with 17 predictive models and 4 ensemble models using the publicly available Pooled Open Access Clinical Trials Database (PRO-ACT). Our experiment showed promising results with the blender-type ensemble model achieving the best prediction accuracy and highest prognostic potential.

The need for effective and efficient clinical decision support (CDS) embedded in electronic health record (EHR) processes is growing. Using choice architecture design strategies may increase effectiveness of CDS solutions. The authors describe implementation of an opioid risk alert and subsequent revisions of that alert to increase effectiveness and reduce alert volumes. The first version of the alert used an opt-in choice architecture when recommending naloxone and the second version used an active choice design. The percentage of opioid prescriptions ordered with naloxone prescribed within the last 12 months increased significantly after implementation of the first version of the alert and then further increased significantly after implementation of the second version. Alert volumes decreased over the same timeframe. An education campaign was also implemented during the timeframe studied and likely also contributed to the naloxone outcomes seen.

Accelerated use of telemedicine during the COVID-19 pandemic enabled uninterrupted healthcare delivery while unmasking care disparities for several vulnerable communities. The social determinants of health (SDOH) serve as a critical model for understanding how the circumstances in which people are born, work, and live impact health outcomes. We performed semi-structured interviews to understand patients and providers' experiences with telemedicine encounters during the COVID-19 pandemic. Through a deductive approach, we applied the SDOH to determine telemedicine's role and impact within this framework. Overall, patient and provider interviews supported the use of existing SDOH domains to describe disparities in Internet access and telemedicine use, rather than reframing technology as a sixth SDOH. In order to mitigate the digital divide, we identify and propose solutions that address SDOH-related barriers that shape the use of health information technologies.