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

Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that leverages nursing surveillance documentation patterns to predict deterioration risks for hospitalized patients. In a retrospective cohort study of 1,013 hospital encounters with unanticipated ICU transfers from a multi-site pragmatic randomized controlled trial, we assessed the influence of CONCERN EWS on in-hospital mortality and length of stay following unanticipated ICU transfers. Chi-square tests, t-tests, multivariate logistic regression, and generalized linear models were used. Our findings showed that patients who had unanticipated ICU transfers from acute care units with CONCERN EWS had a lower in-hospital mortality rate and a shorter average hospital stay than those transferred from units receiving usual care. These results suggest that CONCERN EWS enhances shared situational awareness for care teams, improves communication, and effectively facilitates timely interventions, thereby streamlining care processes and improving patient outcomes.

Collecting images of rare dermatological diseases for machine learning detection applications is a costly, laborious task. It is difficult to collect enough images of these diagnoses to avoid the risk of low accuracy "in the wild". One of the sources of bias in these networks is irrelevant background pixel data. These pixels necessarily have no clinical significance, yet Deep Neural Networks will make weak correlations based on that information. To reduce their ability to do this, we introduce a masking augmentation algorithm, InfoMax-Cutout. It employs unsupervised Information Maximization losses to mask out background pixels. InfoMax-Cutout increased accuracy on classifying 319 diagnoses by 0.76%. These features generalized to an unseen diagnosis task (Fitzpatrick 17k), improving accuracy over a baseline by 43.3% and reducing Gini inequality by 20.9%. This approach of learning to separate out background pixels can increase accuracy in detecting diseases in Lower and Middle Income Countries.

Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO), and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e.g., HPO, CL, and Uberon) and represents 259 kidney-related biomarkers. We have also developed a precision medicine metadata ontology (PMMO) to integrate 50 variables from KPMP and CZ CellxGene data resources and applied PMMO for integrative kidney data analysis. The gene expression profiles of kidney gene biomarkers were specifically analyzed under healthy control or AKI/CKD disease states. This work demonstrates how ontology-based approaches support multi-domain data and knowledge integration in precision medicine.

In many cultures where discussions and care-seeking for sexual and reproductive health (SRH) are stigmatized, unmarried women often suffer silently, facing risks of sexually transmitted infections and gynecological complications. South Korea exemplifies this challenge, with SRH topics remaining stigmatized, potentially contributing to Korean women's high incidence rates of cervical cancer. To address this problem, we designed and studied a protected online community for unmarried Korean women with 9 weeks of guided activities relating to SRH. We describe how these activities helped participants reflect on and discuss the typically taboo topics surrounding SRH. Results indicate that the online community effectively supported participants in initiating additional offline conversations about SRH with more people, and even encouraged some women to seek clinical care. This work sheds light on the potential of supportive and protective online communities to facilitate SRH, offering newfound options for supporting women in cultures where such care is stigmatized.

The complexity of health care processes present significant challenges for using Electronic Health Records (EHR) data to build high fidelity phenotypes. This study leverages a healthcare process modeling (HPM) approach to enable understanding of EHR-based pressure injury (PrI) data patterns needed for building a standardized PrI phenotyping pipeline. The PrI HPM was developed and validated using mixed methods, including exploratory sequential design, through interdisciplinary collaboration among clinical experts, data scientists, database analysts, and informaticians. zThe qualitative analysis identified the dynamics between PrI care and the associated clinical documentation processes. The quantitative analysis identified inherent challenges and limitations of the PrI data. The PrI HPM includes three moderating factors: system configuration, hospital policy, and nurse's individual workflow. We further incorporated the HPM into the PrI phenotype development process to address phenotyping challenges. Moreover, we suggested a set of standardizable recommendations to address PrI phenotyping challenges.

In clinical settings, domain experts sometimes disagree on optimal treatment actions. These "decision points" must be comprehensively characterized, as they offer opportunities for Artificial Intelligence (AI) to provide statistically informed recommendations. To address this, we introduce a pipeline to investigate "decision regions", clusters of decision points, by training classifiers for prediction and applying clustering techniques to the classifier's embedding space. Our methodology includes: a robustness analysis confirming the topological stability of decision regions across diverse design parameters; an empirical study using the MIMIC-III database, focusing on the binary decision to administer vasopressors to hypotensive patients in the ICU; and an expert-validated summary of the decision regions' statistical attributes with novel clinical interpretations. We demonstrate that the topology of these decision regions remains stable across various design choices, reinforcing the reliability of our findings and generalizability of our approach. We encourage future work to extend this approach to other medical datasets.

The aim of this work was to create a gold-standard curated cohort of 10,000+ cases from the Veteran Affairs (VA) corporate data warehouse (CDW) for virtual emulation of a randomized clinical trial (CSP#592). The trial had six inclusion/exclusion criteria lacking adequate structured data. We therefore used a hybrid computer/human approach to extract information from clinical notes. Rule-based NLP output was iteratively adjudicated by a panel of trained non-clinician content experts and non-experts using an easy-to-use spreadsheet-based rapid adjudication display. This group-adjudication process iteratively sharpened both the computer algorithm and clinical decision criteria, while simultaneously training the non-experts. The cohort was successfully created with each inclusion/exclusion decision backed by a source document. Less than 0.5% of cases required referral to specialist clinicians. It is likely that such curated datasets capturing specialist reasoning and using a process-supervised approach will acquire greater importance as training tools for future clinical AI applications.

The nature of paramedic workloads typically results in incomplete or lack of patient care reports on patient handover to emergency department staff. Patient information gaps can increase emergency department staff's workload, cause care delays, and increase risks of adverse events. An integrated hands-free electronic patient care report (ePCR) could eliminate this gap. We conducted an environmental scan of the available literature on technologies to improve paramedic documentation and current advanced paramedic charting systems. Two technologies, speech recognition documentation and live telemetry sharing systems, were identified as potential improvements. A theoretical architecture for an integrated hands-free ePCR charting (IHeC) system was developed by combining these technologies. The ePCR could be completed and available upon patient arrival to the hospital using speech recognition and vital sign sharing technology. The IHeC system could solve the problem of patient information gaps and provide a platform for more advanced integration of paramedic services.

Access to mental healthcare is increasingly technologically-mediated. People with low socioeconomic status (SES) and serious mental illness (SMI) face lower rates of tech ownership and may lack technological skills, called "digital divides." Yet, little is known about how digital divides may impact mental healthcare access. Therefore, a qualitative study (ethnographic observations and interviews) was conducted with stakeholders working with low-SES SMI patients using community mental health care (CMH) (N=14). Findings showed that consumers struggled to maintain consistent internet-and thus mental healthcare-access despite owning smartphones. Consumers frequently faced care disruptions due to broken, lost, or uncharged phones. Staff and patients created effortful but ad-hoc workarounds to restore access during technological access disruptions. These solutions frequently occurred after healthcare appointments were missed. Digital divide concepts should accommodate the work necessary to maintain technology access even after ownership and its impact on care access-especially among low-SES SMI patients.

We introduce an innovative automated system for the search and assessment of genetic variant evidence, meticulously aligned with ACMG guidelines. Leveraging the synergistic power of artificial intelligence (AI), elastic search, and an extensive knowledge base, our system advances the efficiency and accuracy of genetic variant interpretation. Distinct from existing methodologies, it features a pioneering literature filtering mechanism that automates the identification and relevance ranking of scientific articles, significantly reducing the time spending on literature evidence search and optimizing the evidence assessment process. Implemented and rigorously tested by a commercial company hereditary cancer variant curation team, the system demonstrated its effectiveness and scalability by processing over 3 million PMIDs and 1.8 million full-text articles. Throughout the period of active utilization, significant insights were gleaned into the real-world impact and user experience of the system, conclusively affirming its robustness. Our comparative analysis with Mastermind 2.0 highlights the system's enhanced performance in minimizing false positives for various mutation types. The core AI model exhibits exceptional precision, recall, and F1 scores above 0.8, signifying its adeptness in selecting pertinent literature for variant classification. The experience and knowledge acquired from deploying the system in a commercial setting provide a distinctive outlook on its practicality and prospects for future development. The novel integration of AI with traditional genetic variant curation processes heralds a new era in the field, promising significant advancements and broader application prospects.