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200 papers
Evaluating the Performance of Large Language Models for Named Entity Recognition in Ophthalmology Clinical Free-Text Notes.
Majid I, Mishra V, Ravindranath R … +1 more , Wang SY
AMIA Annu Symp Proc · 2024 · PMID 40417582
This study compared large language models (LLMs) and Bidirectional Encoder Representations from Transformers (BERT) models in identifying medication names, routes, and frequencies from publicly available free-text ophtha...
This study compared large language models (LLMs) and Bidirectional Encoder Representations from Transformers (BERT) models in identifying medication names, routes, and frequencies from publicly available free-text ophthalmology progress notes of 480 patients. 5,520 lines of annotated text were divided into train (N=3,864), validation (N=1,104), and test sets (N=552). We evaluated ChatGPT-3.5, ChatGPT-4, PaLM 2, and Gemini to identify these medication entities. We fine-tuned BERT, BioBERT, ClinicalBERT, DistilBERT, and RoBERTa for the same task using the training set. On the test set, GPT-4 achieved the best performance (macro-averaged F1 0.962). Among the BERT models, BioBERT achieved the best performance (macro-averaged F1 0.875). Modern LLMs outperformed BERT models even in the highly domain-specific task of identifying ophthalmic medication information from progress notes, showcasing the potential of LLMs for medical named entity recognition to enhance patient care.
Development of a Flexible Chain of Thought Framework for Automated Routing of Patient Portal Messages.
Gao M, Pejavara K, Balu S … +1 more , Henao R
AMIA Annu Symp Proc · 2024 · PMID 40417581
The increase in utilization of patient portal messages has imposed a considerable burden on healthcare providers, contributing to an increased incidence of provider burnout. This study introduces a framework for leveragi...
The increase in utilization of patient portal messages has imposed a considerable burden on healthcare providers, contributing to an increased incidence of provider burnout. This study introduces a framework for leveraging Large Language Models (LLMs) and Chain-of-Thought (CoT) prompting in order to automatically categorize and route messages to their appropriate location. The modeling framework, which utilizes gold standard annotations from triage nurses, not only facilitates the dynamic adaptation of the model to evolving healthcare workflows and emerging edge-case scenarios, but also significantly improves the model's classification accuracy compared to traditional zero-shot methods. In addition, the framework allows for flexibility in its task and continuous improvement via annotation of exemplar messages. The model is able to accurately categorize messages in an automated fashion, which has potential to dramatically ease the burden on providers and provide faster and safer responses to patients. This framework can also be readily extended to work in a variety of clinical and documentation settings.
Large-scale Text Mining of Suicide Attempt improves Identification of Distinct Suicidal Events in Electronic Health Records.
Lee H, Bejan CA, Walsh CG
AMIA Annu Symp Proc · 2024 · PMID 40417580
In this study, we explore a natural language processing (NLP) algorithm's capacity to identify proximal but distinct suicide attempt (SA) events compared to diagnostic code-based approaches. This study used an NLP algori...
In this study, we explore a natural language processing (NLP) algorithm's capacity to identify proximal but distinct suicide attempt (SA) events compared to diagnostic code-based approaches. This study used an NLP algorithm with high precision in identifying SA events, which processes clinical notes for suicide-related text expressions and generates SA outcome relevance scores on mentioned dates. We chart reviewed all SA visit pairs less than 15 days apart. Despite sample size limitations, our NLP method surpassed the code-based model's performance (0.85 [95% CI: 0.74 - 0.92] vs. 0.78 [95% CI: 0.56 - 0.92], p = 0.71). More importantly, NLP detected three times more SA visit pairs <15 days compared to the code-based approach (71 vs. 23), with only 3 overlaps. This study demonstrates NLP's efficacy in identifying distinct SA visit pairs. Given minimal overlap, we suggest leveraging both clinical notes and diagnostic codes for a comprehensive SA event detection.
Assessing the Seasonality of Lab Tests Among Patients with Alzheimer's Disease and Related Dementias in OneFlorida Data Trust.
Han W, Bhasuran B, Muse VP … +6 more , Brunak S, Lin L, Hanna K, Huang Y, Bian J, He Z
AMIA Annu Symp Proc · 2024 · PMID 40417579
About 1 in 9 older adults over 65 has Alzheimer's disease (AD), many of whom also have multiple other chronic conditions such as hypertension and diabetes, necessitating careful monitoring through laboratory tests. Under...
About 1 in 9 older adults over 65 has Alzheimer's disease (AD), many of whom also have multiple other chronic conditions such as hypertension and diabetes, necessitating careful monitoring through laboratory tests. Understanding the patterns of laboratory tests in this population aids our understanding and management of these chronic conditions along with AD. In this study, we used an unimodal cosinor model to assess the seasonality of lab tests using electronic health record (EHR) data from 34,303 AD patients from the OneFlorida+ Clinical Research Consortium. We observed significant seasonal fluctuations-higher in winter in lab tests such as glucose, neutrophils per 100 white blood cells (WBC), and WBC. Notably, certain leukocyte types like eosinophils, lymphocytes, and monocytes are elevated during summer, likely reflecting seasonal respiratory diseases and allergens. Seasonality is more pronounced in older patients and varies by gender. Our findings suggest that recognizing these patterns and adjusting reference intervals for seasonality would allow healthcare providers to enhance diagnostic precision, tailor care, and potentially improve patient outcomes.
Preemptive Forecasting of Symptom Escalation in Cancer Patients Undergoing Chemotherapy.
Finkelstein J, Smiley A, Echeverria C … +1 more , Mooney K
AMIA Annu Symp Proc · 2024 · PMID 40417578
This study evaluates the utility of machine learning (ML) algorithms in early forecasting of total symptom score changes from daily self-reports of 339 chemotherapy patients. The dataset comprised 12 specific symptoms, w...
This study evaluates the utility of machine learning (ML) algorithms in early forecasting of total symptom score changes from daily self-reports of 339 chemotherapy patients. The dataset comprised 12 specific symptoms, with severity and distress for each symptom rated on a 1 to 10 scale, generating a "total symptom score" ranging from 0 to 230. To address the challenge of an unbalanced original dataset, where Class I (score change ≥ 5) and Class II (score change < 5) were unevenly represented, we created a balanced dataset specifically for model training. This process involved a stratified sampling technique to ensure equitable representation of both classes, enhancing the predictive analysis. Using the MATLAB® Classification Learner application, we investigated nine ML models, including decision trees, discriminant analysis, support vector machines (SVM), and others, each applying various classifiers. The objective was to predict the total symptom score change based on the preceding 3 to 5 days' symptom data. Models were trained on the balanced dataset to mitigate the original imbalance's impact, with comparative evaluations also conducted on the unbalanced data to assess performance differences. The analysis revealed that certain classifiers, such as SVM, delivered optimal performance on the unbalanced dataset, with an accuracy rate peaking at 82%. Yet, these models tended to frequently misclassify Class I as Class II. In contrast, the Ensemble algorithm equipped with the RUSBoost classifier demonstrated exceptional skill in accurately classifying both classes on both datasets, achieving accuracies of 59%, 59.3%, and 59.4% for data from 3, 4, and 5 days prior, respectively. Notably, these figures slightly improved to 61.16%, 58.41%, and 60.05% upon utilizing the balanced dataset for training. The deployment of a balanced dataset for model training underscores the significant potential of ML algorithms in improving symptom management for chemotherapy patients, offering a path to enhanced patient care and quality of life through targeted, personalized symptom monitoring.
SeqTrial: Utility Preserving Sequential Clinical Trial Data Generator.
Das T, Shafquat A, Beigi M … +3 more , Aptekar J, Mezey J, Sun J
AMIA Annu Symp Proc · 2024 · PMID 40417577
Clinical trial data used to evaluate new treatments have value beyond the original studies, but limitations in data access due to privacy concerns make further use of these data challenging. Digital twins offer a solutio...
Clinical trial data used to evaluate new treatments have value beyond the original studies, but limitations in data access due to privacy concerns make further use of these data challenging. Digital twins offer a solution by simulating patient outcomes, providing less restricted data access, reducing costs and increasing sample sizes. However, existing research focuses on synthetic Electronic Healthcare Records (EHRs) and lacks personalized patient record generation. This paper introduces SeqTrial, a framework for generating personalized digital twins for sequential clinical trial event data. The method uses BioBERT word embeddings to capture biomedical term semantics, an attention mechanism to understand visit relationships, and synthesizes digital twins for each patient. SeqTrial generates utility-preserving digital twins capable of estimating clinical outcomes, while addressing data scarcity through self-supervised pretraining. The method demonstrates high fidelity and utility in generating synthetic sequential clinical trial data for patient outcome prediction while ensuring privacy protection. The code is available at.
Student Behavior Analysis using YOLOv5 and OpenPose in Smart Classroom Environment.
Li X, Ji Y, Yang J … +1 more , Li M
AMIA Annu Symp Proc · 2024 · PMID 40417576
In the classroom, artificial intelligence techniques help automate student behavior analysis, and teachers are able to understand students' class status more effectively. We developed an intelligent method for classroom...
In the classroom, artificial intelligence techniques help automate student behavior analysis, and teachers are able to understand students' class status more effectively. We developed an intelligent method for classroom behavior analysis by building a CQStu datasets and annotating 6,687 images through active learning. OpenPose was used to detect the key points of the student's body, and the key points of the key parts of the body were utilized to generate representative points of the student, and the idea of coordinates was used to assign the student's position. Using YOLOV5 to recognize students' classroom behaviors and count the number of times, our experimental results show that the average classroom behavior recognition accuracy is 84.23%, and the overall location accuracy is about 79.6%. In addition, we introduced a nonlinear weighting factor to evaluate the effectiveness of teaching and constructed corresponding classroom behavior weights based on different classroom scenarios. A method for student classroom behavior identification and analysis is provided, and a framework for future intelligent classroom teaching evaluation methods is established, providing objective data support for student performance analysis.
Integrated Hands-Free Electronic Patient Care Report (ePCR) Charting (IHeC): Designing the Architecture.
Hedderson DR, Lai C
AMIA Annu Symp Proc · 2024 · PMID 40417575
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 work...
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.
Voice-Activated Self-Monitoring Application (VoiS): Perspectives from People with Diabetes and Hypertension.
Oh H, Yang L, Zahra TA … +9 more , Rabbani M, Tian S, Anik AA, Upama PB, Park MS, Luo J, Chan E, Whittle J, Ahamed SI
AMIA Annu Symp Proc · 2024 · PMID 40417574
This paper describes the development and usability test processes of the voice-activated self-monitoring (VoiS) application. VoiS is an innovative, theory-driven mobile app on a smart speaker platform that supports routi...
This paper describes the development and usability test processes of the voice-activated self-monitoring (VoiS) application. VoiS is an innovative, theory-driven mobile app on a smart speaker platform that supports routine and convenient self-monitoring of blood pressures, glucose levels, and health behaviors by people with coexisting diabetes and hypertension. It improves the quality of their communication with healthcare providers. The prototype of VoiS includes voice interaction with Amazon Alexa and data representation using smartphones (iOS and Android). Fourteen people with coexisting diabetes and hypertension participated in usability testing. After completing a range of tasks individually, testers participated in group interviews. We used a survey based on the Technology Acceptance Model to measure the ease of use and perceived usefulness of VoiS. All interviews were recorded and transcribed, and then common themes were extracted. Participants found VoiS to be easy to use and useful.
EHR Documentation Frequency Changes Across the COVID-19 Pandemic.
Fan H, Rossetti S, Mugoya R … +6 more , Jia H, Thate J, Finnegan A, Lai AM, Cato K, Yen PY
AMIA Annu Symp Proc · 2024 · PMID 40417573
Documentation is crucial for patient care. However, the increased documentation burden raises questions about its clinical value. The COVID-19 pandemic provided an opportunity to explore nurses' documentation patterns du...
Documentation is crucial for patient care. However, the increased documentation burden raises questions about its clinical value. The COVID-19 pandemic provided an opportunity to explore nurses' documentation patterns due to increased patient care demands and the implementation of documentation relaxation policy. We conducted a trend analysis to examine documentation frequency over time, including phases during the pandemic and the implementation of the Surge Documentation, a documentation relaxation policy at a Midwest academic medical center. We analyzed the trend changes using segmented regression and mixed-effect Poisson regression. We found that documentation frequency increased in response to the pandemic due to the heightened demand for patient care and significantly decreased with the implementation of Surge documentation. The reduction was particularly noticeable in flowsheets unrelated to patient acuity. The study highlighted nurses' critical thinking in prioritizing documentation based on patient care. Future policies should support nurses'autonomy in documentation without imposing excessive requirements.
Emulation of a Target Trial to Estimate the Effect of Selective Serotonin Reuptake Inhibitors on the Development of Antimicrobial-Resistant Infections using Electronic Health Record Data and Causal Machine Learning.
Ser SE, Snigurska UA, Cohen SA … +6 more , Jun I, Bjarnadottir RI, Lucero RJ, Marini S, Bian J, Prosperi M
AMIA Annu Symp Proc · 2024 · PMID 40417572
Antimicrobial resistance is a significant public health concern. The use of selective serotonin reuptake inhibitors (SSRIs), medications commonly prescribed to treat depression, anxiety, and other psychiatric disorders,...
Antimicrobial resistance is a significant public health concern. The use of selective serotonin reuptake inhibitors (SSRIs), medications commonly prescribed to treat depression, anxiety, and other psychiatric disorders, is increasing. Previous in vitro studies have shown that bacteria can become resistant to antibiotics when exposed to SSRIs. In this study, we emulated a target trial to estimate the effect of SSRI usage on the incidence of antibiotic-resistant infection. Our study population consisted of patients with mood, anxiety, or stress-related disorders, and a record of previous antimicrobial susceptibility testing or diagnosis of bacterial infection. Univariable, multivariable survival regression, and causal survival forest analyses all showed that patients treated with SSRIs had a higher risk of developing an antibiotic-resistant infection than those not treated with SSRIs. This study confirms the in vitro findings and may provide insights for future studies exploring the relationship of treatment with SSRIs and subsequent antibiotic-resistant infection.
An Interactive Web Application for School-Based Physical Fitness Testing in California: Geospatial Analysis and Custom Mapping.
Guo Y, Hu K, Hu D … +2 more , Zheng K, Cooper DM
AMIA Annu Symp Proc · 2024 · PMID 40417571
Physical activity is crucial for children's healthy growth and development. In the US, most states have physical education standards. California implemented the mandated School-based Physical Fitness Testing (SB-PFT) ove...
Physical activity is crucial for children's healthy growth and development. In the US, most states have physical education standards. California implemented the mandated School-based Physical Fitness Testing (SB-PFT) over two decades ago. Despite the substantial effort in collecting the SB-PFT data, its research reuse has been limited due to the lack of readily accessible analytical tools. We developed a web application utilizing GeoServer, ArcGIS, and AWS to visualize the SB-PFT data. Education administrators and policymakers can leverage this user-friendly platform to gain insights into children's physical fitness trend, and identify schools and districts with successful programs to gauge the success of new physical education programs. The application also includes a custom mapping tool that allows users to compare external datasets with SB-PFT. We conclude that by incorporating advanced analytical capabilities through an informatics-based user-facing tool, this platform has great potential to encourage a broader engagement in enhancing children's physical fitness.
A Novel Sentence Transformer-based Natural Language Processing Approach for Schema Mapping of Electronic Health Records to the OMOP Common Data Model.
Zhou X, Dhingra LS, Aminorroaya A … +2 more , Adejumo P, Khera R
AMIA Annu Symp Proc · 2024 · PMID 40417570
Mapping electronic health records (EHR) data to common data models (CDMs) enables the standardization of clinical records, enhancing interoperability and enabling large-scale, multi-centered clinical investigations. Usin...
Mapping electronic health records (EHR) data to common data models (CDMs) enables the standardization of clinical records, enhancing interoperability and enabling large-scale, multi-centered clinical investigations. Using 2 large publicly available datasets, we developed transformer-based natural language processing models to map medication-related concepts from the EHR at a large and diverse healthcare system to standard concepts in OMOP CDM. We validated the model outputs against standard concepts manually mapped by clinicians. Our best model reached out-of-box accuracies of 96.5% in mapping the 200 most common drugs and 83.0% in mapping 200 random drugs in the EHR. For these tasks, this model outperformed a state-of-the-art large language model (SFR-Embedding-Mistral, 89.5% and 66.5% in accuracy for the two tasks), a widely used software for schema mapping (Usagi, 90.0% and 70.0% in accuracy), and direct string match (7.5% and 7.5% accuracy). Transformer-based deep learning models outperform existing approaches in the standardized mapping of EHR elements and can facilitate an end-to-end automated EHR transformation pipeline.
Sepsis Prediction Models are Trained on Labels that Diverge from Clinician-Recommended Treatment Times.
Weissman GE, Hubbard RA, Himes BE … +7 more , Goodman-O'Leary KL, Harhay MO, Ginestra JC, Kohn R, Admon AJ, Taylor SP, Halpern SD
AMIA Annu Symp Proc · 2024 · PMID 40417569
Many sepsis prediction models use the Sepsis-3 definition or its variants as a training label. However, among the few sepsis models ever deployed in practice, there is scant evidence that they offer clinically meaningful...
Many sepsis prediction models use the Sepsis-3 definition or its variants as a training label. However, among the few sepsis models ever deployed in practice, there is scant evidence that they offer clinically meaningful decision support at the bedside. As a potential mechanism to explain this limitation, we hypothesized that clinician-recommended treatment times for sepsis would diverge from onset time defined by Sepsis-3. We conducted an electronic survey that was completed by 153 clinicians at three large and geographically diverse medical centers using vignettes derived from eight real cases of sepsis. After reviewing these vignettes, participants suggested antibiotic treatment to start an average of 7.0 hours (95% confidence interval 5.3 to 8.8) before the Sepsis-3 definition onset. Thus, predicting Sepsis-3 onset as a treatment prompt could lead to inappropriate and delayed treatment recommendations. Building predictive decision support systems that identify outcomes aligned with bedside decisions would increase their clinical utility.
EHR Phenotyping Methods for Measuring Treatment Adherence Among People Living With HIV in All of Us: Towards Disparities and Inequalities in HIV Care Continuum.
Yue Y, Khanal A, Lyu T … +2 more , Weissman S, Liang C
AMIA Annu Symp Proc · 2024 · PMID 40417568
HIV treatment adherence is among the most important determinants of HIV outcomes. However, only 50% of people living with HIV in the US were retained in care. Measuring HIV treatment adherence in the clinical settings is...
HIV treatment adherence is among the most important determinants of HIV outcomes. However, only 50% of people living with HIV in the US were retained in care. Measuring HIV treatment adherence in the clinical settings is feasible but when it comes to the growing number of multi-site Electronic Health Records (EHR), there has been a dearth of research for adequate informatics methods to handle EHR. We sought to address this gap by developing a cluster of metrics for measuring HIV treatment adherence via EHR phenotyping methods. Our methods were developed and tested in the All of Us research program. We also performed preliminary analyses to explore disparities in HIV treatment adherence and demographic factors contributing to poor adherence. This study paves the way for systematic data mining and analyses for the HIV care continuum, disparities, and inequality research on All of Us and other EHR normalized with the OMOP Common Data Model.
Probabilistic Graphical Models for Evaluating the Utility of Data-Driven ICD Code Categories in Pediatric Sepsis.
Valdez LA, Hernandez EJ, Matthews O … +3 more , Mulvey M, Crandall H, Eilbeck K
AMIA Annu Symp Proc · 2024 · PMID 40417567
Electronic health records (EHRs) are information systems designed to collect and manage clinical data in order to support various clinical activities. They have emerged as valuable sources of data for outcomes research,...
Electronic health records (EHRs) are information systems designed to collect and manage clinical data in order to support various clinical activities. They have emerged as valuable sources of data for outcomes research, offering vast repositories of patient information for analysis. Definitions for pediatric sepsis diagnosis are ambiguous, resulting in delayed diagnosis and treatment, highlighting the need for precise and efficient patient categorizing techniques. Nevertheless, the use of EHRs in research poses challenges. Although EHRs were originally created to document patient encounters, the medical coding was designed to satisfy billing requirements. As a result, EHR data may lack granularity, potentially leading to misclassification and incomplete representation of patient conditions. We compared data-driven ICD code categories to chart review using probabilistic graphical models (PGMs) due to their ability to handle uncertainty and incorporate prior knowledge. Overall, this paper demonstrates the potential of using PGMs to address these challenges and improve the analysis of ICD codes for sepsis outcomes research.
Unlocking Early Cancer Detection: Leveraging Machine Learning in Cell-Free DNA Analysis for Precision Oncology.
Li H, Wang J, Liu H
AMIA Annu Symp Proc · 2024 · PMID 40417566
This study introduces a groundbreaking approach to early cancer detection through the analysis of cell-free DNA (cfDNA), utilizing machine learning algorithms to navigate the complexities of low circulating tumor DNA (ct...
This study introduces a groundbreaking approach to early cancer detection through the analysis of cell-free DNA (cfDNA), utilizing machine learning algorithms to navigate the complexities of low circulating tumor DNA (ctDNA) fractions and genetic heterogeneity. CfDNA, found in bodily fluids and comprising fragments from apoptotic or necrotic cells, offers a non-invasive means to identify cancer signals. With ctDNA-a subset of cfDNA from cancer cells-serving as a biomarker, the potential for detecting cancer at its earliest stages is vastly improved, enhancing treatment effectiveness and patient prognosis. However, the challenges of distinguishing cancer-specific signatures within cfDNA due to low ctDNA levels and the noise of genetic heterogeneity necessitate advanced methods beyond traditional mutation analysis. Leveraging high-throughput sequencing technologies and the precision of machine learning, our research aims to surmount these obstacles by identifying nuanced cancer signatures within cfDNA sequencing data. Machine learning's capability to model complex data relationships allows for the differentiation of subtle oncogenic patterns from background noise, thereby increasing the diagnostic accuracy of liquid biopsies. This paper outlines our exploration into employing machine learning for early cancer detection via cfDNA, detailing our method of transforming sequencing data into analyzable formats, enhancing signal detection through a sliding window technique, and predicting true tumor-origin fragments. By advancing cfDNA-based cancer diagnostics, this research not only signifies a leap towards more sensitive and specific early-stage cancer detection but also opens avenues for personalized oncology, where treatment strategies are informed by the unique genetic profile unveiled through cfDNA analysis. Our findings underscore the potential of integrating artificial intelligence with liquid biopsy technologies to revolutionize cancer diagnostics, offering new hope for early detection and personalized treatment pathways.
Pregnancy Outcomes in Hidradenitis Suppurativa Patients.
Walsh DP
AMIA Annu Symp Proc · 2024 · PMID 40417565
Hidradenitis suppurativa is an autoinflammatory condition resulting in painful cysts, nodules, and sinus tracts in areas of high skin on skin contact. The microenvironment of affected tissues is high in pro-inflammatory...
Hidradenitis suppurativa is an autoinflammatory condition resulting in painful cysts, nodules, and sinus tracts in areas of high skin on skin contact. The microenvironment of affected tissues is high in pro-inflammatory cytokines and T-helper 17 cells. Other auto-inflammatory diseases, like psoriasis, have an enhanced risk of systemic inflammation and an elevated risk of spontaneous abortion. A cohort of pregnant patients from Cerner Health Facts® was identified using a Python adaptation of a validated pregnancy identification and classification algorithm. The HS population was identified among the pregnant population and was shown to be statistically significantly associated with outcome type by Chi square. A multinomial logistic regression also indicated a statistically significant increase in the odds of a pregnant patient having a spontaneous abortion over a live birth when controlling for thyroid disease, polycystic ovarian syndrome, antiphospholipid syndrome, other inflammatory diseases, and advanced maternal age.
Optimizing the migration of a data warehouse to the cloud using network analysis.
Oliver A, Tariq AA, Riley J … +1 more , Salmasian H
AMIA Annu Symp Proc · 2024 · PMID 40417564
Passage of the HITECH Act in 2009 led to an increase in the adoption of electronic health record (EHR) and health information technology (HIT) systems by hospitals. These systems require hospitals to maintain complex dat...
Passage of the HITECH Act in 2009 led to an increase in the adoption of electronic health record (EHR) and health information technology (HIT) systems by hospitals. These systems require hospitals to maintain complex data warehouses supporting clinical and operational activities. Periodically modernizing this data infrastructure is a critical task for IT departments, but this is often managed heuristically. Our team at Children's Hospital of Philadelphia (CHOP) sought an efficient, data-driven method to assist in this modernization activity by prioritizing data warehouse assets for migration into a cloud environment. We created a network graph to capture the dependencies between assets and used graph theoretic methods to score the relative influence of each asset within the overall network. The influence score, based on centrality measures, is proportional to the number of downstream dependencies of an asset. Using this score, we proposed a data-driven and rational strategy for efficiently migrating assets.
Barriers and Facilitators of Digital Health Use for Self-Management of Hypertensive Disorders by Black Pregnant Women.
Foreman MA, Ross A, Burgess APH … +2 more , Myneni S, Franklin A
AMIA Annu Symp Proc · 2024 · PMID 40417563
Although digital health tools are increasingly common for managing health conditions, these applications are often developed without consideration of differences across user populations. A reproducible framework is neede...
Although digital health tools are increasingly common for managing health conditions, these applications are often developed without consideration of differences across user populations. A reproducible framework is needed to support tailoring applications to include cultural considerations, potentially leading to better adoption and more effective use. As a first step, this study captures a snapshot of Black women's barriers and facilitators in using digital health products for self-management of hypertensive disorders of pregnancy (HDP). One-on-one semi-structured interviews were conducted with 17 Black pregnant women with HDP. We established a unique model for cultural tailoring with these experiences using Black feminist theory and the CDC's Social-Ecological Model (SEM). 38 themes across the four levels of SEM were found through grounded theory. These themes can inform the feature development of a digital health intervention. Future work will instantiate and validate a framework that provides theoretical constructs for developing culturally tailored digital health interventions.
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