205 hot topic(s) found with the query "Electronic health records"
Screening Familial Risk for Hereditary Breast and Ovarian Cancer
(Posted: Oct 04, 2024 11AM)
From the abstract: "In a large health system, how many ungenotyped patients meet family history genetic testing criteria for hereditary breast and ovarian cancer? In this cross-sectional analysis, 2.9% of patients had no evidence of prior genetic testing but had electronic health records indicating they met family history criteria. These criteria were associated with significantly increased prevalence of genetic risk variants among 38?003 genotyped patients. These findings suggest that substantial gaps exist in identifying and testing patients meeting family history criteria for hereditary breast and ovarian cancer, and other methods may be needed to close these gaps. "
Genome-first evaluation with exome sequence and clinical data uncovers underdiagnosed genetic disorders in a large healthcare system
(Posted: Apr 21, 2024 0PM)
From the abstract: "Population-based genomic screening may help diagnose individuals with disease-risk variants. Here, we perform a genome-first evaluation for nine disorders in 29,039 participants with linked exome sequences and electronic health records (EHRs). We identify 614 individuals with 303 pathogenic/likely pathogenic or predicted loss-of-function (P/LP/LoF) variants, yielding 644 observations; 487 observations (76%) lack a corresponding clinical diagnosis in the EHR."
Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records
M Nigau et al, Nature Comm, March 7, 2024
(Posted: Mar 07, 2024 8AM)
From the abstract: "Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks. 8,164 MRSA and 22,393 non-MRSA patient events. "
Combining rare and common genetic variants improves population risk stratification for breast cancer
A Bolze et al, Genetics in Medicine Open, February 2, 2024
(Posted: Feb 05, 2024 11AM)
From the abstract: " This study aimed to evaluate the performance of different genetic screening approaches to identify women at high-risk of breast cancer in the general population. We retrospectively studied 25,591 women with available electronic health records and genetic data, participants in the Healthy Nevada Project. Family history of breast cancer was ascertained on or after the record of breast cancer for 78% of women with both, indicating that this risk assessment method is not being properly utilized for early screening. Genetics offered an alternative method for risk assessment. 11.4% of women were identified as high-risk based on possessing a predicted loss-of-function (pLOF) variant in BRCA1, BRCA2 or PALB2 (hazard ratio = 10.4, 95% confidence interval: 8.1-13.5), or a pLOF variant in ATM or CHEK2 (HR = 3.4, CI: 2.4-4.8), or being in the top 10% of the polygenic risk score (PRS) distribution (HR = 2.4, CI: 2.0-2.8). "
Large language models to identify social determinants of health in electronic health records
M Guevera et al, NPJ Digital Medicine, January 11, 2023
(Posted: Jan 11, 2024 7AM)
From the abstract: "Social determinants of health (SDoH) play a critical role in patient outcomes, yet their documentation is often missing or incomplete in the structured data of electronic health records (EHRs). Large language models (LLMs) could enable high-throughput extraction of SDoH from the EHR to support research and clinical care. "
Data-driven science and diversity in the All of Us Research Program.
Geoffrey S Ginsburg et al. Sci Transl Med 2023 12 (726) eade9214
(Posted: Dec 14, 2023 8AM)
From the paper: "Having >1 million whole-genome sequences integrated with longitudinal data from questionnaires and electronic health records will allow a comprehensive molecular epidemiological approach across the life span. Genetic, environmental, and lifestyle data will be integrated and accessible, promoting an understanding of how their interactions drive transitions from health to disease and enabling a robust assessment of vulnerabilities and resilience for an individual or population. "
Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk.
Xilin Jiang et al. Nat Genet 2023 10
(Posted: Oct 10, 2023 9AM)
From the abstract: " The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. "
Power of Public Investment in Curated Big Health Data.
Paula Anne Newman-Casey et al. JAMA Ophthalmol 2023 9
(Posted: Sep 08, 2023 9AM)
From the paper: "Public investment from the US and the UK in creating the UK Biobank and the All of Us databases has resulted in the generation of critical new knowledge to better understand human health. Both projects have created publicly available data sets to encourage researchers to leverage large quantities of data to identify patterns and advance health care. Moreover, each database has its unique strengths. The UK Biobank data set goes deep into genomics, metabolomics, brain, heart, and ocular imaging, providing granular and specific measurements to inform many fields of study. The All of Us data set includes biospecimens, linkages to electronic health records, and survey results."
A Test of Automated Use of Electronic Health Records to Aid in Diagnosis of Genetic Disease
T Cassini et al, Genetics in Medicine, August 22, 2023
(Posted: Aug 22, 2023 9AM)
Automated use of electronic health records may aid in decreasing the diagnostic delay for rare diseases. The phenotype risk score (PheRS) is a weighted aggregate of syndromically related phenotypes that measures the similarity between an individual’s conditions and features of a disease. For some diseases, there are individuals without a diagnosis of that disease who have scores similar to diagnosed patients. These individuals may have that disease but not yet be diagnosed.
The shaky foundations of large language models and foundation models for electronic health records
M Wornow et al, NPJ Digital Medicine, July 29, 2023
(Posted: Jul 31, 2023 11AM)
The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models’ capabilities. In this narrative review, we examine 84 foundation models trained on non-imaging EMR data (i.e., clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets.
Embracing generative AI in health care
The Lancet Regional Health, July 2023
(Posted: Jul 06, 2023 8AM)
To date, AI technologies have had limited, yet considerable, applications in health care. Such technologies have been used to improve the analysis of medical images such as x-rays, CT scans, and MRIs for disease diagnosis; for extracting and analyzing information from electronic health records; for personalised medicine; for remote monitoring with wearable devices, sensors, and home monitoring systems; and for drug discovery and development. In contrast, GenAI has the potential to transform clinical workflows and the way doctors work. For example, at the basic level, GenAI can help health-care professionals interpret data such as a patient's medical history, imaging records, genomics, or laboratory results with a simple query, even if the information is stored across different formats and locations.
A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records.
Megan M Shuey et al. EBioMedicine 2023 7 104674
(Posted: Jul 05, 2023 7AM)
We developed and tested a genetically-informed drug-repurposing pipeline for diabetes management. This approach mapped genetically-predicted gene expression signals from the largest genome-wide association study for type 2 diabetes mellitus to drug targets using publicly available databases to identify drug–gene pairs. These drug–gene pairs were then validated using a two-step approach: 1) a self-controlled case-series (SCCS) using electronic health records from a discovery and replication population, and 2) Mendelian randomization (MR).
Phenotypic Presentation of Mendelian Disease Across the Diagnostic Trajectory in Electronic Health.
Records Rory J Tinker et al. Genet Med 2023 6 100921
(Posted: Jun 20, 2023 7AM)
Using a novel conceptual model to study the diagnostic trajectory of genetic disease in the EHR, we demonstrated that phenotype ascertainment is, in large part, driven by the clinical exams and studies prompted by clinical suspicion of a genetic disease, a process we term diagnostic convergence. Algorithms designed to detect undiagnosed genetic disease should consider censoring EHR data at the first date of clinical suspicion to avoid data leakage.
Long COVID risk and pre-COVID vaccination in an EHR-based cohort study from the RECOVER program.
M Daniel Brannock et al. Nat Commun 2023 5 (1) 2914
(Posted: May 24, 2023 9AM)
We used electronic health records available through the National COVID Cohort Collaborative to characterize the association between SARS-CoV-2 vaccination and long COVID diagnosis. We found that vaccination was consistently associated with lower odds and rates of long COVID clinical diagnosis and high-confidence computationally derived diagnosis after adjusting for sex, demographics, and medical history.
Essential Electronic Health Record Reforms for This Decade.
Don Eugene Detmer et al. JAMA 2023 5
(Posted: May 06, 2023 7AM)
Few health care innovations have been more intrusive and ubiquitous than electronic health records (EHRs). Despite EHRs’ distinct advantages, the structure of health care services in the US has made it difficult to exploit their most desirable features. Instead of supporting clinicians seeking to deliver care more effectively and efficiently, current EHR design and configurations attempt to manage clinicians and how they do their work.
AI-assisted prediction of differential response to antidepressant classes using electronic health records.
Yi-Han Sheu et al. NPJ Digit Med 2023 4 (1) 73
(Posted: Apr 27, 2023 8AM)
Antidepressant selection is largely a trial-and-error process. We used electronic health record (EHR) data and artificial intelligence (AI) to predict response to four antidepressants classes (SSRI, SNRI, bupropion, and mirtazapine) 4 to 12 weeks after antidepressant initiation. The final data set comprised 17,556 patients. We show that antidepressant response can be accurately predicted from real-world EHR data with AI modeling, and our approach could inform further development of clinical decision support systems for more effective treatment selection.
Foundation models for generalist medical artificial intelligence
M Moor et al, Nature, April 12, 2023
(Posted: Apr 12, 2023 11AM)
We propose a new paradigm for medical AI, which we refer to as generalist medical AI (GMAI). GMAI models will be capable of carrying out a diverse set of tasks using very little or no task-specific labelled data. Built through self-supervision on large, diverse datasets, GMAI will flexibly interpret different combinations of medical modalities, including data from imaging, electronic health records, laboratory results, genomics, graphs or medical text. Models will in turn produce expressive outputs such as free-text explanations, spoken recommendations or image annotations that demonstrate advanced medical reasoning abilities.
Ischemic stroke after COVID-19 bivalent vaccine administration in patients aged 65 years and older: analysis of nation-wide patient electronic health records in the United States
M Gorenflo et al, MEDRXIV, February 14, 2023
(Posted: Feb 16, 2023 6AM)
Empowering underserved groups through access to electronic health records.
Aleena Hossain et al. BMJ (Clinical research ed.) 2023 1 247
(Posted: Feb 03, 2023 7AM)
Patients need support to ensure that access to electronic health records doesn’t create another digital divide. Giving patients access to their electronic health record (EHR) enhances their understanding of their care, empowering them to make informed decisions and better manage their own health, which can ultimately improve outcomes.
Massive health-record review links viral illnesses to brain disease
M Koslov, Nature, January 23, 2023
(Posted: Jan 24, 2023 8AM)
An analysis of about 450,000 electronic health records has found a link between infections from influenza and other common viruses and an elevated risk of having a neurodegenerative condition such as Alzheimer’s or Parkinson’s disease later in life. But researchers caution that the data show only a possible connection, and that it’s still unclear how or whether the infections trigger disease onset.
A machine-learning-derived, in silico marker for CAD identifies underdiagnosed patients.
Huynh Karina et al. Nature reviews. Cardiology 2023 1
(Posted: Jan 21, 2023 6AM)
Current risk prediction tools for coronary artery disease (CAD) do not measure disease on a continuous scale and use only a small number of variables for risk prediction, disregarding much of the data contained in electronic health records (EHRs). In a new study, a machine learning model trained using clinical data from EHRs generated a novel, in silico quantitative score for CAD that can quantify disease pathophysiology and clinical outcomes on a continuous spectrum.
Machine learning identifies long COVID patterns from electronic health records.
et al. Nature medicine 2023 1
(Posted: Jan 17, 2023 9AM)
A machine learning algorithm identifies four reproducible clinical subphenotypes of long COVID from the electronic health records of patients with post-acute sequelae of SARS-CoV-2 infection within 30–180 days of infection; these patterns have implications for the treatment and management of long COVID.
The case for including microbial sequences in the electronic health record.
Sintchenko Vitali et al. Nature medicine 2023 1
(Posted: Jan 17, 2023 9AM)
The growing availability of microbial genomes sequenced for health care rather than research raises the question of whether such data should be included in an individual’s electronic health records (EHR). While integrating human genome data into EHR has been widely discussed, microbial genomic data bring unique and important challenges.
Acute respiratory distress syndrome after SARS-CoV-2 infection on young adult population: International observational federated study based on electronic health records through the 4CE consortium
B Moal et al, PLOS ONE, Jan 4, 2023
(Posted: Jan 05, 2023 5AM)
Among the 75,377 hospitalized patients with positive SARS-CoV-2 PCR, 1001 young adults presented with ARDS (7.8% of young hospitalized adults). Their mortality rate at 90 days was 16.2% and they presented with a similar complication rate for infection than older adults with ARDS. Peptic ulcer disease, paralysis, obesity, congestive heart failure, valvular disease, diabetes, chronic pulmonary disease and liver disease were associated with a higher risk of ARDS. We described a high prevalence of obesity (53%), hypertension (38%- although not significantly associated with ARDS), and diabetes (32%).
Comparative effectiveness of third doses of mRNA-based COVID-19 vaccines in US veterans
BA Dickerman et al, Nat Microbiology, January 22, 2023
(Posted: Jan 02, 2023 0PM)
We emulated a target trial using electronic health records of US veterans who received a third dose of either BNT162b2 or mRNA-1273 vaccines between 20 October 2021 and 8 February 2022, during a period that included Delta- and Omicron-variant waves. The 16-week risks of COVID-19 outcomes were low after a third dose of mRNA-1273 or BNT162b2, although risks were lower with mRNA-1273 than with BNT162b2, particularly for documented infection.
Development and Trends in Artificial Intelligence in Critical Care Medicine: A Bibliometric Analysis of Related Research over the Period of 2010–2021
X Cui et al, J Per Med, December 27, 2022
(Posted: Dec 28, 2022 11AM)
Research related to artificial intelligence in CCM has been increasing over the years. The USA published the most articles and had the top 10 active affiliations. The top ten active journals are bioinformatics journals and are in JCR Q1. Prediction, diagnosis, and treatment strategy exploration of sepsis, pneumonia, and acute kidney injury were the most focused topics. Electronic health records (EHRs) were the most widely used data and the “-omics” data should be integrated further.
A large language model for electronic health records
X Yang et al, PJ Digital Medicine, December 27, 2022
(Posted: Dec 27, 2022 0PM)
In this study, we develop from scratch a large clinical language model—GatorTron—using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on five clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA).
NPCR’s 30th Anniversary
CDC, October 2022,
(Posted: Oct 24, 2022 11AM)
The need for timely, accurate cancer data is greater than ever. While we have data on every cancer case that can be reported in the United States, we need to improve our systems so we can collect data faster at every step. Real-time data would provide a current, more complete picture of cancer trends at all levels, from local to national. This would also help public health professionals make decisions faster to save lives. To accomplish this goal, CDC is working with central cancer registries to move toward a cloud-based computing platform and to use electronic reporting from laboratories and electronic health records. We’re starting to build and test the cloud-based platform now.
Fitbit step counts clarify the association between activity and chronic disease risk
Nature Medicine, October 11, 2022
(Posted: Oct 12, 2022 8AM)
Using electronic health records data from the All of Us Research Program, we show that higher daily step counts in data collected over several years of Fitbit fitness tracker use were associated with lower risk of common, chronic diseases, including diabetes, hypertension, gastroesophageal reflux disease, depression, obesity and sleep apnea.
Artificial intelligence for multimodal data integration in oncology
J Lipkova et al, Cancer Cell, October 10, 2022
(Posted: Oct 11, 2022 6AM)
In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to electronic health records. Current artificial intelligence (AI) models operate mainly in the realm of a single modality, neglecting the broader clinical context, which inevitably diminishes their potential. Integration of different data modalities provides opportunities to increase robustness and accuracy of diagnostic and prognostic models.
Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records.
Onishchenko Dmytro et al. Nature medicine 2022 9
(Posted: Oct 01, 2022 7AM)
Using subtle comorbidity signatures identified from the history of medical encounters of individuals, we developed an algorithm, called the zero-burden comorbidity risk score for IPF (ZCoR-IPF), to predict the future risk of an IPF diagnosis. ZCoR-IPF was trained on a national insurance claims database and validated on three independent databases, comprising a total of 2,983,215 participants, with 54,247 positive cases. The algorithm achieved positive likelihood ratios greater than 30 at a specificity of 0.99 across different cohorts.
Impact of integrating genomic data into the electronic health record on genetics care delivery.
Lau-Min Kelsey S et al. Genetics in medicine : official journal of the American College of Medical Genetics 2022 9
(Posted: Sep 23, 2022 7AM)
Multimodal biomedical AI.
Acosta Julián N et al. Nature medicine 2022 9
(Posted: Sep 20, 2022 5AM)
The increasing availability of biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing have set the stage for the development of multimodal artificial intelligence solutions that capture the complexity of human health and disease. In this Review, we outline the key applications enabled, along with the technical and analytical challenges.
Using deep learning and electronic health records to detect Noonan syndrome in pediatric patien
Z Yang et al, Genetics in Medicine, September 13, 2022
(Posted: Sep 14, 2022 3AM)
Using diagnosis texts extracted from Cincinnati Children’s Hospital’s EHR database, we constructed deep learning models from 162 NS cases and 16,200 putative controls. Performance was evaluated on 2 independent test sets, one containing patients with NS who were previously diagnosed and the other containing patients with undiagnosed NS. Our novel method performed significantly better than the previous method, with the convolutional neural network model achieving the highest area under the precision-recall curve in both test sets (diagnosed: 0.43, undiagnosed: 0.16).
Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative
R Johnson et al, Genome Medicine, September 9, 2022
(Posted: Sep 09, 2022 8AM)
We quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes.
Communicating Precision Medicine Research: Multidisciplinary Teams and Diverse Communities.
Beans Julie A et al. Public health genomics 2022 8 1-9
(Posted: Sep 01, 2022 3PM)
A shared definition of precision medicine research as well as six case examples of precision medicine research involving genetic risk, pharmacogenetics, epigenetics, the microbiome, mobile health, and electronic health records were developed. Discussion/Conclusion: The precision medicine research definition and case examples can be used as planning tools to establish a shared understanding of the scope of precision medicine research across multidisciplinary teams and with the diverse communities.
The Need for Electronic Health Records to Support Delivery of Behavioral Health Preventive Services.
Huffstetler Alison N et al. JAMA 2022 8
(Posted: Aug 06, 2022 8AM)
To accomplish digital health goals, it is essential to adhere to 3 key principles. First, digital health systems need to make it easy for clinicians to deliver national guidelines and quality recommendations. Second, digital health systems need to make information actionable for clinicians and patients. Third, digital health systems need to be easy to use; they should be intuitive for users, easily accessible, and not require complex workflows to enter and retrieve data.
Remote COVID-19 Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies
AE Gonzales et al, The Lancet Digital Health, July 28, 2022
(Posted: Jul 31, 2022 7AM)
Accurate assessment of COVID-19 severity in the community is essential for patient care and requires COVID-19-specific risk prediction scores adequately validated in a community setting. We aimed to develop and validate two COVID-19-specific risk prediction scores. Remote COVID-19 Assessment in Primary Care-General Practice score (RECAP-GP; without peripheral oxygen saturation [SpO2]) and RECAP-oxygen saturation score (RECAP-O2; with SpO2). RECAP was a prospective cohort study that used multivariable logistic regression. Data on signs and symptoms (predictors) of disease were collected from community-based patients with suspected COVID-19 via primary care electronic health records and linked with secondary data on hospital admission (outcome) within 28 days of symptom onset.
Identification of robust deep neural network models of longitudinal clinical measurements
H Javidi et al, NPJ Digital Medicine, July 27, 2022
(Posted: Jul 28, 2022 6AM)
Deep learning (DL) from electronic health records holds promise for disease prediction, but systematic methods for learning from simulated longitudinal clinical measurements have yet to be reported. We compared nine DL frameworks using simulated body mass index (BMI), glucose, and systolic blood pressure trajectories, independently isolated shape and magnitude changes, and evaluated model performance across various parameters (e.g., irregularity, missingness).
Machine learning for real-time aggregated prediction of hospital admission for emergency patients
Z King et al, NPJ Digital Medicine, July 27, 2022
(Posted: Jul 27, 2022 10AM)
Machine learning for hospital operations is under-studied. We present a prediction pipeline that uses live electronic health-records for patients in a UK teaching hospital’s emergency department (ED) to generate short-term, probabilistic forecasts of emergency admissions. A set of XGBoost classifiers applied to 109,465 ED visits yielded AUROCs from 0.82 to 0.90 depending on elapsed visit-time at the point of prediction.
All of Us Seeks Input on Broadening Participants’ Electronic Health Record Data
All of US Research Program, July 26, 2022
(Posted: Jul 27, 2022 10AM)
Request for Information looks for guidance on how to acquire and integrate data from health information networks and health information exchanges.
“Health care in the United States is highly fragmented, so patient data is often siloed in different systems, making it difficult to access. Lack of interoperability and evolving data standards contribute to additional gaps in our EHR dataset.”
Impact of a Population Genomic Screening Program on Health Behaviors Related to Familial Hypercholesterolemia Risk Reduction.
Jones Laney K et al. Circulation. Genomic and precision medicine 2022 101161CIRCGEN121003549
(Posted: Jul 26, 2022 9AM)
We conducted a retrospective cohort study of MyCode participants with an FH risk variant beginning 2 years before disclosure until January 16, 2019. We analyzed lipid-lowering prescriptions (clinician behavior), medication adherence (participant behavior), and LDL (low-density lipoprotein) cholesterol levels (health outcome impact) pre- and post-disclosure. Data were collected from electronic health records and claims. Despite disclosure of an FH risk variant, nonprescribing and nonadherence to lipid-lowering therapy remained high. However, when clinicians intensified medication regimens and participants adhered to medications, lipid levels decreased.
Making Electronic Health Records Both SAFER and SMARTER
KB Johnson et al, JAMA, July 14, 2022
(Posted: Jul 14, 2022 0PM)
Today, it is increasingly clear that electronic health record (EHR) implementation in the US has failed to live up to expectations. Although the benefits of digital infrastructure are substantial, the adverse effects are as well. Today’s focus on interoperability opens the door to integrating novel approaches such as self-documenting clinical environments into tomorrow’s digital connectivity infrastructure. Today’s EHR infrastructure can continue to be used for what it does well, while enabling goal-oriented, interdisciplinary research and development to push toward solutions to the cognitive grand challenges of rapidly advancing biomedical science and health care landscapes.
Systematic pan-cancer analysis of mutation-treatment interactions using large real-world clinicogenomics data.
Liu Ruishan et al. Nature medicine 2022 6
(Posted: Jul 01, 2022 8AM)
We perform a large-scale computational analysis of 40,903 US patients with cancer who have detailed mutation profiles, treatment sequences and outcomes derived from electronic health records. We systematically identify 458 mutations that predict the survival of patients on specific immunotherapies, chemotherapy agents or targeted therapies across eight common cancer types. We further characterize mutation–mutation interactions that impact the outcomes of targeted therapies.
Computational drug repurposing based on electronic health records: a scoping review
N Zhong et al, NPJ Digital Medicine, June 14, 2022
(Posted: Jun 14, 2022 1PM)
Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide rich longitudinal and pathophysiological data that facilitate the generation and validation of drug repurposing. Here, we present an appraisal of recently published research on computational drug repurposing utilizing the EHR.
COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records
JH Thygesen et al, The Lancet Digital Health, June 8, 2022
(Posted: Jun 09, 2022 10AM)
Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework.
In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes).
Understanding Post-Acute Sequelae of SARS-CoV-2 Infection through Data-Driven Analysis with the Longitudinal Electronic Health Records: Findings from the RECOVER Initiative
C Zang et al, MEDRXIV, May 22, 2022
(Posted: May 23, 2022 7AM)
This study aims to characterize PASC using the EHR data warehouses from two large national patient-centered clinical research networks (PCORnet), INSIGHT and OneFlorida+, which include 11 million patients in New York City (NYC) and 16.8 million patients in Florida respectively. With a high-throughput causal inference pipeline using high-dimensional inverse propensity score adjustment, we identified a broad list of diagnoses and medications with significantly higher incidence 30-180 days after the laboratory-confirmed SARS-CoV-2 infection compared to non-infected patients.
Machine learning paves the way toward the prevention of mental health crises
Nature Medicine, May 18, 2022
(Posted: May 19, 2022 10AM)
Experiencing a mental health crisis has a detrimental impact on a patient’s life. A machine learning algorithm trained retrospectively with electronic health records can predict almost 60% of mental health crises 4 weeks in advance. Prospective evaluation of the algorithm in clinical practice reveals its potential to enable preemptive interventions.
Machine learning model to predict mental health crises from electronic health records
R Garriga et al, Nature Medicine, May 16, 2022
(Posted: May 17, 2022 11AM)
We developed a machine learning model that uses electronic health records to continuously monitor patients for risk of a mental health crisis over a period of 28?days. The model achieves an area under the receiver operating characteristic curve of 0.797 and an area under the precision-recall curve of 0.159, predicting crises with a sensitivity of 58% at a specificity of 85%. A follow-up 6-month prospective study evaluated our algorithm’s use in clinical practice and observed predictions to be clinically valuable in terms of either managing caseloads or mitigating the risk of crisis in 64% of cases.
Association of Pathogenic Variants in Hereditary Cancer Genes With Multiple Diseases
C Zheng et al, JAMA Oncology, April 21, 2022
(Posted: Apr 22, 2022 0PM)
This phenome-wide association study used genetic and phenotypic data derived from health-related data from electronic health records in 3 cohorts comprising 214 020 participants to identify 19 new diseases and conditions associated with pathogenic variants in 13 hereditary cancer genes. These new phenotypes included both neoplastic and nonneoplastic diseases.
Comparing medical history data derived from electronic health records and survey answers in the All of Us Research Program.
Sulieman Lina et al. Journal of the American Medical Informatics Association : JAMIA 2022 4
(Posted: Apr 10, 2022 3PM)
The 4th All of Us dataset release includes data from 314 994 participants; 28.3% of whom completed medical history surveys, and 65.5% of whom had EHR data. Hearing and vision category within the survey had the highest number of responses, but the second lowest positive agreement with the EHR (0.21). The Infectious disease category had the lowest positive agreement (0.12). Cancer conditions had the highest positive agreement (0.45) between the 2 data sources.
Waning effectiveness of BNT162b2 and ChAdOx1 COVID-19 vaccines over six months since second dose: a cohort study using linked electronic health records
EMF Horne et al, MEDRXIV, March 23, 2022
(Posted: Mar 24, 2022 8AM)
The BNT162b2, ChAdOx1 and unvaccinated groups comprised 1,773,970, 2,961,011 and 2,433,988 individuals, respectively. Waning of vaccine effectiveness was similar across outcomes and vaccine brands: e.g. in the 65+ years subgroup ratios of aHRs versus unvaccinated for COVID-19 hospitalisation, COVID-19 death and positive SARS-CoV-2 test ranged from 1.23 (95% CI 1.15-1.32) to 1.27 (1.20-1.34) for BNT162b2 and 1.16 (0.98-1.37) to 1.20 (1.14-1.27) for ChAdOx1. Despite waning, rates of COVID-19 hospitalisation and COVID-19 death were substantially lower among vaccinated individuals compared to unvaccinated individuals up to 26 weeks after second dose, with estimated aHRs <0.20 (>80% vaccine effectiveness) for BNT162b2, and <0.26 (>74%) for ChAdOx1. By weeks 23-26, rates of SARS-CoV-2 infection in fully vaccinated individuals were similar to or higher than those in unvaccinated individuals
Comparison of mRNA-1273 and BNT162b2 Vaccines on Breakthrough SARS-CoV-2 Infections, Hospitalizations, and Death During the Delta-Predominant Period.
Wang Lindsey et al. JAMA 2022 1
(Posted: Jan 21, 2022 7AM)
This study examined breakthrough infections, hospitalizations, and mortality in a general population for these 2 vaccines during the Delta period while considering risk characteristics of vaccine recipients and the varying time since vaccination.We used the cloud-based TriNetX Analytics Platform to obtain web-based real-time secure access to fully deidentified electronic health records of 89 million patients from 63 health care organizations including inpatient and outpatient settings, representing 27% of the US population from 50 states. The study found that recipients of mRNA-1273 compared with BNT162b2 had a lower risk of breakthrough SARS-CoV-2 infections and hospitalizations during the Delta period.
Broadening the reach of the FDA Sentinel system: A roadmap for integrating electronic health record data in a causal analysis framework
RJ Desai et al, NPJ Digital Medicine, December 20, 2021
(Posted: Dec 20, 2021 10AM)
We identify key challenges when using EHR data to investigate medical product safety in a scalable and accelerated way, outline potential solutions, and describe the Sentinel Innovation Center’s initiatives to put solutions into practice by expanding and strengthening the existing system with a query-ready, large-scale data infrastructure of linked EHR and claims data. We describe our initiatives in four strategic priority areas: (1) data infrastructure, (2) feature engineering, (3) causal inference, and (4) detection analytics, with the goal of incorporating emerging data science innovations to maximize the utility of EHR data for medical product safety surveillance.
Insights Into Immune-Mediated Disease and Cancer Risk-Delivering on the Promise of UK Biobank Big Data.
Stewart Douglas R et al. JAMA oncology 2021 12
(Posted: Dec 03, 2021 11AM)
Large, longitudinal data sets derived from the electronic health records are powerful tools for discovery. A recent study extracted from the big data provided by the UK Biobank clinically and scientifically useful insights and estimates of cancer risk associated with immune-mediated disease.
Personalized survival probabilities for SARS-CoV-2 positive patients by explainable machine learning
AG Zucco et al, MEDRXIV, October 29, 2021
(Posted: Oct 30, 2021 11AM)
Here we trained a machine learning model to predict mortality within 12 weeks of a first positive SARS-CoV-2 test. By leveraging data on 33,928 confirmed SARS-CoV-2 cases in eastern Denmark, we considered 2,723 variables extracted from electronic health records (EHR) including demographics, diagnoses, medications, laboratory test results and vital parameters. Performance of weighted concordance index 0.95 and precision-recall area under the curve 0.71 were measured on the test set. Age, sex, number of medications, previous hospitalizations and lymphocyte counts were identified as top mortality risk factors.
Imputation of missing values for electronic health record laboratory data
J Li et al, NPJ Digital Medicine, October 11,2021
(Posted: Oct 11, 2021 7AM)
We assess the utility of imputation in two real-world EHR-derived cohorts of ischemic stroke from Geisinger and of heart failure from Sutter Health. Overall, the pattern of missingness in EHR laboratory variables was not at random and was highly associated with patients’ comorbidity data; and the multi-level imputation algorithm showed smaller imputation error than the cross-sectional method.
Distribution of SARS-CoV-2 Variants in a Large Integrated Health Care System - California, March-July 2021.
Malden Deborah E et al. MMWR. Morbidity and mortality weekly report 2021 10 (40) 1415-1419
(Posted: Oct 08, 2021 5AM)
During March 4–July 21, 2021, sequencing data from 6,798 SARS-CoV-2–positive specimens were linked to electronic health records among Kaiser Permanente Southern California members. The weekly percentage of all infections attributed to the Delta variant rapidly increased to 95% during this period. Infection with the Delta variant was more common among younger persons and among non-Hispanic Black persons.
Incidence, co-occurrence, and evolution of long-COVID features: A 6-month retrospective cohort study of 273,618 survivors of COVID-19
M Taquet et al, PLOS Medicine, September 28, 2021
(Posted: Sep 29, 2021 1PM)
This study used data from electronic health records of 273,618 patients diagnosed with COVID-19 and estimated the risk of having long-COVID features in the 6 months after a diagnosis of COVID-19. It compared the risk of long-COVID features in different groups within the population and also compared the risk to that after influenza. The research found that over 1 in 3 patients had one or more features of long-COVID recorded between 3 and 6 months after a diagnosis of COVID-19. This was significantly higher than after influenza.
A Deep Learning Method to Detect Opioid Prescription and Opioid Use Disorder from Electronic Health Records
A Kashyap et al, MEDRXIV, September 21, 2021
(Posted: Sep 22, 2021 9AM)
As the opioid epidemic continues across the United States, methods are needed to accurately and quickly identify patients at risk for opioid use disorder (OUD). The purpose of this study is to develop two predictive algorithms: one to predict opioid prescription and one to predict OUD. Materials and Methods: We developed an informatics algorithm that trains two deep learning models over patient EHRs using the MIMIC-III database. We utilize both the structured and unstructured parts of the EHR and show that it is possible to predict both of these challenging outcomes
Case-finding and genetic testing for familial hypercholesterolaemia in primary care.
Qureshi Nadeem et al. Heart (British Cardiac Society) 2021
(Posted: Sep 21, 2021 9AM)
A novel case-finding tool (Familial Hypercholetserolemia Case Ascertainment Tool, FAMCAT1) was applied to the electronic health records of 86?219 patients with cholesterol readings (44.5% of total practices' population), identifying 3375 at increased risk of FH. Of these, a cohort of 336 consenting to completing Family History Questionnaire and detailed review of their clinical data, were offered FH genetic testing in primary care. Genetic testing was completed by 283 patients, newly identifying 16 with genetically confirmed FH and 10 with variants of unknown significance. All 26 (9%) were recommended for referral and 19 attended specialist assessment. In a further 153 (54%) patients, the test suggested polygenic hypercholesterolemia who were managed in primary care.
Measuring the Value of a Practical Text Mining Approach to Identify Patients With Housing Issues in the Free-Text Notes in Electronic Health Record: Findings of a Retrospective Cohort Study.
Hatef Elham et al. Frontiers in public health 2021 9697501
(Posted: Sep 17, 2021 6AM)
Despite the growing efforts to standardize coding for social determinants of health (SDOH), they are infrequently captured in electronic health records (EHRs). Most SDOH variables are still captured in the unstructured fields (i.e., free-text) of EHRs. In this study we attempt to evaluate a practical text mining approach (i.e., advanced pattern matching techniques) in identifying phrases referring to housing issues, an important SDOH domain affecting value-based healthcare providers.
Association of COVID-19 vaccines ChAdOx1 and BNT162b2 with major venous, arterial, and thrombocytopenic events: whole population cohort study in 46 million adults in England
W Whiteley et al, MEDRXIV, August 23, 2021
(Posted: Aug 24, 2021 8AM)
This study aimed to quantify associations of vaccination with ChAdOx1-S and BNT162b2 with major arterial, venous and thrombocytopenic events. Design: Cohort study based on linked electronic health records among adults registered with an NHS general practice in England. Increases in ICVT and thrombocytopenia after ChAdOx1-S vaccination in adults aged <70 years are small compared with its effect in reducing COVID-19 morbidity and mortality, although more precise estimates for adults <40 years are needed. For people aged =70 years, rates of arterial or venous thrombotic, events were generally lower after either vaccine.
Harnessing electronic health records to study emerging environmental disasters: a proof of concept with perfluoroalkyl substances (PFAS)
MR Boland et al, NPJ Digital Medicine, August 11, 2021
(Posted: Aug 12, 2021 7AM)
We propose the use of electronic health records (EHR) and informatics methods to study the health effects of emergent environmental disasters in a cost-effective manner. An emergent environmental disaster is exposure to perfluoroalkyl substances (PFAS) in the Philadelphia area. Penn Medicine (PennMed) comprises multiple hospitals and facilities within the Philadelphia Metropolitan area, including over three thousand PFAS-exposed women living in one of the highest PFAS exposure areas nationwide.
Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies
D Xu et al, NPJ Digital Medicine, July 23, 2021
(Posted: Jul 24, 2021 7AM)
Labeling clinical data from electronic health records in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies in case definitions across different algorithms. We describe here quantitative disease risk scores based on almost unsupervised methods that require minimal input from clinicians and can be applied to large datasets.
Predicting critical state after COVID-19 diagnosis: model development using a large US electronic health record dataset
MD Rinderknecht et al, NPJ Digital Medicine, July 20, 2021
(Posted: Jul 21, 2021 8AM)
As the COVID-19 pandemic is challenging healthcare systems worldwide, early identification of patients with a high risk of complication is crucial. We present a prognostic model predicting critical state within 28 days following COVID-19 diagnosis trained on data from US electronic health records (IBM Explorys), including demographics, comorbidities, symptoms, and hospitalization. Out of 15753 COVID-19 patients, 2050 went into critical state or deceased.
Neptune: an environment for the delivery of genomic medicine
V Eric et al, Genetics in Medicine, June 13, 2021
(Posted: Jul 14, 2021 7AM)
Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process.
COVID-19 vaccines dampen genomic diversity of SARS-CoV-2: Unvaccinated patients exhibit more antigenic mutational variance
M Niesen et al, MEDRXIV, July 5, 2021
(Posted: Jul 05, 2021 11AM)
We conducted longitudinal analysis over 1.8 million SARS-CoV-2 genomes from 183 countries or territories to capture vaccination-associated viral evolutionary patterns. To augment this macroscale analysis, we performed viral genome sequencing in 23 vaccine breakthrough COVID-19 patients and 30 unvaccinated COVID-19 patients for whom we also conducted machine-augmented curation of the electronic health records (EHRs). Strikingly, we find the diversity of the SARS-CoV-2 lineages is declining at the country-level with increased rate of mass vaccination.
Risk factors for long COVID: analyses of 10 longitudinal studies and electronic health records in the UK
EJ Thompson et al, MEDRXIV, June 25, 2021
(Posted: Jun 26, 2021 7AM)
Fine-scale genetic ancestry as a potential new tool for precision medicine
NP Tatonetti et al, Nature Medicine, June 18, 2021
(Posted: Jun 18, 2021 6AM)
Race and ethnicity (R/E) and genetic ancestry have long been conflated in biomedical research. The use of self-reported R/E as a proxy for genetic ancestry holds back precision medicine by confounding biological risks with those stemming from the environment, economic status, other socioeconomic factors and racism. The advent of large institutional biobanks connected to both genomic databases and electronic health records therefore represents an opportunity for disentangling the social and cultural from the ancestral.
Phenotypic signatures in clinical data enable systematic identification of patients for genetic testing
TJ Morley et al, Nature Medicine, June 3, 2021
(Posted: Jun 04, 2021 11AM)
Around 5% of the population is affected by a rare genetic disease, yet most endure years of uncertainty before receiving a genetic test. A common feature of genetic diseases is the presence of multiple rare phenotypes that often span organ systems. Here, we use diagnostic billing information from longitudinal clinical data in the electronic health records (EHRs) of 2,286 patients who received a chromosomal microarray test, and 9,144 matched controls, to build a model to predict who should receive a genetic test.
Clinical Implications of Pharmacogenomic Testing in the Real World—Insights From a Pediatric Program
CC Coffinier, JAMA Network Open, May 26, 2021
(Posted: May 27, 2021 7AM)
One challenge identified in general PGx implementation, which may be magnified in the pediatric setting, is that the full benefit of acquiring PGx test results may not be apparent until later in life. Integrating PGx information into electronic health records and ensuring its high visibility using reminders and alerts are fundamental steps to facilitate future use; otherwise, even a simple change in clinicians within the same health care system is likely to result in the loss of information.
Toward a fine-scale population health monitoring system
G Belbin et al, Cell, April 15, 2021
(Posted: Apr 21, 2021 7AM)
We propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry. We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated significant differences in segregation of variants contributing to Mendelian diseases.
Establishing the value of genomics in medicine: the IGNITE Pragmatic Trials Network
G Ginsburg et al, Genetics in Medicine, March 29, 2021
(Posted: Mar 30, 2021 10AM)
IGNITE PTN is a network that carries out pragmatic clinical trials in genomic medicine; it is focused on diversity and inclusion of underrepresented minority trial participants; it uses electronic health records and clinical decision support to deliver the interventions. IGNITE PTN will develop the evidence to support (or oppose) the adoption of genomic medicine interventions by patients, providers, and payers.
Artificial intelligence-assisted phenotype discovery of fragile X syndrome in a population-based sample.
Movaghar Arezoo et al. Genetics in medicine : official journal of the American College of Medical Genetics 2021 3
(Posted: Mar 29, 2021 6AM)
We mined the longitudinal electronic health records from more than one million individuals to investigate the health characteristics of patients who have been clinically diagnosed with FXS. Additionally, using machine-learning approaches, we created predictive models to identify individuals with FXS in the general population. We identified associations of FXS with a wide range of medical conditions including circulatory, endocrine, digestive, and genitourinary. We successfully created predictive models to identify cases five years prior to clinical diagnosis of FXS.
Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation.
Zhao Yiqing et al. Journal of medical Internet research 2021 23(3) e22951
(Posted: Mar 12, 2021 9AM)
We developed and validated a machine learning-based algorithm that performed well for identifying incident stroke and for determining type of stroke. The algorithm also performed well on a sample from a general population, further demonstrating its generalizability and potential for adoption by other institutions.
Linking electronic health records for research on a nationwide cohort including over 54 million people in England
A Wood et al, MEDRXIV, February 26, 2021
(Posted: Feb 27, 2021 7AM)
We hereby describe a large-scale community effort to build an open-access, interoperable, and computable repository of COVID-19 molecular mechanisms - the COVID-19 Disease Map. We discuss the tools, platforms, and guidelines necessary for the distributed development of its contents by a multi-faceted community of biocurators, domain experts, bioinformaticians, and computational biologists.
Real-time analysis of a mass vaccination effort via an Artificial Intelligence platform confirms the safety of FDA-authorized COVID-19 vaccines
R McMurry et al, MEDRXIV, February 23, 2021
(Posted: Feb 24, 2021 6AM)
Curation from large-scale electronic health records (EHRs) allows for near real-time safety evaluations that were not previously possible. Here, we advance context- and sentiment-aware deep neural networks over the multi-state Mayo Clinic enterprise (Minnesota, Arizona, Florida, Wisconsin) for automatically curating the adverse effects mentioned by physicians in over 108,000 EHR clinical notes between December 1st 2020 to February 8th 2021.
Feasibility of Embedding a Scalable, Virtually Enabled Biorepository in the Electronic Health Record for Precision Medicine
KM De Merle et al, JAMA Network Open, February 21,2021
(Posted: Feb 23, 2021 6AM)
In this cohort study of 1027 patients with sepsis, a novel infrastructure, termed virtually enabled biorepository and electronic health record–embedded, scalable cohort for precision medicine (VESPRE) was developed. VESPRE appeared to demonstrate feasible digital screening, successful enrollment, biologic sampling, and lower costs compared with a traditional study design.
A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine
M Salvatore et al, MEDRXIV, February 20, 2021
(Posted: Feb 21, 2021 7AM)
Personalized Multimorbidity Management for Patients with Type 2 Diabetes Using Reinforcement Learning of Electronic Health Records.
Zheng Hua et al. Drugs 2021 Feb
(Posted: Feb 16, 2021 9AM)
Comorbid chronic conditions are common among people with type 2 diabetes. We developed an artificial intelligence algorithm, based on reinforcement learning (RL), for personalized diabetes and multimorbidity management, with strong potential to improve health outcomes relative to current clinical practice
Development of Electronic Health Record–Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction
MF Matheny et al, JAMA Network Open, January 2021
(Posted: Jan 30, 2021 7AM)
Exome-wide evaluation of rare coding variants using electronic health records identifies new gene–phenotype associations
J Park et al, Nature Medicine, January 11, 2021
(Posted: Jan 12, 2021 11AM)
The clinical impact of rare loss-of-function variants has yet to be determined for most genes. Integration of DNA sequencing data with electronic health records (EHRs) could enhance our understanding of the contribution of rare genetic variation to human disease1. By leveraging 10,900 whole-exome sequences linked to EHR data in the Penn Medicine Biobank, we addressed the association of the cumulative effects of rare predicted loss-of-function variants.
Healthcare Facilities That Have Implemented COVID-19 Electronic Case Reporting
CDC, December 2020
(Posted: Dec 19, 2020 7AM)
Electronic case reporting (eCR) is the automated, real-time exchange of case report information between electronic health records and public health agencies. As of December 13, 2020, more than 6,500 facilities are sending COVID-19 electronic initial case reports to public health using eCR.
Real-world integration of genomic data into the electronic health record: the PennChart Genomics Initiative.
Lau-Min Kelsey S et al. Genetics in medicine : official journal of the American College of Medical Genetics 2020 Dec
(Posted: Dec 14, 2020 8AM)
The PennChart Genomics Initiative (PGI) is a multidisciplinary collaborative effort including Penn Medicine clinicians, researchers, pathologists, legal staff, and information services with input and efforts from Epic Systems Corporation (Wisconsin). We describe our efforts to operationalize the ACMG guidelines in the “real world”.
Use of Real-World Electronic Health Records to Estimate Risk, Risk Factors, and Disparities for COVID-19 in Patients With Cancer
A Desai et al, JAMA Oncology, December 10, 2020
(Posted: Dec 10, 2020 11AM)
Solutions for disparities observed with COVID-19 relate primarily to public policy. Solutions require (1) large-scale, high-quality epidemiologic data; (2) policies that mitigate socioeconomic risk factors and health care access disparities; and (3) validated risk prediction tools to identify patients at greatest risk from COVID-19 and its complications.
How IT preparedness helped to create a digital field hospital to care for COVID-19 patients in S. Korea
SY Jung et al, NPJ Digital Medicine, December 3, 2020
(Posted: Dec 05, 2020 0PM)
SNUBH’s IT preparedness made it possible to create a fully functional “digital field hospital” in a short time. When designing a CTC as a digital hospital with only a few medical staff on-site, we took three considerations into account. First, we minimized direct contact with the patient. Second, all information was recorded electronically and incorporated into electronic health records. Finally, we implemented a telemonitoring system to intervene effectively when necessary.
Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines
SC Huang et al, NPJ Digital Medicine, October 16, 2020
(Posted: Oct 17, 2020 0PM)
Advancements in deep learning techniques carry the potential to make significant contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis, prognosis, and treatment decisions.
A digital health intervention for cardiovascular disease management in primary care (CONNECT) randomized controlled trial
J Redfern et al, NPJ Digital Medicine, September 10, 2020
(Posted: Sep 11, 2020 7AM)
Digital health applications have the potential to improve health behaviors and outcomes. We aimed to examine the effectiveness of a consumer web-based app linked to primary care electronic health records. CONNECT was a multicenter randomized controlled trial involving patients with or at risk of cardiovascular disease recruited from primary care.
Real-time Prediction of COVID-19 related Mortality using Electronic Health Records
P Schwab et al, ARXIV, August 31, 2020
(Posted: Sep 04, 2020 8AM)
Predicting critical state after COVID-19 diagnosis: Model development using a large US electronic health record dataset
MD Rinderknecht et al, MEDRXIV, August 31, 2020
(Posted: Aug 31, 2020 9AM)
We present a prognostic model predicting critical state within 28 days following COVID-19 diagnosis trained on data from US electronic health records, including demographics, comorbidities, symptoms, insurance types, and hospitalization. Of 15816 COVID-19 patients, 2054 went into critical state or deceased. The model had an ROC AUC of 0.872.
A multidimensional precision medicine approach identifies an autism subtype characterized by dyslipidemia
Y Luo et al, Nature Medicine, August 10, 2020
(Posted: Aug 11, 2020 7AM)
By combining healthcare claims, electronic health records, familial whole-exome sequences and neurodevelopmental gene expression patterns, we identified a subgroup of patients with dyslipidemia-associated autism.
Evidence of gender bias in the diagnosis and management of COVID-19 patients: A Big Data analysis of Electronic Health Records
J Ancochea et al, MEDRXIV, July 26, 2020
(Posted: Jul 26, 2020 11AM)
Clinical outcomes of a genomic screening program for actionable genetic conditions
AH Buchanan et al, Genetics in Medicine, June 30, 2020
(Posted: Jul 01, 2020 8AM)
A study of electronic health records shows that among individuals with variants in tier1 genes (BRCA, Lynch syndrome, familial hypercholesterolemia, 87% did not have a prior genetic diagnosis. Genomic screening programs can identify individuals at increased risk of cancer and heart disease and facilitate risk management and early cancer detection.
The interface of genomic information with the electronic health record: a points to consider statement of the American College of Medical Genetics and Genomics (ACMG)
TA Grebe et al, Genetics in Medicine, June 1, 2020
(Posted: Jun 01, 2020 10AM)
This document discusses types of genomic information in the EHR, mechanisms of placement, data entry, usage, patient/provider access, results disclosure, portability, and privacy. It highlights patient, family, and societal benefits; discuss areas of concern, identifying where modifications are needed; and make recommendations for optimization.
Big Data Begin in Psychiatry
MM Weissman, JAMA Psychiatry, May 13, 2020
(Posted: May 13, 2020 11AM)
The review traces the subsequent evolution of big data in psychiatry to 5 overlapping phases, other population surveys in the US and globally, cohort studies, administrative claims, large genetic data sets, and electronic health records.
Ethical Use of Electronic Health Record Data and Artificial Intelligence: Recommendations of the Primary Care Informatics Working Group of the International Medical Informatics Association
ST Liaw et al, Yearb Med Inform, April 2020
(Posted: Apr 22, 2020 8AM)
International Electronic Health Record-Derived COVID-19 Clinical Course Profile: The 4CE Consortium
GA Brat et al, MEDRXIV, April 18, 2020
(Posted: Apr 18, 2020 8AM)
A consortium of international hospital systems utilizing Informatics for Integrating Biology and the Bedside (i2b2) and Observational Medical Outcomes Partnership (OMOP) platforms was convened to address the COVID-19 epidemic. Over a course of two weeks, the group initially focused on admission comorbidities and temporal changes in key laboratory test values.
Prevalence estimation by joint use of big data and health survey: a demonstration study using electronic health records in New York city.
Kim Ryung S et al. BMC medical research methodology 2020 Apr 20(1) 77
(Posted: Apr 15, 2020 10AM)
Electronic health records and polygenic risk scores for predicting disease risk
R Li et al, Nat Rev Genetics, March 31, 2020
(Posted: Apr 06, 2020 8AM)
Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges.
