Populating the i2b2 Database with Heterogeneous Emr Data: a Semantic Network Approach

Electronic Medical Record (EMR) data are an efficient method for constructing large type 1 diabetes (T1D) cohorts but are limited by availability of accurate diagnosis codes. We sought to derive an algorithm for identifying T1D using Electronic Medical Records - Primary Care (EMRPC) also known as EMRALD, a database including records from >300,000 primary care patients in Ontario, Canada. A 25% random sample of adults with diabetes and at least 1 year of records in EMRPC prior to September 30, 2015 formed the reference cohort. Charts of potential T1D cases (those on insulin±metformin) were abstracted; all others were classified as non-T1D. Algorithms were derived using variables from free-text searches (diagnosis terms in the Cumulative Patient Profile (CPP)) and standardized fields (medications, age, BMI) using 1)a priori combinations of variables; 2)classification and regression tree analysis (CART). Algorithms were evaluated for sensitivity, specificity, positive and negative predictive values (PPV and NPV) with adjustment for optimism for CART. Of 21,547 eligible patients with diabetes, the reference cohort was 5407: 4968 non-abstracted non-T1D, 240 abstracted non-T1D and 199 T1D. The prevalence of T1D was 3.7%. The optimal algorithm using a priori variable combinations was T1D diagnosis in the CPP + rapid-acting insulin (sensitivity 69.3% (95% CI 62.4-75.7%), specificity 99.8% (99.6-99.9), PPV 92.6% (87.2-96.3), NPV 98.8% (98.5-99.1)). CART partitioned on T1D diagnosis in the CPP, any insulin, and non-metformin oral hypoglycemic medications and improved performance with optimism-adjusted sensitivity of 77% (72.0-83.9), specificity 99.8% (99.8-100.0), PPV 96.8% (94.6-99.6), and NPV 99.1% (98.9-99.4). Simple algorithms using EMR variables yielded good diagnostic performance for identification of T1D and CART further improved performance. Pending additional validation these algorithms can be applied to study large T1D cohorts in EMR databases. Disclosure A. Weisman: None. J. Young: None. M. Kumar: None. P. Austin: None. K. Tu: None. L. Jaakkimainen: None. L. Lipscombe: None. G. Booth: None. Funding Diabetes Action Canada

To examine whether data sourced from electronic medical records (EMR) and a large industrial spectacle lens manufacturing database can estimate refractive error distribution within large populations as an alternative to typical population surveys of refractive error. A total of 555,528 patient visits from 28 Irish primary care optometry practices between the years 1980 and 2019 and 141,547,436 spectacle lens sales records from an international European lens manufacturer between the years 1998 and 2016. Anonymized EMR data included demographic, refractive and visual acuity values. Anonymized spectacle lens data included refractive data. Spectacle lens data was separated into lenses containing an addition (ADD) and those without an addition (SV). The proportions of refractive errors from the EMR data and ADD lenses were compared to published results from the European Eye Epidemiology (E3) Consortium and the Gutenberg Health Study (GHS). Age and gender matched proportions of refractive error were comparable in the E3 data and the EMR data, with no significant difference in the overall refractive error distribution (χ2 = 527, p = 0.29, DoF = 510). EMR data provided a closer match to the E3 refractive error distribution by age than the ADD lens data. The ADD lens data, however, provided a closer approximation to the E3 data for total myopia prevalence than the GHS data, up to age 64. The prevalence of refractive error within a population can be estimated using EMR data in the absence of population surveys. Industry derived sales data can also provide insights on the epidemiology of refractive errors in a population over certain age ranges. EMR and industrial data may therefore provide a fast and cost-effective surrogate measure of refractive error distribution that can be used for future health service planning purposes.

This study aims to identify stages of Duchenne muscular dystrophy (DMD) and assess the disease burden by progression stage using real-world administrative claims supplemented by relevant electronic medical record (EMR) data. Claims and EMR data from the Decision Resources Group's Real World Data Repository (2011-2020) were used to identify patients with DMD by diagnosis code and to stratify them into four disease stages by diagnosis and procedure markers reflective of DMD progression. Clinical and medical history data from the Cooperative International Neuromuscular Research Group (CINRG) were used to validate the developed claims-based staging algorithm. The distribution and drivers by disease stage, as well as disease burden, were examined. A total of 938 (94%) of patients with DMD identified in claims/EMR data had sufficient information for stage classification. Patients were classified by stage based on patient characteristics and the presence or absence of progression markers such as genetic testing, wheelchair usage, scoliosis treatment, or ventilation assistance. Average ages at stages1-4 are 7, 13, 18, and 23years, respectively. Using natural history data, the claims-based staging algorithm was validated with high sensitivity and specificity rates. Both healthcare resource utilization and medical charges increased by stage. For example, the average annualized total charges were $17,688 (stage1), $36,868 (stage2), $72,801 (stage3), and $167,285 (stage4). Large-scale claims data supplemented by EMR data can be used to characterize DMD progression and evaluate disease burden which may inform the design of future real-world studies about DMD.

This study analyzed GE Centricity Electronic Medical Record (EMR) data to examine the effects of body mass index (BMI) and obesity, key risk factor components of metabolic syndrome, on the prevalence of 3 chronic diseases: type II diabetes mellitus, hyperlipidemia, and hypertension. These chronic diseases occur with high prevalence and impose high disease burdens. The rationale for using Centricity EMR data is 2-fold. First, EMRs may be a good source of BMI/obesity data, which are often underreported in surveys and administrative databases. Second, EMRs provide an ideal means to track variables over time and, thus, allow longitudinal analyses of relationships between risk factors and disease prevalence and progression. Analysis of Centricity EMR data showed associations of age, sex, race/ethnicity, and BMI with diagnosed prevalence of the 3 conditions. Results include uniform direct correlations between age and BMI and prevalence of each disease; uniformly greater disease prevalence for males than females; varying differences by race/ethnicity (ie, African Americans have the highest prevalence of diagnosed type II diabetes and hypertension, while whites have the highest prevalence of diagnosed hypertension); and adverse effects of comorbidities. The direct associations between BMI and disease prevalence are consistent for males and females and across all racial/ethnic groups. The results reported herein contribute to the growing literature about the adverse effects of obesity on chronic disease prevalence and about the potential value of EMR data to elucidate trends in disease prevalence and facilitate longitudinal analyses.

We integrate literature- and data-driven task analysis methods to derive an initial task taxonomy for electronic health record (EHR) and electronic medical record (EMR) data analysis. An EHR (EMR) is a digital and longitudinal version of a patients health(medical) information and may include all key clinical events relevant to that persons health (medical) history, such as provider, demographics, progress notes, medicine, diagnosis, etc. Our goal is to arrive a task taxonomy for analyzing EHR (EMR) datasets because tasks play an important role in the design and evaluation of visualization techniques. Our method has three stages: data collection, task modelling, and task taxonomy summary. In data collection, we first survey related literature from the past two decades and extract typical tasks and corresponding data by extracting goals and scenarios of the particular work. We introduce multiple continuous relations to describe specific binary or multiple continuous relation-seeking tasks. Finally, we arrive an initial set of task types for EHR/EMR analysis that guide the design and evaluation of visualization techniques.

Severe sepsis and septic shock are global issues with high mortality rates. Early recognition and intervention are essential to optimize patient outcomes. Automated detection using electronic medical record (EMR) data can assist this process. This review describes automated sepsis detection using EMR data. PubMed retrieved publications between January 1, 2005 and January 31, 2015. Thirteen studies met study criteria: described an automated detection approach with the potential to detect sepsis or sepsis-related deterioration in real or near-real time; focused on emergency department and hospitalized neonatal, pediatric, or adult patients; and provided performance measures or results indicating the impact of automated sepsis detection. Detection algorithms incorporated systemic inflammatory response and organ dysfunction criteria. Systems in nine studies generated study or care team alerts. Care team alerts did not consistently lead to earlier interventions. Earlier interventions did not consistently translate to improved patient outcomes. Performance measures were inconsistent. Automated sepsis detection is potentially a means to enable early sepsis-related therapy but current performance variability highlights the need for further research.

The purpose of this study is to explore the symptoms experienced by adolescents with moyamoya disease and to identify the characteristics of each symptom cluster associated with moyamoya disease. A retrospective and descriptive design, which is a secondary data analysis study based on electronic medical record data from hospitals. To assess the symptoms associated with moyamoya disease, a qualitative study was conducted on 12 adolescents, 12 caregivers and 12 experts on moyamoya disease. According to a qualitative study, 12 main symptoms (limb weakness, numbness, pins and needles, difficulty in speech, blurred vision, facial paralysis, hand tremors, involuntary movements, convulsions, dizziness, nausea/vomiting and headaches) were selected. Data were collected around these symptoms. The data collection was carried out through the Electronic Medical Record (EMR) data of 303 adolescents aged 13-19 who were diagnosed with moyamoya disease (ICD 10 Code: I67.5) between January 2010-December 2019 in a hospital in Seoul, South Korea. Cluster analysis was applied to identify symptom clusters with the hierarchical cluster agglomerative approach. We used the log-likelihood distance to measure the similarity of variables. Proximity between groups of variables was measured using the two-step method. The physical symptoms experienced by adolescents with moyamoya disease were 'limb weakness' - the most common - followed by a 'headache,' 'difficulty in speech,' and 'nausea/vomiting.' A total of five symptom clusters were derived: cluster 1 was characterized by 'limb weakness'; cluster 2 participants were asymptomatic or experienced 'convulsions'; cluster 3 experienced 'difficulty in speech' and 'facial paralysis'; cluster 4 is prone to 'dizziness' and 'pins and needles'; and cluster 5 displays 'headaches' and 'nausea/vomiting'. This study offers a multidimensional approach for identifying differences in clinical symptoms of moyamoya disease among adolescents. These results will help provide interventions concerning the characteristics of the symptoms of moyamoya disease among adolescents.

Posttraumatic stress disorder (PTSD) has significant morbidity and economic costs. This study describes the prevalence and characteristics of patients with PTSD using primary care electronic medical record (EMR) data. This retrospective cross-sectional study used EMR data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). This study included 1,574 primary care providers located in 7 Canadian provinces. There were 689,301 patients that visited a CPCSSN provider between 1 January 2017 and 31 December 2019. We describe associations between PTSD and patient characteristics using descriptive statistics, chi-square, and multiple logistic regression models. Among the 689,301 patients included, 8,817 (1.3%, 95% CI 1.2-1.3) had a diagnosis of PTSD. On multiple logistic regression analysis, patients with depression (OR 4.4, 95% CI 4.2-4.7, P < 0.001), alcohol abuse/dependence (OR 1.7, 95% CI 1.6-1.9, P < 0.001), and/or drug abuse/dependence (OR 2.6, 95% CI 2.5-2.8, P < 0.001) had significantly higher odds of PTSD compared with patients without those conditions. Patients residing in community areas considered the most material deprived (OR 2.1, 95% CI 1.5-2.1, P < 0.001) or the most socially deprived (OR 2.8, 95% CI 2.7-5.3, P < 0.001) had higher odds of being diagnosed with PTSD compared with patients in the least deprived areas. The prevalence of PTSD in Canadian primary care is 1.3% (95% CI 1.25-1.31). Using EMR records we confirmed the co-occurrence of PTSD with other mental health conditions within primary care settings suggesting benefit for improved screening and evidence-based resources to manage PTSD.

We present an ontology based approach to identify hypertensive patients who show non-adherence to prescribed medication. Using the Web ontology language (OWL), we have developed an ontology that includes patient prescription details, medication possession ratios (MPRs) and blood pressure measurements (together with other patient related information) that has been populated with production electronic medical record (EMR) data from a general medical practice in New Zealand. We have written queries using the semantic query-enhanced Web rule language (SQWRL) to query this ontology to determine patients who have lapsed medication while having a low MPR. We also discuss some practical issues related to patient recall based on EMR data, as well as the suitability of the proposed scheme.

IntroductionFrailty is a combination of factors that increase vulnerability to functional decline, dependence and/or death. Frailty cannot easily be defined by comorbidities or medical treatment alone. Accurate detection of frailty in practice and at a population level is needed. This may be achieved using a combination of data sources.&#x0D; Objectives and ApproachWe construct algorithms that can identify frailty using electronic medical record (EMR) and administrative data. We linked EMR data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) nodes and the administrative (e.g. billings, hospitalizations) from Population Data BC and the Manitoba Health Policy Centre. Frailty was defined as individuals 65+ who were receiving home services, had specific chronic conditions, received specific diagnoses, and/or had specific lab or other clinical indicators. We describe sociodemographic characteristics, risk factors, prescribed medications, use and costs of healthcare for those identified as frail.&#x0D; ResultsPeople were identified as frail in 2014 and all analysis was completed with 2015 data. Among those who were &gt; 65 years, who had a record in both EMR and administrative data, 5\%-8\% (n=191 of 3,553, BC; n=2,396 of 29,382, MB) were identified as frail. There was a higher likelihood of being frail with increasing age and being a woman. In BC, those identified as frail have higher contacts with primary care (n=20 vs. n=10) and more days in hospital (n=7.4 vs. n=2.0) compared to those who are not frail. Twenty two percent of those identified as frail in 2014 died in 2015, compared to a mortality rate of 2\% among those who are not frail.&#x0D; Conclusion/ImplicationsIdentifying and reporting on those who are frail in primary care as well as in communities could enable targeted communications with patients and families and community based resources in order to improve patient care, patients’ and caregivers’ quality of life and better use of the healthcare system.

BackgroundWith the proliferation of electronic medical record (EMR) systems, there is an increasing interest in utilizing EMR data for medical research; yet, there is no quantitative research on EMR data utilization for medical research purposes in China.ObjectiveThis study aimed to understand how and to what extent EMR data are utilized for medical research purposes in a Healthcare Information and Management Systems Society (HIMSS) Analytics Electronic Medical Record Adoption Model (EMRAM) Stage 7 hospital in Beijing, China. Obstacles and issues in the utilization of EMR data were also explored to provide a foundation for the improved utilization of such data.MethodsFor this descriptive cross-sectional study, cluster sampling from Xuanwu Hospital, one of two Stage 7 hospitals in Beijing, was conducted from 2016 to 2019. The utilization of EMR data was described as the number of requests, the proportion of requesters, and the frequency of requests per capita. Comparisons by year, professional title, and age were conducted by double-sided chi-square tests.ResultsFrom 2016 to 2019, EMR data utilization was poor, as the proportion of requesters was 5.8% and the frequency was 0.1 times per person per year. The frequency per capita gradually slowed and older senior-level staff more frequently used EMR data compared with younger staff.ConclusionsThe value of using EMR data for research purposes is not well studied in China. More research is needed to quantify to what extent EMR data are utilized across all hospitals in Beijing and how these systems can enhance future studies. The results of this study also suggest that young doctors may be less exposed or have less reason to access such research methods.

Population health management approaches can help target diabetes resources like Diabetes Self-Management Education and Support (DSMES) to individuals at the highest risk of complications and poor outcomes. Little is known about patient characteristics associated with DSMES receipt since widespread uptake of telemedicine for diabetes care in 2020. In this retrospective cohort study, we used electronic medical record (EMR) data to assess patterns of DSMES delivery from May 2020 to May 2022 among adults who used telemedicine for type 2 diabetes (T2D) endocrinology care in a large integrated health system. Multilevel regression models were used to evaluate the association of key patient characteristics with DSMES receipt. Of 3530 patients in the overall cohort, 401 patients (11%) received DSMES. In adjusted multivariable logistic regression, higher baseline HbA1c (odds ratios [OR] 3.10 [95% confidence interval 2.22-4.33] for HbA1c ≥9% vs <7%), insulin regimen complexity (OR 3.53 [2.59-4.80] for multiple daily injections vs no insulin), and number of noninsulin medications (OR 1.17 [1.05-1.30] per 1 additional medication) were significantly associated with receipt of DSMES, whereas rurality and area-level deprivation of patient residence were not. Diabetes Self-Management Education and Support remains underutilized in this cohort of adults using telemedicine to access endocrinology care for T2D. Factors contributing to clinical complexity increased the odds of receiving DSMES. These results support a potential population health management approach using EMR data, which could target DSMES resources to those at higher risk of poor outcomes. This risk-stratified approach may be even more effective now that more people can access DSMES via telemedicine in addition to in-person care.

The combination of secure multi-party computations (SMC) algorithms, encryption, public key infrastructure (PKI), certificates, and a certificate authority (CA) is used to implement an infrastructure and a toolset for statistical analysis of electronic medical record (EMR) data. EMR data are typically located at different general practices and hospitals. These general practices and hospitals are considered nodes in a computing graph. The infrastructure and toolset has to take into consideration legal, security and privacy issues involved in statistical analysis of such data. The described approach tries to support a wide range of possible SMC algorithms and computing graphs.

BACKGROUND Timely and accurate prediction of short-term mortality is critical in intensive care units (ICUs), where patients’ conditions change rapidly. Traditional scoring systems, such as the Simplified Acute Physiology Score and Acute Physiology and Chronic Health Evaluation, rely on static variables collected within the first 24 hours of admission and do not account for continuously evolving clinical states. These systems lack real-time adaptability, interpretability, and generalizability. With the increasing availability of high-frequency electronic medical record (EMR) data, machine learning (ML) approaches have emerged as powerful tools to model complex temporal patterns and support dynamic clinical decision-making. However, existing models are often limited by their inability to handle irregular sampling and missing values, and many lack rigorous external validation across institutions. OBJECTIVE We aimed to develop a real-time, interpretable risk prediction model that continuously assesses ICU patient mortality using irregular, longitudinal EMR data, with improved performance and generalizability over traditional static scoring systems. METHODS A time-aware bidirectional attention-based long short-term memory (TBAL) model was developed using EMR data from the MIMIC-IV (Medical Information Mart for Intensive Care) and eICU Collaborative Research Database (eICU-CRD) databases, comprising 176,344 ICU stays. The model incorporated dynamic variables, including vital signs, laboratory results, and medication data, updated hourly, to perform static and continuous mortality risk assessments. External cross-validation and subgroup sensitivity analyses were conducted to evaluate robustness and fairness. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, and &lt;i&gt;F&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt;-score. Interpretability was enhanced using integrated gradients to identify key predictors. RESULTS For the static 12-hour to 1-day mortality prediction task, the TBAL model achieved AUROCs of 95.9 (95% CI 94.2-97.5) and 93.3 (95% CI 91.5-95.3) and AUPRCs of 48.5 and 21.6 in MIMIC-IV and eICU-CRD, respectively. Accuracy and &lt;i&gt;F&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt;-scores reached 94.1 and 46.7 in MIMIC-IV and 92.2 and 28.1 in eICU-CRD. In dynamic prediction tasks, AUROCs reached 93.6 (95% CI 93.2-93.9) and 91.9 (95% CI 91.6-92.1), with AUPRCs of 41.3 and 50, respectively. The model maintained high recall for positive cases (82.6% and 79.1% in MIMIC-IV and eICU-CRD). Cross-database validation yielded AUROCs of 81.3 and 76.1, confirming generalizability. Subgroup analysis showed stable performance across age, sex, and severity strata, with top predictors including lactate, vasopressor use, and Glasgow Coma Scale score. CONCLUSIONS The TBAL model offers a robust, interpretable, and generalizable solution for dynamic real-time mortality risk prediction in ICU patients. Its ability to adapt to irregular temporal patterns and to provide hourly updated predictions positions it as a promising decision-support tool. Future work should validate its utility in prospective clinical trials and investigate its integration into real-world ICU workflows to enhance patient outcomes. CLINICALTRIAL

BackgroundPre-exposure prophylaxis (PrEP) in the form of daily tenofovir disoproxil fumarate (TDF/FTC) is a potentially transformative tool to prevent HIV infection. However, PrEP scale-up in the United States has been slow and difficult to evaluate comprehensively. All payer claims databases (APCDs) are large datasets that contain information on medical and pharmaceutical claims from most public and private payers in each state, and provide an unusual opportunity to evaluate statewide PrEP implementation efforts.MethodsWe used 2012–2017 data from Rhode Island’s APCD and developed an algorithm to identify individuals prescribed TDF/FTC for PrEP. We compared APCD PrEP data to electronic medical record (EMR) data at the largest dedicated PrEP program in the state, and to other comprehensive pharmaceutical claims data (AIDSVu.org). We calculated the PrEP-to-Need ratio (PnR) based on annual HIV incidence, and used multivariable logistic regression to predict ZIP code-level PrEP use, and specialty of prescribing provider (primary care vs. infectious disease).ResultsThe Rhode Island APCD included insurance claims for 917,633 individuals (87% of the Rhode Island population). PrEP use increased substantially in Rhode Island over the 5-year period, from 13 to 331 prescriptions between 2012 and 2017, with 546 total users during this time period. Users were predominantly male (89%) and privately insured (69.1%), and concentrated in Providence County (71.5%). The PnR ratio increased from 0.2 to 4.0 from 2012–2017. Compared with AIDSVu and EMR Data, the APCD underestimated the number of PrEP users in Rhode Island, but improved over time in documenting users. Infectious diseases specialists had 8.4 times the odds (95% CI: 5.4 to 12.9) of being a PrEP prescriber compared with primary care providers. A total of 2.6% of infectious disease specialists were PrEP prescribers compared with 0.33% of PCPs. The proportion of Black or Hispanic individuals in a ZIP-code was not a significant predictor of PrEP use.ConclusionAPCDs offer an innovative approach to evaluate statewide PrEP implementation comprehensively. Engaging PCPs in PrEP implementation is critical to improve overall uptake among populations most at-risk.DisclosuresAll authors: No reported disclosures.