Seeking out SARI: an automated search of electronic health records.
Ontology highlight
ABSTRACT:
SUBMITTER: O'Horo JC
PROVIDER: S-EPMC5997502 | biostudies-literature | 2018 Jun
REPOSITORIES: biostudies-literature
Publications
Seeking out SARI: an automated search of electronic health records.
O'Horo John C JC Dziadzko Mikhail M Sakusic Amra A Ali Rashid R Sohail M Rizwan MR Kor Daryl J DJ Gajic Ognjen O
Epidemiology and infection 20180418 8
The definition of severe acute respiratory infection (SARI) - a respiratory illness with fever and cough, occurring within the past 10 days and requiring hospital admission - has not been evaluated for critically ill patients. Using integrated electronic health records data, we developed an automated search algorithm to identify SARI cases in a large cohort of critical care patients and evaluate patient outcomes. We conducted a retrospective cohort study of all admissions to a medical intensive ...[more]
Similar Datasets
Project description:ObjectiveA full-text search engine can be a useful tool for augmenting the reuse value of unstructured narrative data stored in electronic health records (EHR). A prominent barrier to the effective utilization of such tools originates from users' lack of search expertise and/or medical-domain knowledge. To mitigate the issue, the authors experimented with a 'collaborative search' feature through a homegrown EHR search engine that allows users to preserve their search knowledge and share it with others. This feature was inspired by the success of many social information-foraging techniques used on the web that leverage users' collective wisdom to improve the quality and efficiency of information retrieval.DesignThe authors conducted an empirical evaluation study over a 4-year period. The user sample consisted of 451 academic researchers, medical practitioners, and hospital administrators. The data were analyzed using a social-network analysis to delineate the structure of the user collaboration networks that mediated the diffusion of knowledge of search.ResultsThe users embraced the concept with considerable enthusiasm. About half of the EHR searches processed by the system (0.44 million) were based on stored search knowledge; 0.16 million utilized shared knowledge made available by other users. The social-network analysis results also suggest that the user-collaboration networks engendered by the collaborative search feature played an instrumental role in enabling the transfer of search knowledge across people and domains.ConclusionApplying collaborative search, a social information-foraging technique popularly used on the web, may provide the potential to improve the quality and efficiency of information retrieval in healthcare.
| S-EPMC3078661 | biostudies-literature
Project description:ObjectiveTo develop and validate automated electronic note search strategies (automated digital algorithm) to identify Charlson comorbidities.Patients and methodsThe automated digital algorithm was built by a series of programmatic queries applied to an institutional electronic medical record database. The automated digital algorithm was derived from secondary analysis of an observational cohort study of 1447 patients admitted to the intensive care unit from January 1 through December 31, 2006, and validated in an independent cohort of 240 patients. The sensitivity, specificity, and positive and negative predictive values of the automated digital algorithm and International Classification of Diseases, Ninth Revision (ICD-9) codes were compared with comprehensive medical record review (reference standard) for the Charlson comorbidities.ResultsIn the derivation cohort, the automated digital algorithm achieved a median sensitivity of 100% (range, 99%-100%) and a median specificity of 99.7% (range, 99%-100%). In the validation cohort, the sensitivity of the automated digital algorithm ranged from 91% to 100%, and the specificity ranged from 98% to 100%. The sensitivity of the ICD-9 codes ranged from 8% for dementia to 100% for leukemia, whereas specificity ranged from 86% for congestive heart failure to 100% for leukemia, dementia, and AIDS.ConclusionOur results suggest that search strategies that use automated electronic search strategies to extract Charlson comorbidities from the clinical notes contained within the electronic medical record are feasible and reliable. Automated digital algorithm outperformed ICD-9 codes in all the Charlson variables except leukemia, with greater sensitivity, specificity, and positive and negative predictive values.
| S-EPMC3538495 | biostudies-literature
Project description:BackgroundAccurate influenza activity forecasting helps public health officials prepare and allocate resources for unusual influenza activity. Traditional flu surveillance systems, such as the Centers for Disease Control and Prevention's (CDC) influenza-like illnesses reports, lag behind real-time by one to 2 weeks, whereas information contained in cloud-based electronic health records (EHR) and in Internet users' search activity is typically available in near real-time. We present a method that combines the information from these two data sources with historical flu activity to produce national flu forecasts for the United States up to 4 weeks ahead of the publication of CDC's flu reports.MethodsWe extend a method originally designed to track flu using Google searches, named ARGO, to combine information from EHR and Internet searches with historical flu activities. Our regularized multivariate regression model dynamically selects the most appropriate variables for flu prediction every week. The model is assessed for the flu seasons within the time period 2013-2016 using multiple metrics including root mean squared error (RMSE).ResultsOur method reduces the RMSE of the publicly available alternative (Healthmap flutrends) method by 33, 20, 17 and 21%, for the four time horizons: real-time, one, two, and 3 weeks ahead, respectively. Such accuracy improvements are statistically significant at the 5% level. Our real-time estimates correctly identified the peak timing and magnitude of the studied flu seasons.ConclusionsOur method significantly reduces the prediction error when compared to historical publicly available Internet-based prediction systems, demonstrating that: (1) the method to combine data sources is as important as data quality; (2) effectively extracting information from a cloud-based EHR and Internet search activity leads to accurate forecast of flu.
| S-EPMC5423019 | biostudies-literature
Project description:BACKGROUND:Circulating biomarkers can facilitate diagnosis and risk stratification for complex conditions such as heart failure (HF). Newer molecular platforms can accelerate biomarker discovery, but they require significant resources for data and sample acquisition. OBJECTIVES:The purpose of this study was to test a pragmatic biomarker discovery strategy integrating automated clinical biobanking with proteomics. METHODS:Using the electronic health record, the authors identified patients with and without HF, retrieved their discarded plasma samples, and screened these specimens using a DNA aptamer-based proteomic platform (1,129 proteins). Candidate biomarkers were validated in 3 different prospective cohorts. RESULTS:In an automated manner, plasma samples from 1,315 patients (31% with HF) were collected. Proteomic analysis of a 96-patient subset identified 9 candidate biomarkers (p < 4.42 × 10-5). Two proteins, angiopoietin-2 and thrombospondin-2, were associated with HF in 3 separate validation cohorts. In an emergency department-based registry of 852 dyspneic patients, the 2 biomarkers improved discrimination of acute HF compared with a clinical score (p < 0.0001) or clinical score plus B-type natriuretic peptide (p = 0.02). In a community-based cohort (n = 768), both biomarkers predicted incident HF independent of traditional risk factors and N-terminal pro-B-type natriuretic peptide (hazard ratio per SD increment: 1.35 [95% confidence interval: 1.14 to 1.61; p = 0.0007] for angiopoietin-2, and 1.37 [95% confidence interval: 1.06 to 1.79; p = 0.02] for thrombospondin-2). Among 30 advanced HF patients, concentrations of both biomarkers declined (80% to 84%) following cardiac transplant (p < 0.001 for both). CONCLUSIONS:A novel strategy integrating electronic health records, discarded clinical specimens, and proteomics identified 2 biomarkers that robustly predict HF across diverse clinical settings. This approach could accelerate biomarker discovery for many diseases.
| S-EPMC6501811 | biostudies-literature
Project description:ObjectiveTo demonstrate the utility of growthcleanr, an anthropometric data cleaning method designed for electronic health records (EHR).Materials and methodsWe used all available pediatric and adult height and weight data from an ongoing observational study that includes EHR data from 15 healthcare systems and applied growthcleanr to identify outliers and errors and compared its performance in pediatric data with 2 other pediatric data cleaning methods: (1) conditional percentile (cp) and (2) PaEdiatric ANthropometric measurement Outlier Flagging pipeline (peanof).Results687 226 children (<20 years) and 3 267 293 adults contributed 71 246 369 weight and 51 525 487 height measurements. growthcleanr flagged 18% of pediatric and 12% of adult measurements for exclusion, mostly as carried-forward measures for pediatric data and duplicates for adult and pediatric data. After removing the flagged measurements, 0.5% and 0.6% of the pediatric heights and weights and 0.3% and 1.4% of the adult heights and weights, respectively, were biologically implausible according to the CDC and other established cut points. Compared with other pediatric cleaning methods, growthcleanr flagged the most measurements for exclusion; however, it did not flag some more extreme measurements. The prevalence of severe pediatric obesity was 9.0%, 9.2%, and 8.0% after cleaning by growthcleanr, cp, and peanof, respectively.Conclusiongrowthcleanr is useful for cleaning pediatric and adult height and weight data. It is the only method with the ability to clean adult data and identify carried-forward and duplicates, which are prevalent in EHR. Findings of this study can be used to improve the growthcleanr algorithm.
| S-EPMC9629892 | biostudies-literature
Project description:Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. Am J Psychiatry 172:363-372, 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3330 BD cases and 3952 controls of European ancestry were used to estimate SNP-based heritability (h2g) and genetic correlation (rg) between EHR-based phenotype definitions and traditionally ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and three rule-based algorithms using codified EHR with decreasing levels of stringency-"coded-strict", "coded-broad", and "coded-broad based on a single clinical encounter" (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1968 coded-strict, 2581 coded-broad, 408 coded-broad-SV BD cases, and 3 952 controls. The estimated h2g were 0.24 (p?=?0.015), 0.09 (p?=?0.064), 0.13 (p?=?0.003), 0.00 (p?=?0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h2g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h2g) was 0.12 (p?=?0.004). These h2g were lower or similar to the h2g observed by the ICCBD?+?PGCBD (0.23, p?=?3.17E-80, total N?=?33,181). However, the rg between ICCBD?+?PGCBD and the EHR-based cases were high for 95-NLP (0.66, p?=?3.69?×?10-5), coded-strict (1.00, p?=?2.40?×?10-4), and coded-broad (0.74, p?=?8.11?×?10-7). The rg between EHR-based BD definitions ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.
| S-EPMC5904248 | biostudies-literature
Project description:ObjectiveLarge electronic health record (EHR) datasets are increasingly used to facilitate research on growth, but measurement and recording errors can lead to biased results. We developed and tested an automated method for identifying implausible values in pediatric EHR growth data.Materials and methodsUsing deidentified data from 46 primary care sites, we developed an algorithm to identify weight and height values that should be excluded from analysis, including implausible values and values that were recorded repeatedly without remeasurement. The foundation of the algorithm is a comparison of each measurement, expressed as a standard deviation score, with a weighted moving average of a child's other measurements. We evaluated the performance of the algorithm by (1) comparing its results with the judgment of physician reviewers for a stratified random selection of 400 measurements and (2) evaluating its accuracy in a dataset with simulated errors.ResultsOf 2 000 595 growth measurements from 280 610 patients 1 to 21 years old, 3.8% of weight and 4.5% of height values were identified as implausible or excluded for other reasons. The proportion excluded varied widely by primary care site. The automated method had a sensitivity of 97% (95% confidence interval [CI], 94-99%) and a specificity of 90% (95% CI, 85-94%) for identifying implausible values compared to physician judgment, and identified 95% (weight) and 98% (height) of simulated errors.Discussion and conclusionThis automated, flexible, and validated method for preparing large datasets will facilitate the use of pediatric EHR growth datasets for research.
| S-EPMC7651915 | biostudies-literature
Project description:BACKGROUND:Adverse events in health care entail substantial burdens to health care systems, institutions, and patients. Retrospective trigger tools are often manually applied to detect AEs, although automated approaches using electronic health records may offer real-time adverse event detection, allowing timely corrective interventions. OBJECTIVE:The aim of this systematic review was to describe current study methods and challenges regarding the use of automatic trigger tool-based adverse event detection methods in electronic health records. In addition, we aimed to appraise the applied studies' designs and to synthesize estimates of adverse event prevalence and diagnostic test accuracy of automatic detection methods using manual trigger tool as a reference standard. METHODS:PubMed, EMBASE, CINAHL, and the Cochrane Library were queried. We included observational studies, applying trigger tools in acute care settings, and excluded studies using nonhospital and outpatient settings. Eligible articles were divided into diagnostic test accuracy studies and prevalence studies. We derived the study prevalence and estimates for the positive predictive value. We assessed bias risks and applicability concerns using Quality Assessment tool for Diagnostic Accuracy Studies-2 (QUADAS-2) for diagnostic test accuracy studies and an in-house developed tool for prevalence studies. RESULTS:A total of 11 studies met all criteria: 2 concerned diagnostic test accuracy and 9 prevalence. We judged several studies to be at high bias risks for their automated detection method, definition of outcomes, and type of statistical analyses. Across all the 11 studies, adverse event prevalence ranged from 0% to 17.9%, with a median of 0.8%. The positive predictive value of all triggers to detect adverse events ranged from 0% to 100% across studies, with a median of 40%. Some triggers had wide ranging positive predictive value values: (1) in 6 studies, hypoglycemia had a positive predictive value ranging from 15.8% to 60%; (2) in 5 studies, naloxone had a positive predictive value ranging from 20% to 91%; (3) in 4 studies, flumazenil had a positive predictive value ranging from 38.9% to 83.3%; and (4) in 4 studies, protamine had a positive predictive value ranging from 0% to 60%. We were unable to determine the adverse event prevalence, positive predictive value, preventability, and severity in 40.4%, 10.5%, 71.1%, and 68.4% of the studies, respectively. These studies did not report the overall number of records analyzed, triggers, or adverse events; or the studies did not conduct the analysis. CONCLUSIONS:We observed broad interstudy variation in reported adverse event prevalence and positive predictive value. The lack of sufficiently described methods led to difficulties regarding interpretation. To improve quality, we see the need for a set of recommendations to endorse optimal use of research designs and adequate reporting of future adverse event detection studies.
| S-EPMC6000482 | biostudies-literature
Project description:Electronic health records (EHRs) are an important source of data for detection of adverse drug reactions (ADRs). However, adverse events are frequently due not to medications but to the patients' underlying conditions. Mining to detect ADRs from EHR data must account for confounders. We developed an automated method using natural-language processing (NLP) and a knowledge source to differentiate cases in which the patient's disease is responsible for the event rather than a drug. Our method was applied to 199,920 hospitalization records, concentrating on two serious ADRs: rhabdomyolysis (n = 687) and agranulocytosis (n = 772). Our method automatically identified 75% of the cases, those with disease etiology. The sensitivity and specificity were 93.8% (confidence interval: 88.9-96.7%) and 91.8% (confidence interval: 84.0-96.2%), respectively. The method resulted in considerable saving of time: for every 1 h spent in development, there was a saving of at least 20 h in manual review. The review of the remaining 25% of the cases therefore became more feasible, allowing us to identify the medications that had caused the ADRs.
| S-EPMC3685297 | biostudies-literature
Project description:The objective was to compare case ascertainment, agreement, validity, and missing values for clinical research data obtained, processed, and linked electronically from electronic health records (EHR) compared to "manual" data processing and record abstraction in a cohort of out-of-hospital trauma patients.? This was a secondary analysis of two sets of data collected for a prospective, population-based, out-of-hospital trauma cohort evaluated by 10 emergency medical services (EMS) agencies transporting to 16 hospitals, from January 1, 2006, through October 2, 2007. Eighteen clinical, operational, procedural, and outcome variables were collected and processed separately and independently using two parallel data processing strategies by personnel blinded to patients in the other group. The electronic approach included EHR data exports from EMS agencies, reformatting, and probabilistic linkage to outcomes from local trauma registries and state discharge databases. The manual data processing approach included chart matching, data abstraction, and data entry by a trained abstractor. Descriptive statistics, measures of agreement, and validity were used to compare the two approaches to data processing.? During the 21-month period, 418 patients underwent both data processing methods and formed the primary cohort. Agreement was good to excellent (kappa = 0.76 to 0.97; intraclass correlation coefficient [ICC] = 0.49 to 0.97), with exact agreement in 67% to 99% of cases and a median difference of zero for all continuous and ordinal variables. The proportions of missing out-of-hospital values were similar between the two approaches, although electronic processing generated more missing outcomes (87 of 418, 21%, 95% confidence interval [CI] = 17% to 25%) than the manual approach (11 of 418, 3%, 95% CI = 1% to 5%). Case ascertainment of eligible injured patients was greater using electronic methods (n = 3,008) compared to manual methods (n = 629).? In this sample of out-of-hospital trauma patients, an all-electronic data processing strategy identified more patients and generated values with good agreement and validity compared to traditional data collection and processing methods.
| S-EPMC3278860 | biostudies-literature