The Postencounter Form System: Viewpoint on Efficient Data Collection Within Electronic Health Records.
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ABSTRACT:
SUBMITTER: Held P
PROVIDER: S-EPMC7171586 | biostudies-literature | 2020 Apr
REPOSITORIES: biostudies-literature
Publications
The Postencounter Form System: Viewpoint on Efficient Data Collection Within Electronic Health Records.
Held Philip P Boley Randy A RA Faig Walter G WG O'Toole John A JA Desai Imran I Zalta Alyson K AK Khan Jawad J Sims Shannon S Brennan Michael B MB Van Horn Rebecca R Glover Angela C AC Hota Bala N BN Patty Brian D BD Rab S Shafiq SS Pollack Mark H MH Karnik Niranjan S NS
JMIR formative research 20200406 4
Electronic health records (EHRs) offer opportunities for research and improvements in patient care. However, challenges exist in using data from EHRs due to the volume of information existing within clinical notes, which can be labor intensive and costly to transform into usable data with existing strategies. This case report details the collaborative development and implementation of the postencounter form (PEF) system into the EHR at the Road Home Program at Rush University Medical Center in C ...[more]
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Project description:IntroductionData extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR).Materials and methodsFor 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen's kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies.ResultsFor the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. For 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error: human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%).ConclusionOur findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations on how to improve future iterations.
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Project description:Motivated by two case studies using primary care records from the Clinical Practice Research Datalink, we describe statistical methods that facilitate the analysis of tall data, with very large numbers of observations. Our focus is on investigating the association between patient characteristics and an outcome of interest, while allowing for variation among general practices. We explore ways to fit mixed-effects models to tall data, including predictors of interest and confounding factors as covariates, and including random intercepts to allow for heterogeneity in outcome among practices. We introduce (1) weighted regression and (2) meta-analysis of estimated regression coefficients from each practice. Both methods reduce the size of the dataset, thus decreasing the time required for statistical analysis. We compare the methods to an existing subsampling approach. All methods give similar point estimates, and weighted regression and meta-analysis give similar standard errors for point estimates to analysis of the entire dataset, but the subsampling method gives larger standard errors. Where all data are discrete, weighted regression is equivalent to fitting the mixed model to the entire dataset. In the presence of a continuous covariate, meta-analysis is useful. Both methods are easy to implement in standard statistical software.
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Project description:ObjectiveWhile there has been a substantial increase in health information exchange, levels of outside records use by frontline providers are low. We assessed whether integration between outside data and local data results in increased viewing of outside records, overall and by encounter, provider, and patient type.Materials and methodsUsing data from UCSF Health, we measured change in outside record views after integrating the list of local (UCSF) and outside (other health systems on Epic [Epic Systems, Verona, WI]) encounters on the Chart Review tab. Previously, providers only viewed records from outside encounters on a separate tab. We used an interrupted time series design (with outside record viewing event counts aggregated to the week level) to measure changes in the level and trend over a 1-year period.ResultsThere was a large increase in the level of outside record views of 22 920 per week (P < .001). The change in trend went from a weekly increase of 116 (P < .05) to a decrease of 402 (P = .08), reflecting a small effect decay. There were increases in the level of views for all provider and encounter types: attendings (n = 3675), residents (n = 3277), and nurses (n = 914); and inpatient (n = 1676), emergency (n = 487), and outpatient (n = 7228) (P < .001 for all). Results persisted when adjusted for total encounter volume.DiscussionWhile outside records were readily available before the encounter integration, the simple step of clicking on a separate tab appears to have depressed use.ConclusionsUser interface designs that comingle local and outside data result in higher levels of viewing and should be more broadly pursued.
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Project description:BackgroundPatterns of disease incidence can identify new risk factors for the disease or provide insight into the etiology. For example, allergies and infectious diseases have been shown to follow periodic temporal patterns due to seasonal changes in environmental or infectious agents. Previous work searching for seasonal or other temporal patterns in disease diagnosis rates has been limited both in the scope of the diseases examined and in the ability to distinguish unexpected seasonal patterns. Electronic Health Records (EHR) compile extensive longitudinal clinical information, constituting a unique source for discovery of trends in occurrence of disease. However, the data suffer from inherent biases that preclude an identification of temporal trends.MethodsMotivated by observation of the biases in this data source, we developed a method (Lomb-Scargle periodograms in detrended data, LSP-detrend) to find periodic patterns by adjusting the temporal information for broad trends in incidence, as well as seasonal changes in total hospitalizations. LSP-detrend can sensitively uncover periodic temporal patterns in the corrected data and identify the significance of the trend. We apply LSP-detrend to a compilation of records from 1.5 million patients encoded by ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), including 2,805 disorders with more than 500 occurrences across a 12 year period, recorded from 1.5 million patients.Results and conclusionsAlthough EHR data, and ICD-9 coded records in particular, were not created with the intention of aggregated use for research, these data can in fact be mined for periodic patterns in incidence of disease, if confounders are properly removed. Of all diagnoses, around 10% are identified as seasonal by LSP-detrend, including many known phenomena. We robustly reproduce previous findings, even for relatively rare diseases. For instance, Kawasaki disease, a rare childhood disease that has been associated with weather patterns, is detected as strongly linked with winter months. Among the novel results, we find a bi-annual increase in exacerbations of myasthenia gravis, a potentially life threatening complication of an autoimmune disease. We dissect the causes of this seasonal incidence and propose that factors predisposing patients to this event vary through the year.
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Project description:Objective/backgroundSleep problems are common in people on the autism spectrum. This study reviews one detailed approach to querying the electronic health record (EHR) in a large tertiary care center.Patients/methodsWe developed methods for identifying people on the autism spectrum and defined their sleep problems using the key words, "sleep" or "melatonin", or International Classification of Diseases (ICD) codes. We examined treatment responses of these individuals to melatonin supplementation.ResultsSleep problems were documented in 86% of patients with ages ranging from 6 to 30 years old. Our specific keyword search yielded more patients with sleep diagnoses than ICD codes alone. About two-thirds of patients who received melatonin supplementation reported benefit from its use.ConclusionsOur study provides a framework for using deidentified medical records to characterize sleep, a common co-occurring condition, in people on the autism spectrum. Using specific keywords could be helpful in future work that queries the EHR.
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Project description:The rise of genomically targeted therapies and immunotherapy has revolutionized the practice of oncology in the last 10-15 years. At the same time, new technologies and the electronic health record (EHR) in particular have permeated the oncology clinic. Initially designed as billing and clinical documentation systems, EHR systems have not anticipated the complexity and variety of genomic information that needs to be reviewed, interpreted, and acted upon on a daily basis. Improved integration of cancer genomic data with EHR systems will help guide clinician decision making, support secondary uses, and ultimately improve patient care within oncology clinics. Some of the key factors relating to the challenge of integrating cancer genomic data into EHRs include: the bioinformatics pipelines that translate raw genomic data into meaningful, actionable results; the role of human curation in the interpretation of variant calls; and the need for consistent standards with regard to genomic and clinical data. Several emerging paradigms for integration are discussed in this review, including: non-standardized efforts between individual institutions and genomic testing laboratories; "middleware" products that portray genomic information, albeit outside of the clinical workflow; and application programming interfaces that have the potential to work within clinical workflow. The critical need for clinical-genomic knowledge bases, which can be independent or integrated into the aforementioned solutions, is also discussed.
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Project description:BACKGROUND:In order to examine the potential clinical value of integrating family history information directly from the electronic health records of patients' family members, the electronic health records of individuals in Clalit Health Services, the largest payer/provider in Israel, were linked with the records of their parents. METHODS:We describe the results of a novel approach for creating data-derived family history information for 2 599 575 individuals, focusing on three chronic diseases: asthma, cardiovascular disease (CVD) and diabetes. RESULTS:In our cohort, there were 256 598 patients with asthma, 55 309 patients with CVD and 66 324 patients with diabetes. Of the people with asthma, CVD or diabetes, the percentage that also had a family history of the same disease was 22.0%, 70.8% and 70.5%, respectively. CONCLUSIONS:Linking individuals' health records with their data-derived family history has untapped potential for supporting diagnostic and clinical decision-making.
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Project description:BACKGROUND:Missing data is a challenge for all studies; however, this is especially true for electronic health record (EHR)-based analyses. Failure to appropriately consider missing data can lead to biased results. While there has been extensive theoretical work on imputation, and many sophisticated methods are now available, it remains quite challenging for researchers to implement these methods appropriately. Here, we provide detailed procedures for when and how to conduct imputation of EHR laboratory results. OBJECTIVE:The objective of this study was to demonstrate how the mechanism of missingness can be assessed, evaluate the performance of a variety of imputation methods, and describe some of the most frequent problems that can be encountered. METHODS:We analyzed clinical laboratory measures from 602,366 patients in the EHR of Geisinger Health System in Pennsylvania, USA. Using these data, we constructed a representative set of complete cases and assessed the performance of 12 different imputation methods for missing data that was simulated based on 4 mechanisms of missingness (missing completely at random, missing not at random, missing at random, and real data modelling). RESULTS:Our results showed that several methods, including variations of Multivariate Imputation by Chained Equations (MICE) and softImpute, consistently imputed missing values with low error; however, only a subset of the MICE methods was suitable for multiple imputation. CONCLUSIONS:The analyses we describe provide an outline of considerations for dealing with missing EHR data, steps that researchers can perform to characterize missingness within their own data, and an evaluation of methods that can be applied to impute clinical data. While the performance of methods may vary between datasets, the process we describe can be generalized to the majority of structured data types that exist in EHRs, and all of our methods and code are publicly available.
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Project description:Temporal variability in health-care processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal data-set shifts can present as trends, as well as abrupt or seasonal changes in the statistical distributions of data over time. The latter are particularly complicated to address in multimodal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large sets of historical data from EHRs, there is a need for specific software methods to help delineate temporal data-set shifts to ensure reliable data reuse. EHRtemporalVariability is an open-source R package and Shiny app designed to explore and identify temporal data-set shifts. EHRtemporalVariability estimates the statistical distributions of coded and numerical data over time; projects their temporal evolution through non-parametric information geometric temporal plots; and enables the exploration of changes in variables through data temporal heat maps. We demonstrate the capability of EHRtemporalVariability to delineate data-set shifts in three impact case studies, one of which is available for reproducibility. EHRtemporalVariability enables the exploration and identification of data-set shifts, contributing to the broad examination and repurposing of large, longitudinal data sets. Our goal is to help ensure reliable data reuse for a wide range of biomedical data users. EHRtemporalVariability is designed for technical users who are programmatically utilizing the R package, as well as users who are not familiar with programming via the Shiny user interface.Availability: https://github.com/hms-dbmi/EHRtemporalVariability/Reproducible vignette: https://cran.r-project.org/web/packages/EHRtemporalVariability/vignettes/EHRtemporalVariability.htmlOnline demo: http://ehrtemporalvariability.upv.es/.
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Project description:The availability of data from electronic health records facilitates the development and evaluation of risk-prediction models, but estimation of prediction accuracy could be limited by outcome misclassification, which can arise if events are not captured. We evaluate the robustness of prediction accuracy summaries, obtained from receiver operating characteristic curves and risk-reclassification methods, if events are not captured (i.e., "false negatives"). We derive estimators for sensitivity and specificity if misclassification is independent of marker values. In simulation studies, we quantify the potential for bias in prediction accuracy summaries if misclassification depends on marker values. We compare the accuracy of alternative prognostic models for 30-day all-cause hospital readmission among 4548 patients discharged from the University of Pennsylvania Health System with a primary diagnosis of heart failure. Simulation studies indicate that if misclassification depends on marker values, then the estimated accuracy improvement is also biased, but the direction of the bias depends on the direction of the association between markers and the probability of misclassification. In our application, 29% of the 1143 readmitted patients were readmitted to a hospital elsewhere in Pennsylvania, which reduced prediction accuracy. Outcome misclassification can result in erroneous conclusions regarding the accuracy of risk-prediction models.
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