Why Is the Electronic Health Record So Challenging for Research and Clinical Care?

2021

2021 Jul 19

2511-705X

<p><strong>BACKGROUND: </strong> The electronic health record (EHR) has become increasingly ubiquitous. At the same time, health professionals have been turning to this resource for access to data that is needed for the delivery of health care and for clinical research. There is little doubt that the EHR has made both of these functions easier than earlier days when we relied on paper-based clinical records. Coupled with modern database and data warehouse systems, high-speed networks, and the ability to share clinical data with others are large number of challenges that arguably limit the optimal use of the EHR OBJECTIVES:  Our goal was to provide an exhaustive reference for those who use the EHR in clinical and research contexts, but also for health information systems professionals as they design, implement, and maintain EHR systems.</p>

<p><strong>METHODS: </strong> This study includes a panel of 24 biomedical informatics researchers, information technology professionals, and clinicians, all of whom have extensive experience in design, implementation, and maintenance of EHR systems, or in using the EHR as clinicians or researchers. All members of the panel are affiliated with Penn Medicine at the University of Pennsylvania and have experience with a variety of different EHR platforms and systems and how they have evolved over time.</p>

<p><strong>RESULTS: </strong> Each of the authors has shared their knowledge and experience in using the EHR in a suite of 20 short essays, each representing a specific challenge and classified according to a functional hierarchy of interlocking facets such as usability and usefulness, data quality, standards, governance, data integration, clinical care, and clinical research.</p>

<p><strong>CONCLUSION: </strong> We provide here a set of perspectives on the challenges posed by the EHR to clinical and research users.</p>

10.1055/s-0041-1731784

Methods Inf Med

34282602

Diagnostic biomarkers to differentiate sepsis from cytokine release syndrome in critically ill children.

2020

5174-5183

2020 Oct 27

2473-9537

<p>Chimeric antigen receptor (CAR) T-cells directed against CD19 have drastically altered outcomes for children with relapsed and refractory acute lymphoblastic leukemia (r/r ALL). Pediatric patients with r/r ALL treated with CAR-T are at increased risk of both cytokine release syndrome (CRS) and sepsis. We sought to investigate the biologic differences between CRS and sepsis and to develop predictive models which could accurately differentiate CRS from sepsis at the time of critical illness. We identified 23 different cytokines that were significantly different between patients with sepsis and CRS. Using elastic net prediction modeling and tree classification, we identified cytokines that were able to classify subjects as having CRS or sepsis accurately. A markedly elevated interferon γ (IFNγ) or a mildly elevated IFNγ in combination with a low IL1β were associated with CRS. A normal to mildly elevated IFNγ in combination with an elevated IL1β was associated with sepsis. This combination of IFNγ and IL1β was able to categorize subjects as having CRS or sepsis with 97% accuracy. As CAR-T therapies become more common, these data provide important novel information to better manage potential associated toxicities.</p>

10.1182/bloodadvances.2020002592

Blood Adv

33095872

A maximum likelihood approach to electronic health record phenotyping using positive and unlabeled patients.

2019

2019 Nov 13

1527-974X

<p><strong>OBJECTIVE: </strong>Phenotyping patients using electronic health record (EHR) data conventionally requires labeled cases and controls. Assigning labels requires manual medical chart review and therefore is labor intensive. For some phenotypes, identifying gold-standard controls is prohibitive. We developed an accurate EHR phenotyping approach that does not require labeled controls.</p>

<p><strong>MATERIALS AND METHODS: </strong>Our framework relies on a random subset of cases, which can be specified using an anchor variable that has excellent positive predictive value and sensitivity independent of predictors. We proposed a maximum likelihood approach that efficiently leverages data from the specified cases and unlabeled patients to develop logistic regression phenotyping models, and compare model performance with existing algorithms.</p>

<p><strong>RESULTS: </strong>Our method outperformed the existing algorithms on predictive accuracy in Monte Carlo simulation studies, application to identify hypertension patients with hypokalemia requiring oral supplementation using a simulated anchor, and application to identify primary aldosteronism patients using real-world cases and anchor variables. Our method additionally generated consistent estimates of 2 important parameters, phenotype prevalence and the proportion of true cases that are labeled.</p>

<p><strong>DISCUSSION: </strong>Upon identification of an anchor variable that is scalable and transferable to different practices, our approach should facilitate development of scalable, transferable, and practice-specific phenotyping models.</p>

<p><strong>CONCLUSIONS: </strong>Our proposed approach enables accurate semiautomated EHR phenotyping with minimal manual labeling and therefore should greatly facilitate EHR clinical decision support and research.</p>

10.1093/jamia/ocz170

J Am Med Inform Assoc

31722396