Comparing automated vs. manual data collection for COVID-specific medications from electronic health records.

INTRODUCTION: Data 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 METHODS: For 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. RESULTS: For 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%). CONCLUSION: Our 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.

Exploring a 2-naphthoic acid template for the structure-based design of P2Y14 receptor antagonist molecular probes.

The P2Y14 receptor (P2Y14R), one of eight P2Y G protein-coupled receptors (GPCR), is involved in inflammatory, endocrine, and hypoxic processes and is an attractive pharmaceutical target. The goal of this research is to develop high-affinity P2Y14R fluorescent probes based on the potent and highly selective antagonist 4-(4-(piperidin-4-yl)-phenyl)-7-(4-(trifluoromethyl)-phenyl)-2-naphthoic acid (6, PPTN). A model of hP2Y14R based on recent hP2Y12R X-ray structures together with simulated antagonist docking suggested that the piperidine ring is suitable for fluorophore conjugation while preserving affinity. Chain-elongated alkynyl or amino derivatives of 6 for click or amide coupling were synthesized, and their antagonist activities were measured in hP2Y14R-expressing CHO cells. Moreover, a new Alexa Fluor 488 (AF488) containing derivative 30 (MRS4174, Ki = 80 pM) exhibited exceptionally high affinity, as compared to 13 nM for the alkyne precursor 22. A flow cytometry assay employing 30 as a fluorescent probe was used to quantify specific binding to P2Y14R. Known P2Y receptor ligands inhibited binding of 30 with properties consistent with their previously established receptor selectivities and affinities. These results illustrate that potency in this series of 2-naphthoic acid derivatives can be preserved by chain functionalization, leading to highly potent fluorescent molecular probes for P2Y14R. Such conjugates will be useful tools in expanding the SAR of this receptor, which still lacks chemical diversity in its collective ligands. This approach demonstrates the predictive power of GPCR homology modeling and the relevance of newly determined X-ray structures to GPCR medicinal chemistry.