scholarly journals Structured override reasons for drug-drug interaction alerts in electronic health records

2019 ◽  
Vol 26 (10) ◽  
pp. 934-942 ◽  
Author(s):  
Adam Wright ◽  
Dustin S McEvoy ◽  
Skye Aaron ◽  
Allison B McCoy ◽  
Mary G Amato ◽  
...  

Abstract Objective The study sought to determine availability and use of structured override reasons for drug-drug interaction (DDI) alerts in electronic health records. Materials and Methods We collected data on DDI alerts and override reasons from 10 clinical sites across the United States using a variety of electronic health records. We used a multistage iterative card sort method to categorize the override reasons from all sites and identified best practices. Results Our methodology established 177 unique override reasons across the 10 sites. The number of coded override reasons at each site ranged from 3 to 100. Many sites offered override reasons not relevant to DDIs. Twelve categories of override reasons were identified. Three categories accounted for 78% of all overrides: “will monitor or take precautions,” “not clinically significant,” and “benefit outweighs risk.” Discussion We found wide variability in override reasons between sites and many opportunities to improve alerts. Some override reasons were irrelevant to DDIs. Many override reasons attested to a future action (eg, decreasing a dose or ordering monitoring tests), which requires an additional step after the alert is overridden, unless the alert is made actionable. Some override reasons deferred to another party, although override reasons often are not visible to other users. Many override reasons stated that the alert was inaccurate, suggesting that specificity of alerts could be improved. Conclusions Organizations should improve the options available to providers who choose to override DDI alerts. DDI alerting systems should be actionable and alerts should be tailored to the patient and drug pairs.

2014 ◽  
Vol 10 (1) ◽  
pp. 59-63 ◽  
Author(s):  
Marvin B. Harper ◽  
Christopher A. Longhurst ◽  
Troy L. McGuire ◽  
Rod Tarrago ◽  
Bimal R. Desai ◽  
...  

2020 ◽  
Author(s):  
Abison Logeswaran ◽  
Yu Jeat Chong ◽  
Matthew R Edmunds

BACKGROUND Despite the widespread adoption of EHR globally, there has been increasing dissatisfaction amongst end users about the usability of such technology. To overcome this problem, a variety of frameworks have been published in the United States and the United Kingdom for usability testing. However, these are often overly complex, making it difficult for healthcare professionals (HCP) to engage with the process of usability testing OBJECTIVE To describe evidence based qualitative methods for the evaluation of electronic health records (EHR) for HCPs. METHODS We conducted a review of current qualitative usability methodologies, based on the National Centre for Human Factors in Healthcare EHR framework. Ophthalmology is used as use case for the application of these methods RESULTS We identified several qualitative methodologies that could be used at the major stages of EHR evaluation. These include: 1) Tools for User Centered Design: Shadowing and autoethnography, semi structured interviews and questionnaires 2) Tools for Summative Testing: Card sort and reverse card sort, retrospective think aloud protocol, wireframing, screenshot testing and heatmaps CONCLUSIONS High yield, low fidelity tools can be used to engage HCPs with the process of usability testing of EHR, increasing end user satisfaction. These methods can be used by HCPs without the requirement for prior training in usability science, and by clinical centers without significant technical requirements.


2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S819-S820
Author(s):  
Jonathan Todd ◽  
Jon Puro ◽  
Matthew Jones ◽  
Jee Oakley ◽  
Laura A Vonnahme ◽  
...  

Abstract Background Over 80% of tuberculosis (TB) cases in the United States are attributed to reactivation of latent TB infection (LTBI). Eliminating TB in the United States requires expanding identification and treatment of LTBI. Centralized electronic health records (EHRs) are an unexplored data source to identify persons with LTBI. We explored EHR data to evaluate TB and LTBI screening and diagnoses within OCHIN, Inc., a U.S. practice-based research network with a high proportion of Federally Qualified Health Centers. Methods From the EHRs of patients who had an encounter at an OCHIN member clinic between January 1, 2012 and December 31, 2016, we extracted demographic variables, TB risk factors, TB screening tests, International Classification of Diseases (ICD) 9 and 10 codes, and treatment regimens. Based on test results, ICD codes, and treatment regimens, we developed a novel algorithm to classify patient records into LTBI categories: definite, probable or possible. We used multivariable logistic regression, with a referent group of all cohort patients not classified as having LTBI or TB, to identify associations between TB risk factors and LTBI. Results Among 2,190,686 patients, 6.9% (n=151,195) had a TB screening test; among those, 8% tested positive. Non-U.S. –born or non-English–speaking persons comprised 24% of our cohort; 11% were tested for TB infection, and 14% had a positive test. Risk factors in the multivariable model significantly associated with being classified as having LTBI included preferring non-English language (adjusted odds ratio [aOR] 4.20, 95% confidence interval [CI] 4.09–4.32); non-Hispanic Asian (aOR 5.17, 95% CI 4.94–5.40), non-Hispanic black (aOR 3.02, 95% CI 2.91–3.13), or Native Hawaiian/other Pacific Islander (aOR 3.35, 95% CI 2.92–3.84) race; and HIV infection (aOR 3.09, 95% CI 2.84–3.35). Conclusion This study demonstrates the utility of EHR data for understanding TB screening practices and as an important data source that can be used to enhance public health surveillance of LTBI prevalence. Increasing screening among high-risk populations remains an important step toward eliminating TB in the United States. These results underscore the importance of offering TB screening in non-U.S.–born populations. Disclosures All Authors: No reported disclosures


2018 ◽  
Vol 136 (2) ◽  
pp. 164 ◽  
Author(s):  
Michele C. Lim ◽  
Michael V. Boland ◽  
Colin A. McCannel ◽  
Arvind Saini ◽  
Michael F. Chiang ◽  
...  

2021 ◽  
Vol 12 (04) ◽  
pp. 816-825
Author(s):  
Yingcheng Sun ◽  
Alex Butler ◽  
Ibrahim Diallo ◽  
Jae Hyun Kim ◽  
Casey Ta ◽  
...  

Abstract Background Clinical trials are the gold standard for generating robust medical evidence, but clinical trial results often raise generalizability concerns, which can be attributed to the lack of population representativeness. The electronic health records (EHRs) data are useful for estimating the population representativeness of clinical trial study population. Objectives This research aims to estimate the population representativeness of clinical trials systematically using EHR data during the early design stage. Methods We present an end-to-end analytical framework for transforming free-text clinical trial eligibility criteria into executable database queries conformant with the Observational Medical Outcomes Partnership Common Data Model and for systematically quantifying the population representativeness for each clinical trial. Results We calculated the population representativeness of 782 novel coronavirus disease 2019 (COVID-19) trials and 3,827 type 2 diabetes mellitus (T2DM) trials in the United States respectively using this framework. With the use of overly restrictive eligibility criteria, 85.7% of the COVID-19 trials and 30.1% of T2DM trials had poor population representativeness. Conclusion This research demonstrates the potential of using the EHR data to assess the clinical trials population representativeness, providing data-driven metrics to inform the selection and optimization of eligibility criteria.


2020 ◽  
Vol 159 (6) ◽  
pp. 2221-2225.e6 ◽  
Author(s):  
Shailendra Singh ◽  
Mohammad Bilal ◽  
Haig Pakhchanian ◽  
Rahul Raiker ◽  
Gursimran S. Kochhar ◽  
...  

2018 ◽  
Vol 25 (2) ◽  
pp. 109-125 ◽  
Author(s):  
Mark Chun Moon ◽  
Rebecca Hills ◽  
George Demiris

BackgroundLittle is known about optimisation of electronic health records (EHRs) systems in the hospital setting while adoption of EHR systems continues in the United States.ObjectiveTo understand optimisation processes of EHR systems undertaken in leading healthcare organisations in the United States.MethodsInformed by a grounded theory approach, a qualitative study was undertaken that involved 11 in-depth interviews and a focus group with the EHR experts from the high performing healthcare organisations across the United States.ResultsThe study describes EHR optimisation processes characterised by prioritising exponentially increasing requests with predominant focus on improving efficiency of EHR, building optimisation teams or advisory groups and standardisation. The study discusses 16 types of optimisation that interdependently produced 16 results along with identifying 11 barriers and 20 facilitators to optimisation.ConclusionsThe study describes overall experiences of optimising EHRs in select high performing healthcare organisations in the US. The findings highlight the importance of optimising the EHR after, and even before, go-live and dedicating resources exclusively for optimisation.


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