A usability and safety analysis of electronic health records: a multi-center study.
Ontology highlight
ABSTRACT:
SUBMITTER: Ratwani RM
PROVIDER: S-EPMC7646875 | biostudies-literature | 2018 Sep
REPOSITORIES: biostudies-literature
Publications
A usability and safety analysis of electronic health records: a multi-center study.
Ratwani Raj M RM Savage Erica E Will Amy A Arnold Ryan R Khairat Saif S Miller Kristen K Fairbanks Rollin J RJ Hodgkins Michael M Hettinger A Zachary AZ
Journal of the American Medical Informatics Association : JAMIA 20180901 9
To characterize the variability in usability and safety of EHRs from two vendors across four healthcare systems (2 Epic and 2 Cerner). Twelve to 15 emergency medicine physicians participated from each site and completed six clinical scenarios. Keystroke, mouse click, and video data were collected. From the six scenarios, two diagnostic imaging, laboratory, and medication tasks were analyzed. There was wide variability in task completion time, clicks, and error rates. For certain tasks, there wer ...[more]
Similar Datasets
Project description:PurposeAs of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction.MethodWe first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction.ResultsFor the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort.ConclusionThe developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model.
| S-EPMC7863774 | biostudies-literature
Project description:BackgroundElectronic Health Records (EHRs) can help clinicians to plan, document and deliver care for patients in healthcare services. When used consistently, EHRs can advance patient safety and quality, and reduce clinician's workload. However, usability problems can make it difficult for clinicians to use EHRs effectively, which can negatively impact both healthcare professionals and patients.ObjectiveTo improve usability of EHRs within a mental health service in the UK.MethodsThis was a feasibility study conducted with two mental health teams. A mixed-methods approach was employed. Focus group discussions with clinicians identified existing usability problems in EHRs and changes were made to address these problems. Updated EHR assessment forms were evaluated by comparing the following measures pre and post changes: (1) usability testing to monitor time spent completing and duplicating patient information in EHRs, (2) clinician's experience of using EHRs, and (3) proportion of completed EHR assessment forms.ResultsUsability testing with clinicians (n = 3) showed that the time taken to complete EHR assessment forms and time spent duplicating patient information decreased. Clinician's experience of completing EHR assessment forms also significantly improved post changes compared to baseline (n = 71; p < 0.005). There was a significant increase in completion of most EHR forms by both teams after EHR usability improvements (all at p < 0.01).ConclusionsUsability improvements to EHRs can reduce the time taken to complete forms, advance clinician's experience and increase usage of EHRs. It is important to engage healthcare professionals in the usability improvement process of EHRs in mental health services.
| S-EPMC9177469 | biostudies-literature
Project description:BACKGROUND:Poor EHR design adds further challenges, especially in the areas of order entry and information visualization, with a net effect of increased rates of incidents, accidents, and mortality in ICU settings. OBJECTIVE:The purpose of this study was to propose a novel, mixed-methods framework to understand EHR-related information overload by identifying and characterizing areas of suboptimal usability and clinician frustration within a vendor-based, provider-facing EHR interface. METHODS:A mixed-methods, live observational usability study was conducted at a single, large, tertiary academic medical center in the Southeastern US utilizing a commercial, vendor based EHR. Physicians were asked to complete usability patient cases, provide responses to three surveys, and participant in a semi-structured interview. RESULTS:Of the 25 enrolled ICU physician participants, there were 5(20%) attending physicians, 9 (36%) fellows, and 11 (44%) residents; 52% of participants were females. On average, residents were the quickest in completing the tasks while attending physician took the longest to complete the same task. Poor usability, complex interface screens, and difficulty to navigate the EHR significantly correlated with high frustration levels. Significant association were found between the occurrence of error messages and temporal demand such that more error messages resulted in longer completion time (p?=?.03). DISCUSSION:Physicians remain frustrated with the EHR due to difficulty in finding patient information. EHR usability remains a critical challenge in healthcare, with implications for medical errors, patient safety, and clinician burnout. There is a need for scientific findings on current information needs and ways to improve EHR-related information overload.
| S-EPMC6584062 | biostudies-literature
Project description:Usability testing is increasingly being recognized as a way to increase the usability and safety of health information technology (HIT). Medical simulation centers can serve as testing environments for HIT usability studies. We integrated the quality assurance version of our emergency department (ED) electronic health record (EHR) into our medical simulation center and piloted a clinical care scenario in which emergency medicine resident physicians evaluated a simulated ED patient and documented electronically using the ED EHR. Meticulous planning and close collaboration with expert simulation staff was important for designing test scenarios, pilot testing, and running the sessions. Similarly, working with information systems teams was important for integration of the EHR. Electronic tools are needed to facilitate entry of fictitious clinical results while the simulation scenario is unfolding. EHRs can be successfully integrated into existing simulation centers, which may provide realistic environments for usability testing, training, and evaluation of human-computer interactions.
| S-EPMC3994869 | biostudies-other
Project description:ObjectivePhysician burnout associated with EHRs is a major concern in health care. A comprehensive assessment of differences among physicians in the areas of EHR performance, efficiency, and satisfaction has not been conducted. The study sought to study relationships among physicians' performance, efficiency, perceived workload, satisfaction, and usability in using the electronic health record (EHR) with comparisons by age, gender, professional role, and years of experience with the EHR.Materials and methodsMixed-methods assessments of the medical intensivists' EHR use and perceptions. Using simulated cases, we employed standardized scales, performance measures, and extensive interviews. NASA Task Load Index (TLX), System Usability Scale (SUS), and Questionnaire on User Interface Satisfaction surveys were deployed.ResultsThe study enrolled 25 intensive care unit (ICU) physicians (11 residents, 9 fellows, 5 attendings); 12 (48%) were men, with a mean age of 33 (range, 28-55) years and a mean of 4 (interquartile range, 2.0-5.5) years of Epic experience. Overall task performance scores were similar for men (90% ± 9.3%) and women (92% ± 4.4%), with no statistically significant differences (P = .374). However, female physicians demonstrated higher efficiency in completion time (difference = 7.1 minutes; P = .207) and mouse clicks (difference = 54; P = .13). Overall, men reported significantly higher perceived EHR workload stress compared with women (difference = 17.5; P < .001). Men reported significantly higher levels of frustration with the EHR compared with women (difference = 33.15; P < .001). Women reported significantly higher satisfaction with the ease of use of the EHR interface than men (difference = 0.66; P =.03). The women's perceived overall usability of the EHR is marginally higher than that of the men (difference = 10.31; P =.06).ConclusionsAmong ICU physicians, we measured significant gender-based differences in perceived EHR workload stress, satisfaction, and usability-corresponding to objective patterns in EHR efficiency. Understanding the reasons for these differences may help reduce burnout and guide improvements to physician performance, efficiency, and satisfaction with EHR use.DesignMixed-methods assessments of the medical intensivists' EHR use and perceptions. Using simulated cases, we employed standardized scales, performance measures, and extensive interviews.
| S-EPMC7647147 | biostudies-literature
Project description:ObjectiveIn this systematic review, we aimed to evaluate methodological and reporting trends present in the current literature by investigating published usability studies of electronic health records (EHRs).MethodsA literature search was conducted for articles published through January 2015 using MEDLINE (Ovid), EMBASE, Scopus, and Web of Science, supplemented by citation and reference list reviews. Studies were included if they tested the usability of hospital and clinic EHR systems in the inpatient, outpatient, emergency department, or operating room setting.ResultsA total of 4848 references were identified for title and abstract screening. Full text screening was performed for 197 articles, with 120 meeting the criteria for study inclusion.ConclusionA review of the literature demonstrates a paucity of quality published studies describing scientifically valid and reproducible usability evaluations at various stages of EHR system development. A lack of formal and standardized reporting of EHR usability evaluation results is a major contributor to this knowledge gap, and efforts to improve this deficiency will be one step of moving the field of usability engineering forward.
| S-EPMC7654077 | biostudies-literature
Project description:BackgroundThe use of patient health and treatment information captured in structured and unstructured formats in computerized electronic health record (EHR) repositories could potentially augment the detection of safety signals for drug products regulated by the US Food and Drug Administration (FDA). Natural language processing and other artificial intelligence (AI) techniques provide novel methodologies that could be leveraged to extract clinically useful information from EHR resources.ObjectiveOur aim is to develop a novel AI-enabled software prototype to identify adverse drug event (ADE) safety signals from free-text discharge summaries in EHRs to enhance opioid drug safety and research activities at the FDA.MethodsWe developed a prototype for web-based software that leverages keyword and trigger-phrase searching with rule-based algorithms and deep learning to extract candidate ADEs for specific opioid drugs from discharge summaries in the Medical Information Mart for Intensive Care III (MIMIC III) database. The prototype uses MedSpacy components to identify relevant sections of discharge summaries and a pretrained natural language processing (NLP) model, Spark NLP for Healthcare, for named entity recognition. Fifteen FDA staff members provided feedback on the prototype's features and functionalities.ResultsUsing the prototype, we were able to identify known, labeled, opioid-related adverse drug reactions from text in EHRs. The AI-enabled model achieved accuracy, recall, precision, and F1-scores of 0.66, 0.69, 0.64, and 0.67, respectively. FDA participants assessed the prototype as highly desirable in user satisfaction, visualizations, and in the potential to support drug safety signal detection for opioid drugs from EHR data while saving time and manual effort. Actionable design recommendations included (1) enlarging the tabs and visualizations; (2) enabling more flexibility and customizations to fit end users' individual needs; (3) providing additional instructional resources; (4) adding multiple graph export functionality; and (5) adding project summaries.ConclusionsThe novel prototype uses innovative AI-based techniques to automate searching for, extracting, and analyzing clinically useful information captured in unstructured text in EHRs. It increases efficiency in harnessing real-world data for opioid drug safety and increases the usability of the data to support regulatory review while decreasing the manual research burden.
| S-EPMC10538589 | biostudies-literature
Project description:Healthcare facilities (HF) may identify catchment areas (CA) by selecting criteria that depend on various factors. These refer to hospital activities, geographical definition, patient covariates, and more. The analyses that were traditionally pursued have a limiting factor in the consideration of only static conditions. Instead, some of the CA determinants involve influences occurring at both temporal and spatial scales. The study of CA in the cancer context means choosing between HF, usually divided into general hospitals versus oncological centers (OCs). In the CA context, electronic health records (EHRs) promise to be a valuable source of information, one driving the next-generation patient-driven clinical decision support systems. Among the challenges, digital health requires the re-definition of a role of stochastic modeling to deal with emerging complexities from data heterogeneity. To model CA with cancer EHR, we have chosen a computational framework centered on a logistic model, as a reference, and on a multivariate statistical approach. We also provided a battery of tests for CA assessment. Our results indicate that a more refined CA model's structure yields superior discrimination power between health facilities. The increased significance was also visualized by comparative evaluations with ad hoc geo-localized maps. Notably, a cancer-specific spatial effect can be noticed, especially for breast cancer and through OCs. To mitigate the data distributional influences, bootstrap analysis was performed, and gains in some cancer-specific and spatially concentrated regions were obtained. Finally, when the temporal dynamics are assessed along a 3-year timeframe, negligible differential effects appear between predicted probabilities observed between standard critical values and bootstrapped values. In conclusion, for interpreting CA in terms of both spatial and temporal dynamics, sophisticated models are required. The one here proposed suggests that bootstrap can improve test accuracy. We recommend that evidences from stochastic modeling are merged with visual analytics, as this combination may be exploited by policy-makers in support to quantitative CA assessment.
| S-EPMC5696335 | biostudies-literature
Project description:Healthcare providers previously using older electronic health records (EHRs) with electronic prescribing (e-prescribing) are transitioning to newer systems to be eligible for federal meaningful use incentives. Little is known about the safety effects of transitioning between systems.To assess the effect of transitioning between EHR systems on rates and types of prescribing errors, as well as provider perceptions about the effect on prescribing safety.Prospective, case study of 17 physicians at an academic-affiliated ambulatory clinic from February 2008 through August 2009. All physicians transitioned from an older EHR with minimal clinical decision support (CDS) for e-prescribing to a newer EHR with more robust CDS.Prescribing errors were identified by standardized prescription and chart review. A novel survey instrument was administered to evaluate provider perceptions about prescribing safety.We analyzed 1298 prescriptions at baseline, 1331 prescriptions 12 weeks post-implementation, and 1303 prescriptions one year post-implementation. Overall prescribing error rates were highest at baseline (35.7 per 100 prescriptions, 95% confidence interval (CI) 23.2-54.8) and lowest one year post-implementation (12.2 per 100 prescriptions, 95% CI 8.6-17.4) (p?<?0.001). Improvement in prescribing safety was mainly a result of reducing inappropriate abbreviation errors. However, rates for non-abbreviation prescribing errors were significantly higher at 12 weeks post-implementation than at baseline (17.7 per 100 prescriptions, 95% CI 9.5-33.0 versus 8.5 per 100 prescriptions, 95% CI 4.6-15.9) (p <0.001) and no different at baseline than one year (10.2 per 100 prescriptions, 95% CI 6.2-18.6) (p?=?0.337). Survey results complemented quantitative findings.Results from this case study suggest that transitioning between systems, even to those with more robust CDS, may pose important safety threats. Recognizing the challenges associated with transitions and refining CDS within systems may help maximize safety benefits.
| S-EPMC3138980 | biostudies-literature
Project description:Carfilzomib is a promising anticancer drug for relapsed/refractory multiple myeloma (RRMM). However, real-world evidence has only investigated the cardiovascular safety of carfilzomib, and there is a high demand for thorough safety evaluations. We aimed to comprehensively evaluate the risk of adverse events associated with carfilzomib in Korean patients with RRMM. We followed up with 138 matched patients with RRMM (69 KRd (carfilzomib, lenalidomide, and dexamethasone) and 69 Rd (lenalidomide and dexamethasone) users). A total of 12 adverse events were evaluated. More than 75% of adverse events occurred during the early cycle (1-6 cycles), and the incidence rate showed a tendency to decrease in the later cycle (7-12 and 13-18 cycles). Severities of most adverse events were evaluated as grade 1-2. The KRd regimen were related with significantly increased risks of dyspnea (adjusted HR (aHR) 2.27, 95% confidence interval (CI) 1.24-4.16), muscle spasm (aHR 5.12, 95% CI 1.05-24.9) and thrombocytopenia (aHR 1.84, 95% CI 1.10-3.06). Although the severities were low, carfilzomib has many side effects in treating RRMM; hence, findings on the patterns of its adverse events could lead to both effective and safe use of KRd therapy in real-world settings.
| S-EPMC9603615 | biostudies-literature