Using a medical simulation center as an electronic health record usability laboratory.
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ABSTRACT:
SUBMITTER: Landman AB
PROVIDER: S-EPMC3994869 | biostudies-other | 2014 May-Jun
REPOSITORIES: biostudies-other
Publications
Using a medical simulation center as an electronic health record usability laboratory.
Landman Adam B AB Redden Lisa L Neri Pamela P Poole Stephen S Horsky Jan J Raja Ali S AS Pozner Charles N CN Schiff Gordon G Poon Eric G EG
Journal of the American Medical Informatics Association : JAMIA 20131118 3
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 ...[more]
Similar Datasets
Project description:ObjectivesThis study set out to obtain a general profile of physician time expenditure and electronic health record (EHR) limitations in a large university medical center in Germany. We also aim to illustrate the merit of a tool allowing for easier capture and prioritization of specific clinical needs at the point of care for which the current study will inform development in subsequent work.MethodsNineteen physicians across six different departments participated in this study. Direct clinical observations were conducted with 13 out of 19 physicians for a total of 2,205 minutes, and semistructured interviews were conducted with all participants. During observations, time was measured for larger activity categories (searching information, reading information, documenting information, patient interaction, calling, and others). Semistructured interviews focused on perceived limitations, frustrations, and desired improvements regarding the EHR environment.ResultsOf the observed time, 37.1% was spent interacting with the health records (9.0% searching, 7.7% reading, and 20.5% writing), 28.0% was spent interacting with patients corrected for EHR use (26.9% of time in a patient's presence), 6.8% was spent calling, and 28.1% was spent on other activities. Major themes of discontent were a spread of patient information, high and often repeated documentation burden, poor integration of (new) information into workflow, limits in information exchange, and the impact of such problems on patient interaction. Physicians stated limited means to address such issues at the point of care.ConclusionIn the study hospital, over one-third of physicians' time was spent interacting with the EHR, environment, with many aspects of used systems far from optimal and no convenient way for physicians to address issues as they occur at the point of care. A tool facilitating easier identification and registration of issues, as they occur, may aid in generating a more complete overview of limitations in the EHR environment.
| S-EPMC8695058 | biostudies-literature
Project description:PurposeAn oncology electronic health record (EHR) was implemented without prior usability testing. Before expanding the system to new clinics, this study was initiated to examine the role of usability testing in the evaluation of an EHR product and whether novice users could identify issues with usability that resonated with more experienced users of the system. In addition, our study evaluated whether usability issues with an already implemented system affect efficiency and satisfaction of users.MethodsA general usability guide was developed by a group of five informaticists. Using this guide, four novice users evaluated an EHR product and identified issues. A panel of five experts reviewed the identified issues to determine agreement with and applicability to the already implemented system. A survey of 42 experienced users of the previously implemented EHR was also performed to assess efficiency and general satisfaction.ResultsThe novice users identified 110 usability issues. Our expert panel agreed with 90% of the issues and recommendations for correction identified by the novice users. Our survey had a 54% response rate. The majority of the experienced users of the previously implemented system, which did not benefit from upfront usability testing, had a high degree of dissatisfaction with efficiency and general functionality but higher overall satisfaction than expected.ConclusionIn addition to reviewing features and content of an EHR system, usability testing could improve the chances that the EHR design is integrated with existing workflow and business processes in a clear and efficient way.
| S-EPMC2868635 | biostudies-literature
Project description:BackgroundObtaining accurate clinical information about recent acute care visits is extremely important for outpatient providers. However, documents used to communicate this information are often difficult to use. This puts patients at risk of adverse events. Elderly patients who are seen by more providers and have more care transitions are especially vulnerable.ObjectiveThis study aimed to (1) identify the information about elderly patients' recent acute care visits needed to coordinate their care, (2) use this information to assess discharge summaries, and (3) provide recommendations to help improve the quality of electronic health record (EHR)-generated discharge summaries, thereby increasing patient safety.MethodsA literature review, clinician interviews, and a survey of outpatient providers were used to identify and categorize data needed to coordinate care for recently discharged elderly patients. Based upon those data, 2 guidelines for creating useful discharge summaries were created. The new guidelines, along with 17 previously developed medical documentation usability heuristics, were applied to assess 4 simulated elderly patient discharge summaries.ResultsThe initial research effort yielded a list of 29 items that should always be included in elderly patient discharge summaries and a list of 7 "helpful, but not always necessary" items. Evaluation of 4 deidentified elderly patient discharge summaries revealed that none of the documents contained all 36 necessary items; between 14 and 18 were missing. The documents each had several other issues, and they differed significantly in organization, layout, and formatting.ConclusionsVariations in content and structure of discharge summaries in the United States make them unnecessarily difficult to use. Standardization would benefit both patients, by lowering the risk of care transition-related adverse events, and outpatient providers, by helping reduce frustration that can contribute to burnout. In the short term, acute care providers can help improve the quality of their discharge summaries by working with EHR vendors to follow recommendations based upon this study. Meanwhile, additional human factors work should determine the most effective way to organize and present information in discharge summaries, to facilitate effective standardization.
| S-EPMC8085750 | biostudies-literature
Project description:OBJECTIVE:Electronic medical records (EMRs) can support medical research and discovery, but privacy risks limit the sharing of such data on a wide scale. Various approaches have been developed to mitigate risk, including record simulation via generative adversarial networks (GANs). While showing promise in certain application domains, GANs lack a principled approach for EMR data that induces subpar simulation. In this article, we improve EMR simulation through a novel pipeline that (1) enhances the learning model, (2) incorporates evaluation criteria for data utility that informs learning, and (3) refines the training process. MATERIALS AND METHODS:We propose a new electronic health record generator using a GAN with a Wasserstein divergence and layer normalization techniques. We designed 2 utility measures to characterize similarity in the structural properties of real and simulated EMRs in the original and latent space, respectively. We applied a filtering strategy to enhance GAN training for low-prevalence clinical concepts. We evaluated the new and existing GANs with utility and privacy measures (membership and disclosure attacks) using billing codes from over 1 million EMRs at Vanderbilt University Medical Center. RESULTS:The proposed model outperformed the state-of-the-art approaches with significant improvement in retaining the nature of real records, including prediction performance and structural properties, without sacrificing privacy. Additionally, the filtering strategy achieved higher utility when the EMR training dataset was small. CONCLUSIONS:These findings illustrate that EMR simulation through GANs can be substantially improved through more appropriate training, modeling, and evaluation criteria.
| S-EPMC6913223 | biostudies-literature
Project description:BackgroundPatient portals provide patients and their caregivers online access to limited health results. Health care employees with electronic health record (EHR) access may be able to view their health information not available in the patient portal by looking in the EHR.ObjectiveIn this study, we examine how employees use the patient portal when they also have access to the tethered EHR.MethodsWe obtained patient portal and EHR usage logs corresponding to all employees who viewed their health data at our institution between January 1, 2013 and November 1, 2017. We formed three cohorts based on the systems that employees used to view their health data: employees who used the patient portal only, employees who viewed health data in the EHR only, and employees who used both systems. We compared system accesses and usage patterns for each employee cohort.ResultsDuring the study period, 35,172 employees accessed the EHR as part of patients' treatment and 28,631 employees accessed their health data: 25,193 of them used the patient portal and 13,318 accessed their clinical data in EHR. All employees who accessed their records in the EHR viewed their clinical notes at least once. Among EHR accesses, clinical note accesses comprised more than 42% of all EHR accesses. Provider messaging and appointment scheduling were the most commonly used functions in the patient portal. Employees who had access to their health data in both systems were more likely to engage with providers through portal messages.ConclusionEmployees at a large medical center accessed clinical notes in the EHR to obtain information about their health. Employees also viewed other health data not readily available in the patient portal.
| S-EPMC7299683 | biostudies-literature
Project description:Introduction:The adoption of electronic health records (EHRs) presents new challenges for information gathering, documentation, and patient care. Medical students spend a significant amount of time using the EHR during their clerkship experiences and will continue to do so as they progress to residency. However, formal training varies between institutions and leaves gaps in data-gathering skills, documentation skills, and order entry-these three skills formed the basis for our learning objectives. We designed a workshop using a simulated EHR to teach these skills. Methods:We offered the workshop during a class-wide transition-to-internship course for senior medical students. After a brief didactic, students worked in small groups using a simulated EHR to complete cases addressing each of the three learning objectives. Faculty facilitators assisted groups and then guided a large-group discussion of the challenges encountered during the cases. Results:Twenty-five senior medical students attended the first optional workshop. Of these students, 66.7% recommended that the workshop continue to be included in the curriculum. Comments from those who recommended otherwise suggested that many of them would recommend the workshop if it used our local EHR (Epic). Correct answers to the factual questions increased for most questions between the pretest and the posttest. Confidence to perform all skills targeted in the learning objectives increased between the pretest and the posttest. Discussion:This EHR workshop was well received by senior medical students and increased confidence in EHR skills, including data gathering, documentation, and handling unsolicited information with a plan including order entry.
| S-EPMC6946580 | biostudies-literature
Project description:BackgroundElectronic health record (EHR) usability issues represent an emerging threat to the wellbeing of nurses and patients; however, few large studies have examined these relationships.ObjectiveTo examine associations between EHR usability and nurse job (burnout, job dissatisfaction, and intention to leave) and surgical patient (inpatient mortality and 30-day readmission) outcomes.MethodsA cross-sectional analysis of linked American Hospital Association, state patient discharge, and nurse survey data was conducted. The sample included 343 hospitals, 1,281,848 surgical patients, and 12,004 nurses. Logistic regression models were used to assess relationships between EHR usability and outcomes, before and after accounting for EHR adoption level (comprehensive vs. basic or less) and other confounders.ResultsIn fully adjusted models, nurses who worked in hospitals with poorer EHR usability had significantly higher odds of burnout [odds ratio (OR), 1.41; 95% confidence interval (CI), 1.21-1.64], job dissatisfaction (OR, 1.61; 95% CI, 1.37-1.90) and intention to leave (OR, 1.31; 95% CI, 1.09-1.58) compared with nurses working in hospitals with better usability. Surgical patients treated in hospitals with poorer EHR usability had significantly higher odds of inpatient mortality (OR, 1.21; 95% CI, 1.09-1.35) and 30-day readmission (OR, 1.06; 95% CI, 1.01-1.12) compared with patients in hospitals with better usability. Comprehensive EHR adoption was associated with higher odds of nurse burnout (OR, 1.14; 95% CI, 1.01-1.28).ConclusionEmploying EHR systems with suboptimal usability was associated with higher odds of adverse nurse job outcomes and surgical patient mortality and readmission. EHR usability may be more important to nurse job and patient outcomes than comprehensive EHR adoption.
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Project description:BackgroundCollection of accurate patient race, ethnicity, preferred language (REaL) and gender identity in the electronic health record (EHR) is essential for equitable and inclusive care. Misidentification of these factors limits quality measurement of health outcomes in at-risk populations. Therefore, the aim of our study was to assess the accuracy of REaL and gender identity data at our institution.MethodsA survey was administered to 117 random patients, selected from prior day admissions at a large academic medical center in urban central New York. Patients (or guardians) self-reported REaL and gender identity data, selecting from current EHR options. Variables were coded for the presence or absence of a difference from data recorded in the EHR.ResultsRace was misreported in the EHR for 13% of patients and ethnicity for 6%. For most White and Black patients, race was concordant. However, self-identified data for all multiracial patients were discordant with the EHR. Most Non-Hispanic patients had ethnicity correctly documented. Some Hispanic patients were misidentified. There was a significant association between reporting both a race and an ethnicity which differed from the EHR on chi square analysis (P < 0.001). Of those who reported an alternative ethnicity, 71.4% also reported an alternative race. Gender identity was missing for most patients and 11% of the gender-identity entries present in the EHR were discordant with the patient's self-identity. Preferred language was 100% concordant with the EHR.ConclusionsAt an academic medical center, multiracial and Hispanic patients were more likely to have their demographics misreported in the EHR, and gender identity data were largely missing. Healthcare systems need strategies that support accurate collection of patients' self-reported ReAL and gender identity data to improve the future ability to identify and address healthcare disparities.
| S-EPMC10463428 | biostudies-literature
Project description:BackgroundClinical decision support systems are used to help predict patient stability and mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians with patient management and in allocating finite resources. However, systems currently in common use have limited predictive value in the clinical setting. The increasing availability of Electronic Health Records (EHR) provides an opportunity to use medical information for more accurate patient stability and mortality prediction in the ICU.ObjectiveDevelop and evaluate an algorithm which more accurately predicts patient mortality in the ICU, using the correlations between widely available clinical variables from the EHR.MethodsWe have developed an algorithm, AutoTriage, which uses eight common clinical variables from the EHR to assign patient mortality risk scores. Each clinical variable produces a subscore, and combinations of two or three discretized clinical variables also produce subscores. A combination of weighted subscores produces the overall score. We validated the performance of this algorithm in a retrospective study on the MIMIC III medical ICU dataset.ResultsAutoTriage 12 h mortality prediction yields an Area Under Receiver Operating Characteristic value of 0.88 (95% confidence interval 0.86 to 0.88). At a sensitivity of 80%, AutoTriage maintains a specificity of 81% with a diagnostic odds ratio of 16.26.ConclusionsThrough the multidimensional analysis of the correlations between eight common clinical variables, AutoTriage provides an improvement in the specificity and sensitivity of patient mortality prediction over existing prediction methods.
| S-EPMC5037117 | biostudies-literature
Project description:OBJECTIVE: Using numeric laboratory data and administrative data from hospital electronic health record (EHR) systems, to develop an inpatient mortality predictive model. METHODS: Using EHR data of 1,428,824 adult discharges from 70 hospitals in 2006-2007, we developed the Acute Laboratory Risk of Mortality Score (ALaRMS) using age, gender, and initial laboratory values on admission as candidate variables. We then added administrative variables using the Agency for Healthcare Research and Quality (AHRQ)'s clinical classification software (CCS) and comorbidity software (CS) as disease classification tools. We validated the model using 770,523 discharges in 2008. RESULTS: Mortality predictors with ORs >2.00 included age, deranged albumin, arterial pH, bands, blood urea nitrogen, oxygen partial pressure, platelets, pro-brain natriuretic peptide, troponin I, and white blood cell counts. The ALaRMS model c-statistic was 0.87. Adding the CCS and CS variables increased the c-statistic to 0.91. The relative contributions were 69% (ALaRMS), 25% (CCS), and 6% (CS). Furthermore, the integrated discrimination improvement statistic demonstrated a 127% (95% CI 122% to 133%) overall improvement when ALaRMS was added to CCS and CS variables. In contrast, only a 22% (CI 19% to 25%) improvement was seen when CCS and CS variables were added to ALaRMS. CONCLUSIONS: EHR data can generate clinically plausible mortality predictive models with excellent discrimination. ALaRMS uses automated laboratory data widely available on admission, providing opportunities to aid real-time decision support. Models that incorporate laboratory and AHRQ's CCS and CS variables have utility for risk adjustment in retrospective outcome studies.
| S-EPMC3994855 | biostudies-other