Perceptions of hospital electronic health record (EHR) training, support, and patient safety by staff position and tenure
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ABSTRACT:
SUBMITTER: Campione J
PROVIDER: S-EPMC11337607 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Government regulations have created new incentives for health systems to implement changes in electronic health records (EHRs) to reduce tobacco use among patients. The purpose of this study is to conduct a content analysis of EHR modifications aimed at supporting tobacco cessation and to document these modifications using a 5 A's framework (i.e., Ask, Advise, Assess, Assist, Arrange). Fourteen trials were identified that met inclusion criteria. A content analysis of EHR functionality in these trials was conducted by two independent reviewers between February and June 2015. For "Ask," all trials provided for the documentation of smoking status in the EHR. For "Advise," 35.7 % of EHRs provided functionality related to helping a clinician provide advice to quit. For "Assess," more than half (57.1 %) of EHRs included a feature to document a patient's willingness to quit. For "Assist," EHRs offered features for medication prescribing (78.6 %), providing educational materials to patients (57.1 %), referring a patient to the quitline (50.0 %), referring a patient to a tobacco treatment specialist (42.9 %), and documenting the provision of counseling (35.7 %). Finally, for "Arrange," EHRs supported the following up of patients (35.7 %) and allowed tobacco treatment specialists to "pass back" patient notes to primary care providers (28.6 %). Studies that have modified EHRs for tobacco treatment have done so across the steps in the 5 As model, with most modifications occurring to support documenting smoking status (Ask) and assisting with medication prescribing (Assist). As health systems attempt to comply with Meaningful Use regulations, an understanding of the range of EHR modifications to support tobacco treatment is warranted.
| S-EPMC5526802 | biostudies-other
Project description:ObjectiveElectronic health records (EHR) hold great promise for managing patient information in ways that improve healthcare delivery. Physicians differ, however, in their use of this health information technology (IT), and these differences are not well understood. The authors study the differences in individual physicians' EHR use patterns and identify perceptions of uncertainty as an important new variable in understanding EHR use.DesignQualitative study using semi-structured interviews and direct observation of physicians (n=28) working in a multispecialty outpatient care organization.MeasurementsWe identified physicians' perceptions of uncertainty as an important variable in understanding differences in EHR use patterns. Drawing on theories from the medical and organizational literatures, we identified three categories of perceptions of uncertainty: reduction, absorption, and hybrid. We used an existing model of EHR use to categorize physician EHR use patterns as high, medium, and low based on degree of feature use, level of EHR-enabled communication, and frequency that EHR use patterns change.ResultsPhysicians' perceptions of uncertainty were distinctly associated with their EHR use patterns. Uncertainty reductionists tended to exhibit high levels of EHR use, uncertainty absorbers tended to exhibit low levels of EHR use, and physicians demonstrating both perspectives of uncertainty (hybrids) tended to exhibit medium levels of EHR use.ConclusionsWe find evidence linking physicians' perceptions of uncertainty with EHR use patterns. Study findings have implications for health IT research, practice, and policy, particularly in terms of impacting health IT design and implementation efforts in ways that consider differences in physicians' perceptions of uncertainty.
| S-EPMC3912724 | biostudies-literature
Project description:ObjectiveTo understand if providers who had additional electronic health record (EHR) training improved their satisfaction, decreased personal EHR-use time, and decreased turnaround time on tasks.Materials and methodsThis pre-post study with no controls evaluated the impact of a supplemental EHR training program on a group of academic and community practice clinicians that previously had go-live group EHR training and 20 months experience using this EHR on self-reported data, calculated EHR time, and vendor-reported metrics.ResultsProviders self-reported significant improvements in their knowledge of efficiency tools in the EHR after training and doubled (significant) their preference list entries (mean pre = 38.1 [65.88], post = 63.5 [90.47], P < .01). Of the 7 EHR satisfaction variables, only 1 self-reported variable significantly improved after training: Control over my workload in the EHR (mean pre = 2.7 [0.96], post = 3.0 [1.04], P < .01). There was no significant decrease in their calculated EHR usage outside of clinic (mean pre = 0.39 [0.77] to post = 0.37 [0.48], P = .73). No significant difference was seen in turnaround time for patient calls (mean pre = 2.3 [2.06] days, post = 1.9 [1.76] days, P = .08) and results (mean before = 4.0 [2.79] days, after = 3.2 [2.33] days, P = .03).DiscussionMultiple sources of data provide a holistic view of the provider experience in the EHR. This study suggests that individualized EHR training can improve the knowledge of EHR tools and satisfaction with their perceived control of EHR workload, however this did not translate into less Clinician Logged-In Outside Clinic (CLOC) time, a calculated metric, nor quicker turnaround on in box tasks. CLOC time emerged as a potential less-costly surrogate metric for provider satisfaction in EHR work than surveying clinicians. Further study is required to understand the cost-benefit of various interventions to decrease CLOC time.ConclusionsThis supplemental EHR training session, 20 months post go-live, where most participants elected to receive 2 or fewer sessions did significantly improve provider satisfaction with perceived control over their workload in the EHR, but it was not effective in decreasing EHR-use time outside of clinic. CLOC time, a calculated metric, could be a practical trackable surrogate for provider satisfaction (inverse correlation) with after-hours time spent in the EHR. Further study into interventions that decrease CLOC time and improve turnaround time to respond to inbox tasks are suggested next steps.
| S-EPMC6952029 | biostudies-literature
Project description:ObjectiveWe report the influence of Sprint electronic health record (EHR) training and optimization on clinician time spent in the EHR.Materials and methodsWe studied the Sprint process in one academic internal medicine practice with 26 providers. Program offerings included individualized training sessions, and the ability to clean up, fix, or build new EHR tools during the 2-week intervention. EHR usage log data were available for 24 clinicians, and the average clinical full-time equivalent was 0.44. We used a quasi-experimental study design with an interrupted time series specification, with 8 months of pre- and 12 months of post-intervention data to evaluate clinician time spent in the EHR.ResultsWe discovered a greater than 6 h per day reduction in clinician time spent in the EHR at the clinic level. At the individual clinician level, we demonstrated a time savings of 20 min per clinician per day among those who attended at least 2 training sessions.DiscussionWe can promote EHR time savings for clinicians who engage in robust EHR training and optimization programs. To date, programs have shown a positive correlation between participation and subjective EHR satisfaction, efficiency, or time saved. The impact of EHR training and optimization on objective time savings remains elusive. By measuring time in the EHR, this study contributes to an ongoing conversation about the resources and programs needed to decrease clinician EHR time.ConclusionsWe have demonstrated that Sprint is associated with time savings for clinicians for up to 6 months. We suggest that an investment in EHR optimization and training can pay dividends in clinician time saved.
| S-EPMC8382277 | biostudies-literature
Project description:BackgroundElectronic health record (EHR) transitions are inherently disruptive to healthcare workers who must rapidly learn a new EHR and adapt to altered clinical workflows. Healthcare workers' perceptions of EHR usability and their EHR use patterns following transitions are poorly understood. The Department of Veterans Affairs (VA) is currently replacing its homegrown EHR with a commercial Cerner EHR, presenting a unique opportunity to examine EHR use trends and usability perceptions.ObjectiveTo assess EHR usability and uptake up to 1-year post-transition at the first VA EHR transition site using a novel longitudinal, mixed methods approach.DesignA concurrent mixed methods strategy using EHR use metrics and qualitative interview data.Participants141 clinicians with data from select EHR use metrics in Cerner Lights On Network®. Interviews with 25 healthcare workers in various clinical and administrative roles.ApproachWe assessed changes in total EHR time, documentation time, and order time per patient post-transition. Interview transcripts (n = 90) were coded and analyzed for content specific to EHR usability.Key resultsTotal EHR time, documentation time, and order time all decreased precipitously within the first four months after go-live and demonstrated gradual improvements over 12 months. Interview participants expressed ongoing concerns with the EHR's usability and functionality up to a year after go-live such as tasks taking longer than the old system and inefficiencies related to inadequate training and inherent features of the new system. These sentiments did not seem to reflect the observed improvements in EHR use metrics.ConclusionsThe integration of quantitative and qualitative data yielded a complex picture of EHR usability. Participants described persistent challenges with EHR usability 1 year after go-live contrasting with observed improvements in EHR use metrics. Combining findings across methods can provide a clearer, contextualized understanding of EHR adoption and use patterns during EHR transitions.
| S-EPMC10593729 | biostudies-literature
Project description:AimsUnderstanding atypical forms of diabetes (AD) may advance precision medicine, but methods to identify such patients are needed. We propose an electronic health record (EHR)-based algorithmic approach to identify patients who may have AD, specifically those with insulin-sufficient, non-metabolic diabetes, in order to improve feasibility of identifying these patients through detailed chart review.MethodsPatients with likely T2D were selected using a validated machine-learning (ML) algorithm applied to EHR data. "Typical" T2D cases were removed by excluding individuals with obesity, evidence of dyslipidemia, antibody-positive diabetes, or cystic fibrosis. To filter out likely type 1 diabetes (T1D) cases, we applied six additional "branch algorithms," relying on various clinical characteristics, which resulted in six overlapping cohorts. Diabetes type was classified by manual chart review as atypical, not atypical, or indeterminate due to missing information.ResultsOf 114,975 biobank participants, the algorithms collectively identified 119 (0.1%) potential AD cases, of which 16 (0.014%) were confirmed after expert review. The branch algorithm that excluded T1D based on outpatient insulin use had the highest percentage yield of AD (13 of 27; 48.2% yield). Together, the 16 AD cases had significantly lower BMI and higher HDL than either unselected T1D or T2D cases identified by ML algorithms (P<0.05). Compared to the ML T1D group, the AD group had a significantly higher T2D polygenic score (P<0.01) and lower hemoglobin A1c (P<0.01).ConclusionOur EHR-based algorithms followed by manual chart review identified collectively 16 individuals with AD, representing 0.22% of biobank enrollees with T2D. With a maximum yield of 48% cases after manual chart review, our algorithms have the potential to drastically improve efficiency of AD identification. Recognizing patients with AD may inform on the heterogeneity of T2D and facilitate enrollment in studies like the Rare and Atypical Diabetes Network (RADIANT).
| S-EPMC9744270 | biostudies-literature
Project description:BackgroundThe predominant implementation paradigm of electronic health record (EHR) systems in low- and middle-income countries (LMICs) relies on standalone system installations at facilities. This implementation approach exacerbates the digital divide, with facilities in areas with inadequate electrical and network infrastructure often left behind. Mobile health (mHealth) technologies have been implemented to extend the reach of digital health, but these systems largely add to the problem of siloed patient data, with few seamlessly interoperating with the EHR systems that are now scaled nationally in many LMICs. Robust mHealth applications that effectively extend EHR systems are needed to improve access, improve quality of care, and ameliorate the digital divide.ObjectiveWe report on the development and scaled implementation of mUzima, an mHealth extension of the most broadly deployed EHR system in LMICs (OpenMRS).MethodsThe "Guidelines for reporting of health interventions using mobile phones: mobile (mHealth) evidence reporting assessment (mERA)" checklist was employed to report on the mUzima application. The World Health Organization (WHO) Principles for Digital Development framework was used as a secondary reference framework. Details of mUzima's architecture, core features, functionalities, and its implementation status are provided to highlight elements that can be adapted in other systems.ResultsmUzima is an open-source, highly configurable Android application with robust features including offline management, deduplication, relationship management, security, cohort management, and error resolution, among many others. mUzima allows providers with lower-end Android smartphones (version 4.4 and above) who work remotely to access historical patient data, collect new data, view media, leverage decision support, conduct store-and-forward teleconsultation, and geolocate clients. The application is supported by an active community of developers and users, with feature priorities vetted by the community. mUzima has been implemented nationally in Kenya, is widely used in Rwanda, and is gaining scale in Uganda and Mozambique. It is disease-agnostic, with current use cases in HIV, cancer, chronic disease, and COVID-19 management, among other conditions. mUzima meets all WHO's Principles of Digital Development, and its scaled implementation success has led to its recognition as a digital global public good and its listing in the WHO Digital Health Atlas.ConclusionsGreater emphasis should be placed on mHealth applications that robustly extend reach of EHR systems within resource-limited settings, as opposed to siloed mHealth applications. This is particularly important given that health information exchange infrastructure is yet to mature in many LMICs. The mUzima application demonstrates how this can be done at scale, as evidenced by its adoption across multiple countries and for numerous care domains.
| S-EPMC8715359 | biostudies-literature
Project description:ObjectiveClinicians spend significant time working in the electronic health record (EHR). The US is an outlier in EHR time, suggesting that EHR-related work may be driven in part by the legal environment and threat of malpractice. To assess this, we evaluate the association between state-level malpractice climate and clinician time spent in the EHR.Materials and methodsWe use EHR metadata from 351 ambulatory care health systems in the United States using Epic from January-August 2019 combined with state-level data on malpractice incidence and payouts. We used descriptive statistics to measure variation in clinician EHR time, including total EHR time, documentation time per day, and after-hours EHR time per day. Multi-variable regression evaluated the association between clinicians in high malpractice states and EHR use.ResultsWe found no association between location in a state in the top-quartile of malpractice payouts and time spent in the EHR per day, time spent in the EHR outside of scheduled hours, or time spent documenting per day, except for a subgroup of the clinicians in the highest malpractice specialties, where there was a small increase in EHR time per day (B = 6.08 min, P < 0.001) and time spent documenting notes (B = 2.77 min, P < 0.001).DiscussionState-level differences in malpractice incidence are unlikely to be a significant driver of EHR work for most clinicians.ConclusionPolicymakers seeking to address EHR documentation burden should examine burden driven by other socio-technical demands on clinician time, such as billing or quality measurement.
| S-EPMC9093025 | biostudies-literature
Project description:IntroductionEffective electronic health record (EHR)-based training interventions facilitate improved EHR use for healthcare providers. One such training intervention is simulation-based training that emphasises learning actual tasks through experimentation in a risk-free environment without negative patient outcomes. EHR-specific simulation-based training can be employed to improve EHR use, thereby enhancing healthcare providers' skills and behaviours. Despite the potential advantages of this type of training, no study has identified and mapped the available evidence. To fill that gap, this scoping review will synthesise the current state of literature on EHR simulation-based training.Methods and analysisThe Arksey and O'Malley methodological framework will be employed. Three databases (PubMed, Embase and Cumulative Index to Nursing and Allied Health Literature) will be searched for published articles. ProQuest and Google Scholar will be searched to identify unpublished articles. Databases will be searched from inception to 29 January 2020. Only articles written in English, randomised control trials, cohort studies, cross-sectional studies and case-control studies will be considered for inclusion. Two reviewers will independently screen titles and abstracts against inclusion and exclusion criteria. Then, they will review full texts to determine articles for final inclusion. Citation chaining will be conducted to manually screen references of all included studies to identify additional studies not found by the search. A data abstraction form with relevant characteristics will be developed to help address the research question. Descriptive numerical analysis will be used to describe characteristics of included studies. Based on the extracted data, research evidence of EHR simulation-based training will be synthesised.Ethics and disseminationSince no primary data will be collected, there will be no formal ethical review. Research findings will be disseminated through publications, presentations and meetings with relevant stakeholders.
| S-EPMC7422637 | biostudies-literature
Project description:Physicians currently spend as much as half of their day in front of the computer. The Electronic Health Record (EHR) has been associated with declining bedside skills and physician burnout. Medical student EHR use has not been well studied or characterized. However, student responsibilities for EHR documentation will likely increase as the Centers for Medicare and Medicaid Services (CMS) most recent provisions now allow student notes for billing which will likely increase the role of medical student use of the EHR over time. To gain a better understanding of how medical students use the EHR at our institution, we retrospectively analyzed 6,692,994 EHR interactions from 49 third-year clerkship medical students and their supervising physicians assigned to the inpatient medicine ward rotation between June 25 2015 and June 24 2016 at a tertiary academic medical center. Medical students spent 4.42 hours (37%) of each day at the on the EHR and 35 minutes logging in from home. Improved understanding of student EHR-use and the effects on well-being warrants further attention, especially as EHR use increases with early trainees.
| S-EPMC6697335 | biostudies-literature