Leveraging the Electronic Health Record to Address the COVID-19 Pandemic.
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ABSTRACT:
SUBMITTER: Satterfield BA
PROVIDER: S-EPMC8059945 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveThe COVID-19 pandemic changed clinician electronic health record (EHR) work in a multitude of ways. To evaluate how, we measure ambulatory clinician EHR use in the United States throughout the COVID-19 pandemic.Materials and methodsWe use EHR meta-data from ambulatory care clinicians in 366 health systems using the Epic EHR system in the United States from December 2019 to December 2020. We used descriptive statistics for clinician EHR use including active-use time across clinical activities, time after-hours, and messages received. Multivariable regression to evaluate total and after-hours EHR work adjusting for daily volume and organizational characteristics, and to evaluate the association between messages and EHR time.ResultsClinician time spent in the EHR per day dropped at the onset of the pandemic but had recovered to higher than prepandemic levels by July 2020. Time spent actively working in the EHR after-hours showed similar trends. These differences persisted in multivariable models. In-Basket messages received increased compared with prepandemic levels, with the largest increase coming from messages from patients, which increased to 157% of the prepandemic average. Each additional patient message was associated with a 2.32-min increase in EHR time per day (P < .001).DiscussionClinicians spent more total and after-hours time in the EHR in the latter half of 2020 compared with the prepandemic period. This was partially driven by increased time in Clinical Review and In-Basket messaging.ConclusionsReimbursement models and workflows for the post-COVID era should account for these demands on clinician time that occur outside the traditional visit.
| S-EPMC8689796 | biostudies-literature
Project description:BackgroundBeginning March 2020, the COVID-19 pandemic has disrupted different aspects of life. The impact on children's rate of weight gain has not been analysed.MethodsIn this retrospective cohort study, we used United States (US) Electronic Health Record (EHR) data from Optum® to calculate the age- and sex- adjusted change in BMI (∆BMIadj) in individual 6-to-17-year-old children between two well child checks (WCCs). The mean of individual ∆BMIadj during 2017-2020 was calculated by month. For September-December WCCs, the mean of individual ∆BMIadj (overall and by subgroup) was reported for 2020 and 2017-2019, and the impact of 2020 vs 2017-2019 was tested by multivariable linear regression.FindingsThe mean [95% Confidence Interval - CI] ∆BMIadj in September-December of 2020 was 0·62 [0·59,0·64] kg/m2, compared to 0·31 [0·29, 0·32] kg/m2 in previous years. The increase was most prominent in children with pre-existing obesity (1·16 [1·07,1·24] kg/m2 in 2020 versus 0·56 [0·52,0·61] kg/m2 in previous years), Hispanic children (0·93 [0·84,1·02] kg/m2 in 2020 versus 0·41 [0·36,0·46] kg/m2 in previous years), and children who lack commercial insurance (0·88 [0·81,0·95] kg/m2 in 2020 compared to 0·43 [0·39,0·47] kg/m2 in previous years). ∆BMIadj accelerated most in ages 8-12 and least in ages 15-17.InterpretationChildren's rate of unhealthy weight gain increased notably during the COVID-19 pandemic across demographic groups, and most prominently in children already vulnerable to unhealthy weight gain. This data can inform policy decisions critical to child development and health as the pandemic continues to unfold.FundingAmgen, Inc.
| S-EPMC8318998 | biostudies-literature
Project description:ObjectiveTo rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon sequential organ failure assessment (SOFA) for decision support for a Crisis Standards of Care team.Materials and methodsWe developed, verified, and deployed a stacked generalization model to predict mortality using data available in the electronic health record (EHR) by combining 5 previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index.ResultsThe prospective cohort included 27 296 encounters, of which 1358 (5.0%) were positive for SARS-CoV-2, 4494 (16.5%) required intensive care unit care, 1480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85.DiscussionStacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction.ConclusionWe developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.
| S-EPMC8136054 | biostudies-literature
Project description: Not available
| S-BSST1055 | biostudies-other
Project description:ObjectiveMethods to identify and study safety risks of electronic health records (EHRs) are underdeveloped and largely depend on limited end-user reports. "Safety huddles" have been found useful in creating a sense of collective situational awareness that increases an organization's capacity to respond to safety concerns. We explored the use of safety huddles for identifying and learning about EHR-related safety concerns.DesignData were obtained from daily safety huddle briefing notes recorded at a single midsized tertiary-care hospital in the United States over 1 year. Huddles were attended by key administrative, clinical, and information technology staff. We conducted a content analysis of huddle notes to identify what EHR-related safety concerns were discussed. We expanded a previously developed EHR-related error taxonomy to categorize types of EHR-related safety concerns recorded in the notes.ResultsOn review of daily huddle notes spanning 249 days, we identified 245 EHR-related safety concerns. For our analysis, we defined EHR technology to include a specific EHR functionality, an entire clinical software application, or the hardware system. Most concerns (41.6%) involved " EHR technology working incorrectly, " followed by 25.7% involving " EHR technology not working at all. " Concerns related to "EHR technology missing or absent" accounted for 16.7%, whereas 15.9% were linked to " user errors ."ConclusionsSafety huddles promoted discussion of several technology-related issues at the organization level and can serve as a promising technique to identify and address EHR-related safety concerns. Based on our findings, we recommend that health care organizations consider huddles as a strategy to promote understanding and improvement of EHR safety.
| S-EPMC5391729 | biostudies-literature
Project description:ObjectivesThe recent COVID-19 pandemic has disrupted mental healthcare delivery, with many services shifting from in-person to remote patient contact. We investigated the impact of the pandemic on the use of remote consultation and on the prescribing of psychiatric medications.Design and settingThe Clinical Record Interactive Search tool was used to examine deidentified electronic health records of people receiving mental healthcare from the South London and Maudsley (SLaM) NHS Foundation Trust. Data from the period before and after the onset of the pandemic were analysed using linear regression, and visualised using locally estimated scatterplot smoothing.ParticipantsAll patients receiving care from SLaM between 7 January 2019 and 20 September 2020 (around 37 500 patients per week).Outcome measures(i) The number of clinical contacts (in-person, remote or non-attended) with mental healthcare professionals per week.(ii) Prescribing of antipsychotic and mood stabiliser medications per week.ResultsFollowing the onset of the pandemic, the frequency of in-person contacts was significantly reduced compared with that in the previous year (β coefficient: -5829.6 contacts, 95% CI -6919.5 to -4739.6, p<0.001), while the frequency of remote contacts significantly increased (β coefficient: 3338.5 contacts, 95% CI 3074.4 to 3602.7, p<0.001). Rates of remote consultation were lower in older adults than in working age adults, children and adolescents. Despite this change in the type of patient contact, antipsychotic and mood stabiliser prescribing remained at similar levels.ConclusionsThe COVID-19 pandemic has been associated with a marked increase in remote consultation, particularly among younger patients. However, there was no evidence that this has led to changes in psychiatric prescribing. Nevertheless, further work is needed to ensure that older patients are able to access mental healthcare remotely.
| S-EPMC8728386 | biostudies-literature
Project description:INTRODUCTION:A high proportion of children presenting to pediatric urgent cares are exposed to tobacco smoke. An electronic health record-based clinical decision support system for nurses to facilitate guideline-based tobacco smoke exposure screening and counseling for caregivers who smoke was designed and evaluated. DESIGN:A mixed-methods, 3-month, prospective study that began in November 2015, data were analyzed in June 2016. SETTING/PARTICIPANTS:Five urgent cares that were part of a large children's hospital in Cincinnati, OH. Participants were urgent care nurses. INTERVENTION:The clinical decision support system prompted nurses to Ask, Advise, Assess, and Assist caregivers to quit smoking. Monthly feedback reports were also provided. MAIN OUTCOME MEASURE:Clinical decision support system use rates, nurses' attitudes towards tobacco smoke exposure intervention, and percentage of children screened and caregivers counseled. RESULTS:All nurses used the clinical decision support system. Compared with Month 1, nurses were twice as likely to advise and assess during Months 2 and 3. There was significant improvement in nurses feeling prepared to assist caregivers in quitting. Nurses reported that feedback reports motivated them to use the clinical decision support system, and that it was easy to use. Almost 65% of children were screened for tobacco smoke exposure; 19.5% screened positive. Of caregivers identified as smokers, 26% were advised to quit and 29% were assessed for readiness to quit. Of those assessed, 67% were interested in quitting, and of those, 100% were assisted. CONCLUSIONS:A clinical decision support system increased rates of tobacco smoke exposure screening and intervention in pediatric urgent cares. Rates might further improve by incorporating all components of the clinical decision support system into the electronic health record. TRIAL REGISTRATION:This study is registered at www.clinicaltrials.gov NCT02489708.
| S-EPMC5736447 | biostudies-literature
Project description:Metformin is a first-line antihyperglycemic agent commonly prescribed in type 2 diabetes mellitus (T2DM), but whose pharmacogenomics are not clearly understood. Further, due to accumulating evidence highlighting the potential for metformin in cancer prevention and treatment efforts it is imperative to understand molecular mechanisms of metformin. In this electronic health record(EHR)-based study we explore the potential association of the flavin-containing monooxygenase(FMO)-5 gene, a biologically plausible biotransformer of metformin, and modifying glycemic response to metformin treatment. Using a cohort of 258 T2DM patients who had new metformin exposure, existing genetic data, and longitudinal electronic health records, we compared genetic variation within FMO5 to change in glycemic response. Gene-level and SNP-level analysis identified marginally significant associations for FMO5 variation, representing an EHR-driven pharmacogenetics hypothesis for a potential novel mechanism for metformin biotransformation. However, functional validation of this EHR-based hypothesis is necessary to ascertain its clinical and biological significance.
| S-EPMC4525256 | biostudies-literature
Project description:ObjectiveUsing Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning.MethodsInpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation.ResultsEHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation.ConclusionFindings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.
| S-EPMC5391722 | biostudies-literature
Project description:We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.
| S-EPMC7438496 | biostudies-literature