Dataset Information

Analyses of electronic health records utilization in a large community hospital.

ABSTRACT: INTRODUCTION:The Electronic Health Record (EHR) has become an integral component of healthcare delivery. Survey based studies have estimated that physicians spend 4-6 hours of their workday devoted to EHR. Our study was designed to use computer software to objectively obtain time spent on EHR. METHODS:We recorded EHR time for 248 physiciansover 2 time intervals. EHR active use was defined as more than 15 keystrokes, or 3 mouse clicks, or 1700 "mouse miles" per minute. We recorded total time and % of work hours spent on EHR, and differences in those based on seniority. Physicians reported duty hours using a standardized toolkit. RESULTS:Physicians spent 3.8 (±2) hours on EHR daily, which accounted for 37% (±17%), 41% (±14%), and 45% (±12%) of their day for all clinicians, residents, and interns, respectively. With the progression of training, there was a reduction in EHR time (all p values <0.01). During the first academic quarter, clinicians spent 38% (± 8%) of time on chart review, 17% (± 7%) on orders, 28% (±11%) on documentation (i.e. writing notes) and 17% (±7%) on other activities (i.e. physician hand-off and medication reconciliation). This pattern remained unchanged during the fourth quarter. CONCLUSIONS:Physicians spend close to 40% of their work day on EHR, with interns spending the most time. There is a significant reduction in time spent on EHR with training and greater experience, although the overall amount of time spent on EHR remained high.

SUBMITTER: Verma G

PROVIDER: S-EPMC7329072 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Analyses of electronic health records utilization in a large community hospital.

Verma Gautam G Ivanov Alexander A Benn Francis F Rathi Anil A Tran Nathaniel N Afzal Ashwad A Mehta Parag P Heitner John F JF

PloS one 20200701 7

<h4>Introduction</h4>The Electronic Health Record (EHR) has become an integral component of healthcare delivery. Survey based studies have estimated that physicians spend 4-6 hours of their workday devoted to EHR. Our study was designed to use computer software to objectively obtain time spent on EHR.<h4>Methods</h4>We recorded EHR time for 248 physiciansover 2 time intervals. EHR active use was defined as more than 15 keystrokes, or 3 mouse clicks, or 1700 "mouse miles" per minute. We recorded ...[more]

PMID: 32609757

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Project description:BackgroundHospital antimicrobial stewardship (AMS) programmes are multidisciplinary initiatives to optimize antimicrobial use. Most hospitals depend on time-consuming manual audits to monitor clinicians' prescribing. But much of the information needed could be sourced from electronic health records (EHRs).ObjectivesTo develop an informatics methodology to analyse characteristics of hospital AMS practice using routine electronic prescribing and laboratory records.MethodsFeasibility study using electronic prescribing, laboratory and clinical coding records from adult patients admitted to six specialities at Queen Elizabeth Hospital, Birmingham, UK (September 2017-August 2018). The study involved: (i) a review of AMS standards of care; (ii) their translation into concepts measurable from commonly available EHRs; and (iii) a pilot application in an EHR cohort study (n = 61679 admissions).ResultsWe developed data modelling methods to characterize antimicrobial use (antimicrobial therapy episode linkage methods, therapy table, therapy changes). Prescriptions were linked into antimicrobial therapy episodes (mean 2.4 prescriptions/episode; mean length of therapy 5.8 days), enabling several actionable findings. For example, 22% of therapy episodes for low-severity community-acquired pneumonia were congruent with prescribing guidelines, with a tendency to use broader-spectrum antibiotics. Analysis of therapy changes revealed IV to oral therapy switching was delayed by an average 3.6 days (95% CI: 3.4-3.7). Microbial cultures were performed prior to treatment initiation in just 22% of antibacterial prescriptions. The proposed methods enabled fine-grained monitoring of AMS practice down to specialities, wards and individual clinical teams by case mix, enabling more meaningful peer comparison.ConclusionsIt is feasible to use hospital EHRs to construct rapid, meaningful measures of prescribing quality with potential to support quality improvement interventions (audit/feedback to prescribers), engagement with front-line clinicians on optimizing prescribing, and AMS impact evaluation studies.

Project description:There is an urgent need to monitor the mental health of large populations, especially during crises such as the COVID-19 pandemic, to timely identify the most at-risk subgroups and to design targeted prevention campaigns. We therefore developed and validated surveillance indicators related to suicidality: the monthly number of hospitalisations caused by suicide attempts and the prevalence among them of five known risks factors. They were automatically computed analysing the electronic health records of fifteen university hospitals of the Paris area, France, using natural language processing algorithms based on artificial intelligence. We evaluated the relevance of these indicators conducting a retrospective cohort study. Considering 2,911,920 records contained in a common data warehouse, we tested for changes after the pandemic outbreak in the slope of the monthly number of suicide attempts by conducting an interrupted time-series analysis. We segmented the assessment time in two sub-periods: before (August 1, 2017, to February 29, 2020) and during (March 1, 2020, to June 31, 2022) the COVID-19 pandemic. We detected 14,023 hospitalisations caused by suicide attempts. Their monthly number accelerated after the COVID-19 outbreak with an estimated trend variation reaching 3.7 (95%CI 2.1-5.3), mainly driven by an increase among girls aged 8-17 (trend variation 1.8, 95%CI 1.2-2.5). After the pandemic outbreak, acts of domestic, physical and sexual violence were more often reported (prevalence ratios: 1.3, 95%CI 1.16-1.48; 1.3, 95%CI 1.10-1.64 and 1.7, 95%CI 1.48-1.98), fewer patients died (p = 0.007) and stays were shorter (p < 0.001). Our study demonstrates that textual clinical data collected in multiple hospitals can be jointly analysed to compute timely indicators describing mental health conditions of populations. Our findings also highlight the need to better take into account the violence imposed on women, especially at early ages and in the aftermath of the COVID-19 pandemic.

Dataset Information

Analyses of electronic health records utilization in a large community hospital.

Publications

Analyses of electronic health records utilization in a large community hospital.

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