Dataset Information

Secondary use of electronic health record data for clinical workflow analysis.

ABSTRACT: Objective:Outpatient clinics lack guidance for tackling modern efficiency and productivity demands. Workflow studies require large amounts of timing data that are prohibitively expensive to collect through observation or tracking devices. Electronic health records (EHRs) contain a vast amount of timing data - timestamps collected during regular use - that can be mapped to workflow steps. This study validates using EHR timestamp data to predict outpatient ophthalmology clinic workflow timings at Oregon Health and Science University and demonstrates their usefulness in 3 different studies. Materials and Methods:Four outpatient ophthalmology clinics were observed to determine their workflows and to time each workflow step. EHR timestamps were mapped to the workflow steps and validated against the observed timings. Results:The EHR timestamp analysis produced times that were within 3?min of the observed times for?>80% of the appointments. EHR use patterns affected the accuracy of using EHR timestamps to predict workflow times. Discussion:EHR timestamps provided a reasonable approximation of workflow and can be used for workflow studies. They can be used to create simulation models, analyze EHR use, and quantify the impact of trainees on workflow. Conclusion:The secondary use of EHR timestamp data is a valuable resource for clinical workflow studies. Sample timestamp data files and algorithms for processing them are provided and can be used as a template for more studies in other clinical specialties and settings.

SUBMITTER: Hribar MR

PROVIDER: S-EPMC6080808 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Secondary use of electronic health record data for clinical workflow analysis.

Hribar Michelle R MR Read-Brown Sarah S Goldstein Isaac H IH Reznick Leah G LG Lombardi Lorinna L Parikh Mansi M Chamberlain Winston W Chiang Michael F MF

Journal of the American Medical Informatics Association : JAMIA 20180101 1

<h4>Objective</h4>Outpatient clinics lack guidance for tackling modern efficiency and productivity demands. Workflow studies require large amounts of timing data that are prohibitively expensive to collect through observation or tracking devices. Electronic health records (EHRs) contain a vast amount of timing data - timestamps collected during regular use - that can be mapped to workflow steps. This study validates using EHR timestamp data to predict outpatient ophthalmology clinic workflow tim ...[more]

PMID: 29036581

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Project description:IntroductionIn Australia, general practitioners usually are the first point of contact for patients with non-urgent medical conditions. Appropriate and efficient utilisation of pathology tests by general practitioners forms a key part of diagnosis and monitoring. However overutilisationand underutilisation of pathology tests have been reported across several tests and conditions, despite evidence-based guidelines outlining best practice in pathology testing. There are a limited number of studies evaluating the impact of these guidelines on pathology testing in general practice. The aim of our quantitative observational study is to define how pathology tests are used in general practice and investigate how test ordering practices align with evidence-based pathology guidelines.Methods and analysisAccess to non-identifiable patient data will be obtained through electronic health records from general practices across three primary health networks in Victoria, Australia. Numbers and characteristics of patients, general practices, encounters, pathology tests and problems managed over time will be described. Overall rates of encounters and tests, alongside more detailed investigation between subcategories (encounter year, patient's age, gender, and location and general practice size), will also be undertaken. To evaluate how general practitioner test ordering coincides with evidence-based guidelines, five key candidate indicators will be investigated: full blood counts for patients on clozapine medication; international normalised ratio measurements for patients on warfarin medication; glycated haemoglobin testing for monitoring patients with diabetes; vitamin D testing; and thyroid function testing.Ethics and disseminationEthics clearance to collect data from general practice facilities has been obtained by the data provider from the RACGP National Research and Evaluation Ethics Committee (NREEC 17-008). Approval for the research group to use these data has been obtained from Macquarie University (5201700872). This study is funded by the Australian Government Department of Health Quality Use of Pathology Program (Agreement ID: 4-2QFVW4M). Findings will be reported to the Department of Health and disseminated in peer-reviewed academic journals and presentations (national and international conferences, industry forums).

Project description:BACKGROUND:The transition to the electronic health record (EHR) has brought forth a rapid cultural shift in the world of medicine, presenting both new challenges as well as opportunities for improving health care. As clinicians work to adapt to the changes imposed by the EHR, identification of best practices around the clinically excellent use of the EHR is needed. OBJECTIVE:Using the domains of clinical excellence previously defined by the Johns Hopkins Miller Coulson Academy of Clinical Excellence, this review aims to identify best practices around the clinically excellent use of the EHR. METHODS:The authors searched the PubMed database, using keywords related to clinical excellence domains and the EHR, to capture the English-language, peer-reviewed literature published between January 1, 2000, and August 2, 2016. One author independently reviewed each article and extracted relevant data. RESULTS:The search identified 606 titles, with the majority (393/606, 64.9%) in the domain of communication and interpersonal skills. Twenty-eight of the 606 (4.6%) titles were excluded from full-text review, primarily due to lack of availability of the full-text article. The remaining 578 full-text articles reviewed were related to clinical excellence generally (3/578, 0.5%) or the specific domains of communication and interpersonal skills (380/578, 65.7%), diagnostic acumen (31/578, 5.4%), skillful negotiation of the health care system (4/578, 0.7%), scholarly approach to clinical practice (41/578, 7.1%), professionalism and humanism (2/578, 0.4%), knowledge (97/578, 16.8%), and passion for clinical medicine (20/578, 3.5%). CONCLUSIONS:Results suggest that as familiarity and expertise are developed, clinicians are leveraging the EHR to provide clinically excellent care. Best practices identified included deliberate physical configuration of the clinical space to involve sharing the screen with patients and limiting EHR use during difficult and emotional topics. Promising horizons for the EHR include the ability to augment participation in pragmatic trials, identify adverse drug effects, correlate genomic data to clinical outcomes, and follow data-driven guidelines. Clinician and patient satisfaction with the EHR has generally improved with time, and hopefully continued clinician, and patient input will lead to a system that satisfies all.

Project description:ImportanceAssessing generalizability of clinical trials is important to ensure appropriate application of interventions, but most assessments provide minimal granularity on comparisons of clinical characteristics.ObjectiveTo assess the extent of underlying clinical differences between clinical trial participants and nonparticipants by using a combination of electronic health record and trial enrollment data.Design, setting, and participantsThis cross-sectional study used data obtained from a single academic medical center between September 1996 and January 2019 to identify 1645 clinical trial participants from a diverse set of 202 available trials conducted at the center. Using an aggregated resampling procedure, nonparticipants were matched to participants 1:1 based on trial conditions, number of recent visits to a health care professional, and calendar time.ExposuresClinical trial enrollment vs no enrollment.Main outcomes and measuresThe primary outcome was standardized differences in clinical characteristics between participants and nonparticipants in clinical trials stratified into the 4 most common disease domains.ResultsThis cross-sectional study included 1645 participants from 202 trials (929 [56.5%] male; mean [SD] age, 54.65 [21.38] years) and an aggregated set of 1645 nonparticipants (855 [52.0%] male; mean [SD] age, 57.24 [21.91] years). The most common disease domains for the selected trials were neoplastic disease (86 trials; 737 participants), disorders of the digestive system (31 trials; 321 participants), inflammatory disorders (28 trials; 276 participants), and disorders of the cardiovascular system (27 trials; 319 participants); trials could qualify for multiple disease domains. Among 31 conditions, the percentage of conditions for which the prevalence was lower among participants than among nonparticipants per standardized differences was 64.5% (20 conditions) for neoplastic disease trials, 61.3% (19) for digestive system trials, 58.1% (18) for inflammatory disorder trials, and 38.7% (12) for cardiovascular system trials. Among 17 medications, the percentage of medications for which use was less among participants than among nonparticipants per standardized differences was 64.7% (11) for neoplastic disease trials, 58.8% (10) for digestive system trials, 88.2% (15) for inflammatory disorder trials, and 52.9% (9) for cardiovascular system trials.Conclusions and relevanceUsing a combination of electronic health record and trial enrollment data, this study found that clinical trial participants had fewer comorbidities and less use of medication than nonparticipants across a variety of disease domains. Combining trial enrollment data with electronic health record data may be useful for better understanding of the generalizability of trial results.

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Secondary use of electronic health record data for clinical workflow analysis.

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Secondary use of electronic health record data for clinical workflow analysis.

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