Dataset Information

Clinician characteristics and use of novel electronic health record functionality in primary care.

ABSTRACT: Conventional wisdom holds that older, busier clinicians who see complex patients are less likely to adopt and use novel electronic health record (EHR) functionality.To compare the characteristics of clinicians who did and did not use novel EHR functionality, we conducted a retrospective analysis of the intervention arm of a randomized trial of new EHR-based tobacco treatment functionality.The novel functionality was used by 103 of 207 (50%) clinicians. Staff physicians were more likely than trainees to use the functionality (64% vs 37%; p<0.001). Clinicians who graduated more than 10 years previously were more likely to use the functionality than those who graduated less than 10 years previously (64% vs 42%; p<0.01). Clinicians with higher patient volumes were more likely to use the functionality (lowest quartile of number of patient visits, 25%; 2nd quartile, 38%; 3rd quartile, 65%; highest quartile, 71%; p<0.001). Clinicians who saw patients with more documented problems were more likely to use the functionality (lowest tertile of documented patient problems, 38%; 2nd tertile, 58%; highest tertile, 54%; p=0.04). In multivariable modeling, independent predictors of use were the number of patient visits (OR 1.2 per 100 additional patients; 95% CI 1.1 to 1.4) and number of documented problems (OR 2.9 per average additional problem; 95% CI 1.4 to 6.1).Contrary to conventional wisdom, clinically busier physicians seeing patients with more documented problems were more likely to use novel EHR functionality.

SUBMITTER: Linder JA

PROVIDER: S-EPMC3241168 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Clinician characteristics and use of novel electronic health record functionality in primary care.

Linder Jeffrey A JA Rigotti Nancy A NA Schneider Louise I LI Kelley Jennifer H K JH Brawarsky Phyllis P Schnipper Jeffrey L JL Middleton Blackford B Haas Jennifer S JS

Journal of the American Medical Informatics Association : JAMIA 20110907

<h4>Background</h4>Conventional wisdom holds that older, busier clinicians who see complex patients are less likely to adopt and use novel electronic health record (EHR) functionality.<h4>Methods</h4>To compare the characteristics of clinicians who did and did not use novel EHR functionality, we conducted a retrospective analysis of the intervention arm of a randomized trial of new EHR-based tobacco treatment functionality.<h4>Results</h4>The novel functionality was used by 103 of 207 (50%) clin ...[more]

PMID: 21900702

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Project description:BackgroundElectronic health records (EHRs) enable health data exchange across interconnected systems from varied settings. Epic is among the 5 leading EHR providers and is the most adopted EHR system across the globe. Despite its global reach, there is a gap in the literature detailing how EHR systems such as Epic have been used for health care research.ObjectiveThe objective of this scoping review is to synthesize the available literature on use cases of the Epic EHR for research in various areas of clinical and health sciences.MethodsWe used established scoping review methods and searched 9 major information repositories, including databases and gray literature sources. To categorize the research data, we developed detailed criteria for 5 major research domains to present the results.ResultsWe present a comprehensive picture of the method types in 5 research domains. A total of 4669 articles were screened by 2 independent reviewers at each stage, while 206 articles were abstracted. Most studies were from the United States, with a sharp increase in volume from the year 2015 onwards. Most articles focused on clinical care, health services research and clinical decision support. Among research designs, most studies used longitudinal designs, followed by interventional studies implemented at single sites in adult populations. Important facilitators and barriers to the use of Epic and EHRs in general were identified. Important lessons to the use of Epic and other EHRs for research purposes were also synthesized.ConclusionsThe Epic EHR provides a wide variety of functions that are helpful toward research in several domains, including clinical and population health, quality improvement, and the development of clinical decision support tools. As Epic is reported to be the most globally adopted EHR, researchers can take advantage of its various system features, including pooled data, integration of modules and developing decision support tools. Such research opportunities afforded by the system can contribute to improving quality of care, building health system efficiencies, and conducting population-level studies. Although this review is limited to the Epic EHR system, the larger lessons are generalizable to other EHRs.

Project description:BACKGROUND:The increased use of electronic medical records (EMRs) in Canadian primary health care practice has resulted in an expansion of the availability of EMR data. Potential users of these data need to understand their quality in relation to the uses to which they are applied. Herein, we propose a basic model for assessing primary health care EMR data quality, comprising a set of data quality measures within four domains. We describe the process of developing and testing this set of measures, share the results of applying these measures in three EMR-derived datasets, and discuss what this reveals about the measures and EMR data quality. The model is offered as a starting point from which data users can refine their own approach, based on their own needs. METHODS:Using an iterative process, measures of EMR data quality were created within four domains: comparability; completeness; correctness; and currency. We used a series of process steps to develop the measures. The measures were then operationalized, and tested within three datasets created from different EMR software products. RESULTS:A set of eleven final measures were created. We were not able to calculate results for several measures in one dataset because of the way the data were collected in that specific EMR. Overall, we found variability in the results of testing the measures (e.g. sensitivity values were highest for diabetes, and lowest for obesity), among datasets (e.g. recording of height), and by patient age and sex (e.g. recording of blood pressure, height and weight). CONCLUSIONS:This paper proposes a basic model for assessing primary health care EMR data quality. We developed and tested multiple measures of data quality, within four domains, in three different EMR-derived primary health care datasets. The results of testing these measures indicated that not all measures could be utilized in all datasets, and illustrated variability in data quality. This is one step forward in creating a standard set of measures of data quality. Nonetheless, each project has unique challenges, and therefore requires its own data quality assessment before proceeding.

Dataset Information

Clinician characteristics and use of novel electronic health record functionality in primary care.

Publications

Clinician characteristics and use of novel electronic health record functionality in primary care.

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