Safe Practices for Copy and Paste in the EHR

Tsou, Amy Y.; Lehmann, Christoph U.; Michel, Jeremy J.; Solomon, Ronni P.; Possanza, Lorraine; Gandhi, Tejal K.

doi:10.4338/aci-2016-09-r-0150

Cited by 64 publications

(53 citation statements)

References 25 publications

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“…For example, “note bloat” has become an issue with the rise of copy-and-paste functions in the EHR as physicians and organizations attempt to maximize efficiency and guard against legal disputes 35 . This note bloat can make it more difficult to find and read key clinical information, perpetuating documentation errors and enabling new errors 36 …”

Section: Organizational Influencesmentioning

confidence: 99%

The complex case of EHRs: examining the factors impacting the EHR user experience

Tutty

Carlasare

Lloyd

et al. 2019

Journal of the American Medical Informatics Association

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Physicians can spend more time completing administrative tasks in their electronic health record (EHR) than engaging in direct face time with patients. Increasing rates of burnout associated with EHR use necessitate improvements in how EHRs are developed and used. Although EHR design often bears the brunt of the blame for frustrations expressed by physicians, the EHR user experience is influenced by a variety of factors, including decisions made by entities other than the developers and end users, such as regulators, policymakers, and administrators. Identifying these key influences can help create a deeper understanding of the challenges in developing a better EHR user experience. There are multiple opportunities for regulators, policymakers, EHR developers, payers, health system leadership, and users each to make changes to collectively improve the use and efficacy of EHRs.

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Section: Organizational Influencesmentioning

confidence: 99%

The complex case of EHRs: examining the factors impacting the EHR user experience

Tutty

Carlasare

Lloyd

et al. 2019

Journal of the American Medical Informatics Association

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“…After EHR implementation, nurses had to redocument a complete physical assessment every 8 hours, because IT developers set the system for eight-hour tours of duty for nurses, which was inconsistent with practice. Therefore, numerous nurses expressed the desire for a copy and paste function, but this can inadvertently lead to clinical harm [ 35 ]. Organizations interested in using a copy and paste function may benefit from adopting best practices from the Partnership for Health IT Patient Safety [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, numerous nurses expressed the desire for a copy and paste function, but this can inadvertently lead to clinical harm [ 35 ]. Organizations interested in using a copy and paste function may benefit from adopting best practices from the Partnership for Health IT Patient Safety [ 35 ]. Eventually, the physical documentation assessment policy in the EHR was modified to reflect nursing workflow.…”

Section: Discussionmentioning

confidence: 99%

Nurses’ Experience With Health Information Technology: Longitudinal Qualitative Study

Zadvinskis¹,

Smith²,

Yen³

2018

JMIR Med Inform

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BackgroundNurses are the largest group of health information technology (HIT) users. As such, nurses’ adaptations are critical for HIT implementation success. However, longitudinal approaches to understanding nurses’ perceptions of HIT remain underexplored. Previous studies of nurses’ perceptions demonstrate that the progress and timing for acceptance of and adaptation to HIT varies.ObjectiveThis study aimed to explore nurses’ experience regarding implementation of HIT over time.MethodsA phenomenological approach was used for this longitudinal qualitative study to explore nurses’ perceptions of HIT implementation over time, focusing on three time points (rounds) at 3, 9, and 18 months after implementation of electronic health records and bar code medication administration. The purposive sample was comprised of clinical nurses who worked on a medical-surgical unit in an academic center.ResultsMajor findings were categorized into 7 main themes with 54 subthemes. Nurses reported personal-level and organizational-level factors that facilitated HIT adaptation. We also generated network graphs to illustrate the occurrence of themes. Thematic interconnectivity differed due to nurses’ concerns and satisfaction at different time points. Equipment and workflow were the most frequent themes across all three rounds. Nurses were the most dissatisfied approximately 9 months after HIT implementation. Eighteen months after HIT implementation, nurses’ perceptions appeared more balanced.ConclusionsIt is recommended that organizations invest in equipment (ie, wireless barcode scanners), refine policies to reflect nursing practice, and improve systems to focus on patient safety. Future research is necessary to confirm patterns of nurses’ adaptation to HIT in other samples.

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“…To ensure that our proposed registry design would be useful to clinicians without adding additional complexity to an already overburdened system of documentation [47], we presented the results of our algorithms and a mockup of the registry tables to a physician, a tobacco cessation counselor, several health services researchers, and the New Hampshire Tobacco Quit Line Director. Feedback about the perceived benefit of the proposed pipeline, implementation concerns, and basic data structure design needs for successful clinical integration were incorporated in to the final proposed table designs (Additional file 1: Tables S3-S4).…”

Section: Discussionmentioning

confidence: 99%

Building a tobacco user registry by extracting multiple smoking behaviors from clinical notes

Palmer

Hassanpour

Higgins

et al. 2019

BMC Med Inform Decis Mak

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Background Usage of structured fields in Electronic Health Records (EHRs) to ascertain smoking history is important but fails in capturing the nuances of smoking behaviors. Knowledge of smoking behaviors, such as pack year history and most recent cessation date, allows care providers to select the best care plan for patients at risk of smoking attributable diseases. Methods We developed and evaluated a health informatics pipeline for identifying complete smoking history from clinical notes in EHRs. We utilized 758 patient-visit notes (from visits between 03/28/2016 and 04/04/2016) from our local EHR in addition to a public dataset of 502 clinical notes from the 2006 i2b2 Challenge to assess the performance of this pipeline. We used a machine-learning classifier to extract smoking status and a comprehensive set of text processing regular expressions to extract pack years and cessation date information from these clinical notes. Results We identified smoking status with an F1 score of 0.90 on both the i2b2 and local data sets. Regular expression identification of pack year history in the local test set was 91.7% sensitive and 95.2% specific, but due to variable context the pack year extraction was incomplete in 25% of cases, extracting packs per day or years smoked only. Regular expression identification of cessation date was 63.2% sensitive and 94.6% specific. Conclusions Our work indicates that the development of an EHR-based Smokers’ Registry containing information relating to smoking behaviors, not just status, from free-text clinical notes using an informatics pipeline is feasible. This pipeline is capable of functioning in external EHRs, reducing the amount of time and money needed at the institute-level to create a Smokers’ Registry for improved identification of patient risk and eligibility for preventative and early detection services. Electronic supplementary material The online version of this article (10.1186/s12911-019-0863-3) contains supplementary material, which is available to authorized users.

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Safe Practices for Copy and Paste in the EHR

Cited by 64 publications

References 25 publications

The complex case of EHRs: examining the factors impacting the EHR user experience

The complex case of EHRs: examining the factors impacting the EHR user experience

Nurses’ Experience With Health Information Technology: Longitudinal Qualitative Study

Building a tobacco user registry by extracting multiple smoking behaviors from clinical notes

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