Public health nurse perceptions of Omaha System data visualization

Lee, Seonah; Kim, Era; Monsen, Karen A.

doi:10.1016/j.ijmedinf.2015.06.010

Cited by 28 publications

(25 citation statements)

References 11 publications

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“…Data generated through the use of the Omaha System become actionable resulting in rapid quantification of who receives which interventions, how many, when, and by whom. From the data, tables and graphs can be easily created to track patient care across the care continuum to ensure timely interventions (Lee, Kim, & Monsen, ; Monsen et al., ). Because the Omaha System encoding structure and clinical guidelines are in the public domain, anyone throughout the globe with access to the Internet and basic Excel skills can access and create guidelines.…”

Section: Resultsmentioning

confidence: 99%

Toward Improving Quality of End‐of‐Life Care: Encoding Clinical Guidelines and Standing Orders Using the Omaha System

Slipka

Monsen

2017

Worldviews Ev Based Nurs

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Section: Resultsmentioning

confidence: 99%

Toward Improving Quality of End‐of‐Life Care: Encoding Clinical Guidelines and Standing Orders Using the Omaha System

Slipka

Monsen

2017

Worldviews Ev Based Nurs

Self Cite

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“…Hence, many studies have focused on the redesign of EMR interfaces, hopefully facilitating clinicians to keep track of relevant patient information. Well-designed data visualization in EMR systems not only allows healthcare professionals to communicate information efficiently and effectively but also improves data interpretation and clinical reasoning [16,17]. Besides, it is recommended that copied material should be displayed in a different font or color, so that they can be easily identified [10].…”

Section: Introductionmentioning

confidence: 99%

A Natural Language Processing Approach to Automated Highlighting of New Information in Clinical Notes

Chao

Hung

et al. 2020

Applied Sciences

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Electronic medical records (EMRs) have been used extensively in most medical institutions for more than a decade in Taiwan. However, information overload associated with rapid accumulation of large amounts of clinical narratives has threatened the effective use of EMRs. This situation is further worsened by the use of “copying and pasting”, leading to lots of redundant information in clinical notes. This study aimed to apply natural language processing techniques to address this problem. New information in longitudinal clinical notes was identified based on a bigram language model. The accuracy of automated identification of new information was evaluated using expert annotations as the reference standard. A two-stage cross-over user experiment was conducted to evaluate the impact of highlighting of new information on task demands, task performance, and perceived workload. The automated method identified new information with an F1 score of 0.833. The user experiment found a significant decrease in perceived workload associated with a significantly higher task performance. In conclusion, automated identification of new information in clinical notes is feasible and practical. Highlighting of new information enables healthcare professionals to grasp key information from clinical notes with less perceived workload.

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“…to provide feedback or alerts to clinicians to encourage them to carry out evidence based care (15), or to combine and display information about a patient's condition to help inform clinical decision making (6,16,17). Initial evaluations of dashboards have documented reduced response times for finding relevant information (6,12,18,19), improved accuracy in information retrieval (6,18,19) and increased adherence to evidence based care interventions (12).…”

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confidence: 99%

Usability Evaluation of a Dashboard for Home Care Nurses

2019

CIN: Computers, Informatics, Nursing

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The introduction of electronic health records has produced many challenges for clinicians. These include integrating technology into clinical workflow and fragmentation of relevant information across systems. Dashboards, that use visualized data to summarize key patient information, have the potential to address these issues. In this paper we outline a usability evaluation of a dashboard designed for home care nurses. An iterative design process was used which consisted of a) contextual inquiry (observation and interviews) with 2 home care nurses b) rapid feedback on paper prototypes of the dashboard (10 nurses) and c) usability evaluation of the final dashboard prototype (20 nurses). Usability methods and assessments included observation of nurses interacting with the dashboard, the system usability scale (SUS) and the Questionnaire for User Interaction Satisfaction (QUIS) short form. The dashboard prototype was deemed to have high usability (mean SUS 73.2; SD 18.8) and was positively evaluated by nurse users. It is important to ensure that technology solutions such as the one proposed here, are designed with clinical users in mind, to meet their information needs. The design elements of the dashboard outlined here, could be translated to other EHRs used in home care settings.

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Public health nurse perceptions of Omaha System data visualization

Cited by 28 publications

References 11 publications

Toward Improving Quality of End‐of‐Life Care: Encoding Clinical Guidelines and Standing Orders Using the Omaha System

Toward Improving Quality of End‐of‐Life Care: Encoding Clinical Guidelines and Standing Orders Using the Omaha System

A Natural Language Processing Approach to Automated Highlighting of New Information in Clinical Notes

Usability Evaluation of a Dashboard for Home Care Nurses

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