scholarly journals Characterizing and Visualizing Display and Task Fragmentation in the Electronic Health Record: Mixed Methods Design (Preprint)

2020 ◽  
Author(s):  
Yalini Senathirajah ◽  
David R Kaufman ◽  
Kenrick D Cato ◽  
Elizabeth M Borycki ◽  
Jaime Allen Fawcett ◽  
...  
Keyword(s):  
User Interfaces ◽  
Mixed Methods Design ◽  
Patient Case ◽  
Health Records ◽  
Health Care Data ◽  
Fragmentation Index ◽  
Navigation Structure ◽  
Electronic Health ◽  
Visualization Techniques ◽  
And Task

BACKGROUND The complexity of health care data and workflow presents challenges to the study of usability in electronic health records (EHRs). Display fragmentation refers to the distribution of relevant data across different screens or otherwise far apart, requiring complex navigation for the user’s workflow. Task and information fragmentation also contribute to cognitive burden. OBJECTIVE This study aims to define and analyze some of the main sources of fragmentation in EHR user interfaces (UIs); discuss relevant theoretical, historical, and practical considerations; and use granular microanalytic methods and visualization techniques to help us understand the nature of fragmentation and opportunities for EHR optimization or redesign. METHODS Sunburst visualizations capture the EHR navigation structure, showing levels and sublevels of the navigation tree, allowing calculation of a new measure, the Display Fragmentation Index. Time belt visualizations present the sequences of subtasks and allow calculation of proportion per instance, a measure that quantifies task fragmentation. These measures can be used separately or in conjunction to compare EHRs as well as tasks and subtasks in workflows and identify opportunities for reductions in steps and fragmentation. We present an example use of the methods for comparison of 2 different EHR interfaces (commercial and composable) in which subjects apprehend the same patient case. RESULTS Screen transitions were substantially reduced for the composable interface (from 43 to 14), whereas clicks (including scrolling) remained similar. CONCLUSIONS These methods can aid in our understanding of UI needs under complex conditions and tasks to optimize EHR workflows and redesign.

scholarly journals Characterizing and Visualizing Display and Task Fragmentation in the Electronic Health Record: Mixed Methods Design

10.2196/18484 ◽  
2020 ◽  
Vol 7 (4) ◽  
pp. e18484
Author(s):  
Yalini Senathirajah ◽  
David R Kaufman ◽  
Kenrick D Cato ◽  
Elizabeth M Borycki ◽  
Jaime Allen Fawcett ◽  
...  
Keyword(s):  
User Interfaces ◽  
Mixed Methods Design ◽  
Patient Case ◽  
Health Records ◽  
Health Care Data ◽  
Fragmentation Index ◽  
Navigation Structure ◽  
Electronic Health ◽  
Visualization Techniques ◽  
And Task

Background The complexity of health care data and workflow presents challenges to the study of usability in electronic health records (EHRs). Display fragmentation refers to the distribution of relevant data across different screens or otherwise far apart, requiring complex navigation for the user’s workflow. Task and information fragmentation also contribute to cognitive burden. Objective This study aims to define and analyze some of the main sources of fragmentation in EHR user interfaces (UIs); discuss relevant theoretical, historical, and practical considerations; and use granular microanalytic methods and visualization techniques to help us understand the nature of fragmentation and opportunities for EHR optimization or redesign. Methods Sunburst visualizations capture the EHR navigation structure, showing levels and sublevels of the navigation tree, allowing calculation of a new measure, the Display Fragmentation Index. Time belt visualizations present the sequences of subtasks and allow calculation of proportion per instance, a measure that quantifies task fragmentation. These measures can be used separately or in conjunction to compare EHRs as well as tasks and subtasks in workflows and identify opportunities for reductions in steps and fragmentation. We present an example use of the methods for comparison of 2 different EHR interfaces (commercial and composable) in which subjects apprehend the same patient case. Results Screen transitions were substantially reduced for the composable interface (from 43 to 14), whereas clicks (including scrolling) remained similar. Conclusions These methods can aid in our understanding of UI needs under complex conditions and tasks to optimize EHR workflows and redesign.

scholarly journals Identifying Multisite Chronic Pain with Electronic Health Records Data

Pain Medicine ◽  
2020 ◽  
Vol 21 (12) ◽  
pp. 3387-3392
Author(s):  
Michael Von Korff ◽  
Lynn L DeBar ◽  
Richard A Deyo ◽  
Meghan Mayhew ◽  
Robert D Kerns ◽  
...  
Keyword(s):  
Health Care ◽  
Chronic Pain ◽  
Electronic Health Records ◽  
Single Site ◽  
Self Report ◽  
Survey Questionnaire ◽  
Health Records ◽  
Health Care Data ◽  
Electronic Health ◽  
Weighted Prevalence

Abstract Background Multisite chronic pain (MSCP) is associated with increased chronic pain impact, but methods for identifying MSCP for epidemiological research have not been evaluated. Objective We assessed the validity of identifying MSCP using electronic health care data compared with survey questionnaires. Methods Stratified random samples of adults served by Kaiser Permanente Northwest and Washington (N = 2,059) were drawn for a survey, oversampling persons with frequent use of health care for pain. MSCP and single-site chronic pain were identified by two methods, with electronic health care data and with self-report of common chronic pain conditions by survey questionnaire. Analyses were weighted to adjust for stratified sampling. Results MSCP was somewhat less common when ascertained by electronic health records (14.7% weighted prevalence) than by survey questionnaire (25.9% weighted prevalence). Agreement of the two MSCP classifications was low (kappa agreement statistic of 0.21). Ascertainment of MSCP with electronic health records was 30.9% sensitive, 91.0% specific, and had a positive predictive value of 54.5% relative to MSCP identified by self-report as the standard. After adjusting for age and gender, patients with MSCP identified by either electronic health records or self-report showed higher levels of pain-related disability, pain severity, depressive symptoms, and long-term opioid use than persons with single-site chronic pain identified by the same method. Conclusions Identification of MSCP with electronic health care data was insufficiently accurate to be used as a surrogate or screener for MSCP identified by self-report, but both methods identified persons with heightened chronic pain impact.

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