scholarly journals Electronic Health Records in Specialty Care: A Time-Motion Study

2007 ◽  
Vol 14 (5) ◽  
pp. 609-615 ◽  
Author(s):  
H. G. Lo ◽  
L. P. Newmark ◽  
C. Yoon ◽  
L. A. Volk ◽  
V. L. Carlson ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Specialty Care ◽  
Health Records ◽  
Time Motion ◽  
Motion Study ◽  
Electronic Health

Electronic health records and resident workflow: A time-motion study of otolaryngology residents

2014 ◽  
Vol 125 (3) ◽  
pp. 594-598 ◽  
Author(s):  
Andrew J. Victores ◽  
Kenneth Coggins ◽  
Mas Takashima
Keyword(s):  
Electronic Health Records ◽  
Health Records ◽  
Time Motion ◽  
Motion Study ◽  
Electronic Health

scholarly journals Detecting moderate or complex congenital heart defects in adults from an electronic health records system

2018 ◽  
Vol 25 (12) ◽  
pp. 1634-1642
Author(s):  
Alpha Oumar Diallo ◽  
Asha Krishnaswamy ◽  
Stuart K Shapira ◽  
Matthew E Oster ◽  
Mary G George ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Risk Score ◽  
Congenital Heart Defects ◽  
Congenital Heart ◽  
Heart Defects ◽  
Specialty Care ◽  
Brier Score ◽  
Health Records ◽  
Logistic Regression Models ◽  
Electronic Health

Abstract Background The prevalence of moderate or complex (moderate-complex) congenital heart defects (CHDs) among adults is increasing due to improved survival, but many patients experience lapses in specialty care or their CHDs are undocumented in the medical system. There is, to date, no efficient approach to identify this population. Objective To develop and assess the performance of a risk score to identify adults aged 20-60 years with undocumented specific moderate-complex CHDs from electronic health records (EHR). Methods We used a case-control study (596 adults with specific moderate-complex CHDs and 2384 controls). We extracted age, race/ethnicity, electrocardiogram (EKG), and blood tests from routine outpatient visits (1/2009 through 12/2012). We used multivariable logistic regression models and a split-sample (4: 1 ratio) approach to develop and internally validate the risk score, respectively. We generated receiver operating characteristic (ROC) c-statistics and Brier scores to assess the ability of models to predict the presence of specific moderate-complex CHDs. Results Out of six models, the non-blood biomarker model that included age, sex, and EKG parameters offered a high ROC c-statistic of 0.96 [95% confidence interval: 0.95, 0.97] and low Brier score (0.05) relative to the other models. The adult moderate-complex congenital heart defect risk score demonstrated good accuracy with 96.4% sensitivity and 80.0% specificity at a threshold score of 10. Conclusions A simple risk score based on age, sex, and EKG parameters offers early proof of concept and may help accurately identify adults with specific moderate-complex CHDs from routine EHR systems who may benefit from specialty care.

scholarly journals Impact of Electronic Health Record Implementation on Ophthalmology Trainee Time Expenditures

2019 ◽  
Vol 11 (02) ◽  
pp. e65-e72 ◽  
Author(s):  
Helena E. Gali ◽  
Sally L. Baxter ◽  
Lina Lander ◽  
Abigail E. Huang ◽  
Marlene Millen ◽  
...  
Keyword(s):  
Mixed Effects Models ◽  
Health Records ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Time Motion ◽  
Motion Study ◽  
Time Requirements ◽  
Electronic Health ◽  
Time Expenditures ◽  
Shed Light

Abstract Objective Electronic health records (EHRs) are widely adopted, but the time demands of EHR use on ophthalmology trainees are not well understood. This study evaluated ophthalmology trainee time spent on clinical activities in an outpatient clinic undergoing EHR implementation. Design Prospective, manual time-motion observations of ophthalmology trainees in 2018. Participants Eleven ophthalmology residents and fellows observed during 156 patient encounters. Methods Prospective time-motion study of ophthalmology trainees 2 weeks before and 6 weeks after EHR implementation in an academic ophthalmology department. Manual time-motion observations were conducted for 11 ophthalmology trainees in 6 subspecialty clinics during 156 patient encounters. Time spent documenting, examining, and talking with patients were recorded. Factors influencing time requirements were evaluated using linear mixed effects models. Main Outcome Measures Total time spent by ophthalmology residents and fellows per patient, time spent on documentation, examination, and talking with patients. Results Seven ophthalmology residents and four ophthalmology fellows with mean (standard deviation) postgraduate year of 3.7 (1.2) were observed during 156 patient encounters. Using paper charts, mean total time spent on each patient was 11.6 (6.5) minutes, with 5.4 (3.5) minutes spent documenting (48%). After EHR implementation, mean total time spent on each patient was 11.8 (6.9) minutes, with 6.8 (4.7) minutes spent documenting (57%). Total time expenditure per patient did not significantly change after EHR implementation (+0.17 minutes, 95% confidence interval [CI] for difference in means: –2.78, 2.45; p = 0.90). Documentation time did not change significantly after EHR implementation in absolute terms (+1.42 minutes, 95% CI: –3.13, 0.29; p = 0.10), but was significantly greater as a proportion of total time (48% on paper to 57% on EHR; +9%, 95% CI: 2.17, 15.83; p = 0.011). Conclusion Total time spent per patient and absolute time spent on documentation was not significantly different whether ophthalmology trainees used paper charts or the recently implemented EHR. Percentage of total time spent on documentation increased significantly with early EHR use. Evaluating EHR impact on ophthalmology trainees may improve understanding of how trainees learn to use the EHR and may shed light on strategies to address trainee burnout.

scholarly journals Finding Meaning in Medication Reconciliation Using Electronic Health Records: Qualitative Analysis in Safety Net Primary and Specialty Care

10.2196/10167 ◽  
2018 ◽  
Vol 6 (2) ◽  
pp. e10167 ◽  
Author(s):  
George Yaccoub Matta ◽  
Elaine C Khoong ◽  
Courtney R Lyles ◽  
Dean Schillinger ◽  
Neda Ratanawongsa
Keyword(s):  
Electronic Health Records ◽  
Qualitative Analysis ◽  
Medication Reconciliation ◽  
Safety Net ◽  
Specialty Care ◽  
Health Records ◽  
Finding Meaning ◽  
Electronic Health

Characterizing Multitasking and Workflow Fragmentation in Electronic Health Records among Emergency Department Clinicians: Using Time-Motion Data to Understand Documentation Burden

2021 ◽  
Vol 12 (05) ◽  
pp. 1002-1013
Author(s):  
Amanda J. Moy ◽  
Lucy Aaron ◽  
Kenrick D. Cato ◽  
Jessica M. Schwartz ◽  
Jonathan Elias ◽  
...  
Keyword(s):  
Emergency Department ◽  
Electronic Health Records ◽  
Average Duration ◽  
Clinical Information ◽  
Future Research ◽  
Clinical Workflow ◽  
Health Records ◽  
Time Motion ◽  
Electronic Health ◽  
The Impact

Abstract Background The impact of electronic health records (EHRs) in the emergency department (ED) remains mixed. Dynamic and unpredictable, the ED is highly vulnerable to workflow interruptions. Objectives The aim of the study is to understand multitasking and task fragmentation in the clinical workflow among ED clinicians using clinical information systems (CIS) through time-motion study (TMS) data, and inform their applications to more robust and generalizable measures of CIS-related documentation burden. Methods Using TMS data collected among 15 clinicians in the ED, we investigated the role of documentation burden, multitasking (i.e., performing physical and communication tasks concurrently), and workflow fragmentation in the ED. We focused on CIS-related tasks, including EHRs. Results We captured 5,061 tasks and 877 communications in 741 locations within the ED. Of the 58.7 total hours observed, 44.7% were spent on CIS-related tasks; nearly all CIS-related tasks focused on data-viewing and data-entering. Over one-fifth of CIS-related task time was spent on multitasking. The mean average duration among multitasked CIS-related tasks was shorter than non-multitasked CIS-related tasks (20.7 s vs. 30.1 s). Clinicians experienced 1.4 ± 0.9 task switches/min, which increased by one-third when multitasking. Although multitasking was associated with a significant increase in the average duration among data-entering tasks, there was no significant effect on data-viewing tasks. When engaged in CIS-related task switches, clinicians were more likely to return to the same CIS-related task at higher proportions while multitasking versus not multitasking. Conclusion Multitasking and workflow fragmentation may play a significant role in EHR documentation among ED clinicians, particularly among data-entering tasks. Understanding where and when multitasking and workflow fragmentation occurs is a crucial step to assessing potentially burdensome clinician tasks and mitigating risks to patient safety. These findings may guide future research on developing more scalable and generalizable measures of CIS-related documentation burden that do not necessitate direct observation techniques (e.g., EHR log files).

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