Neurological Dashboards and Consultation Turnaround Time at an Academic Medical Center

Abstract Background Neurologists perform a significant amount of consultative work. Aggregative electronic health record (EHR) dashboards may help to reduce consultation turnaround time (TAT) which may reflect time spent interfacing with the EHR. Objectives This study was aimed to measure the difference in TAT before and after the implementation of a neurological dashboard. Methods We retrospectively studied a neurological dashboard in a read-only, web-based, clinical data review platform at an academic medical center that was separate from our institutional EHR. Using our EHR, we identified all distinct initial neurological consultations at our institution that were completed in the 5 months before, 5 months after, and 12 months after the dashboard go-live in December 2017. Using log data, we determined total dashboard users, unique page hits, patient-chart accesses, and user departments at 5 months after go-live. We calculated TAT as the difference in time between the placement of the consultation order and completion of the consultation note in the EHR. Results By April 30th in 2018, we identified 269 unique users, 684 dashboard page hits (median hits/user 1.0, interquartile range [IQR] = 1.0), and 510 unique patient-chart accesses. In 5 months before the go-live, 1,434 neurology consultations were completed with a median TAT of 2.0 hours (IQR = 2.5) which was significantly longer than during 5 months after the go-live, with 1,672 neurology consultations completed with a median TAT of 1.8 hours (IQR = 2.2; p = 0.001). Over the following 7 months, 2,160 consultations were completed and median TAT remained unchanged at 1.8 hours (IQR = 2.5). Conclusion At a large academic institution, we found a significant decrease in inpatient consult TAT 5 and 12 months after the implementation of a neurological dashboard. Further study is necessary to investigate the cognitive and operational effects of aggregative dashboards in neurology and to optimize their use.

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Leveraging Structural Changes in an Electronic Health Record Tool to Standardize Written Handoff

American Journal of Medical Quality ◽

10.1177/1062860618808018 ◽

2018 ◽

Vol 34 (4) ◽

pp. 354-359 ◽

Cited By ~ 1

Author(s):

Jillian Zavodnick ◽

Rebecca Jaffe ◽

Marc Altshuler ◽

Scott Cowan ◽

Alexis Wickersham ◽

...

Keyword(s):

Electronic Health Record ◽

Structural Changes ◽

Medical Center ◽

Academic Medical Center ◽

Health Record ◽

Patient Handoff ◽

Academic Medical ◽

Effective Training ◽

Before And After ◽

Electronic Health

Miscommunications during patient handoff can lead to harm. The I-PASS bundle has been shown to improve safety outcomes. Although effective training reliably improves verbal handoffs, research has demonstrated a lack of effect on written handoffs. The objective was to compare written handoff before and after integration of a standardized electronic health record (EHR) tool. Interns at a large urban academic medical center underwent I-PASS handoff training. The EHR handoff tool was then revised to prompt the I-PASS components. Handoff documents were obtained before and after the intervention. More handoffs included Illness Severity (33% to 59%, P < .001) and Action List (65% to 83%, P = .005) after the intervention. There was no change in handoffs with miscommunications (12.5% to 10%, P = .566) or omissions (8% to 11%, P = .447). Handoffs including tangential or unrelated information decreased (20% to 4%, P = .001). A written handoff tool can reinforce the effect of training and increase adherence to I-PASS.

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Top ten challenges when interfacing a laboratory information system to an electronic health record: Experience at a large academic medical center

International Journal of Medical Informatics ◽

10.1016/j.ijmedinf.2017.06.008 ◽

2017 ◽

Vol 106 ◽

pp. 9-16 ◽

Cited By ~ 4

Author(s):

Athena K. Petrides ◽

Milenko J. Tanasijevic ◽

Ellen M. Goonan ◽

Adam B. Landman ◽

Michalis Kantartjis ◽

...

Keyword(s):

Information System ◽

Electronic Health Record ◽

Medical Center ◽

Academic Medical Center ◽

Health Record ◽

Laboratory Information System ◽

Academic Medical ◽

Electronic Health

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The Use of an Electronic Health Record Patient Portal to Access Diagnostic Test Results by Emergency Patients at an Academic Medical Center: Retrospective Study

Journal of Medical Internet Research ◽

10.2196/13791 ◽

2019 ◽

Vol 21 (6) ◽

pp. e13791 ◽

Cited By ~ 3

Author(s):

Brody Foster ◽

Matthew David Krasowski

Keyword(s):

Retrospective Study ◽

Electronic Health Record ◽

Diagnostic Test ◽

Medical Center ◽

Academic Medical Center ◽

Health Record ◽

Patient Portal ◽

Test Results ◽

Emergency Patients ◽

Academic Medical

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Moving Radiology Workflow to the Electronic Health Record: Quantitative and Qualitative Experience From a Large Academic Medical Center

Academic Radiology ◽

10.1016/j.acra.2019.02.006 ◽

2020 ◽

Vol 27 (2) ◽

pp. 253-259 ◽

Cited By ~ 1

Author(s):

Benjamin Wildman-Tobriner ◽

Matthew P. Thorpe ◽

Nicholas Said ◽

Wendy L. Ehieli ◽

Christopher J. Roth ◽

...

Keyword(s):

Electronic Health Record ◽

Medical Center ◽

Academic Medical Center ◽

Radiology Workflow ◽

Health Record ◽

Academic Medical ◽

Electronic Health

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Impact of Changes in EHR Use during COVID-19 on Physician Trainee Mental Health

Applied Clinical Informatics ◽

10.1055/s-0041-1731000 ◽

2021 ◽

Vol 12 (03) ◽

pp. 507-517

Author(s):

Katherine J. Holzer ◽

Sunny S. Lou ◽

Charles W. Goss ◽

Jaime Strickland ◽

Bradley A. Evanoff ◽

...

Keyword(s):

Stress Disorder ◽

Medical Center ◽

Academic Medical Center ◽

Well Being ◽

Health Record ◽

Depression And Anxiety ◽

Web Based ◽

Academic Medical ◽

Multivariable Models ◽

Physician Trainee

Abstract Objectives This article investigates the association between changes in electronic health record (EHR) use during the coronavirus disease 2019 (COVID-19) pandemic on the rate of burnout, stress, posttraumatic stress disorder (PTSD), depression, and anxiety among physician trainees (residents and fellows). Methods A total of 222 (of 1,375, 16.2%) physician trainees from an academic medical center responded to a Web-based survey. We compared the physician trainees who reported that their EHR use increased versus those whose EHR use stayed the same or decreased on outcomes related to depression, anxiety, stress, PTSD, and burnout using univariable and multivariable models. We examined whether self-reported exposure to COVID-19 patients moderated these relationships. Results Physician trainees who reported increased use of EHR had higher burnout (adjusted mean, 1.48 [95% confidence interval [CI] 1.24, 1.71] vs. 1.05 [95% CI 0.93, 1.17]; p = 0.001) and were more likely to exhibit symptoms of PTSD (adjusted mean = 15.09 [95% CI 9.12, 21.05] vs. 9.36 [95% CI 7.38, 11.28]; p = 0.035). Physician trainees reporting increased EHR use outside of work were more likely to experience depression (adjusted mean, 8.37 [95% CI 5.68, 11.05] vs. 5.50 [95% CI 4.28, 6.72]; p = 0.035). Among physician trainees with increased EHR use, those exposed to COVID-19 patients had significantly higher burnout (2.04, p < 0.001) and depression scores (14.13, p = 0.003). Conclusion Increased EHR use was associated with higher burnout, depression, and PTSD outcomes among physician trainees. Although preliminary, these findings have implications for creating systemic changes to manage the wellness and well-being of trainees.

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Data on the activation and utilization of an electronic health record patient portal in an adult inpatient population at an academic medical center

Data in Brief ◽

10.1016/j.dib.2021.106806 ◽

2021 ◽

Vol 35 ◽

pp. 106806

Author(s):

Corey T. Allard ◽

Matthew D. Krasowski

Keyword(s):

Electronic Health Record ◽

Medical Center ◽

Academic Medical Center ◽

Health Record ◽

Patient Portal ◽

Academic Medical ◽

Adult Inpatient ◽

Inpatient Population ◽

Electronic Health

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A Pilot Study to Document the Return on Investment for Implementing an Ambulatory Electronic Health Record at an Academic Medical Center

Journal of the American College of Surgeons ◽

10.1016/j.jamcollsurg.2007.02.074 ◽

2007 ◽

Vol 205 (1) ◽

pp. 89-96 ◽

Cited By ~ 38

Author(s):

Dara L. Grieger ◽

Stephen H. Cohen ◽

David A. Krusch

Keyword(s):

Pilot Study ◽

Electronic Health Record ◽

Return On Investment ◽

Medical Center ◽

Academic Medical Center ◽

Health Record ◽

Academic Medical ◽

Electronic Health

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Abstract 15537: An Electronic Health Record Intervention to Optimize Utilization of Transthoracic Echocardiography: Opportunity for Cost Savings in the Era of Value Based-care

Circulation ◽

10.1161/circ.142.suppl_3.15537 ◽

2020 ◽

Vol 142 (Suppl_3) ◽

Author(s):

Jason Castaneda ◽

corey rearick ◽

Joseph Weber ◽

Eve Edstrom ◽

Kimisha Cassidy ◽

...

Keyword(s):

Electronic Health Record ◽

Transthoracic Echocardiography ◽

Medical Center ◽

Academic Medical Center ◽

Cost Savings ◽

Health Record ◽

Post Intervention ◽

Academic Medical ◽

Electronic Health ◽

Value Based Care

Introduction: In the current value-based era, targeting diagnostic resources and minimizing unnecessary testing is of paramount importance. Transthoracic echocardiography (TTE) is a common and costly test, and available Appropriate Use Criteria (AUC) guide optimal utilization. Limited TTE (L-TTE) shortens sonographer time, lowers cost and may be ideal for repeat TTEs (R-TTE) with a focused indication. However, many clinicians are unfamiliar with the AUC and opportunities for L-TTE. We prospectively tested an Electronic Health Record (EHR)-based intervention aimed at optimizing TTE utilization in a large academic medical center. Methods: TTE utilization at the University of Chicago Medicine was assessed over a 6-month period and complete TTE (C-TTE), L-TTE and R-TTE (TTE repeated within 6 months) were recorded. An EHR-based intervention was then implemented and TTE utilization was assessed over the ensuing 8 weeks. The intervention included presenting new descriptive L-TTE options (i.e. “Limited TTE: EF or Effusion Only”) when any “echo” was searched in the EHR order panel, an alert to prior TTEs (i.e. date & LVEF) and a link to AUC-based guidance for TTE ordering. Educational materials were also distributed to frequent TTE ordering providers. Results: Among 9121 TTEs (53% inpatient) pre-intervention , 11% (n=1002) were L-TTEs and 25% (n=2320) were R-TTEs. There were more L-TTEs and R-TTEs in pre-intervention inpatients compared to outpatients (L-TTE 14% vs 7%, p<0.0001, R-TTE 33% vs 17%, p<0.0001). Post-intervention (2879 TTEs, 53% inpatient), R-TTEs significantly decreased (22.6% vs 25.4%, p=0.0019) and L-TTEs significantly increased (14% vs 11%, p<0.0001) compared to pre-intervention, with inpatient TTEs most impacted (R-TTE 28% vs 33%, p=0.0016, L-TTE 19% vs 14%, p<0.001). The intervention’s greatest impact was to markedly increase L-TTEs among inpatient R-TTEs (44% vs 35%, p=0.0002). Conclusions: Despite AUC discouraging frequent repeat TTEs, R-TTEs are common in an academic medical center and utilization of L-TTE is rare. An EHR-based intervention with prior TTE alerts and descriptive L-TTE options increases L-TTEs and reduces R-TTEs. Further study is warranted to describe the full clinical and financial impact of this intervention.

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