scholarly journals Characterizing Inpatient Medicine Resident Electronic Health Record Usage Patterns Using Event Log Data

2018 ◽  
Author(s):  
Jonathan H. Chen ◽  
Jason K. Wang ◽  
David Ouyang ◽  
Jason Hom ◽  
Jeffrey Chi
Keyword(s):  
Electronic Health Record ◽  
General Medicine ◽  
Health Record ◽  
Log Data ◽  
Targeted Interventions ◽  
Patient Interactions ◽  
Event Log ◽  
Usage Patterns ◽  
Team Activity ◽  
Electronic Health

ABSTRACTAmid growing rates of burnout, physicians report increasing electronic health record (EHR) usage alongside decreasing clinical facetime with patients. There exists a pressing need to improve physician-computer-patient interactions by streamlining EHR workflow.To identify interventions to improve EHR design and usage, we systematically characterize EHR activity among internal medicine residents at a tertiary academic hospital across various inpatient rotations and roles from June 2013 to November 2016.Logged EHR timestamps were extracted from Stanford Hospital’s EHR system (Epic) and cross-referenced against resident rotation schedules. We tracked the quantity of EHR logs across 24-hour cycles to reveal daily usage patterns. In addition, we decomposed daily EHR time into time spent on specific EHR actions (e.g. chart review, note entry and review, results review).In examining 24-hour usage cycles from general medicine day and night team rotations, we identified a prominent trend in which night team activity promptly ceased at the shift’s end, while day team activity tended to linger post-shift. Across all rotations and roles, residents spent on average 5.38 hours (standard deviation=2.07) using the EHR. PGY1 (post-graduate year one) interns and PGY2+ residents spent on average 2.4 and 4.1 times the number of EHR hours on information review (chart, note, and results review) as information entry (note and order entry).Analysis of EHR event log data can enable medical educators and programs to develop more targeted interventions to improve physician-computer-patient interactions, centered on specific EHR actions.

scholarly journals Characterizing electronic health record usage patterns of inpatient medicine residents using event log data

PLoS ONE ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. e0205379 ◽  
Author(s):  
Jason K. Wang ◽  
David Ouyang ◽  
Jason Hom ◽  
Jeffrey Chi ◽  
Jonathan H. Chen
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Log Data ◽  
Event Log ◽  
Usage Patterns ◽  
Inpatient Medicine ◽  
Electronic Health

scholarly journals Optimizing the electronic health record: An inpatient sprint addresses provider burnout and improves electronic health record satisfaction

2020 ◽  
Author(s):  
Jennifer R Simpson ◽  
Chen-Tan Lin ◽  
Amber Sieja ◽  
Stefan H Sillau ◽  
Jonathan Pell
Keyword(s):  
Confidence Interval ◽  
Electronic Health Record ◽  
Health Record ◽  
Advanced Practice ◽  
Log Data ◽  
Net Promoter Score ◽  
Net Promoter ◽  
Electronic Health ◽  
Emotional Recovery ◽  
Very High

Abstract Objective We sought reduce electronic health record (EHR) burden on inpatient clinicians with a 2-week EHR optimization sprint. Materials and Methods A team led by physician informaticists worked with 19 advanced practice providers (APPs) in 1 specialty unit. Over 2 weeks, the team delivered 21 EHR changes, and provided 39 one-on-one training sessions to APPs, with an average of 2.8 hours per provider. We measured Net Promoter Score, thriving metrics, and time spent in the EHR based on user log data. Results Of the 19 APPs, 18 completed 2 or more sessions. The EHR Net Promoter Score increased from 6 to 60 postsprint (1.0; 95% confidence interval, 0.3-1.8; P = .01). The NPS for the Sprint itself was 93, a very high rating. The 3-axis emotional thriving, emotional recovery, and emotional exhaustion metrics did not show a significant change. By user log data, time spent in the EHR did not show a significant decrease; however, 40% of the APPs responded that they spent less time in the EHR. Conclusions This inpatient sprint improved satisfaction with the EHR.

scholarly journals When order sets do not align with clinician workflow: assessing practice patterns in the electronic health record

2019 ◽  
pp. bmjqs-2018-008968 ◽  
Author(s):  
Ron C Li ◽  
Jason K Wang ◽  
Christopher Sharp ◽  
Jonathan H Chen
Keyword(s):  
Electronic Health Record ◽  
Point Of Care ◽  
Set Design ◽  
Health Record ◽  
Additional Item ◽  
Order Sets ◽  
Usage Patterns ◽  
Electronic Health ◽  
Order Set ◽  
A La Carte

BackgroundOrder sets are widely used tools in the electronic health record (EHR) for improving healthcare quality. However, there is limited insight into how well they facilitate clinician workflow. We assessed four indicators based on order set usage patterns in the EHR that reflect potential misalignment between order set design and clinician workflow needs.MethodsWe used data from the EHR on all orders of medication, laboratory, imaging and blood product items at an academic hospital and an itemset mining approach to extract orders that frequently co-occurred with order set use. We identified the following four indicators: infrequent ordering of order set items, rapid retraction of medication orders from order sets, additional a la carte ordering of items not included in order sets and a la carte ordering of items despite being listed in the order set.ResultsThere was significant variability in workflow alignment across the 11 762 order set items used in the 77 421 inpatient encounters from 2014 to 2017. The median ordering rate was 4.1% (IQR 0.6%–18%) and median medication retraction rate was 4% (IQR 2%–10%). 143 (5%) medications were significantly less likely while 68 (3%) were significantly more likely to be retracted than if the same medication was ordered a la carte. 214 (39%) order sets were associated with least one additional item frequently ordered a la carte and 243 (45%) order sets contained at least one item that was instead more often ordered a la carte.ConclusionOrder sets often do not align with what clinicians need at the point of care. Quantitative insights from EHRs may inform how order sets can be optimised to facilitate clinician workflow.

A minimalist electronic health record-based intervention to reduce standing lab utilisation

2020 ◽  
pp. postgradmedj-2019-136992
Author(s):  
Kuo-Kai Chin ◽  
Amrita Krishnamurthy ◽  
Talhah Zubair ◽  
Tara Ramaswamy ◽  
Jason Hom ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Complete Blood Count ◽  
Tertiary Care Hospital ◽  
Tertiary Care ◽  
General Medicine ◽  
Health Record ◽  
Care Hospital ◽  
Before And After ◽  
Electronic Health ◽  
One Year

BackgroundRepetitive laboratory testing in stable patients is low-value care. Electronic health record (EHR)-based interventions are easy to disseminate but can be restrictive.ObjectiveTo evaluate the effect of a minimally restrictive EHR-based intervention on utilisation.SettingOne year before and after intervention at a 600-bed tertiary care hospital. 18 000 patients admitted to General Medicine, General Surgery and the Intensive Care Unit (ICU).InterventionProviders were required to specify the number of times each test should occur instead of being able to order them indefinitely.MeasurementsFor eight tests, utilisation (number of labs performed per patient day) and number of associated orders were measured.ResultsUtilisation decreased for some tests on all services. Notably, complete blood count with differential decreased 9% (p<0.001) on General Medicine and 21% (p<0.001) in the ICU.ConclusionsRequiring providers to specify the number of occurrences of labs changes significantly reduces utilisation in some cases.

scholarly journals Metrics for assessing physician activity using electronic health record log data

2020 ◽  
Vol 27 (4) ◽  
pp. 639-643 ◽  
Author(s):  
Christine A Sinsky ◽  
Adam Rule ◽  
Genna Cohen ◽  
Brian G Arndt ◽  
Tait D Shanafelt ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Clinical Environment ◽  
Log Data ◽  
Multiple Dimensions ◽  
Time Work ◽  
Physician Time ◽  
Electronic Health ◽  
Practice Efficiency ◽  
Core Measures

Abstract Electronic health record (EHR) log data have shown promise in measuring physician time spent on clinical activities, contributing to deeper understanding and further optimization of the clinical environment. In this article, we propose 7 core measures of EHR use that reflect multiple dimensions of practice efficiency: total EHR time, work outside of work, time on documentation, time on prescriptions, inbox time, teamwork for orders, and an aspirational measure for the amount of undivided attention patients receive from their physicians during an encounter, undivided attention. We also illustrate sample use cases for these measures for multiple stakeholders. Finally, standardization of EHR log data measure specifications, as outlined here, will foster cross-study synthesis and comparative research.

scholarly journals Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study (Preprint)

2018 ◽  
Author(s):  
Azraa Amroze ◽  
Terry S Field ◽  
Hassan Fouayzi ◽  
Devi Sundaresan ◽  
Laura Burns ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Primary Care Physicians ◽  
Controlled Trial ◽  
Patient Characteristics ◽  
Clinical Decision ◽  
Descriptive Study ◽  
Health Record ◽  
Log Data ◽  
Electronic Health

BACKGROUND Electronic health record (EHR) access and audit logs record behaviors of providers as they navigate the EHR. These data can be used to better understand provider responses to EHR–based clinical decision support (CDS), shedding light on whether and why CDS is effective. OBJECTIVE This study aimed to determine the feasibility of using EHR access and audit logs to track primary care physicians’ (PCPs’) opening of and response to noninterruptive alerts delivered to EHR InBaskets. METHODS We conducted a descriptive study to assess the use of EHR log data to track provider behavior. We analyzed data recorded following opening of 799 noninterruptive alerts sent to 75 PCPs’ InBaskets through a prior randomized controlled trial. Three types of alerts highlighted new medication concerns for older patients’ posthospital discharge: information only (n=593), medication recommendations (n=37), and test recommendations (n=169). We sought log data to identify the person opening the alert and the timing and type of PCPs’ follow-up EHR actions (immediate vs by the end of the following day). We performed multivariate analyses examining associations between alert type, patient characteristics, provider characteristics, and contextual factors and likelihood of immediate or subsequent PCP action (general, medication-specific, or laboratory-specific actions). We describe challenges and strategies for log data use. RESULTS We successfully identified the required data in EHR access and audit logs. More than three-quarters of alerts (78.5%, 627/799) were opened by the PCP to whom they were directed, allowing us to assess immediate PCP action; of these, 208 alerts were followed by immediate action. Expanding on our analyses to include alerts opened by staff or covering physicians, we found that an additional 330 of the 799 alerts demonstrated PCP action by the end of the following day. The remaining 261 alerts showed no PCP action. Compared to information-only alerts, the odds ratio (OR) of immediate action was 4.03 (95% CI 1.67-9.72) for medication-recommendation and 2.14 (95% CI 1.38-3.32) for test-recommendation alerts. Compared to information-only alerts, ORs of medication-specific action by end of the following day were significantly greater for medication recommendations (5.59; 95% CI 2.42-12.94) and test recommendations (1.71; 95% CI 1.09-2.68). We found a similar pattern for OR of laboratory-specific action. We encountered 2 main challenges: (1) Capturing a historical snapshot of EHR status (number of InBasket messages at time of alert delivery) required incorporation of data generated many months prior with longitudinal follow-up. (2) Accurately interpreting data elements required iterative work by a physician/data manager team taking action within the EHR and then examining audit logs to identify corresponding documentation. CONCLUSIONS EHR log data could inform future efforts and provide valuable information during development and refinement of CDS interventions. To address challenges, use of these data should be planned before implementing an EHR–based study.

Predicting next-day discharge via electronic health record access logs

2021 ◽  
Author(s):  
Xinmeng Zhang ◽  
Chao Yan ◽  
Bradley A Malin ◽  
Mayur B Patel ◽  
You Chen
Keyword(s):  
Electronic Health Record ◽  
Medical Center ◽  
Influential Factors ◽  
Health Record ◽  
Operating Characteristics ◽  
Log Data ◽  
Time Estimates ◽  
Electronic Health ◽  
Improve Hospital ◽  
Access Logs

Abstract Objective Hospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation by a clinician requires an excessively large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. This study aims to support next-day discharge predictions with machine learning by incorporating electronic health record (EHR) audit log data, a resource that captures EHR users’ granular interactions with patients’ records by communicating various semantics and has been neglected in outcome predictions. Materials and Methods This study focused on the EHR data for all adults admitted to Vanderbilt University Medical Center in 2019. We learned multiple advanced models to assess the value that EHR audit log data adds to the daily prediction of discharge likelihood within 24 h and to compare different representation strategies. We applied Shapley additive explanations to identify the most influential types of user-EHR interactions for discharge prediction. Results The data include 26 283 inpatient stays, 133 398 patient-day observations, and 819 types of user-EHR interactions. The model using the count of each type of interaction in the recent 24 h and other commonly used features, including demographics and admission diagnoses, achieved the highest area under the receiver operating characteristics (AUROC) curve of 0.921 (95% CI: 0.919–0.923). By contrast, the model lacking user-EHR interactions achieved a worse AUROC of 0.862 (0.860–0.865). In addition, 10 of the 20 (50%) most influential factors were user-EHR interaction features. Conclusion EHR audit log data contain rich information such that it can improve hospital-wide discharge predictions.

scholarly journals Association of Perceived Electronic Health Record Usability With Patient Interactions and Work-Life Integration Among US Physicians

2020 ◽  
Vol 3 (6) ◽  
pp. e207374
Author(s):  
Edward R. Melnick ◽  
Christine A. Sinsky ◽  
Liselotte N. Dyrbye ◽  
Mickey Trockel ◽  
Colin P. West ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Work Life ◽  
Health Record ◽  
Patient Interactions ◽  
Electronic Health
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