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 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 Are specific elements of electronic health record use associated with clinician burnout more than others?

2020 ◽  
Vol 27 (9) ◽  
pp. 1401-1410
Author(s):  
Ross W Hilliard ◽  
Jacqueline Haskell ◽  
Rebekah L Gardner
Keyword(s):  
Confidence Interval ◽  
Electronic Health Record ◽  
Odds Ratio ◽  
Adjusted Odds Ratio ◽  
Well Being ◽  
Health Record ◽  
Face To Face ◽  
Efficiency Measures ◽  
Electronic Health ◽  
Copy And Paste

Abstract Objective The study sought to examine the association between clinician burnout and measures of electronic health record (EHR) workload and efficiency, using vendor-derived EHR action log data. Materials and Methods We combined data from a statewide clinician survey on burnout with Epic EHR data from the ambulatory sites of 2 large health systems; the combined dataset included 422 clinicians. We examined whether specific EHR workload and efficiency measures were independently associated with burnout symptoms, using multivariable logistic regression and controlling for clinician characteristics. Results Clinicians with the highest volume of patient call messages had almost 4 times the odds of burnout compared with clinicians with the fewest (adjusted odds ratio, 3.81; 95% confidence interval, 1.44-10.14; P = .007). No other workload measures were significantly associated with burnout. No efficiency variables were significantly associated with burnout in the main analysis; however, in a subset of clinicians for whom note entry data were available, clinicians in the top quartile of copy and paste use were significantly less likely to report burnout, with an adjusted odds ratio of 0.22 (95% confidence interval, 0.05-0.93; P = .039). Discussion High volumes of patient call messages were significantly associated with clinician burnout, even when accounting for other measures of workload and efficiency. In the EHR, “patient calls” encompass many of the inbox tasks occurring outside of face-to-face visits and likely represent an important target for improving clinician well-being. Conclusions Our results suggest that increased workload is associated with burnout and that EHR efficiency tools are not likely to reduce burnout symptoms, with the exception of copy and paste.

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.

scholarly journals Reducing electronic health record-related burnout in providers through a personalized efficiency improvement program

2020 ◽  
Author(s):  
Eli M Lourie ◽  
Levon Haig Utidjian ◽  
Maria F Ricci ◽  
Linda Webster ◽  
Carola Young ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Efficiency Improvement ◽  
Improvement Program ◽  
Improve Efficiency ◽  
After Hours ◽  
The Mean ◽  
Net Promoter ◽  
Electronic Health ◽  
The One

Abstract Objective To give providers a better understanding of how to use the electronic health record (EHR), improve efficiency, and reduce burnout. Materials and Methods All ambulatory providers were offered at least 1 one-on-one session with an “optimizer” focusing on filling gaps in EHR knowledge and lack of customization. Success was measured using pre- and post-surveys that consisted of validated tools and homegrown questions. Only participants who returned both surveys were included in our calculations. Results Out of 1155 eligible providers, 1010 participated in optimization sessions. Pre-survey return rate was 90% (1034/1155) and post-survey was 54% (541/1010). 451 participants completed both surveys. After completing their optimization sessions, respondents reported a 26% improvement in mean knowledge of EHR functionality (P < .01), a 19% increase in the mean efficiency in the EHR (P < .01), and a 17% decrease in mean after-hours EHR usage (P < .01). Of the 401 providers asked to rate their burnout, 32% reported feelings of burnout in the pre-survey compared to 23% in the post-survey (P < .01). Providers were also likely to recommend colleagues participate in the program, with a Net Promoter Score of 41. Discussion It is possible to improve provider efficiency and feelings of burnout with a personalized optimization program. We ascribe these improvements to the one-on-one nature of our program which provides both training as well as addressing the feeling of isolation many providers feel after implementation. Conclusion It is possible to reduce burnout in ambulatory providers with personalized retraining designed to improve efficiency and knowledge of the EHR.

scholarly journals Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial

BMJ ◽  
2021 ◽  
pp. m4786
Author(s):  
F Perry Wilson ◽  
Melissa Martin ◽  
Yu Yamamoto ◽  
Caitlin Partridge ◽  
Erica Moreira ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Relative Risk ◽  
Confidence Interval ◽  
Electronic Health Record ◽  
Usual Care ◽  
Primary Outcome ◽  
Kidney Injury ◽  
Health Record ◽  
Care Practices ◽  
Electronic Health

Abstract Objective To determine whether electronic health record alerts for acute kidney injury would improve patient outcomes of mortality, dialysis, and progression of acute kidney injury. Design Double blinded, multicenter, parallel, randomized controlled trial. Setting Six hospitals (four teaching and two non-teaching) in the Yale New Haven Health System in Connecticut and Rhode Island, US, ranging from small community hospitals to large tertiary care centers. Participants 6030 adult inpatients with acute kidney injury, as defined by the Kidney Disease: Improving Global Outcomes (KDIGO) creatinine criteria. Interventions An electronic health record based “pop-up” alert for acute kidney injury with an associated acute kidney injury order set upon provider opening of the patient’s medical record. Main outcome measures A composite of progression of acute kidney injury, receipt of dialysis, or death within 14 days of randomization. Prespecified secondary outcomes included outcomes at each hospital and frequency of various care practices for acute kidney injury. Results 6030 patients were randomized over 22 months. The primary outcome occurred in 653 (21.3%) of 3059 patients with an alert and in 622 (20.9%) of 2971 patients receiving usual care (relative risk 1.02, 95% confidence interval 0.93 to 1.13, P=0.67). Analysis by each hospital showed worse outcomes in the two non-teaching hospitals (n=765, 13%), where alerts were associated with a higher risk of the primary outcome (relative risk 1.49, 95% confidence interval 1.12 to 1.98, P=0.006). More deaths occurred at these centers (15.6% in the alert group v 8.6% in the usual care group, P=0.003). Certain acute kidney injury care practices were increased in the alert group but did not appear to mediate these outcomes. Conclusions Alerts did not reduce the risk of our primary outcome among patients in hospital with acute kidney injury. The heterogeneity of effect across clinical centers should lead to a re-evaluation of existing alerting systems for acute kidney injury. Trial registration ClinicalTrials.gov NCT02753751 .

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 Is a switch to a different electronic health record associated with a change in patient satisfaction?

2020 ◽  
Vol 27 (6) ◽  
pp. 867-876 ◽  
Author(s):  
Frederick North ◽  
Jennifer L Pecina ◽  
Sidna M Tulledge-Scheitel ◽  
Rajeev Chaudhry ◽  
John C Matulis ◽  
...  
Keyword(s):  
Patient Satisfaction ◽  
Confidence Interval ◽  
Electronic Health Record ◽  
Piecewise Linear ◽  
Health Record ◽  
Patient Response ◽  
Survey Questions ◽  
Before And After ◽  
Electronic Health ◽  
Financial Impacts

Abstract Objective Financial impacts associated with a switch to a different electronic health record (EHR) have been documented. Less attention has been focused on the patient response to an EHR switch. The Mayo Clinic was involved in an EHR switch that occurred at 6 different locations and with 4 different “go-live” dates. We sought to understand the relationship between patient satisfaction and the transition to a new EHR. Materials and Methods We used patient satisfaction data collected by Press Ganey from July 2016 through December 2019. Our patient satisfaction measure was the percent of patients responding “very good” (top box) to survey questions. Twenty-four survey questions were summarized by Press Ganey into 6 patient satisfaction domains. Piecewise linear regression was used to model patient satisfaction before and after the EHR switch dates. Results Significant drops in patient satisfaction were associated with the EHR switch. Patient satisfaction with access (ease of getting clinic on phone, ease of scheduling appointments, etc.) was most affected (range of 6 sites absolute decline: -3.4% to -8.8%; all significant at 99% confidence interval). Satisfaction with providers was least affected (range of 6 sites absolute decline: -0.5% to -2.8%; 4 of 6 sites significant at 99% confidence interval). After 9-15 months, patient satisfaction with access climbed back to pre-EHR switch levels. Conclusions Patient satisfaction in several patient experience domains dropped significantly and stayed lower than pre–“go-live” for several months after a switch in EHR. Satisfaction with providers declined less than satisfaction with access.

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.

Use of an Academic Electronic Health Record With an Interprofessional Simulation for Advanced Practice Nursing Students

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Tara Spalla King ◽  
Carolyn Schubert ◽  
Oralea Pittman ◽  
Lisa Rohrig ◽  
Carolyn McClerking ◽  
...  
Keyword(s):  
Nursing Students ◽  
Electronic Health Record ◽  
Advanced Practice Nursing ◽  
Health Record ◽  
Advanced Practice ◽  
Electronic Health
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