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 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 Electronic Health Record Adoption – Maybe It’s not about the Money

2011 ◽  
Vol 02 (04) ◽  
pp. 460-471 ◽  
Author(s):  
A. Skinner ◽  
J. Windle ◽  
L. Grabenbauer
Keyword(s):  
Electronic Health Record ◽  
Patient Care ◽  
Resistance To Change ◽  
Medical Center ◽  
Academic Medical Center ◽  
Care Physician ◽  
Health Record ◽  
Record System ◽  
Intuitive Interfaces ◽  
Electronic Health

SummaryObjective: The slow adoption of electronic health record (EHR) systems has been linked to physician resistance to change and the expense of EHR adoption. This qualitative study was conducted to evaluate benefits, and clarify limitations of two mature, robust, comprehensive EHR Systems by tech-savvy physicians where resistance and expense are not at issue.Methods: Two EHR systems were examined – the paperless VistA / Computerized Patient Record System used at the Veterans‘ Administration, and the General Electric Centricity Enterprise system used at an academic medical center. A series of interviews was conducted with 20 EHR-savvy multi-institutional internal medicine (IM) faculty and house staff. Grounded theory was used to analyze the transcribed data and build themes. The relevance and importance of themes were constructed by examining their frequency, convergence, and intensity.Results: Despite eliminating resistance to both adoption and technology as drivers of acceptance, these two robust EHR’s are still viewed as having an adverse impact on two aspects of patient care, physician workflow and team communication. Both EHR’s had perceived strengths but also significant limitations and neither were able to satisfactorily address all of the physicians’ needs.Conclusion: Difficulties related to physician acceptance reflect real concerns about EHR impact on patient care. Physicians are optimistic about the future benefits of EHR systems, but are frustrated with the non-intuitive interfaces and cumbersome data searches of existing EHRs.

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 Learning relational policies from electronic health record access logs

2011 ◽  
Vol 44 (2) ◽  
pp. 333-342 ◽  
Author(s):  
Bradley Malin ◽  
Steve Nyemba ◽  
John Paulett
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health ◽  
Access Logs

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 Development and Validation of a Computable Phenotype for Bleeding in Hospitalized Patients: The Medical Inpatients Thrombosis and Hemostasis (MITH) Study

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 45-46
Author(s):  
Mansour Gergi ◽  
Katherine Wilkinson ◽  
Insu Koh ◽  
Jordan Munger ◽  
Nicholas L Smith ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Major Bleeding ◽  
Medical Center ◽  
Hospitalized Patients ◽  
Health Record ◽  
Bleeding Site ◽  
Bleeding Events ◽  
Electronic Health Record Data ◽  
Common Error ◽  
Electronic Health

Introduction: Bleeding is an uncommon event but it is causes significant increase in morbidity and mortality. Identifying bleeding events using electronic health record data (both resulting from hospitalization and causing hospitalization) would allow the development of risk assessment models (RAM) to identify those at most risk. Traditional prospective cohorts for rare events are time consuming and expensive. We suggest a more efficient method using the electronic health record (EHR) data by developing and validating an algorithm to detect bleeding in hospitalized patients, ie, a "computable phenotype". Methods: We captured all admissions to the University of Vermont (UVM) Medical Center between 2010-19, a tertiary care medical center in northwest Vermont. Using International Classification of Disease (ICD) 9 and 10 discharge diagnoses, "present on admission" flags, problem lists, laboratory values, vital signs, current procedure terminology (CPT) codes, medication administration, and flowsheet data for transfusion support, we developed computable phenotypes for bleeding. Classification was based on the gold standard International Society of Thrombosis and Haemostasis definitions for clinically relevant non-major bleeding (CRNMB) and major bleeding (MB) and validated by medical record review. To improve sensitivity and specificity, algorithms were developed by bleeding site (intracerebral, intraspinal, pericardial, retroperitoneal, orbital, intramuscular, gastrointestinal, genitourinary, gynecologic, pulmonary, nasal, post-procedure, or miscellaneous). We preliminary validated the computable phenotype by randomly abstracting 10 medical records from each bleeding site. Results: Among 62,468 admissions, our computable phenotype for bleeding identified 10,202 bleeding events associated with hospitalization; 4,650 were CRNMB and 5,552 were MB. On chart abstraction, 135 of 153 hospitalizations had either a MB or CRNMB (88%, Figure). For MB, 95 of 119 (80%) of the computed MB phenytope events were validated. Of the 24 of 119 (20%) not validated, 14% (16) were CRNMB and 7% (8) the bleeding was present on coding but was not detected by chart review. Only 29%(10/34) of the CRNMB were validated. The most common error in the CRNMB computable phenotype was misclassification of 14 MB as CRNMB (41% of CRNMB. For individual bleeding sites, (figure), the algorithms performed well for most sites including intracerebral hemorrhage, gastrointestinal, and intramuscular bleeding, but performed less well for unusual and rarer bleeding sites (i.e. nasal). Conclusion: We developed a computable phenotype for bleeding which can be applied to our EHR system. The computable phenotype was specific for MB, but underestimated the severity of potential CRNMB. Importantly, we correctly classified specific important bleeding sites such as intracerebral, gastrointestinal, and retroperitoneal. This computable phenotype forms the basis for further refinement, and provides a road map for future studies on epidemiology of hospital-acquired bleeding and hospitalization for bleeding. Figure: Major and Clinically relevant non-major bleeding as detected by Electronic Health Record compared to the chart validation Figure 1 Disclosures No relevant conflicts of interest to declare.

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

2020 ◽  
Vol 27 (2) ◽  
pp. 253-259 ◽  
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

Incidence of interruptive penicillin allergy alerts in patients with previously documented beta-lactam exposure: Potential for leveraging the electronic health record to identify erroneous allergies

2021 ◽  
pp. 1-4
Author(s):  
Nicole Van Groningen ◽  
Ray Duncan ◽  
Galen Cook-Wiens ◽  
Aaron Kwong ◽  
Matthew Sonesen ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Medical Center ◽  
Tertiary Care ◽  
Penicillin Allergy ◽  
Health Record ◽  
Beta Lactam ◽  
Tertiary Care Medical Center ◽  
Lactam Antibiotic ◽  
Electronic Health ◽  
Clinically Significant

Abstract Background: Approximately 10% of patients report allergies to penicillin, yet >90% of these allergies are not clinically significant. Patients reporting penicillin allergies are often treated with second-line, non–β-lactam antibiotics that are typically broader spectrum and more toxic. Orders for β-lactam antibiotics for these patients trigger interruptive alerts, even when there is electronic health record (EHR) data indicating prior β-lactam exposure. Objective: To describe the rate that interruptive penicillin allergy alerts display for patients who have previously had a β-lactam exposure. Design: Retrospective EHR review from January 2013 through June 2018. Setting: A nonprofit health system including 1 large tertiary-care medical center, a smaller associated hospital, 2 emergency departments, and ˜250 outpatient clinics. Participants: All patients with EHR-documented of penicillin allergies. Methods: We examined interruptive penicillin allergy alerts and identified the number and percentage of alerts that display for patients with a prior administration of a penicillin class or other β-lactam antibiotic. Results: Of 115,081 allergy alerts that displayed during the study period, 8% were displayed for patients who had an inpatient administration of a penicillin antibiotic after the allergy was noted, and 49% were displayed for patients with a prior inpatient administration of any β-lactam. Conclusions: Many interruptive penicillin allergy alerts display for patients who would likely tolerate a penicillin, and half of all alerts display for patients who would likely tolerate another β-lactam.

An Analysis of Electronic Health Record Work to Manage Asynchronous Clinical Messages among Breast Cancer Care Teams

2021 ◽  
Vol 12 (04) ◽  
pp. 877-887
Author(s):  
Bryan D. Steitz ◽  
Kim M. Unertl ◽  
Mia A. Levy
Keyword(s):  
Breast Cancer ◽  
Electronic Health Record ◽  
Cancer Care ◽  
Medical Center ◽  
Asynchronous Communication ◽  
Care Team ◽  
Health Record ◽  
Breast Cancer Care ◽  
Team Members ◽  
Electronic Health

Abstract Objective Asynchronous messaging is an integral aspect of communication in clinical settings, but imposes additional work and potentially leads to inefficiency. The goal of this study was to describe the time spent using the electronic health record (EHR) to manage asynchronous communication to support breast cancer care coordination. Methods We analyzed 3 years of audit logs and secure messaging logs from the EHR for care team members involved in breast cancer care at Vanderbilt University Medical Center. To evaluate trends in EHR use, we combined log data into sequences of events that occurred within 15 minutes of any other event by the same employee about the same patient. Results Our cohort of 9,761 patients were the subject of 430,857 message threads by 7,194 employees over a 3-year period. Breast cancer care team members performed messaging actions in 37.5% of all EHR sessions, averaging 29.8 (standard deviation [SD] = 23.5) messaging sessions per day. Messaging sessions lasted an average of 1.1 (95% confidence interval: 0.99–1.24) minutes longer than nonmessaging sessions. On days when the cancer providers did not otherwise have clinical responsibilities, they still performed messaging actions in an average of 15 (SD = 11.9) sessions per day. Conclusion At our institution, clinical messaging occurred in 35% of all EHR sessions. Clinical messaging, sometimes viewed as a supporting task of clinical work, is important to delivering and coordinating care across roles. Measuring the electronic work of asynchronous communication among care team members affords the opportunity to systematically identify opportunities to improve employee workload.

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