Electronic Health Record Workflow Research Articles

Specialty Tobacco Treatment Implementation in Oncology: A Qualitative Study.

In 2017, the National Cancer Institute (NCI) funded the Cancer Center Cessation Initiative (C3I) to implement and expand tobacco treatment programs in routine oncology care. Many C3I programs developed specialty care programs staffed by tobacco treatment specialists (TTSs) to deliver evidence-based treatment to adult patients who smoke. People involved in specialty tobacco treatment programs can help to identify implementation strategies and adaptations that may enhance tobacco treatment reach and effectiveness in cancer care and help more patients with cancer quit using tobacco. We conducted semistructured interviews with TTSs from 21 C3I-funded cancer centers and applied content analysis to interview transcripts from 37 TTSs and 17 respondents in other program roles. We used the Consolidated Framework for Implementation Research to code interview data. We identified final themes and implementation strategies and adaptations recommended by respondents on the basis of these codes. Respondents shared that implementation of specialty tobacco treatment programs in cancer care settings could be facilitated by training staff to provide patient connection to services, incorporating prescription of no- or low-cost cessation medications, hiring additional staff to deliver tobacco treatment, allocating space to the program, and automating electronic health record workflows. TTSs and others involved in specialty tobacco treatment in NCI-designated cancer centers identified ways to improve tobacco treatment access, use, and effectiveness by (1) adapting specialty tobacco treatment delivery to meet patient needs; (2) facilitating referrals and pharmacotherapy coordination; and (3) committing staffing, space, and support resources to tobacco treatment programs. Key program participants suggested that these approaches would help more oncology patients connect with evidence-based tobacco treatment and quit smoking.

Facilitators and Barriers to Integrating Patient-Generated Blood Pressure Data into Primary Care EHR Workflows.

Evidence supports using patient-generated blood pressure data for better outcomes in hypertension management. However, obstacles like dealing with home-generated paper data sets and questions of validity slowed the meaningful incorporation of home blood pressure into clinical care. As clinicians value patient data more, reliance on digital health solutions for data collection and shared decision-making grows. The purpose of this study is to evaluate the design and early implementation of an electronic health record (EHR)-based data visualization tool and explore the barriers or facilitators to integrating) patients' home blood pressure data into the electronic workflow in the clinical setting. Findings can inform potential next steps for implementation and provide recommendations for leveraging patient-generated health data (PGHD) in hypertension management. We qualitatively explored pre- and early-implementation factors for integrating PGHD into clinicians' EHR interfaces intended to support shared decision-making using the Consolidated Framework for Implementation Research (CFIR). We collected data in the form of notes and transcripts from clinician focus groups, administrative leadership feedback sessions, research team observations, and recurring team meetings. This study took place at a midwestern academic health center. We identify implementation facilitating factors, adoption considerations, and next steps across CFIR domains focusing on large-scale implementation. Key recommendations include aligning internal and external priorities, empowering champions to facilitate uptake, using intuitive design, and anticipating and planning for unintended consequences. These findings can guide future efforts to include PGHD in workflows, thus enhancing shared decision-making and laying the groundwork for larger implementations. Understanding the implementation barriers and facilitators to connect PGHD to clinician apps in the EHR workspace can promote their adoption and maintenance.

Multisite Pragmatic Cluster-Randomized Controlled Trial of the CONCERN Early Warning System.

Late predictions of hospitalized patient deterioration, resulting from early warning systems (EWS) with limited data sources and/or a care team's lack of shared situational awareness, contribute to delays in clinical interventions. The COmmunicating Narrative Concerns Entered by RNs (CONCERN) Early Warning System (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify patients' deterioration risk up to 42 hours earlier than other EWSs. To test our a priori hypothesis that patients with care teams informed by the CONCERN EWS intervention have a lower mortality rate and shorter length of stay (LOS) than the patients with teams not informed by CONCERN EWS. One-year multisite, pragmatic controlled clinical trial with cluster-randomization of acute and intensive care units to intervention or usual-care groups. Two large U.S. health systems. Adult patients admitted to acute and intensive care units, excluding those on hospice/palliative/comfort care, or with Do Not Resuscitate/Do Not Intubate orders. The CONCERN EWS intervention calculates patient deterioration risk based on nurses' concern levels measured by surveillance documentation patterns, and it displays the categorical risk score (low, increased, high) in the electronic health record (EHR) for care team members. Primary outcomes: in-hospital mortality, LOS; survival analysis was used. Secondary outcomes: cardiopulmonary arrest, sepsis, unanticipated ICU transfers, 30-day hospital readmission. A total of 60 893 hospital encounters (33 024 intervention and 27 869 usual-care) were included. Both groups had similar patient age, race, ethnicity, and illness severity distributions. Patients in the intervention group had a 35.6% decreased risk of death (adjusted hazard ratio [HR], 0.644; 95% confidence interval [CI], 0.532-0.778; P<.0001), 11.2% decreased LOS (adjusted incidence rate ratio, 0.914; 95% CI, 0.902-0.926; P<.0001), 7.5% decreased risk of sepsis (adjusted HR, 0.925; 95% CI, 0.861-0.993; P=.0317), and 24.9% increased risk of unanticipated ICU transfer (adjusted HR, 1.249; 95% CI, 1.093-1.426; P=.0011) compared with patients in the usual-care group. A hospital-wide EWS based on nursing surveillance patterns decreased in-hospital mortality, sepsis, and LOS when integrated into the care team's EHR workflow. ClinicalTrials.gov Identifier: NCT03911687.

Integration of a machine learning model-generated surgical risk score into the EHR and preoperative workflow.

11163 Background: We previously developed a machine learning model (MLM) that accurately predicts postoperative complications (POC) for cancer inpatients. As laborious manual entry of patient data is a barrier for risk calculator use, and risk constantly changes depending on clinical status, we sought to integrate our MLM into the electronic health record (EHR) and preoperative workflow as a readily available surgical risk score (SRS) that is generated using real-time data. Here, we report on the integration process and survey results regarding its functionality and impact on patient care. Methods: A MLM that predicts severe POC (CD Grade ≥3) for cancer inpatients undergoing same-hospitalization operations was previously developed using EHR data. To integrate our MLM into the Epic EHR, we developed a real-time infrastructure (RTI) using the cloud-based platform Azure. EHR data is inputted to RTI and processed in real-time to generate model outputs. These outputs are then sent back to the EHR through the Epic Cognitive Computing Platform to generate each patient’s specific SRS. The SRS is dichotomized as “high” or “low” risk based upon a previously determined threshold and is displayed as a “Best Practice Advisory (BPA)” when a surgical case request is entered. In addition to the automated BPA, the SRS is sent to the primary surgeon as an InBasket Case Message. To elicit feedback of our EHR-integrated SRS, a survey was distributed to surgical providers at our institution. Results: After integration of our MLM into the EHR preoperative workflow, 185 surgeries were completed between November 16, 2021 to July 31, 2023. The 30-day severe POC rate was 27.6%, and our MLM had precision of 44.7% and recall of 74.5%, resulting in AUROC of 0.78 and AUPRC of 0.53. 35 surgical providers (40% primary surgeons, 34% surgical fellows, 26% advanced practice providers) from the divisions of surgical oncology, colorectal surgery, and gynecologic oncology completed our survey in July 2022 and July 2023. 28 (80%) of providers stated that they have interacted with the SRS through either BPA or InBasket Case Messages. Of these providers, 21 (75%) strongly agreed or agreed that the SRS was visible at appropriate times, and 18 (64%) strongly agreed or agreed that the SRS contains relevant information to assess surgical risk. 8 (29%) strongly agreed or agreed that the SRS was used to facilitate preoperative discussion. The 2023 survey additionally asked whether the SRS impacted the decision to operate; among providers who had interacted with the SRS, 4 (36%) strongly agreed or agreed with this statement. Conclusions: Integration of a real-time, MLM-generated SRS into the EHR and preoperative workflow can be successfully performed. While our integrated SRS is visible to surgeons and contains risk assessment felt relevant to the operation, the impact of the SRS on clinical care and decision making remains unclear.

An Electronic Health Record-Integrated Application for Standardizing Care and Monitoring Patients With Autosomal Dominant Polycystic Kidney Disease Enrolled in a Tolvaptan Clinic: Design and Implementation Study.

Tolvaptan is the only US Food and Drug Administration-approved drug to slow the progression of autosomal dominant polycystic kidney disease (ADPKD), but it requires strict clinical monitoring due to potential serious adverse events. We aimed to share our experience in developing and implementing an electronic health record (EHR)-based application to monitor patients with ADPKD who were initiated on tolvaptan. The application was developed in collaboration with clinical informatics professionals based on our clinical protocol with frequent laboratory test monitoring to detect early drug-related toxicity. The application streamlined the clinical workflow and enabled our nursing team to take appropriate actions in real time to prevent drug-related serious adverse events. We retrospectively analyzed the characteristics of the enrolled patients. As of September 2022, a total of 214 patients were enrolled in the tolvaptan program across all Mayo Clinic sites. Of these, 126 were enrolled in the Tolvaptan Monitoring Registry application and 88 in the Past Tolvaptan Patients application. The mean age at enrollment was 43.1 (SD 9.9) years. A total of 20 (9.3%) patients developed liver toxicity, but only 5 (2.3%) had to discontinue the drug. The 2 EHR-based applications allowed consolidation of all necessary patient information and real-time data management at the individual or population level. This approach facilitated efficient staff workflow, monitoring of drug-related adverse events, and timely prescription renewal. Our study highlights the feasibility of integrating digital applications into the EHR workflow to facilitate efficient and safe care delivery for patients enrolled in a tolvaptan program. This workflow needs further validation but could be extended to other health care systems managing chronic diseases requiring drug monitoring.

Frontiers in Operations: Does Physician’s Choice of When to Perform EHR Tasks Influence Total EHR Workload?

Problem definition: Physicians spend more than five hours a day working on Electronic Health Record (EHR) systems and more than an hour doing EHR tasks after the end of the workday. Numerous studies have identified the detrimental effects of excessive EHR use and after-hours work, including physician burnout, physician attrition, and appointment delays. However, EHR time is not purely an exogenous factor because it depends on physician usage behavior that could have important operational consequences. Interestingly, prior literature has not considered this topic rigorously. In this paper, we investigate how physicians’ workflow decisions on when to perform EHR tasks affect: (1) total time on EHR and (2) time spent after work. Methodology/results: Our data comprise around 150,000 appointments from 74 physicians from a large Academic Medical Center Family Medicine unit. Our data set contains detailed, process-level time stamps of appointment progression and EHR use. We find that the effect of working on EHR systems depends on whether the work is done before or after an appointment. Pre-appointment EHR work reduces total EHR workload and after-work hours spent on EHR. Post-appointment EHR work reduces after-work hours on EHR but increases total EHR time. We find that increasing idle time between appointments can encourage both pre- and post-appointment EHR work. Managerial implications: Our results not only help us understand the timing and structure of work on secondary tasks more generally but also will help healthcare administrators create EHR workflows and appointment schedules to reduce physician burnout associated with excessive EHR use. History: This paper has been accepted in the Manufacturing &amp; Service Operations Management Frontiers in Operations Initiative. Funding: The research conducted for this paper received partial funding from the Center of Business for Health at the Kenan-Flagler Business School, University of North Carolina at Chapel Hill. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0028 .

Factors Influencing Electronic Health Record Workflow Integration Among Nurses in Saudi Arabia: Cross-Sectional Study.

Globally, healthcare organizations have transitioned from paper-based documentation to electronic health records (EHR), including in Saudi Arabia. However, the adoption of EHR at the national level in Saudi Arabia needs more attention. Thus, this study aimed to determine the workflow integration of EHR and associated factors. The specific aims were to examine the level of EHR use and workflow integration among nurses, to determine the differences in EHR use and workflow integration based on nurses' demographic characteristics, and to determine the association between the predictive factors and EHR workflow integration. This is a cross-sectional, correlational descriptive study. The data were collected from 293 nurses using the convenience sampling method. The participating nurses completed a questionnaire that included two measures: the Information System Use Survey and the Workflow Integration Survey (WIS). The data were analyzed using descriptive and multivariate statistics with SPSS software. The nurses had a positive perception of EHR use and workflow. The EHR use scores differed based on workplace (P < .01), education level (P < .05), and area of practice (P < .001). Similarly, the EHR workflow integration scores varied according to workplace (P < .05), education level (P < .05), and area of practice (P < .001). Education level and workplace significantly predicted information system use. Furthermore, education level and information system use significantly predicted the EHR integration into nurses' workflow. The nurses expressed a greater perceived use of EHR regarding the integrated health information system, which was a predictor of EHR integration into nurses' workflow.

The Design of the Electronic Health Record in Type 1 Diabetes Centers: Implications for Metrics and Data Availability for a Quality Collaborative.

Systematic and comprehensive data acquisition from the electronic health record (EHR) is critical to the quality of data used to improve patient care. We described EHR tools, workflows, and data elements that contribute to core quality metrics in the Type 1 Diabetes Exchange Quality Improvement Collaborative (T1DX-QI). We conducted interviews with quality improvement (QI) representatives at 13 T1DX-QI centers about their EHR tools, clinic workflows, and data elements. All centers had access to structured data tools, nine had access to patient questionnaires and two had integration with a device platform. There was significant variability in EHR tools, workflows, and data elements, thus the number of available metrics per center ranged from four to 17 at each site. Thirteen centers had information about glycemic outcomes and diabetes technology use. Seven centers had measurements of additional self-management behaviors. Centers captured patient-reported outcomes including social determinants of health (n = 9), depression (n = 11), transition to adult care (n = 7), and diabetes distress (n = 3). Various stakeholders captured data including health care professionals, educators, medical assistants, and QI coordinators. Centers that had a paired staffing model in clinic encounters distributed the burden of data capture across the health care team and was associated with a higher number of available data elements. The lack of standardization in EHR tools, workflows, and data elements captured resulted in variability in available metrics across centers. Further work is needed to support measurement and subsequent improvement in quality of care for individuals with type 1 diabetes.

Electronic Health Record Prompt to Improve Lung Cancer Screening in Primary Care

Lung cancer is the leading cause of cancer death in the U.S. Combusted tobacco use, the primary risk factor, accounts for 90% of all lung cancers. Early detection of lung cancer improves survival, yet lung cancer screening rates are much lower than those of other cancer screening tests. Electronic health record (EHR) systems are an underutilized tool that could improve screening rates. This study was conducted in the Rutgers Robert Wood Johnson Medical Group, a university-affiliated network in New Brunswick, NJ. Two novel EHR workflow prompts were implemented on July 1, 2018. These prompts included fields to determine tobacco use and lung cancer screening eligibility and facilitated low-dose computed tomography ordering for eligible patients. The prompts were designed to improve tobacco use data entry, allowing for better lung cancer screening eligibility identification. Data were analyzed in 2022 retrospectively for the period July 1, 2017 to June 30, 2019. The analyses represented 48,704 total patient visits. The adjusted odds of patient record completeness to determine eligibility for low-dose computed tomography (AOR=1.19, 95% CI=1.15, 1.23), eligibility for low-dose computed tomography (AOR=1.59, 95% CI=1.38, 1.82), and whether low-dose computed tomography was ordered (AOR=1.04, 95% CI=1.01, 1.07) all significantly increased after the electronic medical record prompts were implemented. These findings show the utility and benefit of EHR prompts in primary care settings to increase identification for lung cancer screening eligibility as well as increased low-dose computed tomography ordering.

Identifying a Clinical Informatics or Electronic Health Record Expert Witness for Medical Professional Liability Cases.

The health care field is experiencing widespread electronic health record (EHR) adoption. New medical professional liability (i.e., malpractice) cases will likely involve the review of data extracted from EHRs as well as EHR workflows, audit logs, and even the potential role of the EHR in causing harm. Reviewing printed versions of a patient's EHRs can be difficult due to differences in printed versus on-screen presentations, redundancies, and the way printouts are often grouped by document or information type rather than chronologically. Simply recreating an accurate timeline often requires experts with training and experience in designing, developing, using, and reviewing EHRs and audit logs. Additional expertise is required if questions arise about data's meaning, completeness, accuracy, and timeliness or ways that the EHR's user interface or automated clinical decision support tools may have contributed to alleged events. Such experts often come from the sociotechnical field of clinical informatics that studies the design, development, implementation, use, and evaluation of information and communications technology, specifically, EHRs. Identifying well-qualified EHR experts to aid a legal team is challenging. Based on literature review and experience reviewing cases, we identified seven criteria to help in this assessment. The criteria are education in clinical informatics; clinical informatics knowledge; experience with EHR design, development, implementation, and use; communication skills; academic publications on clinical informatics; clinical informatics certification; and membership in informatics-related professional organizations. While none of these criteria are essential, understanding the breadth and depth of an individual's qualifications in each of these areas can help identify a high-quality, clinical informatics expert witness.

Effects of the electronic health record on job satisfaction of academic pediatric faculty

Objective(s)To evaluate the association of Electronic Health Record (EHR) skills and available support with job satisfaction for pediatric faculty at an academic institution. To identify key opportunities for improvement. Study DesignCross-sectional study of pediatric academic faculty physicians using a REDCap survey to inquire about faculty EHR skills, support services, and associations between EHR workflow and job satisfaction. Results were analyzed using bivariate testing. ResultsThe majority of respondents (n = 127, response rate 37%), rated the effect of EHR workflow on job satisfaction as neutral (36%) or negative (44%). Users with more EHR skills were more likely to indicate a positive effect of the EHR on overall job satisfaction (p = 0.019). 7% of respondents had none of the EHR skills queried and few felt that initial training (35%) or the Information Technology department (26%) were useful in acquiring skills. Two similar divisions, one with three and one without Physician Builders (providers with specialized training in EHR personalization), had statistically significant different EHR satisfaction ratings (p = 0.0012). ConclusionsMost faculty indicated a negative/neutral effect of the EHR on their overall job satisfaction. Users who indicated more EHR skills had a higher satisfaction rating. Existing training and support were not helpful to users. The division with the most Physician Builders ranked highest in satisfaction. We speculate that 1) adding EHR skills could increase overall job satisfaction and 2) adding Physician Builder resources could increase skills and satisfaction.

User centered clinical decision support to implement initiation of buprenorphine for opioid use disorder in the emergency department: EMBED pragmatic cluster randomized controlled trial

ObjectiveTo determine the effect of a user centered clinical decision support tool versus usual care on rates of initiation of buprenorphine in the routine emergency care of individuals with opioid...

Improved 30-Day Heart Failure Readmissions Following Implementation of an Advanced Cardiovascular Education (ACE) Academy.

Heart failure readmissions are common, though some are preventable through evidence-based management. Despite outperforming national benchmarks for 30-day readmissions, compliance with an evidence-based institutional heart failure management pathway was inconsistent. The purpose of this project was to reduce 30-day heart failure readmission rates through an educational intervention and an electronic health record (EHR) redesign. The cardiac services nursing leadership team conducted an education and documentation needs assessment to identify knowledge gaps and practical barriers to effective utilization of evidence-based interventions for heart failure management. This intervention included an Advanced Cardiovascular Education (ACE) Academy and an EHR workflow redesign for clinical and supportive nursing staff. The 30-day heart failure readmission rates reduced immediately following the intervention, and rates continued to decrease over a 3-year follow-up. Even among hospitals outperforming national benchmarks, 30-day heart failure readmissions can be reduced and sustained with enhanced education and EHR redesign.

Usability of a perioperative medication-related clinical decision support software application: a randomized controlled trial.

We developed a comprehensive, medication-related clinical decision support (CDS) software prototype for use in the operating room. The purpose of this study was to compare the usability of the CDS software to the current standard electronic health record (EHR) medication administration and documentation workflow. The primary outcome was the time taken to complete all simulation tasks. Secondary outcomes were the total number of mouse clicks and the total distance traveled on the screen in pixels. Forty participants were randomized and assigned to complete 7 simulation tasks in 1 of 2 groups: (1) the CDS group (n = 20), who completed tasks using the CDS and (2) the Control group (n = 20), who completed tasks using the standard medication workflow with retrospective manual documentation in our anesthesia information management system. Blinding was not possible. We video- and audio-recorded the participants to capture quantitative data (time on task, mouse clicks, and pixels traveled on the screen) and qualitative data (think-aloud verbalization). The CDS group mean total task time (402.2 ± 85.9 s) was less than the Control group (509.8 ± 103.6 s), with a mean difference of 107.6 s (95% confidence interval [CI], 60.5-179.5 s, P < .001). The CDS group used fewer mouse clicks (26.4 ± 4.5 clicks) than the Control group (56.0 ± 15.0 clicks) with a mean difference of 29.6 clicks (95% CI, 23.2-37.6, P < .001). The CDS group had fewer pixels traveled on the computer monitor (59.5 ± 20.0 thousand pixels) than the Control group (109.3 ± 40.8 thousand pixels) with a mean difference of 49.8 thousand pixels (95% CI, 33.0-73.7, P < .001). The perioperative medication-related CDS software prototype substantially outperformed standard EHR workflow by decreasing task time and improving efficiency and quality of care in a simulation setting.

57 EMBED: A Pragmatic Trial of User-Centered Clinical Decision Support to Implement EMergency Department-Initiated BuprenorphinE for Opioid Use Disorder

Emergency departments (ED) are a critical access point for individuals with opioid use disorder (OUD). Medications for OUD decrease: (1) mortality after nonfatal overdose, (2) withdrawal symptoms, (3) craving, and (4) illicit opioid use. Buprenorphine (BUP) can be safely initiated in the ED and increases retention in outpatient treatment programs. Adoption of ED-initiated BUP has been slow due to lack of physician knowledge, experience, and time. The study objective was to determine the effect of a user-friendly clinical decision support system (CDS) on rates of ED-initiated BUP in the routine emergency care of individuals with OUD. A parallel group randomized pragmatic trial was conducted in 18 EDs across 5 health care systems in the United States. The EMBED intervention is a user-centered, physician-facing CDS system integrated into the electronic health record (EHR) to facilitate ED-initiation of BUP by: (1) determining a diagnosis of OUD, (2) assessing withdrawal severity, (3) motivating patients to accept treatment, and (4) automating EHR workflow including clinical and after visit documentation, order entry, prescribing, and referral. EDs were randomly allocated to the intervention versus usual care using stratified covariate constrained randomization of clusters to arms with a ratio of 1:1. Constraining covariates included EHR vendor, patient volume, practice setting, ongoing resources for OUD patients, and proportion of attending physicians waivered to prescribe BUP. Participants included adult ED OUD patients meeting a predetermined EHR phenotype for OUD and the attending emergency physicians caring for them. Control site visits associated with attending physicians who practiced at both intervention and control sites were excluded. The primary outcome was the rate of ED-initiated BUP (administration or prescription of BUP). Implementation outcomes as compared to usual care were measured using the RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework. 354, 800 ED visits (166, 184 intervention and 188, 616 control) from November 2019-March 2021 were assessed for eligibility. Overall, 4, 561 OUD patients (2, 457 intervention, 2, 104 controls) under the care of 607 attending physicians (335 intervention, 272 controls) were included in the analysis. Among these patients, 33% and 36% were female and the mean age (SD) was 39.4 y (13.3) and 38.5 y (13.0) in the intervention and control groups, respectively. Though the analysis is not complete at this time, full results are forthcoming and will be available in advance of the ACEP21 Research Forum. This pragmatic trial involving patients with OUD will report the effect of a user-centered CDS system that automates EHR workflow to initiate BUP in the ED.

Otolaryngology resident clinic participation and attending electronic health record efficiency-A user activity logs study.

ObjectivesIn an era of increasing electronic health record (EHR) use monitoring and optimization, this study aims to quantify resident contributions and measure the effect of otolaryngology resident coverage in clinic on attending otolaryngologist EHR usage.MethodsIn one academic otolaryngology department, monthly attending provider efficiency profile metrics, data collected by the EHR vendor (Epic Systems Corporation) between January and June 2019 were accessed. Using weekly resident schedules, resident coverage of attending outpatient clinics was categorized by junior (post‐graduate year [PGY] 1‐3) and senior levels (PGY‐4 through fellows) and correlated with attending EHR metrics using linear mixed effect models.ResultsThirteen attending otolaryngologists on average spent 58.8 minutes per day interacting with the EHR. In modeling, one day of trainee clinic coverage was associated with a 22 minutes reduction (95% CI [−37, −6]) in total daily attending EHR time and a 12 minutes reduction (95% CI [−21, −3]) in per day note time (P < .05). When stratifying by trainee level, senior coverage was associated with significantly reduced total daily time in EHR, per day time in clinical review, notes, and orders, as well as per appointment time in notes and clinical review (P < .05). Junior coverage was only associated with reduced per day note time (P < .05).ConclusionsIncreasing resident clinic coverage was inversely related to attending time spent in the EHR and writing notes. Resident contributions to EHR workflows and hospital system productivity should continue to be studied and considered in EHR use measurement studies.Level of EvidenceLevel 4.

Integrated Digital Health System Tools to Support Decision Making and Treatment Preparation in CKD: The PREPARE NOW Study

Rationale & ObjectiveDigital health system tools to support shared decision making and preparation for kidney replacement treatments for patients with chronic kidney disease (CKD) are needed.Study DesignDescriptive study of the implementation of digital infrastructure to support a patient-centered health system intervention.Setting & Participants4 CKD clinics within a large integrated health system.ExposureWe developed an integrated suite of digital engagement tools to support patients’ shared decision making and preparation for kidney failure treatments. Tools included an automated CKD patient registry and risk prediction algorithm within the electronic health record (EHR) to identify and prioritize patients in need of nurse case management to facilitate shared decision making and preparation for kidney replacement treatments, an electronic patient-facing values clarification tool, a tracking application to document patients’ preparation for treatments, and an EHR work flow to broadcast patients’ treatment preferences to all health care providers.OutcomesUptake and acceptability.Analytic ApproachMixed methods.ResultsFrom July 1, 2017, through June 30, 2018, the CKD registry identified 1,032 patients in 4 nephrology clinics, of whom 243 (24%) were identified as high risk for progressing to kidney failure within 2 years. Kidney Transitions Specialists enrolled 117 (48%) high-risk patients by the end of year 1. The values tool was completed by 30/33 (91%) patients who attended kidney modality education. Nurse case managers used the tracking application for 100% of patients to document 287 planning steps for kidney replacement therapy. Most (87%) high-risk patients had their preferred kidney replacement modality documented and displayed in the EHR. Nurse case managers reported that the tools facilitated their identification of patients needing support and their navigation activities.LimitationsSingle institution, short duration.ConclusionsDigital health system tools facilitated rapid identification of patients needing shared and informed decision making and their preparation for kidney replacement treatments.FundingThis work was supported through a Patient-Centered Outcomes Research Institute (PCORI) Project Program Award (IHS-1409-20967).Trial registrationClinicalTrials.gov NCT02722382.

Using Log Data to Measure Provider EHR Activity at a Cancer Center during Rapid Telemedicine Deployment.

Accurate metrics of provider activity within the electronic health record (EHR) are critical to understand workflow efficiency and target optimization initiatives. We utilized newly described, log-based core metrics at a tertiary cancer center during rapid escalation of telemedicine secondary to initial coronavirus disease-2019 (COVID-19) peak onset of social distancing restrictions at our medical center (COVID-19 peak). These metrics evaluate the impact on total EHR time, work outside of work, time on documentation, time on prescriptions, inbox time, teamwork for orders, and undivided attention patients receive during an encounter. Our study aims were to evaluate feasibility of implementing these metrics as an efficient tool to optimize provider workflow and to track impact on workflow to various provider groups, including physicians, advanced practice providers (APPs), and different medical divisions, during times of significant policy change in the treatment landscape. Data compilation and analysis was retrospectively performed in Tableau utilizing user and schedule data obtained from Cerner Millennium PowerChart and our internal scheduling software. We analyzed three distinct time periods: the 3 months prior to the initial COVID-19 peak, the 3 months during peak, and 3 months immediately post-peak. Application of early COVID-19 restrictions led to a significant increase of telemedicine encounters from baseline <1% up to 29.2% of all patient encounters. During initial peak period, there was a significant increase in total EHR time, work outside of work, time on documentation, and inbox time for providers. Overall APPs spent significantly more time in the EHR compared with physicians. All of the metrics returned to near baseline after the initial COVID-19 peak in our area. Our analysis showed that implementation of these core metrics is both feasible and can provide an accurate representation of provider EHR workflow adjustments during periods of change, while providing a basis for cross-vendor and cross-institutional analysis.

Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning.

The characteristics of clinician activities while interacting with electronic health record (EHR) systems can influence the time spent in EHRs and workload. This study aims to characterize EHR activities as tasks and define novel, data-driven metrics. We leveraged unsupervised learning approaches to learn tasks from sequences of events in EHR audit logs. We developed metrics characterizing the prevalence of unique events and event repetition and applied them to categorize tasks into 4 complexity profiles. Between these profiles, Mann-Whitney U tests were applied to measure the differences in performance time, event type, and clinician prevalence, or the number of unique clinicians who were observed performing these tasks. In addition, we apply process mining frameworks paired with clinical annotations to support the validity of a sample of our identified tasks. We apply our approaches to learn tasks performed by nurses in the Vanderbilt University Medical Center neonatal intensive care unit. We examined EHR audit logs generated by 33 neonatal intensive care unit nurses resulting in 57234 sessions and 81 tasks. Our results indicated significant differences in performance time for each observed task complexity profile. There were no significant differences in clinician prevalence or in the frequency of viewing and modifying event types between tasks of different complexities. We presented a sample of expert-reviewed, annotated task workflows supporting the interpretation of their clinical meaningfulness. The use of the audit log provides an opportunity to assist hospitals in further investigating clinician activities to optimize EHR workflows.

Building the evidence-base to reduce electronic health record-related clinician burden.

Clinicians face competing pressures of being clinically productive while using imperfect electronic health record (EHR) systems and maximizing face-to-face time with patients. EHR use is increasingly associated with clinician burnout and underscores the need for interventions to improve clinicians' experiences. With an aim of addressing this need, we share evidence-based informatics approaches, pragmatic next steps, and future research directions to improve 3 of the highest contributors to EHR burden: (1) documentation, (2) chart review, and (3) inbox tasks. These approaches leverage speech recognition technologies, natural language processing, artificial intelligence, and redesign of EHR workflow and user interfaces. We also offer a perspective on how EHR vendors, healthcare system leaders, and policymakers all play an integral role while sharing responsibility in helping make evidence-based sociotechnical solutions available and easy to use.