AI-Generated Draft Replies Integrated Into Health Records and Physicians' Electronic Communication.

Importance: Timely tests are warranted to assess the association between generative artificial intelligence (GenAI) use and physicians' work efforts. Objective: To investigate the association between GenAI-drafted replies for patient messages and physician time spent on answering messages and the length of replies. Design, Setting, and Participants: Randomized waiting list quality improvement (QI) study from June to August 2023 in an academic health system. Primary care physicians were randomized to an immediate activation group and a delayed activation group. Data were analyzed from August to November 2023. Exposure: Access to GenAI-drafted replies for patient messages. Main Outcomes and Measures: Time spent (1) reading messages, (2) replying to messages, (3) length of replies, and (4) physician likelihood to recommend GenAI drafts. The a priori hypothesis was that GenAI drafts would be associated with less physician time spent reading and replying to messages. A mixed-effects model was used. Results: Fifty-two physicians participated in this QI study, with 25 randomized to the immediate activation group and 27 randomized to the delayed activation group. A contemporary control group included 70 physicians. There were 18 female participants (72.0%) in the immediate group and 17 female participants (63.0%) in the delayed group; the median age range was 35-44 years in the immediate group and 45-54 years in the delayed group. The median (IQR) time spent reading messages in the immediate group was 26 (11-69) seconds at baseline, 31 (15-70) seconds 3 weeks after entry to the intervention, and 31 (14-70) seconds 6 weeks after entry. The delayed group's median (IQR) read time was 25 (10-67) seconds at baseline, 29 (11-77) seconds during the 3-week waiting period, and 32 (15-72) seconds 3 weeks after entry to the intervention. The contemporary control group's median (IQR) read times were 21 (9-54), 22 (9-63), and 23 (9-60) seconds in corresponding periods. The estimated association of GenAI was a 21.8% increase in read time (95% CI, 5.2% to 41.0%; P = .008), a -5.9% change in reply time (95% CI, -16.6% to 6.2%; P = .33), and a 17.9% increase in reply length (95% CI, 10.1% to 26.2%; P < .001). Participants recognized GenAI's value and suggested areas for improvement. Conclusions and Relevance: In this QI study, GenAI-drafted replies were associated with significantly increased read time, no change in reply time, significantly increased reply length, and some perceived benefits. Rigorous empirical tests are necessary to further examine GenAI's performance. Future studies should examine patient experience and compare multiple GenAIs, including those with medical training.

Artificial Intelligence-Generated Draft Replies to Patient Inbox Messages.

Importance: The emergence and promise of generative artificial intelligence (AI) represent a turning point for health care. Rigorous evaluation of generative AI deployment in clinical practice is needed to inform strategic decision-making. Objective: To evaluate the implementation of a large language model used to draft responses to patient messages in the electronic inbox. Design, Setting, and Participants: A 5-week, prospective, single-group quality improvement study was conducted from July 10 through August 13, 2023, at a single academic medical center (Stanford Health Care). All attending physicians, advanced practice practitioners, clinic nurses, and clinical pharmacists from the Divisions of Primary Care and Gastroenterology and Hepatology were enrolled in the pilot. Intervention: Draft replies to patient portal messages generated by a Health Insurance Portability and Accountability Act-compliant electronic health record-integrated large language model. Main Outcomes and Measures: The primary outcome was AI-generated draft reply utilization as a percentage of total patient message replies. Secondary outcomes included changes in time measures and clinician experience as assessed by survey. Results: A total of 197 clinicians were enrolled in the pilot; 35 clinicians who were prepilot beta users, out of office, or not tied to a specific ambulatory clinic were excluded, leaving 162 clinicians included in the analysis. The survey analysis cohort consisted of 73 participants (45.1%) who completed both the presurvey and postsurvey. In gastroenterology and hepatology, there were 58 physicians and APPs and 10 nurses. In primary care, there were 83 physicians and APPs, 4 nurses, and 8 clinical pharmacists. The mean AI-generated draft response utilization rate across clinicians was 20%. There was no change in reply action time, write time, or read time between the prepilot and pilot periods. There were statistically significant reductions in the 4-item physician task load score derivative (mean [SD], 61.31 [17.23] presurvey vs 47.26 [17.11] postsurvey; paired difference, -13.87; 95% CI, -17.38 to -9.50; P < .001) and work exhaustion scores (mean [SD], 1.95 [0.79] presurvey vs 1.62 [0.68] postsurvey; paired difference, -0.33; 95% CI, -0.50 to -0.17; P < .001). Conclusions and Relevance: In this quality improvement study of an early implementation of generative AI, there was notable adoption, usability, and improvement in assessments of burden and burnout. There was no improvement in time. Further code-to-bedside testing is needed to guide future development and organizational strategy.

Drivers of telemedicine in primary care clinics at a large academic medical centre.

BACKGROUND: COVID-19 disrupted healthcare routines and prompted rapid telemedicine implementation. We investigated the drivers of visit modality selection (telemedicine versus in-person) in primary care clinics at an academic medical centre. METHODS: We used electronic medical record data from March 2020 to May 2022 from 13 primary care clinics (N = 21,031 new, N = 207,292 return visits), with 55% overall telemedicine use. Hierarchical logistic regression and cross-validation methods were used to estimate the variation in visit modality explained by the patient, clinician and visit factors as measured by the mean-test area under the curve (AUC). RESULTS: There was significant variation in telemedicine use across clinicians (ranging from 0-100%) for the same visit diagnosis. The strongest predictors of telemedicine were the clinician seen for new visits (mean AUC of 0.79) and the primary visit diagnosis for return visits (0.77). Models based on all patient characteristics combined accounted for relatively little variation in modality selection, 0.54 for new and 0.58 for return visits, respectively. Amongst patient characteristics, males, patients over 65 years, Asians and patient's with non-English language preferences used less telemedicine; however, those using interpreter services used significantly more telemedicine. CONCLUSION: Clinician seen and primary visit diagnoses were the best predictors of visit modality. The distinction between new and return visits and the minimal impact of patient characteristics on visit modality highlights the complexity of clinical care and warrants research approaches that go beyond linear models to uncover the emergent causal effects of specific technology features mediated by tasks, people and organisations.

Perspectives on the Intersection of Electronic Health Records and Health Care Team Communication, Function, and Well-being.

Importance: Understanding of the interplay between the electronic health record (EHR), health care team relations, and physician well-being is currently lacking. Approaches to cultivate interpersonal interactions may be necessary to complement advancements in health information technology with high-quality team function. Objective: To examine ways in which the EHR, health care team functioning, and physician well-being intersect and interact. Design, Setting, and Participants: Secondary qualitative analysis of semistructured interview data from 2 studies used keyword-in-context approaches to identify excerpts related to teams. Thematic analysis was conducted using pattern coding, then organized using the relationship-centered organization model. Two health care organizations in California from March 16 to October 13, 2017, and February 28 to April 21, 2022, participated, with respondents including attending and resident physicians. Main Outcome and Measures: Across data sets, themes centered around the interactions between the EHR, health care team functioning, and physician well-being. The first study data focused on EHR-related distressing events and their role in attending physician and resident physician emotions and actions. The second study focused on EHR use and daily EHR irritants. Results: The 73 respondents included attending physicians (53 [73%]) and resident physicians (20 [27%]). Demographic data were not collected. Participants worked in ambulatory specialties (33 [45%]), hospital medicine (10 [14%]), and surgery (10 [14%]). The EHR was reported to be the dominant communication modality among all teams. Interviewees indicated that the EHR facilitates task-related communication and is well suited to completing simple, uncomplicated tasks. However, EHR-based communication limited the rich communication and social connection required for building relationships and navigating conflict. The EHR was found to negatively impact team function by promoting disagreement and introducing areas of conflict into team relationships related to medical-legal pressures, role confusion, and undefined norms around EHR-related communication. In addition, interviewees expressed that physician EHR-related distress affects interactions within the team, eroding team well-being. Conclusions and Relevance: In this study, the EHR supported task-oriented and efficient communication among team members to get work done and care for patients; however, participants felt that the technology shifts attention away from the human needs of the care team that are necessary for developing relationships, building trust, and resolving conflicts. Interventions to cultivate interpersonal interactions and team function are necessary to complement the efficiency benefits of health information technology.

The impact of expanded telehealth availability on primary care utilization.

The expanded availability of telehealth due to the COVID-19 pandemic presents a concern that telehealth may result in an unnecessary increase in utilization. We analyzed 4,114,651 primary care encounters (939,134 unique patients) from three healthcare systems between 2019 and 2021 and found little change in utilization as telehealth became widely available. Results suggest telehealth availability is not resulting in additional primary care visits and federal policies should support telehealth use.

Interruptive Electronic Alerts for Choosing Wisely Recommendations: A Cluster Randomized Controlled Trial.

OBJECTIVE: To assess the efficacy of interruptive electronic alerts in improving adherence to the American Board of Internal Medicine's Choosing Wisely recommendations to reduce unnecessary laboratory testing. MATERIALS AND METHODS: We administered 5 cluster randomized controlled trials simultaneously, using electronic medical record alerts regarding prostate-specific antigen (PSA) testing, acute sinusitis treatment, vitamin D testing, carotid artery ultrasound screening, and human papillomavirus testing. For each alert, we assigned 5 outpatient clinics to an interruptive alert and 5 were observed as a control. Primary and secondary outcomes were the number of postalert orders per 100 patients at each clinic and number of triggered alerts divided by orders, respectively. Post hoc analysis evaluated whether physicians experiencing interruptive alerts reduced their alert-triggering behaviors. RESULTS: Median postalert orders per 100 patients did not differ significantly between treatment and control groups; absolute median differences ranging from 0.04 to 0.40 for PSA testing. Median alerts per 100 orders did not differ significantly between treatment and control groups; absolute median differences ranged from 0.004 to 0.03. In post hoc analysis, providers receiving alerts regarding PSA testing in men were significantly less likely to trigger additional PSA alerts than those in the control sites (Incidence Rate Ratio 0.12, 95% CI [0.03-0.52]). DISCUSSION: Interruptive point-of-care alerts did not yield detectable changes in the overall rate of undesired orders or the order-to-alert ratio between active and silent sites. Complementary behavioral or educational interventions are likely needed to improve efforts to curb medical overuse. CONCLUSION: Implementation of interruptive alerts at the time of ordering was not associated with improved adherence to 5 Choosing Wisely guidelines. TRIAL REGISTRATION: NCT02709772.

Assessing the impact of the COVID-19 pandemic on clinician ambulatory electronic health record use.

OBJECTIVE: The COVID-19 pandemic changed clinician electronic health record (EHR) work in a multitude of ways. To evaluate how, we measure ambulatory clinician EHR use in the United States throughout the COVID-19 pandemic. MATERIALS AND METHODS: We use EHR meta-data from ambulatory care clinicians in 366 health systems using the Epic EHR system in the United States from December 2019 to December 2020. We used descriptive statistics for clinician EHR use including active-use time across clinical activities, time after-hours, and messages received. Multivariable regression to evaluate total and after-hours EHR work adjusting for daily volume and organizational characteristics, and to evaluate the association between messages and EHR time. RESULTS: Clinician time spent in the EHR per day dropped at the onset of the pandemic but had recovered to higher than prepandemic levels by July 2020. Time spent actively working in the EHR after-hours showed similar trends. These differences persisted in multivariable models. In-Basket messages received increased compared with prepandemic levels, with the largest increase coming from messages from patients, which increased to 157% of the prepandemic average. Each additional patient message was associated with a 2.32-min increase in EHR time per day (P < .001). DISCUSSION: Clinicians spent more total and after-hours time in the EHR in the latter half of 2020 compared with the prepandemic period. This was partially driven by increased time in Clinical Review and In-Basket messaging. CONCLUSIONS: Reimbursement models and workflows for the post-COVID era should account for these demands on clinician time that occur outside the traditional visit.

Using a Real-Time Locating System to Evaluate the Impact of Telemedicine in an Emergency Department During COVID-19: Observational Study.

BACKGROUND: Telemedicine has been deployed by health care systems in response to the COVID-19 pandemic to enable health care workers to provide remote care for both outpatients and inpatients. Although it is reasonable to suspect telemedicine visits limit unnecessary personal contact and thus decrease the risk of infection transmission, the impact of the use of such technology on clinician workflows in the emergency department is unknown. OBJECTIVE: This study aimed to use a real-time locating system (RTLS) to evaluate the impact of a new telemedicine platform, which permitted clinicians located outside patient rooms to interact with patients who were under isolation precautions in the emergency department, on in-person interaction between health care workers and patients. METHODS: A pre-post analysis was conducted using a badge-based RTLS platform to collect movement data including entrances and duration of stay within patient rooms of the emergency department for nursing and physician staff. Movement data was captured between March 2, 2020, the date of the first patient screened for COVID-19 in the emergency department, and April 20, 2020. A new telemedicine platform was deployed on March 29, 2020. The number of entrances and duration of in-person interactions per patient encounter, adjusted for patient length of stay, were obtained for pre- and postimplementation phases and compared with t tests to determine statistical significance. RESULTS: There were 15,741 RTLS events linked to 2662 encounters for patients screened for COVID-19. There was no significant change in the number of in-person interactions between the pre- and postimplementation phases for both nurses (5.7 vs 7.0 entrances per patient, P=.07) and physicians (1.3 vs 1.5 entrances per patient, P=.12). Total duration of in-person interactions did not change (56.4 vs 55.2 minutes per patient, P=.74) despite significant increases in telemedicine videoconference frequency (0.6 vs 1.3 videoconferences per patient, P<.001 for change in daily average) and duration (4.3 vs 12.3 minutes per patient, P<.001 for change in daily average). CONCLUSIONS: Telemedicine was rapidly adopted with the intent of minimizing pathogen exposure to health care workers during the COVID-19 pandemic, yet RTLS movement data did not reveal significant changes for in-person interactions between staff and patients under investigation for COVID-19 infection. Additional research is needed to better understand how telemedicine technology may be better incorporated into emergency departments to improve workflows for frontline health care clinicians.

Malingering in the Emergency Setting.

Malingering is the intentional fabrication of symptoms for material gain. Malingering among frequent utilizers and patients with psychiatric symptoms is suspected to be common in emergency settings but difficult to detect and manage. We present a case report of a 50-year-old man feigning psychosis and suicidality in order to obtain shelter. Strategies to identify malingered psychiatric symptoms are presented. Understanding how malingering is adaptational can help clinicians begin to manage these patients and symptoms in a compassionate manner that preserves healthcare resources, improves patient care, and reduces the risk of burnout for clinicians.

Novel Nonproprietary Measures of Ambulatory Electronic Health Record Use Associated with Physician Work Exhaustion.

BACKGROUND: Accumulating evidence indicates an association between physician electronic health record (EHR) use after work hours and occupational distress including burnout. These studies are based on either physician perception of time spent in EHR through surveys which may be prone to bias or by utilizing vendor-defined EHR use measures which often rely on proprietary algorithms that may not take into account variation in physician's schedules which may underestimate time spent on the EHR outside of scheduled clinic time. The Stanford team developed and refined a nonproprietary EHR use algorithm to track the number of hours a physician spends logged into the EHR and calculates the Clinician Logged-in Outside Clinic (CLOC) time, the number of hours spent by a physician on the EHR outside of allocated time for patient care. OBJECTIVE: The objective of our study was to measure the association between CLOC metrics and validated measures of physician burnout and professional fulfillment. METHODS: Physicians from adult outpatient Internal Medicine, Neurology, Dermatology, Hematology, Oncology, Rheumatology, and Endocrinology departments who logged more than 8 hours of scheduled clinic time per week and answered the annual wellness survey administered in Spring 2019 were included in the analysis. RESULTS: We observed a statistically significant positive correlation between CLOC ratio (defined as the ratio of CLOC time to allocated time for patient care) and work exhaustion (Pearson's r = 0.14; p = 0.04), but not interpersonal disengagement, burnout, or professional fulfillment. CONCLUSION: The CLOC metrics are potential objective EHR activity-based markers associated with physician work exhaustion. Our results suggest that the impact of time spent on EHR, while associated with exhaustion, does not appear to be a dominant factor driving the high rates of occupational burnout in physicians.

Physicians Leading Physicians: A Physician Engagement Intervention Decreases Inappropriate Use of IICU Level of Care Accommodations.

Following the adoption of an acuity-adaptable unit model in an academic medical center, a $13M increase in cost of intermediate intensive care unit (IICU) accommodations was observed. The authors followed A3 methodology to determine the root cause of this increase and developed a 3-prong intervention centered on physician engagement, given that physicians have the ability to order a patient's level of care. This intervention consisted of: (1) identifying physician champions to promote appropriate IICU use, (2) visual changes to essential electronic medical record tools, and (3) data-driven feedback to physician champions. In the year following intervention deployment, average IICU length of stay decreased from 1.08 to 0.62 days and average IICU use decreased from 21.4% to 12.3%, corresponding to ~$5.7M cost savings with no significant change in balancing measures observed. Together, these results demonstrate that a multicomponent intervention aimed at engaging physicians reduced inappropriate IICU use with no increase in adverse events.

Distress Screening Through Patient-Reported Outcomes Measurement Information System (PROMIS) at an Academic Cancer Center and Network Site: Implementation of a Hybrid Model.

PURPOSE: Cancer care guidelines recommend regular distress screening of patients, with approximately one in three patients with cancer experiencing significant distress. However, the implementation of such programs is variable and inconsistent. We sought to assess the feasibility of implementing a hybrid electronic and paper screening tool for distress in all patients coming to a large academic cancer center and an associated integrated network site. METHODS: Patients at an academic cancer center (Stanford Cancer Center) and its associated integrated network site received either an electronic or on-paper modified Patient-Reported Outcomes Measurement Information System-Global Health questionnaire, to assess overall health and distress. We used the Reach, Effectiveness, Adoption, Implementation, and Maintenance implementation framework to test and report on the feasibility of using this questionnaire. Iterative workflow changes were made to implement the questionnaire throughout the healthcare system, including processes to integrate with existing electronic health records. RESULTS: From June 2015 to December 2017, 53,954 questionnaires representing 26,242 patients were collected. Approximately 30% of the questionnaires were completed before the visit on an electronic patient portal. The number of patients meeting the positive screen threshold remained around 40% throughout the study period. Following assessment, there were 3,763 referrals to cancer supportive services. Of note, those with a positive screen were more likely to have a referral to supportive care (odds ratio, 6.4; 95% CI, 5.8 to 6.9; P < .0001). CONCLUSION: The hybrid electronic and on-paper use of a commonly available patient-reported outcome tool, Patient-Reported Outcomes Measurement Information System-Global Health, as a large-scale distress screening method, is feasible at a large integrated cancer center.

Assessment of Electronic Health Record Use Between US and Non-US Health Systems.

Importance: Understanding how the electronic health record (EHR) system changes clinician work, productivity, and well-being is critical. Little is known regarding global variation in patterns of use. Objective: To provide insights into which EHR activities clinicians spend their time doing, the EHR tools they use, the system messages they receive, and the amount of time they spend using the EHR after hours. Design, Setting, and Participants: This cross-sectional study analyzed the deidentified metadata of ambulatory care health systems in the US, Canada, Northern Europe, Western Europe, the Middle East, and Oceania from January 1, 2019, to August 31, 2019. All of these organizations used the EHR software from Epic Systems and represented most of Epic Systems's ambulatory customer base. The sample included all clinicians with scheduled patient appointments, such as physicians and advanced practice practitioners. Exposures: Clinician EHR use was tracked by deidentified and aggregated metadata across a variety of clinical activities. Main Outcomes and Measures: Descriptive statistics for clinician EHR use included time spent on clinical activities, note documentation (as measured by the percentage of characters in the note generated by automated or manual data entry source), messages received, and time spent after hours. Results: A total of 371 health systems were included in the sample, of which 348 (93.8%) were located in the US and 23 (6.2%) were located in other countries. US clinicians spent more time per day actively using the EHR compared with non-US clinicians (mean time, 90.2 minutes vs 59.1 minutes; P < .001). In addition, US clinicians vs non-US clinicians spent significantly more time performing 4 clinical activities: notes (40.7 minutes vs 30.7 minutes; P < .001), orders (19.5 minutes vs 8.75 minutes; P < .001), in-basket messages (12.5 minutes vs 4.80 minutes; P < .001), and clinical review (17.6 minutes vs 14.8 minutes; P = .01). Clinicians in the US composed more automated note text than their non-US counterparts (77.5% vs 60.8% of note text; P < .001) and received statistically significantly more messages per day (33.8 vs 12.8; P < .001). Furthermore, US clinicians used the EHR for a longer time after hours, logging in 26.5 minutes per day vs 19.5 minutes per day for non-US clinicians (P = .01). The median US clinician spent as much time actively using the EHR per day (90.1 minutes) as a non-US clinician in the 99th percentile of active EHR use time per day (90.7 minutes) in the sample. These results persisted after controlling for organizational characteristics, including structure, type, size, and daily patient volume. Conclusions and Relevance: This study found that US clinicians compared with their non-US counterparts spent substantially more time actively using the EHR for a wide range of clinical activities or tasks. This finding suggests that US clinicians have a greater EHR burden that may be associated with nontechnical factors, which policy makers and health system leaders should consider when addressing clinician wellness.

Tolman's Electronic Parameter of the Ligand Predicts Phase in the Cation Exchange to CuFeS2Nanoparticles.

The metastable and thermodynamically favored phases of CuFeS2 are shown to be alternatively synthesized during partial cation exchange of hexagonal Cu2S using various phosphorus-containing ligands. Transmission electron microscopy and energy dispersive spectroscopy mapping confirm the retention of the particle morphology and the approximate CuFeS2 stoichiometry. Powder X-ray diffraction patterns and refinements indicate that the resulting phase mixtures of metastable wurtzite-like CuFeS2 versus tetragonal chalcopyrite are correlated with the Tolman electronic parameter of the tertiary phosphorus-based ligand used during the cation exchange. Strong L-type donors lead to the chalcopyrite phase and weak donors to the wurtzite-like phase. To our knowledge, this is the first demonstration of phase control in nanoparticle synthesis using solely L-type donors.

Rapid Deployment of Inpatient Telemedicine In Response to COVID-19 Across Three Health Systems.

OBJECTIVE: To reduce pathogen exposure, conserve personal protective equipment, and facilitate health care personnel work participation in the setting of the COVID-19 pandemic, three affiliated institutions rapidly and independently deployed inpatient telemedicine programs during March 2020. We describe key features and early learnings of these programs in the hospital setting. METHODS: Relevant clinical and operational leadership from an academic medical center, pediatric teaching hospital, and safety net county health system met to share learnings shortly after deploying inpatient telemedicine. A summative analysis of their learnings was re-circulated for approval. RESULTS: All three institutions faced pressure to urgently standup new telemedicine systems while still maintaining secure information exchange. Differences across patient demographics and technological capabilities led to variation in solution design, though key technical considerations were similar. Rapid deployment in each system relied on readily available consumer-grade technology, given the existing familiarity to patients and clinicians and minimal infrastructure investment. Preliminary data from the academic medical center over one month suggested positive adoption with 631 inpatient video calls lasting an average (standard deviation) of 16.5 minutes (19.6) based on inclusion criteria. DISCUSSION: The threat of an imminent surge of COVID-19 patients drove three institutions to rapidly develop inpatient telemedicine solutions. Concurrently, federal and state regulators temporarily relaxed restrictions that would have previously limited these efforts. Strategic direction from executive leadership, leveraging off-the-shelf hardware, vendor engagement, and clinical workflow integration facilitated rapid deployment. CONCLUSION: The rapid deployment of inpatient telemedicine is feasible across diverse settings as a response to the COVID-19 pandemic.

E-HeaRT BPA: electronic health record telemetry BPA.

INTRODUCTION: Continuous cardiac monitoring in non-critical care settings is expensive and overutilised. As such, it is an important target of hospital interventions to establish cost-effective, high-quality care. Since inappropriate telemetry use was persistently elevated at our institution, we devised an electronic best practice alert (BPA) and tested it in a randomised controlled fashion. METHODS: Between 4 March 2018 and 5 July 2018 at our 600-bed academic hospital, all non-critical care patients who had at least one telemetry order were randomised to the control or intervention group. The intervention group received daily BPAs if telemetry was active. RESULTS: 275 and 283 patients were randomised to the intervention and control groups, respectively. The intervention group triggered 1042 alerts and trended toward fewer telemetry days (3.8 vs 5.0, p=0.017). The intervention group stopped telemetry 31.7% of the alerted patient-days compared with 23.3% for the control group (OR 1.53, 95% CI 1.24 to 1.88, p<0.001). There were no significant differences in length of stay, rapid responses, code blues, or mortality between the two groups. CONCLUSIONS: Using a randomised controlled design, we show that BPAs significantly reduce telemetry without negatively affecting patient outcomes. They should have a role in promoting high-value telemetry use.

Metrics for assessing physician activity using electronic health record log data.

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.