Medical Undistancing Through Telemedicine: A Model Enabling Rapid Telemedicine Deployment in an Academic Health Center During the COVID-19 Pandemic.

Background: The authors draw upon their experience with a successful, enterprise-level, telemedicine program implementation to present a "How To" paradigm for other academic health centers that wish to rapidly deploy such a program in the setting of the COVID-19 pandemic. The advent of social distancing as essential for decreasing viral transmission has made it challenging to provide medical care. Telemedicine has the potential to medically undistance health care providers while maintaining the quality of care delivered and fulfilling the goal of social distancing. Methods: Rather than simply reporting enterprise telemedicine successes, the authors detail key telemedicine elements essential for rapid deployment of both an ambulatory and inpatient telemedicine solution. Such a deployment requires a multifaceted strategy: (1) determining the appropriateness of telemedicine use, (2) understanding the interface with the electronic health record, (3) knowing the equipment and resources needed, (4) developing a rapid rollout plan, (5) establishing a command center for post go-live support, (6) creating and disseminating reference materials and educational guides, (7) training clinicians, patients, and clinic schedulers, (8) considering billing and credentialing implications, (9) building a robust communications strategy, and (10) measuring key outcomes. Results: Initial results are reported, showing a telemedicine rate increase to 45.8% (58.6% video and telephone) in just the first week of rollout. Over a 5-month period, the enterprise has since conducted over 119,500 ambulatory telemedicine evaluations (a 1,000-fold rate increase from the pre-COVID-19 time period). Conclusion: This article is designed to offer a "How To" potential best practice approach for others wishing to quickly implement a telemedicine program during the COVID-19 pandemic.

Public Health Role of Academic Medical Center in Community Outbreak of Hepatitis A, San Diego County, California, USA, 2016-2018.

During 2016-2018, San Diego County, California, USA, experienced one of the largest hepatitis A outbreaks in the United States in 2 decades. In close partnership with local healthcare systems, San Diego County Public Health led a public health response to the outbreak that focused on a 3-pronged strategy to vaccinate, sanitize, and educate. Healthcare systems administered nearly half of the vaccinations delivered in San Diego County. At University of California San Diego Health, the use of informatics tools assisted with the identification of at-risk populations and with vaccine delivery across outpatient and inpatient settings. In addition, acute care facilities helped prevent further disease transmission by delaying the discharge of patients with hepatitis A who were experiencing homelessness. We assessed the public health roles that acute care hospitals can play during a large community outbreak and the critical nature of ongoing collaboration between hospitals and public health systems in controlling such outbreaks.

Generative artificial intelligence responses to patient messages in the electronic health record: early lessons learned.

Background: Electronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed. Methods: A retrospective sample (n = 50) of negative patient messages was extracted from a health system EHR, de-identified, and inputted into an LLM (ChatGPT). Qualitative analyses were conducted to compare LLM responses to actual care team responses. Results: Some LLM-generated draft responses varied from human responses in relational connection, informational content, and recommendations for next steps. Occasionally, the LLM draft responses could have potentially escalated emotionally charged conversations. Conclusion: Further work is needed to optimize the use of LLMs for responding to negative patient messages in the EHR.

Patients' Perspectives on Plans Generated During Primary Care Visits and Self-Reported Adherence at 3 Months: Data From a Randomized Trial.

BACKGROUND: Effective primary care necessitates follow-up actions by the patient beyond the visit. Prior research suggests room for improvement in patient adherence. OBJECTIVE: This study sought to understand patients' views on their primary care visits, the plans generated therein, and their self-reported adherence after 3 months. METHODS: As part of a large multisite cluster randomized pragmatic trial in 3 health care organizations, patients completed 2 surveys-the first within 7 days after the index primary care visit and another 3 months later. For this analysis of secondary outcomes, we combined the results across all study participants to understand patient adherence to care plans. We recorded patient characteristics and survey responses. Cross-tabulation and chi-square statistics were used to examine bivariate associations, adjusting for multiple comparisons when appropriate. We used multivariable logistic regression to assess how patients' intention to follow, agreement, and understanding of their plans impacted their plan adherence, allowing for differences in individual characteristics. Qualitative content analysis was conducted to characterize the patient's self-reported plans and reasons for adhering (or not) to the plan 3 months later. RESULTS: Of 2555 patients, most selected the top box option (9=definitely agree) that they felt they had a clear plan (n=2011, 78%), agreed with the plan (n=2049, 80%), and intended to follow the plan (n=2108, 83%) discussed with their provider at the primary care visit. The most common elements of the plans reported included reference to exercise (n=359, 14.1%), testing (laboratory, imaging, etc; n=328, 12.8%), diet (n=296, 11.6%), and initiation or adjustment of medications; (n=284, 11.1%). Patients who strongly agreed that they had a clear plan, agreed with the plan, and intended to follow the plan were all more likely to report plan completion 3 months later (P<.001) than those providing less positive ratings. Patients who reported plans related to following up with the primary care provider (P=.008) to initiate or adjust medications (P≤.001) and to have a specialist visit were more likely to report that they had completely followed the plan (P=.003). Adjusting for demographic variables, patients who indicated intent to follow their plan were more likely to follow-through 3 months later (P<.001). Patients' reasons for completely following the plan were mainly that the plan was clear (n=1114, 69.5%), consistent with what mattered (n=1060, 66.1%), and they were determined to carry through with the plan (n=887, 53.3%). The most common reasons for not following the plan were lack of time (n=217, 22.8%), having decided to try a different approach (n=105, 11%), and the COVID-19 pandemic impacted the plan (n=105, 11%). CONCLUSIONS: Patients' initial assessment of their plan as clear, their agreement with the plan, and their initial willingness to follow the plan were all strongly related to their self-reported completion of the plan 3 months later. Patients whose plans involved lifestyle changes were less likely to report that they had "completely" followed their plan. TRIAL REGISTRATION: ClinicalTrials.gov NCT03385512; https://clinicaltrials.gov/study/NCT03385512. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/30431.

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.

Classification of Patient Recovery from COVID-19 Symptoms using Consumer Wearables and Machine Learning.

Current remote monitoring of COVID-19 patients relies on manual symptom reporting, which is highly dependent on patient compliance. In this research, we present a machine learning (ML)-based remote monitoring method to estimate patient recovery from COVID-19 symptoms using automatically collected wearable device data, instead of relying on manually collected symptom data. We deploy our remote monitoring system, namely eCOVID, in two COVID-19 telemedicine clinics. Our system utilizes a Garmin wearable and symptom tracker mobile app for data collection. The data consists of vitals, lifestyle, and symptom information which is fused into an online report for clinicians to review. Symptom data collected via our mobile app is used to label the recovery status of each patient daily. We propose a ML-based binary patient recovery classifier which uses wearable data to estimate whether a patient has recovered from COVID-19 symptoms. We evaluate our method using leave-one-subject-out (LOSO) cross-validation, and find that Random Forest (RF) is the top performing model. Our method achieves an F1-score of 0.88 when applying our RF-based model personalization technique using weighted bootstrap aggregation. Our results demonstrate that ML-assisted remote monitoring using automatically collected wearable data can supplement or be used in place of manual daily symptom tracking which relies on patient compliance.

Benefits and Challenges of Transitioning Occupational Health to an Enterprise Electronic Health Record.

OBJECTIVE: Occupational health (OH) documentation has traditionally been separate from health system electronic health records (EHRs), but this can create patient safety and care continuity challenges. Herein, we describe outcomes and challenges of such integration including how one health system managed compliance with laws, regulations, and ethical principles concerning digital privacy. METHODS: Occupational health integration with the enterprise EHR at the University of California San Diego Health was started in June 2021 and completed in December 2021. RESULTS: Integrating with the enterprise EHR allowed for a secure telehealth system, faster visit times, digitization of questionnaires medical clearance forms, and improved reporting capabilities. CONCLUSIONS: Integration and interoperability are fundamental building blocks to any OH EHR solution and will allow for evaluation of worker population trends, and targeted interventions to improve worker health status.

Association of physician burnout with perceived EHR work stress and potentially actionable factors.

OBJECTIVE: Physicians of all specialties experienced unprecedented stressors during the COVID-19 pandemic, exacerbating preexisting burnout. We examine burnout's association with perceived and actionable electronic health record (EHR) workload factors and personal, professional, and organizational characteristics with the goal of identifying levers that can be targeted to address burnout. MATERIALS AND METHODS: Survey of physicians of all specialties in an academic health center, using a standard measure of burnout, self-reported EHR work stress, and EHR-based work assessed by the number of messages regarding prescription reauthorization and use of a staff pool to triage messages. Descriptive and multivariable regression analyses examined the relationship among burnout, perceived EHR work stress, and actionable EHR work factors. RESULTS: Of 1038 eligible physicians, 627 responded (60% response rate), 49.8% reported burnout symptoms. Logistic regression analysis suggests that higher odds of burnout are associated with physicians feeling higher level of EHR stress (odds ratio [OR], 1.15; 95% confidence interval [CI], 1.07-1.25), having more prescription reauthorization messages (OR, 1.23; 95% CI, 1.04-1.47), not feeling valued (OR, 3.38; 95% CI, 1.69-7.22) or aligned in values with clinic leaders (OR, 2.81; 95% CI, 1.87-4.27), in medical practice for ≤15 years (OR, 2.57; 95% CI, 1.63-4.12), and sleeping for <6 h/night (OR, 1.73; 95% CI, 1.12-2.67). DISCUSSION: Perceived EHR stress and prescription reauthorization messages are significantly associated with burnout, as are non-EHR factors such as not feeling valued or aligned in values with clinic leaders. Younger physicians need more support. CONCLUSION: A multipronged approach targeting actionable levers and supporting young physicians is needed to implement sustainable improvements in physician well-being.

Implementing clinical informatics tools for primary care-based diabetic retinopathy screening.

OBJECTIVES: To improve diabetic retinopathy (DR) screening rates through a primary care-based "teleretina" screening program incorporating clinical informatics tools. STUDY DESIGN: Quality improvement study at an academic institution. METHODS: Existing DR screening workflows using in-person eye examinations were analyzed via a needs assessment. We identified gaps, which clarified the need for expanding DR screening to primary care settings. We developed informatics tools and described associated challenges and solutions. We also longitudinally monitored imaging volume and quality. RESULTS: The needs assessment identified several gaps in baseline DR screening workflows. Health information technology (IT) considerations for the new primary care-based teleretina screening program included integrating the new program with existing information systems, facilitating care coordination, and decreasing barriers to adoption by incorporating automation and other features aimed at decreasing end-user burden. We successfully developed several tools fulfilling these goals, including integration with the ophthalmology picture and archiving communication system, a customized aggregated report in the electronic health record to monitor screenings, automation of billing and health maintenance documentation, and automated results notification to primary care physicians. Of 316 primary care patients screened between October 2020 and July 2021, 73 (23%) were found to have ocular pathology, including DR, glaucoma, age-related macular degeneration, and a range of other eye conditions that were previously undiagnosed. CONCLUSIONS: New models of health care delivery, including telemedicine workflows, have become increasingly important for complex diabetic care coordination and require substantial health IT engagement. This program illustrates how clinical informatics tools can make substantial contributions to improving diabetes care.

Association of Electronic Health Record Inbasket Message Characteristics With Physician Burnout.

Importance: Physician burnout is an ongoing epidemic; electronic health record (EHR) use has been associated with burnout, and the burden of EHR inbasket messages has grown in the context of the COVID-19 pandemic. Understanding how EHR inbasket messages are associated with physician burnout may uncover new insights for intervention strategies. Objective: To evaluate associations between EHR inbasket message characteristics and physician burnout. Design, Setting, and Participants: Cross-sectional study in a single academic medical center involving physicians from multiple specialties. Data collection took place April to September 2020, and data were analyzed September to December 2020. Exposures: Physicians responded to a survey including the validated Mini-Z 5-point burnout scale. Main Outcomes and Measures: Physician burnout according to the self-reported burnout scale. A sentiment analysis model was used to calculate sentiment scores for EHR inbasket messages extracted for participating physicians. Multivariable modeling was used to model risk of physician burnout using factors such as message characteristics, physician demographics, and clinical practice characteristics. Results: Of 609 physicians who responded to the survey, 297 (48.8%) were women, 343 (56.3%) were White, 391 (64.2%) practiced in outpatient settings, and 428 (70.28%) had been in medical practice for 15 years or less. Half (307 [50.4%]) reported burnout (score of 3 or higher). A total of 1 453 245 inbasket messages were extracted, of which 630 828 (43.4%) were patient messages. Among negative messages, common words included medical conditions, expletives and/or profanity, and words related to violence. There were no significant associations between message characteristics (including sentiment scores) and burnout. Odds of burnout were significantly higher among Hispanic/Latino physicians (odds ratio [OR], 3.44; 95% CI, 1.18-10.61; P = .03) and women (OR, 1.60; 95% CI, 1.13-2.27; P = .01), and significantly lower among physicians in clinical practice for more than 15 years (OR, 0.46; 95% CI, 0.30-0.68; P < .001). Conclusions and Relevance: In this cross-sectional study, message characteristics were not associated with physician burnout, but the presence of expletives and violent words represents an opportunity for improving patient engagement, EHR portal design, or filters. Natural language processing represents a novel approach to understanding potential associations between EHR inbasket messages and physician burnout and may also help inform quality improvement initiatives aimed at improving patient experience.

Bringing student health and Well-Being onto a health system EHR: the benefits of integration in the COVID-19 era.

ObjectiveTo detail the implementation, benefits and challenges of onboarding campus-based health services onto a health system's electronic health record.ParticipantsUC San Diego Student Health and Well-Being offers medical services to over 39,000 students. UC San Diego Health is an academic medical center.Methods20 workstreams and 9 electronic modules, systems, or interfaces were converted to new electronic systems.Results36,023 student-patient medical records were created. EHR-integration increased security while creating visibility to 19,700 shared patient visits and records from 236 health systems across the country over 6 months. Benefits for the COVID-19 response included access to screening tools, decision support, telehealth, patient alerting system, reporting and analytics, COVID-19 dashboard, and increased testing capabilities.ConclusionIntegration of an interoperable EHR between neighboring campus-based health services and an affiliated academic medical center can streamline case management, improve quality and safety, and increase access to valuable health resources in times of need. Pertinent examples during the COVID-19 pandemic included uninterrupted and safe provision of clinical services through access to existing telehealth platforms and increased testing capacity.

Implementation of Patient Engagement Tools in Electronic Health Records to Enhance Patient-Centered Communication: Protocol for Feasibility Evaluation and Preliminary Results.

BACKGROUND: Patient-physician communication during clinical encounters is essential to ensure quality of care. Many studies have attempted to improve patient-physician communication. Incorporating patient priorities into agenda setting and medical decision-making are fundamental to patient-centered communication. Efficient and scalable approaches are needed to empower patients to speak up and prepare physicians to respond. Leveraging electronic health records (EHRs) in engaging patients and health care teams has the potential to enhance the integration of patient priorities in clinical encounters. A systematic approach to eliciting and documenting patient priorities before encounters could facilitate effective communication in such encounters. OBJECTIVE: In this paper, we report the design and implementation of a set of EHR tools built into clinical workflows for facilitating patient-physician joint agenda setting and the documentation of patient concerns in the EHRs for ambulatory encounters. METHODS: We engaged health information technology leaders and users in three health care systems for developing and implementing a set of EHR tools. The goal of these tools is to standardize the elicitation of patient priorities by using a previsit "patient important issue" questionnaire distributed through the patient portal to the EHR. We built additional EHR documentation tools to facilitate patient-staff communication when the staff records the vital signs and the reason for the visit in the EHR while in the examination room, with a simple transmission method for physicians to incorporate patient concerns in EHR notes. RESULTS: The study is ongoing. The anticipated completion date for survey data collection is November 2021. A total of 34,037 primary care patients from three health systems (n=26,441; n=5136; and n=2460 separately recruited from each system) used the previsit patient important issue questionnaire in 2020. The adoption of the digital previsit questionnaire during the COVID-19 pandemic was much higher in one health care system because it expanded the use of the questionnaire from physicians participating in trials to all primary care providers midway through the year. It also required the use of this previsit questionnaire for eCheck-ins, which are required for telehealth encounters. Physicians and staff suggested anecdotally that this questionnaire helped patient-clinician communication, particularly during the COVID-19 pandemic. CONCLUSIONS: EHR tools have the potential to facilitate the integration of patient priorities into agenda setting and documentation in real-world primary care practices. Early results suggest the feasibility and acceptability of such digital tools in three health systems. EHR tools can support patient engagement and clinicians' work during in-person and telehealth visits. They could potentially exert a sustained influence on patient and clinician communication behaviors in contrast to prior ad hoc educational efforts targeting patients or clinicians. TRIAL REGISTRATION: ClinicalTrials.gov NCT03385512; https://clinicaltrials.gov/ct2/show/NCT03385512. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30431.

A case study of the 1115 waiver using population health informatics to address disparities.

As participants in the California Medicaid 1115 waiver, the University of California San Diego Health (UCSDH) used population health informatics tools to address health disparities. This case study describes a modern application of health informatics to improve data capture, describe health disparities through demographic stratification, and drive reliable care through electronic medical record-based registries. We provide a details in our successful approach using (1) standardized collection of race, ethnicity, language, sexual orientation, and gender identity data, (2) stratification of 8 quality measures by demographic profile, and (3) improved quality performance through registries for wellness, social determinants of health, and chronic disease. A strong population health platform paired with executive support, physician leadership, education and training, and workflow redesign can improve the representation of diversity and drive reliable processes for care delivery that improve health equity.

Rapid response to COVID-19: health informatics support for outbreak management in an academic health system.

OBJECTIVE: To describe the implementation of technological support important for optimizing clinical management of the COVID-19 pandemic. MATERIALS AND METHODS: Our health system has confirmed prior and current cases of COVID-19. An Incident Command Center was established early in the crisis and helped identify electronic health record (EHR)-based tools to support clinical care. RESULTS: We outline the design and implementation of EHR-based rapid screening processes, laboratory testing, clinical decision support, reporting tools, and patient-facing technology related to COVID-19. DISCUSSION: The EHR is a useful tool to enable rapid deployment of standardized processes. UC San Diego Health built multiple COVID-19-specific tools to support outbreak management, including scripted triaging, electronic check-in, standard ordering and documentation, secure messaging, real-time data analytics, and telemedicine capabilities. Challenges included the need to frequently adjust build to meet rapidly evolving requirements, communication, and adoption, and to coordinate the needs of multiple stakeholders while maintaining high-quality, prepandemic medical care. CONCLUSION: The EHR is an essential tool in supporting the clinical needs of a health system managing the COVID-19 pandemic.

Promoting Quality Face-to-Face Communication during Ophthalmology Encounters in the Electronic Health Record Era.

OBJECTIVE: To evaluate informatics-enabled quality improvement (QI) strategies for promoting time spent on face-to-face communication between ophthalmologists and patients. METHODS: This prospective study involved deploying QI strategies during implementation of an enterprise-wide vendor electronic health record (EHR) in an outpatient academic ophthalmology department. Strategies included developing single sign-on capabilities, activating mobile- and tablet-based applications, EHR personalization training, creating novel workflows for team-based orders, and promoting problem-based charting to reduce documentation burden. Timing data were collected during 648 outpatient encounters. Outcomes included total time spent by the attending ophthalmologist on the patient, time spent on documentation, time spent on examination, and time spent talking with the patient. Metrics related to documentation efficiency, use of personalization features, use of team-based orders, and note length were also measured from the EHR efficiency portal and compared with averages for ophthalmologists nationwide using the same EHR. RESULTS: Time spent on exclusive face-to-face communication with patients initially decreased with EHR implementation (2.9 to 2.3 minutes, p = 0.005) but returned to the paper baseline by 6 months (2.8 minutes, p = 0.99). Observed participants outperformed national averages of ophthalmologists using the same vendor system on documentation time per appointment, number of customized note templates, number of customized order lists, utilization of team-based orders, note length, and time spent after-hours on EHR use. CONCLUSION: Informatics-enabled QI interventions can promote patient-centeredness and face-to-face communication in high-volume outpatient ophthalmology encounters. By employing an array of interventions, time spent exclusively talking with the patient returned to levels equivalent to paper charts by 6 months after EHR implementation. This was achieved without requiring EHR redesign, use of scribes, or excessive after-hours work. Documentation efficiency can be achieved using interventions promoting personalization and team-based workflows. Given their efficacy in preserving face-to-face physician-patient interactions, these strategies may help alleviate risk of physician burnout.

Time Requirements of Paper-Based Clinical Workflows and After-Hours Documentation in a Multispecialty Academic Ophthalmology Practice.

PURPOSE: To assess time requirements for patient encounters and estimate after-hours demands of paper-based clinical workflows in ophthalmology. DESIGN: Time-and-motion study with a structured survey. METHODS: This study was conducted in a single academic ophthalmology department. A convenience sample consisted of 7 attending ophthalmologists from 6 subspecialties observed during 414 patient encounters for the time-motion analysis and 12 attending ophthalmologists for the survey. Outcome measurements consisted of total time spent by attending ophthalmologists per patient and time spent on documentation, examination, and talking with patients. The survey assessed time requirements of documentation-related activities performed outside of scheduled clinic hours. RESULTS: Among the 7 attending ophthalmologists observed (6 men and 1 woman), mean ± SD age 43.9 ± 7.1 years, during encounters with 414 patients (57.8 ± 24.6 years of age), total time spent per patient was 8.1 ± 4.8 minutes, with 2.8 ± 1.4 minutes (38%) for documentation, 1.2 ± 0.9 minutes (17%) for examination, and 3.3 ± 3.1 minutes (37%) for talking with patients. New patient evaluations required significantly more time than routine follow-up visits and postoperative visits. Higher clinical volumes were associated with less time per patient. Survey results indicated that paper-based documentation was associated with minimal after-hours work on weeknights and weekends. CONCLUSIONS: Paper-based documentation takes up a substantial portion of the total time spent for patient care in outpatient ophthalmology clinics but is associated with minimal after-hours work. Understanding paper-based clinical workflows may help inform targeted strategies for improving electronic health record use in ophthalmology.

Impact of Electronic Health Record Implementation on Ophthalmology Trainee Time Expenditures.

OBJECTIVE: Electronic health records (EHRs) are widely adopted, but the time demands of EHR use on ophthalmology trainees are not well understood. This study evaluated ophthalmology trainee time spent on clinical activities in an outpatient clinic undergoing EHR implementation. DESIGN: Prospective, manual time-motion observations of ophthalmology trainees in 2018. PARTICIPANTS: Eleven ophthalmology residents and fellows observed during 156 patient encounters. METHODS: Prospective time-motion study of ophthalmology trainees 2 weeks before and 6 weeks after EHR implementation in an academic ophthalmology department. Manual time-motion observations were conducted for 11 ophthalmology trainees in 6 subspecialty clinics during 156 patient encounters. Time spent documenting, examining, and talking with patients were recorded. Factors influencing time requirements were evaluated using linear mixed effects models. MAIN OUTCOME MEASURES: Total time spent by ophthalmology residents and fellows per patient, time spent on documentation, examination, and talking with patients. RESULTS: Seven ophthalmology residents and four ophthalmology fellows with mean (standard deviation) postgraduate year of 3.7 (1.2) were observed during 156 patient encounters. Using paper charts, mean total time spent on each patient was 11.6 (6.5) minutes, with 5.4 (3.5) minutes spent documenting (48%). After EHR implementation, mean total time spent on each patient was 11.8 (6.9) minutes, with 6.8 (4.7) minutes spent documenting (57%). Total time expenditure per patient did not significantly change after EHR implementation (+0.17 minutes, 95% confidence interval [CI] for difference in means: -2.78, 2.45; p = 0.90). Documentation time did not change significantly after EHR implementation in absolute terms (+1.42 minutes, 95% CI: -3.13, 0.29; p = 0.10), but was significantly greater as a proportion of total time (48% on paper to 57% on EHR; +9%, 95% CI: 2.17, 15.83; p = 0.011). CONCLUSION: Total time spent per patient and absolute time spent on documentation was not significantly different whether ophthalmology trainees used paper charts or the recently implemented EHR. Percentage of total time spent on documentation increased significantly with early EHR use. Evaluating EHR impact on ophthalmology trainees may improve understanding of how trainees learn to use the EHR and may shed light on strategies to address trainee burnout.

The Impact of Physician EHR Usage on Patient Satisfaction.

Background and Significance: The increased emphasis on patient satisfaction has coincided with the growing adoption of electronic health records (EHRs) throughout the U.S. The 2001 Institute of Medicine Report, "Crossing the Quality Chasm," identified patient-centered care as a key element of quality health care.[1] In response to this call, the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey was developed to assess patients' health care experiences in the inpatient setting. Simultaneously, financial incentives have facilitated the rapid adoption of EHR applications, with 84% of hospitals maintaining at least a basic EHR in 2015 (a ninefold increase since 2008).[2]Despite the concurrent deployment of patient satisfaction surveys and EHRs, there is a poor understanding of the relationship that may exist between physician usage of the EHR and patient satisfaction. Most prior research into the impact of the EHR on physician­patient communication has been observational, describing the behaviors of physicians and patients when the clinician accesses an EHR in the exam room. Past research has shown that encounters where physicians access the EHR are often filled with long pauses,[3] and that few clinicians attempt to engage patients by sharing what they are looking at on the screen.[4] A recent meta-analysis reviewing 53 papers found that only 7 studies attempted to correlate objective observations of physician communication behaviors with patient perceptions by eliciting feedback from the patients.[5] No study used a standardized assessment tool of patient satisfaction. The authors conclude that additional work is necessary to better understand the patient perspective of the presence of an EHR during a clinical encounter.Additionally, increasing EHR adoption and emphasis on patient satisfaction have also corresponded with rising physician burnout rates.[6] [7] Prior work suggests that EHR adoption may be contributing to this trend.[8] Burnout from the EHR may be due in part to the significant amount of time physicians spend logged into systems, documenting long after clinic has ended in effort to avoid disrupting the patient­physician relationship.[9]We used existing data sources to describe the relationship between the amount of time physicians spend logged in to the EHR­both during daytime hours as well after clinic hours­and performance on a validated patient satisfaction survey. Our null hypothesis is that there is no relationship between increased time logged in to the EHR and patient satisfaction.