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Purpose:  We performed an exploratory evaluation of gender-specific differences in speakers and their introductions at internal medicine grand rounds., Method:  Internal medicine grand rounds video archives from three sites between December 2013 and September 2020 were manually transcribed and analyzed using natural language processing techniques. Differences in word usage by gender were compared., Results:  Four hundred and sixty-two grand rounds held at three institutions were examined. There were 167 (34.6%) speakers who were women and 316 (65.4%) who were men. The proportion of women speakers was significantly lower than that of women in the internal medicine workforce (34.6% vs. 39.2%, p = 0.04). Among 191 external speakers, only 57 (29.8%) were women. The use of professional titles was equivalent between genders. Despite equal mention of specific achievements in both male and female speaker introductions, there was a trend toward casting female speakers as being less established., Conclusion:  There is a need to adopt processes that will decrease inequities in the representation of women in grand rounds and in their introductions., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Hanna et al.)

Competing Interests: Disclosures: All relevant financial relationships have been mitigated. Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=A23-0003.

Background: Typical solutions for improving discharge planning often rely on one-way communication mechanisms, static data entry into the electronic health record (EHR), or in-person meetings. Lack of timely and effective communication can adversely affect patients and their care teams., Objective: Applying robust user-centered design strategies, we aimed to design an innovative EHR-based discharge readiness communication tool (the Discharge Today tool) to enable care teams to communicate any barriers to discharge, the status of patient discharge readiness, and patient discharge needs in real time across hospital settings., Methods: We employed multiple user-centered design strategies, including exploration of the current state for documenting discharge readiness and directing discharge planning, iterative low-fidelity prototypes, multidisciplinary stakeholder meetings, a brainwriting premortem exercise, and preproduction user testing. We iteratively collected feedback from users via meetings and surveys., Results: We conducted 28 meetings with 20 different stakeholder groups. From these stakeholder meetings, we developed 14 low-fidelity prototypes prior to deploying the Discharge Today tool for our pilot study. During the pilot study, stakeholders requested 46 modifications, of which 25 (54%) were successfully executed. We found that most providers who responded to the survey reported that the tool either saved time or did not change the amount of time required to complete their discharge workflow (21/24, 88%). Responses to open-ended questions offered both positive feedback and opportunities for improvement in the domains of efficiency, integration into workflow, avoidance of redundancies, expedited communication, and patient-centeredness., Conclusions: Survey data suggest that this electronic discharge readiness tool has been successfully adopted by providers and clinical staff. Frequent stakeholder engagement and iterative user-centered design were critical to the successful implementation of this tool., (©Angela Keniston, Lauren McBeth, Jonathan Pell Sr, Kasey Bowden, Stephen Ball, Kristin Stoebner, Elaina Scherzberg, Susan L Moore, Jamie Nordhagen, Amanda Anthony, Marisha Burden. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 23.04.2021.)

Background: In nearly all areas of academic medicine, disparities still exist for women and underrepresented minorities (URMs)., Objectives: Develop a strategic plan for advancing diversity, equity, and inclusion (DEI); implement and evaluate the plan, specifically focusing on compensation, recruitment, and policies., Design, Setting, Participants: Programmatic evaluation conducted in the division of hospital medicine (DHM) at a major academic medical center involving DHM faculty and staff., Measurements: (1) Development and implementation of strategic plan, including policies, processes, and practices related to key components of DEI program; (2) assessment of specific DEI outcomes, including plan implementation, pre-post salary data disparities based on academic rank, and pre-post disparities for protected time for similar roles., Results: Using information gathered from a focus group with DHM faculty, an iterative strategic plan for DEI was developed and deployed, with key components of focus being institutional structures, our people, our environments, and our core mission areas. A director of DEI was established to help oversee these efforts. Using a two-phase approach, salary disparities by rank were eliminated. Internally funded protected time was standardized for leadership roles. A data dashboard has been developed to track high-level successes and areas for future focus., Conclusion: Using a systematic evidence-based approach with key stakeholder involvement, a division-wide DEI strategy was developed and implemented. While this work is ongoing, short-term wins are possible, in particular around salary equity and development of policies and structures to promote DEI.

Physicians' beliefs and attitudes about COVID-19 are important to ascertain because of their central role in providing care to patients during the pandemic. Identifying topics and sentiments discussed by physicians and other healthcare workers can lead to identification of gaps relating to theCOVID-19 pandemic response within the healthcare system. To better understand physicians' perspectives on the COVID-19 response, we extracted Twitter data from a specific user group that allows physicians to stay anonymous while expressing their perspectives about the COVID-19 pandemic. All tweets were in English. We measured most frequent bigrams and trigrams, compared sentiment analysis methods, and compared our findings to a larger Twitter dataset containing general COVID-19 related discourse. We found significant differences between the two datasets for specific topical phrases. No statistically significant difference was found in sentiments between the two datasets, and both trended slightly more positive than negative. Upon comparison to manual sentiment analysis, it was determined that these sentiment analysis methods should be improved to accurately capture sentiments of anonymous physician data. Anonymous physician social media data is a unique source of information that provides important insights into COVID-19 perspectives.

Background: Clinicians' time with patients has become increasingly limited due to regulatory burden, documentation and billing, administrative responsibilities, and market forces. These factors limit clinicians' time to deliver thorough explanations to patients. OpenNotes began as a research initiative exploring the ability of sharing medical notes with patients to help patients understand their health care. Providing patients access to their medical notes has been shown to have many benefits, including improved patient satisfaction and clinical outcomes. OpenNotes has since evolved into a national movement that helps clinicians share notes with patients. However, a significant barrier to the widespread adoption of OpenNotes has been clinicians' concerns that OpenNotes may cost additional time to correct patient confusion over medical language. Recent advances in artificial intelligence (AI) technology may help resolve this concern by converting medical notes to plain language with minimal time required of clinicians., Objective: This pilot study assesses patient comprehension and perceived benefits, concerns, and insights regarding an AI-simplified note through comprehension questions and guided interview., Methods: Synthea, a synthetic patient generator, was used to generate a standardized medical-language patient note which was then simplified using AI software. A multiple-choice comprehension assessment questionnaire was drafted with physician input. Study participants were recruited from inpatients at the University of Colorado Hospital. Participants were randomly assigned to be tested for their comprehension of the standardized medical-language version or AI-generated plain-language version of the patient note. Following this, participants reviewed the opposite version of the note and participated in a guided interview. A Student t test was performed to assess for differences in comprehension assessment scores between plain-language and medical-language note groups. Multivariate modeling was performed to assess the impact of demographic variables on comprehension. Interview responses were thematically analyzed., Results: Twenty patients agreed to participate. The mean number of comprehension assessment questions answered correctly was found to be higher in the plain-language group compared with the medical-language group; however, the Student t test was found to be underpowered to determine if this was significant. Age, ethnicity, and health literacy were found to have a significant impact on comprehension scores by multivariate modeling. Thematic analysis of guided interviews highlighted patients' perceived benefits, concerns, and suggestions regarding such notes. Major themes of benefits were that simplified plain-language notes may (1) be more useable than unsimplified medical-language notes, (2) improve the patient-clinician relationship, and (3) empower patients through an enhanced understanding of their health care., Conclusions: AI software may translate medical notes into plain-language notes that are perceived as beneficial by patients. Limitations included sample size, inpatient-only setting, and possible confounding factors. Larger studies are needed to assess comprehension. Insight from patient responses to guided interviews can guide the future study and development of this technology., (©Sandeep Bala, Angela Keniston, Marisha Burden. Originally published in JMIR Formative Research (http://formative.jmir.org), 05.06.2020.)

Background: Little is known about the state of research in academic hospital medicine (HM) despite the substantial growth of this specialty., Methods: We used the Society of Hospital Medicine (SHM) membership database to identify research programs and their leadership. In addition, the members of the SHM Research Committee identified individuals who lead research programs in HM. A convenience sample of programs and individuals was thus created. A survey instrument containing questions regarding institutional information, research activities, training opportunities, and funding sources was pilot tested and refined for electronic dissemination. Data were summarized using descriptive statistics., Results: A total of 100 eligible programs and corresponding individuals were identified. Among these programs, 28 completed the survey in its entirety (response rate 28%). Among the 1,586 faculty members represented in the 28 programs, 192 (12%) were identified as engaging in or having obtained extramural funding for research, and 656 (41%) were identified as engaging in quality improvement efforts. Most programs (61%) indicated that they received $500,000 or less in research funding, whereas 29% indicated that they received >$1 million in funding. Major sources of grant support included the Agency for Healthcare Research and Quality, National Institutes of Health, and the Veterans Health Administration. Only five programs indicated that they currently have a research fellowship program in HM. These programs cited lack of funding as a major barrier to establishing fellowships. Almost half of respondents (48%) indicated that their faculty published between 11-50 peer-reviewed manuscripts each year., Conclusion: This survey provides the first national summary of research activities in HM. Future waves of the survey can help determine whether the research footprint of the field is growing.

Objectives: To determine whether treating hospitalists can identify and address early postdischarge problems through a structured telephone call., Study Design: Prospective cohort study., Methods: We studied patients insured through a managed care program who were discharged from a general internal medicine service of a university-affiliated public safety net hospital (Denver Health Medical Center) between March 1, 2012, and October 31, 2013. The hospitalist who treated the patient during their hospitalization contacted them 48 to 72 hours after discharge and completed a structured telephone assessment. We assessed the type and frequency of problems identified, the proportion of calls in which problems were independently addressed by the hospitalist, the proportion referred for additional managed care services, and the duration of calls and subsequent care coordination., Results: Treating hospitalists identified 1 or more problems in 74 of the 131 patients (56%) contacted. The most common categories of problems were: new or worsening symptoms (41%), difficulty accessing recommended follow-up care (21%), and medication issues (20%). Hospitalists independently managed the problems identified in 68% of the calls; additional services were required in 32%. Median time spent per call was 8 minutes (interquartile range, 5-12)., Conclusions: Treating hospitalists identified problems in over half of patients contacted by telephone shortly after discharge, the largest proportion of which were new or worsening symptoms. Hospitalists were able to address the majority of problems identified through the single, brief telephone encounter without utilizing additional resources.