As hospitals and EDs strive to manage crowding, meet rising demand, and enhance operational efficiency, operational and capacity command centers have emerged as a key solution for patient flow optimization, length-of-stay management, and quality and safety risk reduction. This article outlines the process of and outcomes from the implementation of the Michigan Medicine Capacity Optimization and Real-Time Engagement Center, launched in 2022 after 14 months of planning by more than 200 faculty and staff. Through actionable analytics, collaboration, and new processes, patient flow improved significantly. Average time spent waiting for an available adult inpatient bed decreased 33% between 2022 and 2024, and adult ED wait time decreased 37%; the children's hospital saw a 13% decrease. New priority transfer processes and increased visibility to available beds enabled improved access for transfers: the University of Michigan Health Academic Medical Center admitted 70% of adult transfer requests in 2022 and 80% in 2024. New procedures also reduced the time from discharge order to patient exit by 12% in the adult hospitals and 9% in the children's hospital. The combined impact of these efforts yielded throughput improvements that equate to opening 13 inpatient beds, with incremental annual revenue of US$19.5 million. In addition, there was an 8% reduction in absolute and risk-adjusted length of stay in the health system's adult hospitals, representing an additional 50 beds of capacity. These gains resulted in a significant positive return after accounting for both one-time capital costs and incremental staffing.

STUDY OBJECTIVE:Emergency department (ED) boarding is a critical threat to patient safety. ED leaders consider inpatient hallway boarding-moving admitted patients from the ED to inpatient unit corridors while awaiting inpatient beds-a best-practice countermeasure. However, the extent of inpatient hallway boarding usage in practice is unknown. Our objective was to survey hospitals to characterize the adoption and implementation of inpatient hallway boarding. METHODS:We designed, piloted, and administered an online survey addressing inpatient hallway boarding adoption and implementation to all members of the American Hospital Association's Hospital Capacity Management Consortium, representing capacity leaders from 91 hospitals in 34 states. We assessed adoption by the proportion of respondents reporting inpatient hallway boarding usage within the last 12 months. We assessed implementation by the number of boarding patients moved from the ED to inpatient hallway spaces. RESULTS:The response rate was 80.2% (73/91). Thirty-one of 73 (42.5%) respondents reported using inpatient hallway boarding during the last 12 months. Of these, 15 tracked inpatient hallway boarding patient volumes. The median reported number of patients moved from the ED to inpatient hallway spaces was <1 patient per day (mean=1.5, SD=1.9). The median ED boarding census (respondent-reported number of boarding patients in the ED at midnight each day, averaged over the year) for these hospitals was 40 (mean=41, SD=16). Respondents from 5 states reported their state health departments restricted inpatient hallway boarding usage. CONCLUSION:Although inpatient hallway boarding is an evidence-based countermeasure to ED boarding, only a minority of surveyed hospitals adopted inpatient hallway boarding. Among hospitals adopting inpatient hallway boarding, the number of patients moved from the ED to inpatient hallways was minimal.

Background: Delayed hospital and emergency department (ED) patient throughput, which occurs when demand for inpatient care exceeds hospital capacity, is a critical threat to safety, quality, and hospital financial performance. In response, many hospitals are deploying capacity command centers (CCCs), which co-locate key work groups and aggregate real-time data to proactively manage patient flow. Only a narrow body of peer-reviewed articles have characterized CCCs to date. To equip health system leaders with initial insights into this emerging intervention, the authors sought to survey US health systems to benchmark CCC motivations, design, and key performance indicators.Methods: An online survey on CCC design and performance was administered to members of a hospital capacity management consortium, which included a convenience sample of capacity leaders at US health systems ( N = 38). Responses were solicited through a targeted e-mail campaign. Results were summarized using descriptive statistics.Results: The response rate was 81.6% (31/38). Twenty-five respondents were operating CCCs, varying in scope (hospital, region of a health system, or entire health system) and number of beds managed. The most frequent motivation for CCC implementation was reducing ED boarding ( n = 24). The most common functions embedded in CCCs were bed management ( n = 25) and interhospital transfers ( n = 25). Eighteen CCCs (72.0%) tracked financial return on investment (ROI); all reported positive ROI. Conclusion: This survey addresses a gap in the literature by providing initial aggregate data for health system leaders to consider, plan, and benchmark CCCs. The researchers identify motivations for, functions in, and key performance indicators used to assess CCCs. Future research priorities are also proposed.

Journal of Hospital MedicineVolume 19, Issue 2 p. 149-150 EDITORIAL Time to move past midnight census: Adopting modern methods to guide hospital medicine staffing Marcus Calderon MD, Marcus Calderon MD Division of Hospital Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USASearch for more papers by this authorVikas I. Parekh MD, Corresponding Author Vikas I. Parekh MD [email protected] orcid.org/0000-0001-8394-9307 @VikasParekhMD Division of Hospital Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA University of Michigan Health, Ann Arbor, Michigan, USA Correspondence Vikas I. Parekh, MD, University of Michigan Health, Med Inn Bldg. C252, 1500 East Medical Center Dr, Ann Arbor, MI 48109-5825, USA. Email: [email protected]; Twitter: @VikasParekhMDSearch for more papers by this author Marcus Calderon MD, Marcus Calderon MD Division of Hospital Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USASearch for more papers by this authorVikas I. Parekh MD, Corresponding Author Vikas I. Parekh MD [email protected] orcid.org/0000-0001-8394-9307 @VikasParekhMD Division of Hospital Medicine, University of Michigan Medical School, Ann Arbor, Michigan, USA University of Michigan Health, Ann Arbor, Michigan, USA Correspondence Vikas I. Parekh, MD, University of Michigan Health, Med Inn Bldg. C252, 1500 East Medical Center Dr, Ann Arbor, MI 48109-5825, USA. Email: [email protected]; Twitter: @VikasParekhMDSearch for more papers by this author First published: 26 December 2023 https://doi.org/10.1002/jhm.13267Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. CONFLICT OF INTEREST STATEMENT The authors declare no conflict of interest. REFERENCES 1CMS Publication No. 100-02 Medicare Benefit Policy Manual, Chapter 3, Section 20.1: Counting Inpatient Days. 2019. Accessed December 10, 2023. https://www.cms.gov/regulations-and-guidance/guidance/manuals/downloads/bp102c03pdf Google Scholar 2Saville C, Monks T, Griffiths P, Ball JE. Costs and consequences of using average demand to plan baseline nurse staffing levels: a computer simulation study. BMJ Qual Safety. 2021; 30(1): 7-16. 10.1136/bmjqs-2019-010569 PubMedWeb of Science®Google Scholar 3Pierce L, Judson TJ, Mourad M. Finding the time: hourly variation in average daily census on a hospital medicine service. J Hosp Med. 2024; 19: 108-111. doi:10.1002/jhm.13233 10.1002/jhm.13233 PubMedWeb of Science®Google Scholar 4Griner TE, Thompson M, High H, Buckles J. Artificial intelligence forecasting census and supporting early decisions. Nurs Adm Q. 2020; 44(4): 316-328. 10.1097/NAQ.0000000000000436 PubMedGoogle Scholar 5Meyer KR, Fraser PB, Emeny RT. Development of a nursing assignment tool using workload acuity scores. J Nurs Admin. 2020; 50(6): 322-327. 10.1097/NNA.0000000000000892 PubMedWeb of Science®Google Scholar Volume19, Issue2February 2024Pages 149-150 ReferencesRelatedInformation

Background Academic hospitalists engage in many non-clinical domains. Success in these domains requires support, mentorship, protected time, and networks. To address these non-clinical competencies, faculty development programs have been implemented. We aim to describe the demographics, job characteristics, satisfiers, and barriers to success of early-career academic hospitalists who attended the Academic Hospitalist Academic (AHA), a professional development conference from 2009 to 2019. Methods Survey responses from attendees were evaluated; statistical analyses and linear regression were performed for numerical responses and qualitative coding was performed for textual responses. Results A total of 965 hospitalists attended the AHA from 2009 to 2019. Of those, 812 (84%) completed the survey. The mean age of participants was 34 years and the mean time in hospitalist practice was 3.2 years. Most hospitalists were satisfied with their job, and teaching and clinical care were identified as the best parts of the job. The proportion of female hospitalists increased from 42.2% in 2009 to 60% in 2019 ( p = 0.001). No other demographics or job characteristics significantly changed over the years. Lack of time and confidence in individual skills were the most common barriers identified in both bedside teaching and providing feedback, and providing constructive feedback was an additional challenge identified in giving feedback. Conclusions Though early-career hospitalists reported high levels of job satisfaction driven by teaching and clinical care, barriers to success include time constraints and confidence. Awareness of these factors of satisfaction and barriers to success can help shape faculty development curricula for early-career hospitalists.

ABSTRACTAn outbreak of SARS-CoV-2 has led to a global pandemic affecting virtually every country. As of August 31, 2020, globally, there have been approximately 25,500,000 confirmed cases and 850,000 deaths; in the United States (50 states plus District of Columbia), there have been more than 6,000,000 confirmed cases and 183,000 deaths. We propose a Bayesian mixture model to predict and monitor COVID-19 mortality across the United States. The model captures skewed unimodal (prolonged recovery) or multimodal (multiple surges) curves. The results show that across all states, the first peak dates of mortality varied between April 4, 2020 for Alaska and June 18, 2020 for Arkansas. As of August 31, 2020, 31 states had a clear bimodal curve showing a strong second surge. The peak date for a second surge ranged from July 1, 2020 for Virginia to September 12, 2020 for Hawaii. The first peak for the United States occurred about April 16, 2020—dominated by New York and New Jersey—and a second peak on August 6, 2020—dominated by California, Texas, and Florida. Reliable models for predicting the COVID-19 pandemic are essential to informing resource allocation and intervention strategies. A Bayesian mixture model was able to more accurately predict the shape of the mortality curves across the United States than other models, including the timing of multiple peaks. However, given the dynamic nature of the pandemic, it is important that the results be updated regularly to identify and better monitor future waves, and characterize the epidemiology of the pandemic.

Emergency department (ED) crowding is recognized as a critical threat to patient safety, while sub-optimal ED patient flow also contributes to reduced patient satisfaction and efficiency of care. Provider in triage (PIT) programs-which typically involve, at a minimum, a physician or advanced practice provider conducting an initial screening exam and potentially initiating treatment and diagnostic testing at the time of triage-are frequently endorsed as a mechanism to reduce ED length of stay (LOS) and therefore mitigate crowding, improve patient satisfaction, and improve ED operational and financial performance. However, the peer-reviewed evidence regarding the impact of PIT programs on measures including ED LOS, wait times, and costs (as variously defined) is mixed. Mechanistically, PIT programs exert their effects by initiating diagnostic work-ups earlier and, sometimes, by equipping triage providers to directly disposition patients. However, depending on local contextual factors-including the co-existence of other front-end interventions and delays in ED throughput not addressed by PIT-we demonstrate how these features may or may not ultimately translate into reduced ED LOS in different settings. Consequently, site-specific analysis of the root causes of excessive ED LOS, along with mechanistic assessment of potential countermeasures, is essential for appropriate deployment and successful design of PIT programs at individual EDs. Additional motivations for implementing PIT programs may include their potential to enhance patient safety, patient satisfaction, and team dynamics. In this conceptual article, we address a gap in the literature by demonstrating the mechanisms underlying PIT program results and providing a framework for ED decision-makers to assess the local rationale for, operational feasibility of, and financial impact of PIT programs.

Importance:The influence of the COVID-19 pandemic on fertility rates has been suggested in the lay press and anticipated based on documented decreases in fertility and pregnancy rates during previous major societal and economic shifts. Anticipatory planning for birth rates is important for health care systems and government agencies to accurately estimate size of economy and model working and/or aging populations. Objective:To use projection modeling based on electronic health care records in a large US university medical center to estimate changes in pregnancy and birth rates prior to and after the COVID-19 pandemic societal lockdowns. Design, Setting, and Participants:This cohort study included all pregnancy episodes within a single US academic health care system retrospectively from 2017 and modeled prospectively to 2021. Data were analyzed September 2021. Exposures:Pre- and post-COVID-19 pandemic societal shutdown measures. Main Outcomes and Measures:The primary outcome was number of new pregnancy episodes initiated within the health care system and use of those episodes to project birth volumes. Interrupted time series analysis was used to assess the degree to which COVID-19 societal changes may have factored into pregnancy episode volume. Potential reasons for the changes in volumes were compared with historical pregnancy volumes, including delays in starting prenatal care, interruptions in reproductive endocrinology and infertility services, and preterm birth rates. Results:This cohort study documented a steadily increasing number of pregnancy episodes over the study period, from 4100 pregnancies in 2017 to 4620 in 2020 (28 284 total pregnancies; median maternal [interquartile range] age, 30 [27-34] years; 18 728 [66.2%] White women, 3794 [13.4%] Black women; 2177 [7.7%] Asian women). A 14% reduction in pregnancy episode initiation was observed after the societal shutdown of the COVID-19 pandemic (risk ratio, 0.86; 95% CI, 0.79-0.92; P < .001). This decrease appeared to be due to a decrease in conceptions that followed the March 15 mandated COVID-19 pandemic societal shutdown. Prospective modeling of pregnancies currently suggests that a birth volume surge can be anticipated in summer 2021. Conclusions and Relevance:This cohort study using electronic medical record surveillance found an initial decline in births associated with the COVID-19 pandemic societal changes and an anticipated increase in birth volume. Future studies can further explore how pregnancy episode volume changes can be monitored and birth rates projected in real-time during major societal events.

Background: Crowding is a major challenge faced by EDs and is associated with poor outcomes. Objectives: Determine the effect of high ED occupancy on disposition decisions, return ED visits, and hospitalizations. Methods: We conducted a retrospective analysis of electronic health records of patients evaluated at an adult, urban, and academic ED over 20 months between the years 2012 and 2014. Using a logistic regression model predicting admission, we obtained estimates of the effect of high occupancy on admission disposition, adjusted for key covariates. We then stratified the analysis based on the presence or absence of high boarder patient counts. Results: Disposition decisions during a high occupancy hour decreased the odds of admission (OR = 0.93, 95% CI: [0.89, 0.98]). Among those who were not admitted, high occupancy was not associated with increased odds of return in the combined (OR = 0.94, 95% CI: [0.87, 1.02]), with-boarders (OR = 0.96, 95% CI: [0.86, 1.09]), and no-boarders samples (OR = 0.93, 95% CI: [0.83, 1.04]). Among those who were not admitted and who did return within 14 days, disposition during a high occupancy hour on the initial ED visit was not associated with a significant increased odds of hospitalization in the combined (OR = 1.04, 95% CI: [0.87, 1.24]), the with-boarders (OR = 1.12, 95% CI: [0.87, 1.44]), and the no-boarders samples (OR = 0.98, 95% CI: [0.77, 1.24]). Conclusion: ED crowding was associated with reduced likelihood of hospitalization without increased likelihood of 2-week return ED visit or hospitalization. Furthermore, high occupancy disposition hours with high boarder patient counts were associated with decreased likelihood of hospitalization.

BACKGROUND:As clinical demands increase, understanding the features that allow academic hospital medicine programs (AHPs) to thrive has become increasingly important.OBJECTIVE:To develop and validate a quantifiable definition of academic success for AHPs.METHODS:A working group of academic hospitalists was formed. The group identified grant funding, academic promotion, and scholarship as key domains reflective of success, and specific metrics and approaches to assess these domains were developed. Self-reported data on funding and promotion were available from a preexisting survey of AHP leaders, including total funding/group, funding/full-time equivalent (FTE), and number of faculty at each academic rank. Scholarship was defined in terms of research abstracts presented over a 2-year period. Lists of top performers in each of the 3 domains were constructed. Programs appearing on at least 1 list (the SCHOLAR cohort [SuCcessful HOspitaLists in Academics and Research]) were examined. We compared grant funding and proportion of promoted faculty within the SCHOLAR cohort to a sample of other AHPs identified in the preexisting survey.RESULTS:Seventeen SCHOLAR programs were identified, with a mean age of 13.2 years (range, 6-18 years) and mean size of 36 faculty (range, 18-95). The mean total grant funding/program was $4 million (range, $0-$15 million), with mean funding/FTE of $364,000 (range, $0-$1.4 million); both were significantly higher than the comparison sample. The majority of SCHOLAR faculty (82%) were junior, a lower percentage than the comparison sample. The mean number of research abstracts presented over 2 years was 10.8 (range, 9-23).DISCUSSION:Our approach effectively identified a subset of successful AHPs. Despite the relative maturity and large size of the programs in the SCHOLAR cohort, they were comprised of relatively few senior faculty members and varied widely in the quantity of funded research and scholarship. Journal of Hospital Medicine 2016;11:708-713. © 2016 Society of Hospital Medicine.

The term “vasculitis” represents heterogeneous and often overlapping groups of clinicopathologic syndromes that cause vascular damage and organ dysfunction. Diagnoses are categorized into primary and secondary types, by size of affected vessel, and by patient characteristics. Vasculitis should be included in the differential diagnosis of patients presenting with unexplained constitutional symptoms and red-flag findings such as mononeuritis multiplex. Inpatient evaluation may include subspecialty consultation, arteriography, electromyography, or histologic biopsy. Therapy is aimed at inducing and maintaining remission of disease activity and preventing complications. Effective transitions of care and regular monitoring of disease activity and adverse effects from treatment are critical.

Physicians increasingly investigate, work, and teach to improve the quality of care and safety of care delivery. The Society of General Internal Medicine Academic Hospitalist Task Force sought to develop a practical tool, the quality portfolio, to systematically document quality and safety achievements. The quality portfolio was vetted with internal and external stakeholders including national leaders in academic medicine. The portfolio was refined for implementation to include an outlined framework, detailed instructions for use and an example to guide users. The portfolio has eight categories including: (1) a faculty narrative, (2) leadership and administrative activities, (3) project activities, (4) education and curricula, (5) research and scholarship, (6) honors, awards, and recognition, (7) training and certification, and (8) an appendix. The authors offer this comprehensive, yet practical tool as a method to document quality and safety activities. It is relevant for physicians across disciplines and institutions and may be useful as a standalone document or as an adjunct to traditional promotion documents. As the Next Accreditation System is implemented, academic medical centers will require faculty who can teach and implement the systems-based practice requirements. The quality portfolio is a method to document quality improvement and safety activities.

In 2003, Accreditation Council for Graduate Medical Education (ACGME) announced the first in a series of guidelines related to the residency training. The most recent recommendations include explicit recommendations regarding the provision of on-site clinical supervision for trainees of internal medicine. To meet these standards, many internal medicine residency programs turned to hospitalist programs to fill that need. However, much is unknown about the current relationships between hospitalist and residency programs, specifically with regard to supervisory roles and supervision policies. We aimed to describe how academic hospitalists currently supervise housestaff during the on-call, or overnight, period and hospitalist program leader their perceptions of how these new policies would impact trainee-hospitalist interactions.

Udhay Krishnan, MD Vikas I. Parekh, MD Phuc Nguyen, MD Sara A. Bowling, BA Sanjay Saint, MD, MPH Zachary D. Goldberger, MD Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Michigan. Division of Cardiovascular Medicine, University of Michigan Health System, Ann Arbor, Michigan. 3 VA Health Services Research and Development Center for Excellence and Department of Medicine, University of Michigan Medical School, Ann Arbor, Michigan.

BACKGROUND:The need to provide efficient, effective, and safe patient care is of paramount importance. However, most physicians receive little or no formal training to prepare them to address patient safety challenges within their clinical practice.METHODS:We describe a comprehensive Patient Safety Learning Program (PSLP) for internal medicine and medicine-pediatrics residents. The curriculum is designed to teach residents key concepts of patient safety and provided opportunities to apply these concepts in the "real" world in an effort to positively transform patient care. Residents were assigned to faculty expert-led teams and worked longitudinally to identify and address patient safety conditions and problems. The PSLP was assessed by using multiple methods.RESULTS:Resident team-based projects resulted in changes in several patient care processes, with the potential to improve clinical outcomes. However, faculty evaluations of residents were lower for the Patient Safety Improvement Project rotation than for other rotations. Comments on "unsatisfactory" evaluations noted lack of teamwork, project participation, and/or responsiveness to faculty communication. Participation in the PSLP did not change resident or faculty attitudes toward patient safety, as measured by a comprehensive survey, although there was a slight increase in comfort with discussing medical errors.CONCLUSIONS:Development of the PSLP was intended to create a supportive environment to enhance resident education and involve residents in patient safety initiatives, but it produced lower faculty evaluations of resident for communication and professionalism and did not have the intended positive effect on resident or faculty attitudes about patient safety. Further research is needed to design or refine interventions that will develop more proactive resident learners and shift the culture to a focus on patient safety.

BACKGROUND Academic hospital medicine is a new and rapidly growing field. Hospitalist faculty members often fill roles not typically held by other academic faculty, maintain heavy clinical workloads, and participate in nontraditional activities. Because of these differences, there is concern about how academic hospitalists may fare in the promotions process. OBJECTIVE To determine factors critical to the promotion of successfully promoted hospitalists who have achieved the rank of either associate professor or professor. DESIGN A cross-sectional survey. PARTICIPANTS Thirty-three hospitalist faculty members at 22 academic medical centers promoted to associate professor rank or higher between 1995 and 2008. MEASUREMENTS Respondents were asked to describe their institution, its promotions process, and the activities contributing to their promotion. We identified trends across respondents. RESULTS Twenty-six hospitalists responded, representing 20 institutions (79% response rate). Most achieved promotion in a nontenure track (70%); an equal number identified themselves as clinician-administrators and clinician educators (40%). While hospitalists were engaged in a wide range of activities in the traditional domains of service, education, and research, respondents considered peer-reviewed publication to be the most important activity in achieving promotion. Qualitative responses demonstrated little evidence that being a hospitalist was viewed as a hindrance to promotion. CONCLUSIONS Successful promotion in academic hospital medicine depends on accomplishment in traditional academic domains, raising potential concerns for academic hospitalists with less traditional roles. This study may provide guidance for early-career academic hospitalists and program leaders.