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Background Following the publication of 2014 consensus statement regarding mass critical care during public health emergencies, much has been learned about surge responses and the care of overwhelming numbers of patients during the COVID-19 pandemic.1 Gaps in prior pandemic planning were identified and require modification in the midst of ongoing surge throughout the world. Methods The Task Force for Mass Critical Care (TFMCC) adopted a modified version of established rapid guideline methodologies from the World Health Organization2 and the Guidelines International Network-McMaster Guideline Development Checklist.3 With a consensus development process incorporating expert opinion to define important questions and extract evidence, TFMCC developed relevant pandemic surge suggestions in a structured manner, incorporating peer-reviewed literature, “gray” evidence from lay media sources, and anecdotal experiential evidence. Results Ten suggestions were identified regarding staffing, load-balancing, communication, and technology. Staffing models are suggested with resilience strategies to support critical care staff. Intensive care unit (ICU) surge strategies and strain indicators are suggested to enhance ICU prioritization tactics to maintain contingency level care and avoid crisis triage, with early transfer strategies to further load-balance care. We suggest intensivists and hospitalists be engaged with the incident command structure to ensure two-way communication, situational awareness, and the use of technology to support critical care delivery and families of patients in intensive care units (ICUs). Conclusions A subcommittee from the Task Force for Mass Critical Care offers interim evidence-informed operational strategies to assist hospitals and communities to plan for and respond to surge capacity demands from COVID-19.

Background Hospital capacity strain impacts quality of care and hospital throughput and may also impact the well being of clinical staff and teams as well as their ability to do their job. Institutions have implemented a wide array of tactics to help manage hospital capacity strain with variable success. Objective Through qualitative interviews, our study explored interventions used to address hospital capacity strain and the perceived impact of these interventions, as well as how hospital capacity strain impacts patients, the workforce, and other institutional priorities. Design, Setting, and Participants Qualitative study utilizing semi-structured interviews at 13 large urban academic medical centers across the USA from June 21, 2019, to August 22, 2019 (pre-COVID-19). Interviews were recorded, professionally transcribed verbatim, coded, and then analyzed using a mixed inductive and deductive method at the semantic level. Main Outcome Measures Themes and subthemes of semi-structured interviews were identified. Results Twenty-nine hospitalist leaders and hospital leaders were interviewed. Across the 13 sites, a multitude of provider, care team, and institutional tactics were implemented with perceived variable success. While there was some agreement between hospitalist leaders and hospital leaders, there was also some disagreement about the perceived successes of the various tactics deployed. We found three main themes: (1) hospital capacity strain is complex and difficult to predict, (2) the interventions that were perceived to have worked the best when facing strain were to ensure appropriate resources; however, less costly solutions were often deployed and this may lead to unanticipated negative consequences, and (3) hospital capacity strain and the tactics deployed may negatively impact the workforce and can lead to conflict. Conclusions While institutions have employed many different tactics to manage hospital capacity strain and see this as a priority, tactics seen as having the highest yield are often not the first employed. Supplementary Information The online version contains supplementary material available at 10.1007/s11606-021-07106-8.

Hospitalists are well poised to serve in key leadership roles and in frontline care in particular when facing a pandemic such as the SARS-CoV-2 (COVID-19) infection. Much of the disaster planning in hospitals around the country addresses overcrowded emergency departments and decompressing these locations; however, in the case of COVID-19, intensive care units, emergency departments, and medical wards ran the risk of being overwhelmed by a large influx of patients needing high-level medical care. In a matter of days, our Division of Hospital Medicine, in partnership with our hospital, health system, and academic institution, was able to modify and deploy existing disaster plans to quickly care for an influx of medically complex patients. We describe a scaled approach to managing hospitalist clinical operations during the COVID-19 pandemic.

Little is known about physician perceptions of nurse practitioners (NPs) and physician assistants (PAs) on hospitalist teams and the factors that impact their perceptions. Academic physician hospitalists were surveyed to better understand those factors, focusing on NP/PA experience, training, and physician experience with NPs/PAs. Perceptions by hospitalist physicians of NPs/PAs were strongly positive. Notable findings include the perceived positive impacts of NPs/PAs on clinical care provided, practice efficiency, and physician satisfaction. Most respondents prefer working with NPs and PAs with multiple years of clinical experience, particularly those who have participated in training beyond NP/PA graduate programs of study.

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.

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.

BACKGROUND In the face of hospital capacity strain, hospitals have developed multifaceted plans to try to improve patient flow. Many of these initiatives have focused on the timing of discharges and on lowering lengths of stay, and they have met with variable success. We deployed a novel tool in the electronic health record to enhance discharge communication. OBJECTIVE The aim of this study is to evaluate the effectiveness of a discharge communication tool. METHODS This was a prospective, single-center, pre-post study. Hospitalist physicians and advanced practice providers (APPs) used the Discharge Today Tool to update patient discharge readiness every morning and at any time the patient status changed throughout the day. Primary outcomes were tool use, time of day the clinician entered the discharge order, time of day the patient left the hospital, and hospital length of stay. We used linear mixed modeling and generalized linear mixed modeling, with team and discharging provider included in all the models to account for patients cared for by the same team and the same provider. RESULTS During the pilot implementation period from March 5, 2019, to July 31, 2019, a total of 4707 patients were discharged (compared with 4558 patients discharged during the preimplementation period). A total of 352 clinical staff had used the tool, and 84.85% (3994/4707) of the patients during the pilot period had a discharge status assigned at least once. In a survey, most respondents reported that the tool was helpful (32/34, 94% of clinical staff) and either saved time or did not add additional time to their workflow (21/24, 88% of providers, and 34/34, 100% of clinical staff). Although improvements were not observed in either unadjusted or adjusted analyses, after including starting morning census per team as an effect modifier, there was a reduction in the time of day the discharge order was entered into the electronic health record by the discharging physician and in the time of day the patient left the hospital (decrease of 2.9 minutes per additional patient, P=.07, and 3 minutes per additional patient, P=.07, respectively). As an effect modifier, for teams that included an APP, there was a significant reduction in the time of day the patient left the hospital beyond the reduction seen for teams without an APP (decrease of 19.1 minutes per patient, P=.04). Finally, in the adjusted analysis, hospital length of stay decreased by an average of 3.7% (P=.06). CONCLUSIONS The Discharge Today tool allows for real time documentation and sharing of discharge status. Our results suggest an overall positive response by care team members and that the tool may be useful for improving discharge time and length of stay if a team is staffed with an APP or in higher-census situations.

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.

Background In the face of hospital capacity strain, hospitals have developed multifaceted plans to try to improve patient flow. Many of these initiatives have focused on the timing of discharges and on lowering lengths of stay, and they have met with variable success. We deployed a novel tool in the electronic health record to enhance discharge communication. Objective The aim of this study is to evaluate the effectiveness of a discharge communication tool. Methods This was a prospective, single-center, pre-post study. Hospitalist physicians and advanced practice providers (APPs) used the Discharge Today Tool to update patient discharge readiness every morning and at any time the patient status changed throughout the day. Primary outcomes were tool use, time of day the clinician entered the discharge order, time of day the patient left the hospital, and hospital length of stay. We used linear mixed modeling and generalized linear mixed modeling, with team and discharging provider included in all the models to account for patients cared for by the same team and the same provider. Results During the pilot implementation period from March 5, 2019, to July 31, 2019, a total of 4707 patients were discharged (compared with 4558 patients discharged during the preimplementation period). A total of 352 clinical staff had used the tool, and 84.85% (3994/4707) of the patients during the pilot period had a discharge status assigned at least once. In a survey, most respondents reported that the tool was helpful (32/34, 94% of clinical staff) and either saved time or did not add additional time to their workflow (21/24, 88% of providers, and 34/34, 100% of clinical staff). Although improvements were not observed in either unadjusted or adjusted analyses, after including starting morning census per team as an effect modifier, there was a reduction in the time of day the discharge order was entered into the electronic health record by the discharging physician and in the time of day the patient left the hospital (decrease of 2.9 minutes per additional patient, P=.07, and 3 minutes per additional patient, P=.07, respectively). As an effect modifier, for teams that included an APP, there was a significant reduction in the time of day the patient left the hospital beyond the reduction seen for teams without an APP (decrease of 19.1 minutes per patient, P=.04). Finally, in the adjusted analysis, hospital length of stay decreased by an average of 3.7% (P=.06). Conclusions The Discharge Today tool allows for real time documentation and sharing of discharge status. Our results suggest an overall positive response by care team members and that the tool may be useful for improving discharge time and length of stay if a team is staffed with an APP or in higher-census situations.

e14007 Background: The medical oncology patient population is at high risk for adverse effects and complications related to anti-cancer treatments. The cost of oncology care is projected to reach almost $160 billion in 2020. The ongoing cost trajectory has placed a significant financial burden on patients, families, and society as a whole. The Centers for Medicare and Medicaid Service (CMS) has developed a new payment and delivery model that identifies potentially preventable conditions for ED visits/rehospitalizations among patients that are receiving anti-cancer therapies. Proposed CMS changes have created increased urgency around avoiding preventable ED visits/rehospitalizations. In response to the need for high quality, patient centered, cost effective care, we have established an APP-led acute care clinic that specializes in oncology care. Methods: The Clinical Assessment and Rapid Evaluation (CARE) Clinic offers acute care and symptom management catered to the oncology population. Patients are able to receive lab and imaging work up in addition to intervention with IV fluids, anti-emetics, opioids, electrolytes or antibiotics, as well as blood/platelets as warranted. To assess patient satisfaction, we created an anonymous four question survey delivered to CARE clinic patients over a two month period. Results: Seventy five surveys were collected. Pain and nausea were the leading reasons for encounters. 91% of patients rated their care as excellent, while 9% of patients rated their care as good. 33% of patients reported they would have been seen in the ED if the CARE Clinic was not available. Conclusions: The CARE Clinic represents a unique, one of the first of its kind, acute care and symptom management entity that utilizes an APP model to provide high quality care at a low cost. More research is needed to identify if this model can create statistically significant reduction in ED visits and rehospitalizations, however current data suggests that patients are highly satisfied with this unique method of care delivery. Sources Handley NR, Schuchter LM, Bekelman JE. Best Practices for Reducing Unplanned Acute Care for Patients With Cancer. Journal of Oncology Practice. 2018;14(5):306-313. doi:10.1200/jop.17.00081.

The United States currently has 700,000 new heart failure (HF) diagnoses each year (Lloyd-Jones et al., 2009). More than 45% of patients with HF experience repeated episodes of serious depression (Artinian, 2003; Chung et al., 2008; Guck, Elsasser, Kavan, & Barone, 2003), have a 4-fold increase in negative HF outcomes compared with those with no depression (Rutledge, Reis, Linke, Greenberg, & Mills, 2006), and often have higher health costs (Welch, Czerwinski, Ghimire, & Bertsimas, 2009) and reduced quality of life (Lichtman et al., 2008). Depressive symptoms double the risk for mortality and other cardiac events in patients with HF, often exacerbating their HF symptoms (Carney, Freedland,& Jaffe,2009). In longitudinal studies, depressed HF patients were found to (a) be less engaged in essential self-care behaviors, such as maintaining sodium and fluid restrictions and medication regimens (Pandya, Metz, & Patten, 2005); (b) have greater loss of physical function; and (c) die sooner (Jiang et al., 2001; Luttik, Jaarsma, Moser, Sanderman, & van Veldhuisen, 2005). Notably, depression is a strong predictor of repeated HF hospital admissions (Jiang et al., 2001), yet fewer than 25% of HF patients are screened for depression, and even fewer are treated (Heritage, Robinson, Elliott, Beckett, & Wilkes, 2007). Both HF and depression can be characterized by fatigue, loss of energy, poor appetite, sleep disturbances, psychomotor retardation, and concentration deficits. Patients experiencing HF are often not screened for depression because of this overlap of depressive and HF symptoms (Holzapfel et al., 2008). All HF patients should be screened for depressive symptoms because of the negative associations between depressive symptoms and mortality, comorbidities, and poor HF self-management. Although the recent national guidelines from the American Heart Association Prevention Committee, led by nurse practitioners, introduced new recommendations that all patients with heart disease be given a questionnaire to screen for depressive symptoms (Lichtman et al., 2008), many patients are still not screened. Even less common than screening for and treating depressive symptoms is the identification of the causative factors of depressive symptoms from patients’ points of view. The identification of depression and its causative factors is imperative to increase the likelihood of HF patients receiving the proper treatment for depression, considering the untoward impact of depression among these patients. The purpose of this data collection was to screen HF patients for depression, to elicit the causative factors participants self-reported, and to compare those factors identified by a psychiatric nurse specialist during a one-on-one assessment session. Comparing factors identified by participant self-report with those uncovered by a psychiatric nurse specialist will help determine if self-report and one-on-one assessment sessions capture similar details. Further, this project allowed for the completion of validity calculations to determine the adequacy of a relatively new clinical depression-screening questionnaire compared with a depression-screening questionnaire that has been used for decades as the gold standard in many research studies.

The purpose of this study was to compare participants' and a psychiatric nurse specialist's reports on factors precipitating depression and to validate a depression screening instrument.Participants were screened for and asked to self-report causative factors of their depression. Participants with moderately severe and severe depressive symptoms received a psychiatric nurse specialist assessment.Participants self-reported several causative factors of depression. The psychiatric nurse specialist discovered these plus additional factors. The screening instrument was found to be reliable and valid for the measurement of depressive symptoms.Participant self-report identifies many causative factors of depression. The psychiatric nurse specialist identifies additional factors, allowing individualized diagnoses and treatments.