BackgroundLittle is known about how team processes impact providers’ abilities to prepare patients for a safe hospital discharge. Teamwork Shared Mental Models (teamwork-SMMs) are the teams’ organised understanding of individual member’s roles, interactions and behaviours needed to perform a task like hospital discharge. Teamwork-SMMs are linked to team effectiveness in other fields, but have not been readily investigated in healthcare. This study examines teamwork-SMMs to understand how interprofessional teams coordinate care when discharging patients.MethodsThis mixed methods study examined teamwork-SMMs of inpatient interprofessional discharge teams at a single hospital. For each discharge event, we collected data from the patient and their discharge team (nurse, physician and coordinator) using interviews and questionnaires. We quantitatively determined the discharge teams’ teamwork-SMM components of quality and convergence using the Shared Mental Model Scale, and then explored their relationships to patient-reported preparation for posthospital care. We used qualitative thematic analysis of narrative cases to examine the contextual differences of discharge teams with higher versus lower teamwork-SMMs.ResultsThe sample included a total of 106 structured patient interviews, 192 provider day-of-discharge questionnaires and 430 observation hours to examine 64 discharge events. We found that inpatient teams with better teamwork-SMMs (ie, higher perceptions of teamwork quality or greater convergence) were more effective at preparing patients for post-hospital care. Additionally, teams with high and low teamwork-SMMs had different experiences with team cohesion, communication openness and alignment on the patient situation.ConclusionsExamining the quality and agreement of teamwork-SMMs among teams provides a better understanding of how teams coordinate care and may facilitate the development of specific team-based interventions to improve patient care at hospital discharge.

IMPORTANCE There are areas of skilled nursing facility (SNF) experience of importance to the public that are not currently included in public reporting initiatives on SNF quality. Whether patients, hospitals, and payers can leverage the information available from unsolicited online reviews to reduce avoidable rehospitalizations from SNFs is unknown. OBJECTIVES To assess the association between rehospitalization rates and online ratings of SNFs; to compare the association of rehospitalization with ratings from a review website vs Medicare Nursing Home Compare (NHC) ratings; and to identify specific topics consistently reported in reviews of SNFs with the highest vs lowest rehospitalization rates using natural language processing. DESIGN, SETTING, AND PARTICIPANTS A retrospective cross-sectional study of 1536 SNFs with online reviews on Yelp (a website that allows consumers to rate and review businesses and services, scored on a 1-to 5-star rating scale, with 1 star indicating the lowest rating and 5 stars indicating the highest rating) posted between January 1, 2014, and December 31, 2018. The combined data set included 1536 SNFs with 8548 online reviews, NHC ratings, and readmission rates. MAIN OUTCOMES AND MEASURES A mean rating from the review website was calculated through the end of each year. Risk-standardized rehospitalization rates were obtained from NHC. Linear regression was used to measure the association between the rehospitalization rate of a SNF and the online ratings. Natural language processing was used to identify topics associated with reviews of SNFs in the top and bottom quintiles of rehospitalization rates. RESULTS The 1536 SNFs in the sample had a median of 6 reviews (interquartile range, 3-13 reviews), with a mean (SD) review website rating of 2.7 (1.1). The SNFs with the highest rating on both the review website and NHC had 2.0% lower rehospitalization rates compared with the SNFs with the lowest rating on both websites (21.3%; 95% CI, 20.7%-21.8%; vs 23.3%; 95% CI, 22.7%-24.0%; P = .04). Compared with the NHC ratings alone, review website ratings were associated with an additional 0.4% of the variation in rehospitalization rates across SNFs (adjusted R 2 = 0.009 vs adjusted R 2 = 0.013; P = .003). Thematic analysis of qualitative comments on the review website for SNFs with high vs low rehospitalization rates identified several areas of importance to the reviewers, such as the quality of physical infrastructure and equipment, staff attitudes and communication with caregivers. CONCLUSIONS AND RELEVANCE Skilled nursing facilities with the best rating on both a review website and NHC had slightly lower rehospitalization rates than SNFs with the best rating on NHC alone. However, there was marked variation in the volume of reviews, and many SNF characteristics were underrepresented. Further refinement of the review process is warranted.

The study purpose was to describe how bedside nurses can use nursing bedside shift report (NBSR) to keep patients safe. NBSR has been recommended as a means of increasing patient safety, but little is known about how or whether it does so. Grounded theory methods were used. Data were collected from 2014 to 2015 with bedside nurses in a pediatric unit with an established NBSR process. The primary process by which bedside nurses use NBSR to keep patients safe is reducing risk of harm through conveying the patient story from shift to shift. Having a perspective from the bedside is a key antecedent to reducing risk of harm, as it supports the nurses' ability to subsequently identify and address risks. Although often seen as a routine exchange of information, how nursing shift report is conducted can impact patient safety. The study reinforces the value of targeting nursing communication to improve patient safety.

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Thousands of health systems are now recognized as “Age‐Friendly Health Systems,” making this model one of the most widely disseminated – and most promising‐ models to redesign care delivery for older adults. Sustaining these gains will require demonstrating the impact on care delivery and outcomes of older adults. We propose a new measurement model to more tightly link Age‐Friendly Health System transformation to outcomes within each “M” (What Matters, Medications, Mobility, and Mentation). We evaluated measures based on the following characteristics: (1) conceptual responsiveness to changes brought about by practicing “4Ms” care; (2) degree to which they represent outcomes that matter to older adults; and (3) how they can be feasibly, reliably, and validly measured. We offer specific examples of how novel measures are currently being used where available. Finally, we present measures that could capture system‐level effects across “M”s. We tie these suggestions together into a conceptual measurement model for AFHS transformation, with the intent to spur discussion, debate, and iterative improvement in measures over time.

Implementation of TeamSTEPPS for improving patient safety is examined via descriptive qualitative analysis of semistructured interviews with 21 informants at 12 hospitals. Implementation approaches fit 3 strategies: top-down, bottom-up, and combination. The top-down approach failed to develop enough commitment to spread implementation. The bottom-up approach was unable to marshal the resources necessary to spread implementation. Combining top-down and bottom-up processes best facilitated the implementation and spread of the TeamSTEPPS safety initiative.

Facilitating team development is challenging, yet critical for ongoing improvement across healthcare settings. The purpose of this exemplary case study is to examine the role of nurse leaders in facilitating the development of a high-performing Change Team in implementing a patient safety initiative (TeamSTEPPs) using the Tuckman Model of Group Development as a guiding framework. The case study is the synthesis of 2.5 years of critical access hospital key informant interviews (n = 50). Critical juncture points related to team development and key nurse leader actions are analyzed, suggesting that nurse leaders are essential to maximize clinical teams' performance.

Globally, it is estimated that 25 million people identify as transgender and nonbinary gender (TGNB). Approximately 1.5 million TGNB adults live in the United States. Under the Affordable Care Act Section 1557 (2011), a federal law was established that made it illegal to discriminate based on "race, color, national origin, sex, age, or disability in certain health programs and activities." Yet, on June 12, 2020, the U.S. Department of Health and Human Services published a new final rule on its interpretation of Section 1557, which remove gender identity or expression from protections included under sex nondiscrimination, thus permitting healthcare discrimination against TGNB individuals. As clinicians, we are deeply troubled by the elimination of gender identity discrimination protections in health care and ongoing violent acts against transgender people. Globally, it is estimated that 25 million people identify as transgender and nonbinary gender (TGNB; Winter et al., 2016), approximately 1.5 million of whom reside in the United States (Kaiser Health News, 2020; Meerwijk & Sevelius, 2017). Transgender is an umbrella term for people "whose gender identity is different from their sex defined at birth," whereas nonbinary refers to a gender expression that is neither male nor female (National Center for Transgender Equality, 2016). Internationally, TGNB persons face significant barriers to accessing appropriate quality health care (i.e., health insurance and limited specialist services) and experience discrimination within healthcare systems (Bakko & Kattari, 2020; Thomas et al., 2017). These barriers to care are reflected in the disproportionate burden of violence and victimization among transgender persons (Johns et al., 2019); insignificant receipt of preventive health screenings (e.g., cholesterol, cancer; Edmiston et al., 2016); and high prevalence of clinical depression, anxiety, somatization, homelessness, and suicide attempts among transgender youth and adults (Keuroghlian, Shtasel, & Bassuk, 2014; Safer et al., 2016). Given the injustices experienced in the healthcare system, discrimination protection laws are necessary to help ensure that TGNB individuals have the fullest civil rights when receiving health care.