Background:Guidance for measuring team effectiveness in dynamic clinical settings is necessary; however, there are no consensus strategies to help health care organizations achieve optimal teamwork. This systematic review aims to identify validated survey instruments of team effectiveness by clinical settings.Methods:PubMed, MEDLINE, and ISI Web of Knowledge were searched for team effectiveness surveys deployed from 1990 to 2016. Validity and reliability were evaluated using 4 psychometric properties: interrater agreement, internal consistency, content validity, and structural integrity. Two conceptual frameworks, the Donabedian model and the Command Team Effectiveness model, assess conceptual dimensions most measured in each health care setting.Results:The 22 articles focused on surgical, primary care, and other health care settings. Few instruments report the required psychometric properties or feature non-self-reported outcomes. The major conceptual dimensions measured in the survey instruments differed across settings. Team cohesion and overall perceived team effectiveness can be found in all the team effectiveness measurement tools regardless of the health care setting. We found that surgical settings have distinctive conditions for measuring team effectiveness relative to primary or ambulatory care.Discussion:Further development of setting-specific team effectiveness measurement tools can help further enhance continuous quality improvements and clinical outcomes in the future.
Only one quarter of U.S. hospitals demonstrated low enough levels of 30 day readmission rates to avoid penalties imposed by the Hospital Readmissions Reduction Program (HRRP) in 2016. Previous work describes interventions for reducing hospital readmission rates; however, without a comprehensive analysis of these interventions, healthcare leaders cannot prioritize strategies for implementation within their healthcare environment. This comparative study identifies the most effective interventions to reduce unplanned 30-day readmissions. The MEDLINE-PubMed database was used to conduct a systematic review of existing literature about interventions for 30-day readmission reduction published from 2006 through 2017. Data were extracted on hospital type, setting, disease type, intervention type, study sample, and impact level. Of 4,886 citations, 508 articles were reviewed in full-text, and 90 articles met the inclusion criteria. Based on the three analytic methodologies of means, weighted means, and pooled estimated impact level, the most effective interventions to reduce unplanned 30-day admissions were identified as collaboration with clinical teams and/or community providers, post-discharge home visits, telephone follow-up calls, patient/family education, and discharge planning. Commonly, all five interventions identify patient level engagement for success. The findings reveal the need for shared accountability towards desired outcomes among health systems, providers, and patients while providing hospital leaders with actionable strategies that can effectively reduce 30-day readmission rates.
Fundamental to democratic societies, citizen participation is an important tool for promoting active, informed, and empowered citizenry as well as responsive and accountable administration. Past literature on citizen participation has focused on its determinants, forms, and prevalence. This study examines the relationship between a specific form of citizen participation—client participation—and organizational performance. We use hybrid data on U.S. nursing homes that combine a survey of nursing home administrators’ managerial practices with federal performance appraisal data. Our empirical findings suggest that more intense levels of client participation, such as the use of clients’ feedback in decision-making, are positively associated with performance: They increase the overall five-star ratings and lower health deficiencies. In contrast, less intense client participation efforts, such as merely communicating with client/family groups, are not significantly related to performance. This study highlights the role of participation intensity, suggesting that public administrators should not only go beyond informing and listening to their stakeholders, but also take steps to use the obtained feedback in organizational decision-making.
ObjectiveTo compare hospital‐community partnerships among safety‐net hospitals relative to non–safety‐net hospitals, and explore whether hospital‐community partnerships are associated with reductions in readmission rates.Data SourcesData from four nationwide hospital‐level datasets for 2015‐2016, including American Hospital Association (AHA) annual survey, Hospital Inpatient Prospective Payment System (IPPS) data, CMS Hospital Compare, and County Health Rankings National (CHRN) data.Study DesignWe first examined how safety‐net hospitals partner with nine different community providers, and how the overall and individual partnership patterns differ from those in non–safety‐net hospitals. We then explored their association with 30‐day readmission rates by diagnosis and hospital wide.Data Collection/Extraction MethodsWe included 1979 hospitals across 50 US states.Principal FindingsSafety‐net hospitals were more engaged in hospital‐community partnerships, especially with local public health, local governments, social services, nonprofits, and insurance companies, relative to their non–safety‐net peers. However, we found that such partnerships were not significantly related to reductions in readmission rates. The findings indicated that merely partnering with various community organizations may not be associated with readmission rate reduction.ConclusionsBefore promoting partnerships with various community organizations for its own sake, further prospective, longitudinal, and evidence‐based guidance derived from the study of hospital‐community partnerships is needed to make meaningful recommendations aimed at readmission rate reduction in safety‐net hospitals.
This study evaluated the utility and performance of the LACE index and HOSPITAL score with consideration of the type of diagnoses and assessed the accuracy of these models for predicting readmission risks in patient cohorts from 2 large academic medical centers. Admissions to 2 hospitals from 2011 to 2015, derived from the Vizient Clinical Data Base and regional health information exchange, were included in this study (291 886 encounters). Models were assessed using Bayesian information criterion and area under the receiver operating characteristic curve. They were compared in CMS diagnosis-based cohorts and in 2 non-CMS cancer diagnosis-based cohorts. Overall, both models for readmission risk performed well, with LACE performing slightly better (area under the receiver operating characteristic curve 0.73 versus 0.69; P ≤ 0.001). HOSPITAL consistently outperformed LACE among 4 CMS target diagnoses, lung cancer, and colon cancer. Both LACE and HOSPITAL predict readmission risks well in the overall population, but performance varies by salient, diagnosis-based risk factors.
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