Background Despite recommendations and the need to accelerate redesign of delivery models to be team-based and patient-centred, professional silos and cultural and structural barriers that inhibit working together and communicating effectively still predominate in the hospital setting. Aiming to improve team-based rounding, we developed, implemented and evaluated the Interprofessional Teamwork Innovation Model (ITIM). Methods This quality improvement (QI) study was conducted at an academic medical centre. We followed the system’s QI framework, FOCUS-PDSA, with Lean as guiding principles. Primary outcomes included 30-day all-cause same-hospital readmissions and 30-day emergency department (ED) visits. The intervention group consisted of patients receiving care on two hospitalist ITIM teams, and patients receiving care from other hospitalist teams were matched with a control group. Outcomes were assessed using difference-in-difference analysis. Results Team members reported enhanced communication and overall time savings. In multivariate modelling, patients discharged from hospitalist teams using the ITIM approach were associated with reduced 30-day same-hospital readmissions with an estimated point OR of 0.56 (95% CI 0.34 to 0.92), but there was no impact on 30-day same-hospital ED visits. Difference-in-difference analysis showed that ITIM was not associated with changes in average total direct costs nor average cost per patient day, after adjusting for all other covariates in the models, despite the addition of staff resources in the ITIM model. Conclusion The ITIM approach facilitates a collaborative environment in which patients and their family caregivers, physicians, nurses, pharmacists, case managers and others work and share in the process of care.

Poorly managed hospital discharges and care transitions between health care facilities can cause poor outcomes for both patients and their caregivers. Unfortunately, the usual approach to health care delivery does not support continuity and coordination across the settings of hospital, doctors’ offices, home or nursing homes. Though complex efforts with multiple components can improve patient outcomes and reduce 30-day readmissions, research has not identified which components are necessary. Also we do not know how delivery of core components may need to be adjusted based on patient, caregiver, setting or characteristics of the community, or how system redesign can be accelerated. Project ACHIEVE focuses on diverse Medicare populations such as individuals with multiple chronic diseases, patients with low health literacy/numeracy and limited English proficiency, racial and ethnic minority groups, low-income groups, residents of rural areas, and individuals with disabilities. During the first phase, we will use focus groups to identify the transitional care outcomes and components that matter most to patients and caregivers to inform development and validation of assessment instruments. During the second phase, we will evaluate the comparative effectiveness of multi-component care transitions programs occurring across the U.S. Using a mixed-methods approach for this evaluation, we will study historical (retrospective) and current and future (prospective) groups of patients, caregivers and providers using site visits, surveys, and clinical and claims data. In this natural experiment observational study, we use a fractional factorial study design to specify comparators and estimate the individual and combined effects of key transitional care components. Our study will determine which evidence-based transitional care components and/or clusters most effectively produce patient and caregiver desired outcomes overall and among diverse patient and caregiver populations in different healthcare settings. Using the results, we will develop concrete, actionable recommendations regarding how best to implement these strategies. Finally, this work will provide tools for hospitals, community-based organizations, patients, caregivers, clinicians and other stakeholders to help them make informed decisions about which strategies are most effective and how best to implement them in their communities. Registered as NCT02354482 on clinicaltrials.gov on 1/29/2015

Background and Objectives: Overuse and inappropriate use of testing and hospital admission are common in syncope evaluation and management. Though guidelines are available to optimize syncope care, research indicates that current clinical guidelines have not significantly impacted resource utilization surrounding emergency department (ED) evaluation of syncope. Matching implementation strategies to barriers and facilitators and tailoring strategies to local context hold significant promise for a successful implementation of clinical practice guidelines (CPG). Our team applied implementation science principles to develop a stakeholder-based implementation strategy. Methods and Materials: We partnered with patients, family caregivers, frontline clinicians and staff, and health system administrators at four health systems to conduct quantitative surveys and qualitative interviews for context assessment. The identification of implementation strategies was done by applying the CFIR-ERIC Implementation Strategy Matching Tool and soliciting stakeholders’ inputs. We then co-designed with patients and frontline teams, and developed and tested specific strategies. Results: A total of 114 clinicians completed surveys and 32 clinicians and stakeholders participated in interviews. Results from the surveys and interviews indicated low awareness of syncope guidelines, communication challenges with patients, lack of CPG protocol integration into ED workflows, and organizational process to change as major barriers to CPG implementation. Thirty-one patients and their family caregivers participated in interviews and expressed their expectations: clarity regarding their diagnosis, context surrounding care plan and diagnostic testing, and a desire to feel cared about. Identifying change methods to address the clinician barriers and patients and family caregivers expectations informed development of the multilevel, multicomponent implementation strategy, MISSION, which includes patient educational materials, mentored implementation, academic detailing, Syncope Optimal Care Pathway and a corresponding mobile app, and Lean quality improvement methods. The pilot of MISSION demonstrated feasibility, acceptability and initial success on appropriate testing. Conclusions: Effective multifaceted implementation strategies that target individuals, teams, and healthcare systems can be employed to plan successful implementation and promote adherence to syncope CPGs.

Federated scheduling is a strategy to schedule parallel real-time tasks: It allocates a dedicated cluster of cores to each high-utilization task (utilization ≥ 1); It uses a multiprocessor scheduling algorithm to schedule and execute all low-utilization tasks sequentially, on a shared cluster of the remaining cores. Prior work has shown that federated scheduling has the best known capacity augmentation bound of 2 for parallel tasks with implicit deadlines. In this paper, we explore the soft real-time performance of federated scheduling and address average-case workloads instead of worst-case ones. In particular, we consider stochastic tasks - tasks for which execution time and critical-path length are random variables. In this case, we use bounded expected tardiness as the schedulability criterion. We define a stochastic capacity augmentation bound and prove that federated scheduling algorithms guarantee the same bound of 2 for stochastic tasks. We present three federated mapping algorithms with different complexities for core allocation. All of them guarantee bounded expected tardiness and provide the same capacity augmentation bound. In practice, however, we expect them to provide different performance, both in terms of the task sets they can schedule and the actual tardiness they guarantee. Therefore, we present numerical evaluations using randomly generated task sets to examine the practical differences between the three algorithms.