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Goal:The goal of this study was to describe the prevalence and pattern of population health partnerships by hospitals and examine whether these partnerships were associated with different types of payment model programs. Methods:We conducted a cross-sectional analysis of 3,012 U.S. hospitals using data from the American Hospital Association's Annual Survey, the Area Health Resources File, and the County Health Rankings & Roadmaps data. We ran a multivariable Poisson regression model to examine the relationship between value-based payment designs and the number of population health partnerships. Binary logistic regression models were used to assess whether participation in value-based payment design programs was associated with specific types of population health partnerships.Principal Findings: We found that two thirds or more of hospitals used more informal collaborative partnerships with local or state government, faith-based organizations, and local businesses; formal alliances were most common with health insurance companies and other healthcare providers. Accountable care organizations and bundled payment program participation were associated with greater numbers of population health partnerships, whereas hospital ownership of a health plan was not associated with significantly greater numbers of population health partnerships.Applications to Practice: Hospitals were engaged in an intermediate number of partnerships (mean = 3.5, out of 8.0 possible), with opportunities for more partnerships with specific types of organizations (faith-based organizations, health insurance companies). Our findings also suggest that certain types of payment models, particularly those that are less capital intensive and entail less extensive organizational transformation on the part of hospitals, may support

PurposeThe purpose of this study was to examine the organizational context that may support learning and change readiness climates that previous research has found to be conducive to implementing evidence-based interventions.Design/methodology/approachAn exploratory, mixed method evaluation that included 15 rheumatology clinics throughout the United States was performed. Quantitative data were collected using a web-based survey completed by 135 clinic members. Qualitative data were collected via semi-structured interviews with 88 clinic members.FindingsIn general, clinics reported strong, positive learning and change readiness climates. More complex organizations (e.g. multispecialty, academic medical centers) with rational/hierarchical cultures and members with longer tenure were associated with less supportive learning and change readiness climates. The authors’ findings highlight opportunities for organizational leaders and evidence-based intervention sponsors to focus their attention and allocate resources to settings that may be most susceptible to implementation challenges.Originality/valueFirst, the authors address a deficit in previous research by describing both the level and strength of the learning and change readiness climates for implementing an evidence-based shared decision-making aid (SDMA) and examine how these vary as a function of the organizational context. Second, the study examines a broader set of factors to assess the organizational context (e.g. organizational culture, organizational structure, ownership) than previous research, which may be especially salient for shaping the climate in smaller specialty clinics like those we study. Third, the authors utilize a mixed methods analysis to provide greater insights into questions of how and why organizational factors such as size and structure may influence the learning and change readiness climate.

Background The successful implementation of evidence-based innovations to improve healthcare delivery often requires a well-planned strategy to support their use. With a greater recognition of the importance of an implementation process, researchers have turned their attention to implementation strategies and their customization to target specific organizational barriers and facilitators. Further, there is a paucity of empirical evidence demonstrating the link between implementation determinants and the number of selected implementation strategies. The purpose of this mixed methods analysis is to examine how formatively assessed barriers and facilitators to implementation relate to the number and type of implementation strategies adopted to address context-specific factors. Methods A mixed methods evaluation that included 15 rheumatology clinics throughout the United States that were planning for implementation of an evidence-based shared decision-making aid for patients with lupus. Quantitative data consisted of a count of the number of implementation strategies used by a clinic. Qualitative data collection was guided by the Consolidated Framework for Implementation Research (CFIR) and relied upon semi-structured interviews with 90 clinic members between November 2018 and August 2019. Results Using the CFIR, we found that local clinic factors (Inner Setting Domain) resulted in different perceptions about Planning and Executing the DA (Process Domain); these domains were most likely to distinguish between the number and type of implementation strategies adopted by the clinics. In contrast, Intervention characteristics, Individual Characteristics, and the Outer Setting did not differentiate between the groups with different numbers of implementation strategies. The number and type of chosen strategies were not those associated with the context-specific factors. Conclusions Findings show that, despite recognition of the value of customizing implementation strategies for the contexts in which they are applied, they are too often chosen in a manner that fail to adequately reflect the diverse settings that may present unique factors associated with implementation. Our findings also highlight the importance of the inner context – both in terms of structural characteristics and existing work processes – as a driving factor for why some organizations select different numbers and types of implementation strategies.

BACKGROUND While cardiometabolic diseases are leading causes of morbidity and mortality in the United States, computerized tools for risk assessment of cardiometabolic disease are rarely integral components of primary care practice. Embedding cardiometabolic disease staging systems (CMDS) into computerized clinical decision support systems (CDSS) may assist with identifying and treating patients at greatest risk for developing cardiometabolic disease. OBJECTIVE This study aimed to explore the current approach to medical management of obesity and the need for CMDS system designed to aid medical management of people living with obesity, at risk of being obese, or diabetic at the point of care. METHODS Using a general inductive approach, this qualitative research study was guided by an interpretive epistemology. The method included semi-structured in-depth interviews with primary care providers (PCP) from university-based community health clinics. The literature informed interview protocol and included questions on PCPs’ experiences and the need for a tool to improve their ability to manage and prevent complications from overweight and obesity. RESULTS PCPs (N=10) described their current approaches and emphasized behavioral treatments consisting of combined diet, physical activity and behavior therapy as the first line of treatment for people who were overweight or obese. Results suggest that beneficial features of CDSS include: (1) clinically relevant and customizable support; (2) provision of comprehensive medical summary with trends; (3) availability of patient education materials and community resources; and (4) simplicity and easiness to navigate. CONCLUSIONS Implementation of a cardiometabolic disease staging system via CDSS could enable primary care providers to better manage overweight, obesity and prevent diabetes based on risk assessment. Results from this study provide unique insight to developers and researchers to identify areas for design optimization for improved end user experience to ensure successful adoption of the CDSS.