Association of intervention outcomes with practice capacity for change: Subgroup analysis from a group randomized trial

<p>Abstract</p> <p>Background</p> <p>The relationship between health care practices' capacity for change and the results and sustainability of interventions to improve health care delivery is unclear.</p> <p>Methods</p> <p>In the setting of an intervention to increase preventive service delivery (PSD), we assessed practice capacity for change by rating motivation to change and instrumental ability to change on a one to four scale. After combining these ratings into a single score, random effects models tested its association with change in PSD rates from baseline to immediately after intervention completion and 12 months later.</p> <p>Results</p> <p>Our measure of practices' capacity for change varied widely at baseline (range 2–8; mean 4.8 ± 1.6). Practices with greater capacity for change delivered preventive services to eligible patients at higher rates after completion of the intervention (2.7% per unit increase in the combined effort score, p < 0.001). This relationship persisted for 12 months after the intervention ended (3.1%, p < 0.001).</p> <p>Conclusion</p> <p>Greater capacity for change is associated with a higher probability that a practice will attain and sustain desired outcomes. Future work to refine measures of this practice characteristic may be useful in planning and implementing interventions that result in sustained, evidence-based improvements in health care delivery.</p

A Practice Change Model for Quality Improvement in Primary Care Practice.

Faced with a rapidly changing healthcare environment, primary care practices often have to change how they practice medicine. Yet change is difficult, and the process by which practice improvement can be understood and facilitated has not been well elucidated. Therefore, we developed a model of practice change using data from a quality improvement intervention that was successful in creating a sustainable practice improvement. A multidisciplinary team evaluated data from the Study To Enhance Prevention by Understanding Practice (STEP-UP), a randomized clinical trial conducted to improve the delivery of evidence-based preventive services in 79 northeastern Ohio practices. The team conducted comparative case-study analyses of high- and low-improvement practices to identify variables that are critical to the change process and to create a conceptual model for the change. The model depicts the critical elements for understanding and guiding practice change and emphasizes the importance of these elements\u27 evolving interrelationships. These elements are (1) motivation of key stakeholders to achieve the target for change; (2) instrumental, personal, and interactive resources for change; (3) motivators outside the practice, including the larger healthcare environment and community; and (4) opportunities for change--that is, how key stakeholders understand the change options. Change is influenced by the complex interaction of factors inside and outside the practice. Interventions that are based on understanding the four key elements and their interrelationships can yield sustainable quality improvements in primary care practice

Charting brain growth and aging at high spatial precision

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2–100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making

Charting brain growth and aging at high spatial precision.

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2-100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making

Charting brain growth and aging at high spatial precision

Defining reference models for population variation, and the ability to study individual deviations is essential for understanding inter-individual variability and its relation to the onset and progression of medical conditions. In this work, we assembled a reference cohort of neuroimaging data from 82 sites (N=58,836; ages 2–100) and used normative modeling to characterize lifespan trajectories of cortical thickness and subcortical volume. Models are validated against a manually quality checked subset (N=24,354) and we provide an interface for transferring to new data sources. We showcase the clinical value by applying the models to a transdiagnostic psychiatric sample (N=1985), showing they can be used to quantify variability underlying multiple disorders whilst also refining case-control inferences. These models will be augmented with additional samples and imaging modalities as they become available. This provides a common reference platform to bind results from different studies and ultimately paves the way for personalized clinical decision-making