Dataset Information

Improving the Quality of Evaluation Data in Simulation-Based Healthcare Improvement Projects: A Practitioner's Guide to Choosing and Using Published Measurement Tools.

ABSTRACT: STATEMENT:Simulation is increasingly being used in healthcare improvement projects. The aims of such projects can be extremely diverse. Accordingly, the outcomes or participant attributes that need to be measured can vary dramatically from project-to-project and may include a wide range of nontechnical skills, technical skills, and psychological constructs. Consequently, there is a growing need for simulation practitioners to be able to identify suitable measurement tools and incorporate them into their work. This article provides a practical introduction and guide to the key considerations for practitioners when selecting and using such tools. It also offers a substantial selection of example tools, both to illustrate the key considerations in relation to choosing a measure (including reliability and validity) and to serve as a convenient resource for those planning a study. By making well-informed choices, practitioners can improve the quality of the data they collect, and the likelihood that their projects will succeed.

SUBMITTER: Santomauro CM

PROVIDER: S-EPMC7531509 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Improving the Quality of Evaluation Data in Simulation-Based Healthcare Improvement Projects: A Practitioner's Guide to Choosing and Using Published Measurement Tools.

Santomauro Chiara M CM Hill Andrew A McCurdie Tara T McGlashan Hannah L HL

Simulation in healthcare : journal of the Society for Simulation in Healthcare 20201001 5

<h4>Statement</h4>Simulation is increasingly being used in healthcare improvement projects. The aims of such projects can be extremely diverse. Accordingly, the outcomes or participant attributes that need to be measured can vary dramatically from project-to-project and may include a wide range of nontechnical skills, technical skills, and psychological constructs. Consequently, there is a growing need for simulation practitioners to be able to identify suitable measurement tools and incorporate ...[more]

PMID: 32520766

Similar Datasets

Project description:BackgroundAtopic dermatitis (AD) is a heterogeneous disease with a multifactorial aetiology and complex pathophysiology. This heterogeneity translates into different trajectories of disease progression with respect to severity, persistence and risk of development of atopic comorbidities. Determining which possible disease trajectories or comorbidities any individual child might develop is challenging in clinical practice. Tools that help identify paediatric patients at higher risk of disease progression would greatly aid clinicians.MethodsWe reviewed recent cohort studies to synthesize and simplify the epidemiological data to try to identify shared clinically relevant characteristics that may help physicians estimate the risk of disease progression in paediatric patients with AD.ResultsDespite the variability in data collection and methods of analysis and their limitations, there are common patterns of early-childhood AD that may aid in the estimation of risk for disease progression. Factors associated with risk of AD progression include younger age of onset, family history of atopy, greater AD severity, filaggrin mutations, urban environment and polysensitization and/or allergic multimorbidity. Based on these factors, we provide a practitioner's guide for identifying, counselling and/or referring infants and children with AD at potentially higher risk of developing persistent AD and atopic comorbidities. We also present clinical scenarios to illustrate how these data relate to real-life situations.ConclusionsUseful insights are provided for physicians and patients to inform them better about the risk of AD progression and to help guide care pathways for the paediatric population with AD. What's already known about this topic? The complex pathophysiology of atopic dermatitis (AD) translates into a heterogeneous clinical presentation and trajectories of disease progression. Although the consensus is that most paediatric patients with AD will eventually 'outgrow' the disease or follow the longitudinal trajectory known as the 'atopic march', a significant proportion will develop persistent AD and/or other atopic conditions. No known factors conclusively predict the risk of progression or development of comorbidities. What does this study add? Recent analyses of data from large cohorts of paediatric patients with AD have suggested the existence of potentially discrete clusters of patients who present with relatively common AD phenotypes. These studies have shed some light onto the factors associated with risk of progression, which we review in this article. A practitioner's guide with clinical scenarios is provided to help identify patients at high risk of progression to determine whether a patient should be monitored and/or would require specialist referral.

Dataset Information

Improving the Quality of Evaluation Data in Simulation-Based Healthcare Improvement Projects: A Practitioner's Guide to Choosing and Using Published Measurement Tools.

Publications

Improving the Quality of Evaluation Data in Simulation-Based Healthcare Improvement Projects: A Practitioner's Guide to Choosing and Using Published Measurement Tools.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets