Browsing by keyword "Measurement"
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Further Experience with the Practice Integration Profile: A Measure of Behavioral Health and Primary Care IntegrationValid measures of behavioral health integration have the potential to enable comparisons of various models of integration, contribute to the overall development of high-quality care, and evaluate outcomes that are strategically aligned with standard improvement efforts. The Practice Integration Profile has proven to discriminate among clinic types and integration efforts. We continued the validation of the measure's internal consistency, intra-rater consistency, and inter-rater consistency with a separate and larger sample from a broader array of practices. We found that the Practice Integration Profile demonstrated a high level of internal consistency, suggesting empirically sound measurement of independent attributes of integration, and high reliability over time. The Practice Integration Profile provides internally consistent and interpretable results and can serve as both a quality improvement and health services research tool.
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Measurement in Health: Advancing Assessment of DeliriumRationale: Delirium is a serious, morbid condition affecting 2.6 million older Americans annually. A major problem plaguing delirium research is difficulty in identification, given a plethora of existing tools. The lack of consensus on key features and approaches has stymied progress in delirium research. The goal of this project was to use advanced measurement methods to improve delirium’s identification. Aims and Findings: (1) Determine the 4 most commonly used and well-validated instruments for delirium identification. Through a rigorous systematic review, I identified the Confusion Assessment Method (CAM), Delirium Observation Screening Scale (DOSS), Delirium Rating Scale-Revised-98 (DRS-R-98), and Memorial Delirium Assessment Scale (MDAS). (2) Harmonize the 4 instruments to generate a delirium item bank (DEL-IB), a dataset containing items and estimates of their population level parameters. In a secondary analysis of 3 datasets, I equated instruments on a common metric and created crosswalks. (3) Explore applications of the harmonized item bank through several approaches. First, identifying different cut-points that will optimize: (a) balanced high accuracy (Youden’s J-Statistic), (b) screening (sensitivity), and (c) confirmation of diagnosis (specificity) in identification of delirium. Second, comparing performance characteristics of example forms developed from the DEL-IB. Impact: The knowledge gained includes harmonization of 4 instruments for identification of delirium, with crosswalks on a common metric. This will pave the way for combining studies, such as meta-analyses of new treatments, essential for developing guidelines and advancing clinical care. Additionally, the DEL-IB will facilitate creating big datasets, such as for omics studies to advance pathophysiologic understanding of delirium.
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Non-Adherence Tree Analysis (NATA)-An adherence improvement framework: A COVID-19 case studyPoor medication adherence is a global phenomenon that has received a significant amount of research attention yet remains largely unsolved. Medication non-adherence can blur drug efficacy results in clinical trials, lead to substantial financial losses, increase the risk of relapse and hospitalisation, or lead to death. The most common methods of measuring adherence are post-treatment measures; that is, adherence is usually measured after the treatment has begun. What the authors are proposing in this multidisciplinary study is a new technique for predicting the factors that are likely to cause non-adherence before or during medication treatment, illustrated in the context of potential non-adherence to COVID-19 antiviral medication. Fault Tree Analysis (FTA), allows system analysts to determine how combinations of simple faults of a system can propagate to cause a total system failure. Monte Carlo simulation is a mathematical algorithm that depends heavily on repeated random sampling to predict the behaviour of a system. In this study, the authors propose a new technique called Non-Adherence Tree Analysis (NATA), based on the FTA and Monte Carlo simulation techniques, to improve adherence. Firstly, the non-adherence factors of a medication treatment lifecycle are translated into what is referred to as a Non-Adherence Tree (NAT). Secondly, the NAT is coded into a format that is translated into the GoldSim software for performing dynamic system modelling and analysis using Monte Carlo. Finally, the GoldSim model is simulated and analysed to predict the behaviour of the NAT. NATA is dynamic and able to learn from emerging datasets to improve the accuracy of future predictions. It produces a framework for improving adherence by analysing social and non-social adherence barriers. Novel terminologies and mathematical expressions have been developed and applied to real-world scenarios. The results of the application of NATA using data from six previous studies in relation to antiviral medication demonstrate a predictive model which suggests that the biggest factor that could contribute to non-adherence to a COVID-19 antiviral treatment is a therapy-related factor (the side effects of the medication). This is closely followed by a condition-related factor (asymptomatic nature of the disease) then patient-related factors (forgetfulness and other causes). From the results, it appears that side effects, asymptomatic factors and forgetfulness contribute 32.44%, 22.67% and 18.22% respectively to discontinuation of medication treatment of COVID-19 antiviral medication treatment. With this information, clinicians can implement relevant interventions and measures and allocate resources appropriately to minimise non-adherence.
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www.common-metrics.org: a web application to estimate scores from different patient-reported outcome measures on a common scaleBACKGROUND: Recently, a growing number of Item-Response Theory (IRT) models has been published, which allow estimation of a common latent variable from data derived by different Patient Reported Outcomes (PROs). When using data from different PROs, direct estimation of the latent variable has some advantages over the use of sum score conversion tables. It requires substantial proficiency in the field of psychometrics to fit such models using contemporary IRT software. We developed a web application ( http://www.common-metrics.org ), which allows estimation of latent variable scores more easily using IRT models calibrating different measures on instrument independent scales. RESULTS: Currently, the application allows estimation using six different IRT models for Depression, Anxiety, and Physical Function. Based on published item parameters, users of the application can directly estimate latent trait estimates using expected a posteriori (EAP) for sum scores as well as for specific response patterns, Bayes modal (MAP), Weighted likelihood estimation (WLE) and Maximum likelihood (ML) methods and under three different prior distributions. The obtained estimates can be downloaded and analyzed using standard statistical software. CONCLUSIONS: This application enhances the usability of IRT modeling for researchers by allowing comparison of the latent trait estimates over different PROs, such as the Patient Health Questionnaire Depression (PHQ-9) and Anxiety (GAD-7) scales, the Center of Epidemiologic Studies Depression Scale (CES-D), the Beck Depression Inventory (BDI), PROMIS Anxiety and Depression Short Forms and others. Advantages of this approach include comparability of data derived with different measures and tolerance against missing values. The validity of the underlying models needs to be investigated in the future.
