Improving Quality and Achieving Equity: A Guide for Hospital Leaders

Outlines the need to address racial/ethnic disparities in health care, highlights model practices, and makes step-by-step recommendations on creating a committee, collecting data, setting quality measures, evaluating, and implementing new strategies

GW Nursing, Spring 2019

https://hsrc.himmelfarb.gwu.edu/son_gwnursmag/1008/thumbnail.jp

Resource Modelling: The Missing Piece of the HTA Jigsaw?

Within health technology assessment (HTA), cost-effectiveness analysis and budget impact analyses have been broadly accepted as important components of decision making. However, whilst they address efficiency and affordability, the issue of implementation and feasibility has been largely ignored. HTA commonly takes place within a deliberative framework that captures issues of implementation and feasibility in a qualitative manner. We argue that only through a formal quantitative assessment of resource constraints can these issues be fully addressed. This paper argues the need for resource modelling to be considered explicitly in HTA. First, economic evaluation and budget impact models are described along with their limitations in evaluating feasibility. Next, resource modelling is defined and its usefulness is described along with examples of resource modelling from the literature. Then, the important issues that need to be considered when undertaking resource modelling are described before setting out recommendations for the use of resource modelling in HTA

Machine learning and computational methods to identify molecular and clinical markers for complex diseases – case studies in cancer and obesity

In biomedical research, applied machine learning and bioinformatics are the essential disciplines heavily involved in translating data-driven findings into medical practice. This task is especially accomplished by developing computational tools and algorithms assisting in detection and clarification of underlying causes of the diseases. The continuous advancements in high-throughput technologies coupled with the recently promoted data sharing policies have contributed to presence of a massive wealth of data with remarkable potential to improve human health care. In concordance with this massive boost in data production, innovative data analysis tools and methods are required to meet the growing demand. The data analyzed by bioinformaticians and computational biology experts can be broadly divided into molecular and conventional clinical data categories. The aim of this thesis was to develop novel statistical and machine learning tools and to incorporate the existing state-of-the-art methods to analyze bio-clinical data with medical applications. The findings of the studies demonstrate the impact of computational approaches in clinical decision making by improving patients risk stratification and prediction of disease outcomes. This thesis is comprised of five studies explaining method development for 1) genomic data, 2) conventional clinical data and 3) integration of genomic and clinical data. With genomic data, the main focus is detection of differentially expressed genes as the most common task in transcriptome profiling projects. In addition to reviewing available differential expression tools, a data-adaptive statistical method called Reproducibility Optimized Test Statistic (ROTS) is proposed for detecting differential expression in RNA-sequencing studies. In order to prove the efficacy of ROTS in real biomedical applications, the method is used to identify prognostic markers in clear cell renal cell carcinoma (ccRCC). In addition to previously known markers, novel genes with potential prognostic and therapeutic role in ccRCC are detected. For conventional clinical data, ensemble based predictive models are developed to provide clinical decision support in treatment of patients with metastatic castration resistant prostate cancer (mCRPC). The proposed predictive models cover treatment and survival stratification tasks for both trial-based and realworld patient cohorts. Finally, genomic and conventional clinical data are integrated to demonstrate the importance of inclusion of genomic data in predictive ability of clinical models. Again, utilizing ensemble-based learners, a novel model is proposed to predict adulthood obesity using both genetic and social-environmental factors. Overall, the ultimate objective of this work is to demonstrate the importance of clinical bioinformatics and machine learning for bio-clinical marker discovery in complex disease with high heterogeneity. In case of cancer, the interpretability of clinical models strongly depends on predictive markers with high reproducibility supported by validation data. The discovery of these markers would increase chance of early detection and improve prognosis assessment and treatment choice

Data Collection Instrument for Full Accreditation Surveys

The Liaison Committee on Medical Education’s (LCME) Data Collection Instrument for the UNM Health and Science Center. This third party accreditation gives exhaustive information on the HSC and its consistency with established standards

Financial Costs Incurred by Living Kidney Donors: Findings from a Canadian Multi-centre Prospective Cohort Study

This prospective cohort study across 12 Canadian transplant centres evaluated the costs incurred by 912 living kidney donors. Expenses and resources were captured to 3-months post-donation, and micro-costing was used to appraise the costs incurred by donors. Living kidney donors incurred average total costs of $4790, and direct and indirect costs of$2110 and $2679, respectively. 13.3$10,000, and 8.6% of donors incurred costs \u3e25% of their annual household income. Costs incurred by spousal donors were not significantly different from either unrelated or closely related donors. Similarly, costs incurred by kidney paired donors were not significantly different from other donors. In multivariable analyses, living \u3e100 km from the transplant evaluation centre and being employed were associated with higher total costs. In conclusion, many living kidney donors incur substantial costs associated with donation, and our findings can be used to improve the donation experience

i-FRAME – Assessing impacts of social policy innovation in the EU: Proposed methodological framework to evaluate socio-economic returns on investment of social policy innovations

This report presents the final proposal for developing a methodological framework to assess the impacts generated by social policy innovations which promote social investment in the EU, in short i-FRAME. This framework has the objective to provide a structured approach that shall serve as a comprehensive framework for conducting analysis of the economic and social returns on investments of social policy innovations. It also aims to act as a guide to gather insights into replicability and transferability of initiatives which promote social investment across the EU. The report outlines the reviewed and improved theoretical and methodological approach developed by the JRC with help from external experts, and validated by testing the operational components proposed on a number of case studies and scenarios of use. After outlining the conceptual and methodological approach underpinning the i-FRAME (V1.0), the report discusses the proposal for building its operational components according to a structured theoretical framework of a dynamic simulation model for social impact assessment (V1.5). The final proposal for i-FRAME (V2.0) and an overview of the operational components for its implementation are then presented discussing the key elements that should be developed to build a comprehensive i-FRAME Web-Platform and simulator for social impact assessment. Conclusions are then offered in terms of implications for policy and directions for future research. These were drawn after consulting experts from different research disciplines, practitioners and representatives of relevant stakeholders and policymakers, and they include .recommendations for further developing the operational components proposed, paving the way towards building the i-FRAME (V3.0) and beyond.JRC.B.4-Human Capital and Employmen