David A. Muñoz

A Mixed-Methods Research Framework for Healthcare Process Improvement

UNLABELLED The healthcare system in the United States is spiraling out of control due to ever-increasing costs without significant improvements in quality, access to care, satisfaction, and efficiency. Efficient workflow is paramount to improving healthcare value while maintaining the utmost standards of patient care and provider satisfaction in high stress environments. This article provides healthcare managers and quality engineers with a practical healthcare process improvement framework to assess, measure and improve clinical workflow processes. DESIGN AND METHODS The proposed mixed-methods research framework integrates qualitative and quantitative tools to foster the improvement of processes and workflow in a systematic way. The framework consists of three distinct phases: 1) stakeholder analysis, 2a) survey design, 2b) time-motion study, and 3) process improvement. RESULTS The proposed framework is applied to the pediatric intensive care unit of the Penn State Hershey Childrens Hospital. The implementation of this methodology led to identification and categorization of different workflow tasks and activities into both value-added and non-value added in an effort to provide more valuable and higher quality patient care. CONCLUSIONS Based upon the lessons learned from the case study, the three-phase methodology provides a better, broader, leaner, and holistic assessment of clinical workflow. The proposed framework can be implemented in various healthcare settings to support continuous improvement efforts in which complexity is a daily element that impacts workflow. PRACTICAL IMPLICATIONS We proffer a general methodology for process improvement in a healthcare setting, providing decision makers and stakeholders with a useful framework to help their organizations improve efficiency.

Assessing the Research Efficiency of Higher Education Institutions in Chile: A Data Envelopment Analysis Approach.

Purpose – The purpose of this paper is to assess the research efficiency of the Chilean higher education institutions (HEIs). As it has been argued in the literature, universities in Chile are far from being considered research-oriented institutions. Current governmental reforms have put pressures on the efficient use of public resources, especially, public expenditures in higher education. In response, the proposed data-driven approach can be used to inform educational managers and policy makers about research efficiency. Therefore, a better allocation of the scarce educational resources can be achieved. Design/methodology/approach – Data envelopment analysis is used to assess the research efficiency of a set of Chilean universities. Four models are proposed based on different parameters to cover various drivers of the research productivity. Findings – The paper provides evidence that only a few universities in Chile are efficient in regards to research. Moreover, interesting results in terms of the diff...

Assessing collaboration networks in educational research: A co-authorship-based social network analysis approach

Purpose – The purpose of this paper is to analyze collaboration networks and their patterns among higher education institutions (HEIs) in Chile and the Latin American region. This will provide evidence to educational managements in order to properly allocate their efforts to improve collaboration. Design/methodology/approach – This quantitative study examines collaboration using a social network analysis (SNA) approach. The main source used to quantify collaboration is co-authorship of scholarly publications retrieved from the Web of Science scientific search engine. Findings – The paper provides evidence that there is a low collaboration rate within-country as well cross-country among HEIs in Latin America. The collaboration network in Chile is highly dependent on two institutions; Pontificia Universidad Catolica and Universidad de Chile. These institutions are considered leaders of opinion and knowledge facilitators. The density of the whole network is relatively low; only 5 percent of the potential con...

Quantifying complexity in translational research: an integrated approach

PURPOSE The purpose of this paper is to quantify complexity in translational research. The impact of major operational steps and technical requirements is calculated with respect to their ability to accelerate moving new discoveries into clinical practice. DESIGN/METHODOLOGY/APPROACH A three-phase integrated quality function deployment (QFD) and analytic hierarchy process (AHP) method was used to quantify complexity in translational research. A case study in obesity was used to usability. FINDINGS Generally, the evidence generated was valuable for understanding various components in translational research. Particularly, the authors found that collaboration networks, multidisciplinary team capacity and community engagement are crucial for translating new discoveries into practice. RESEARCH LIMITATIONS/IMPLICATIONS As the method is mainly based on subjective opinion, some argue that the results may be biased. However, a consistency ratio is calculated and used as a guide to subjectivity. Alternatively, a larger sample may be incorporated to reduce bias. PRACTICAL IMPLICATIONS The integrated QFD-AHP framework provides evidence that could be helpful to generate agreement, develop guidelines, allocate resources wisely, identify benchmarks and enhance collaboration among similar projects. ORIGINALITY/VALUE Current conceptual models in translational research provide little or no clue to assess complexity. The proposed method aimed to fill this gap. Additionally, the literature review includes various features that have not been explored in translational research.

A goal programming approach to address the proposal selection problem: a case study of a clinical and translational science institute

One of the most challenging tasks faced by healthcare organizations is to properly allocate resources that support the accomplishment of their organizational strategy. This challenge is particularly pressing for healthcare organizations that provide funding through call for proposals processes. In this study, we propose a goal programming (GP) model for the proposal selection problem within a clinical and translational science institute (CTSI) in an academic institution. The proposed GP model seeks to formalize and operationalize the strategy and long-term vision of the healthcare organization into the selection of a mix of proposals. A case study is presented to illustrate the practical use of the GP model. Additionally, this model was validated using real data from a recent call for proposal cycle at the Pennsylvania State University CTSI. The results provided a match on seven of the nine selected proposals from a pool of 24.

A dynamic network analysis approach for evaluating knowledge dissemination in a multi-disciplinary collaboration network in obesity research

Effective knowledge dissemination is important to promote the adoption of new concepts and tools. This study aims to provide a framework that assesses strategies for successful knowledge dissemination in a research collaboration network. We propose a Markov-chain Monte Carlo (MCMC) approach along with Dynamic Network Analysis (DNA) to model a social network and understand how different knowledge dissemination strategies can be used in a research collaboration network. The proposed method was demonstrated through a case study that uses a multi-disciplinary collaboration network in obesity research at an academic medical center. To assess the impact of initial disseminators on knowledge dissemination, four different strategies were considered. The simulation results indicated that the best strategy to disseminate knowledge within this obesity research network may be to use central agents in clusters when considering the coverage and speed of knowledge dissemination.

Healthcare Systems Engineering: Griffin/Healthcare Systems Engineering

The Healthcare Systems Engineering program at Johns Hopkins University provides engineers and healthcare professionals with the indepth knowledge and skills necessary to apply systems engineering principles and best practices to address today’s healthcare challenges and create healthcare of the future. Students will be well prepared to re-engineer healthcare delivery on a broad scale by using a systems approach. This approach will lead to solutions that seamlessly integrate technology into the cultural and workflow dynamics prevalent in healthcare, while holistically addressing interoperability, security/ privacy, safety, cost, performance (i.e., outcomes, etc.), and other key requirements.

Evaluating the cost-effectiveness of an early detection of Parkinson's disease through innovative technology

ABSTRACT Early detection of Parkinsons disease (PD) is critically important because it can increase patient quality of life and save treatment costs. An innovative approach for early detection of PD is to use nonwearable sensors that are capable of capturing skeletal joint data. This article evaluates the cost-effectiveness of this sensor-based intervention considering the quality-adjusted life years (QALYs) and the associated costs. The results indicate that the intervention would be cost-effective if devices were deployed for community health screening in public places such as health fairs and pharmacies.

Assessing the efficiency of secondary schools in Chile: a data envelopment analysis

Purpose The purpose of this paper was to evaluate the efficiency of secondary education schools in Chile. Since the early 1980s, several educational reforms have been passed with the main objective of improving the quality, equity and efficiency of the Chilean education system. This has initiated a debate about the efficient use of public educational resources. In response, this study provides insights into identifying the most efficient types of schools based on a set of different inputs and outputs. Design/methodology/approach This quantitative research study used data envelopment analysis (DEA), which estimates a single index of efficiency to identify schools performing at superior levels compared to other schools with similar characteristics. Two sets of models are created for evaluating efficiency. The first set of analyses provides a longitudinal efficiency comparison based on student performance on two national standardized tests as outputs, and the second model incorporates socioeconomic characteristics of students attending different schools as inputs in the efficiency estimation. Findings Based on the longitudinal models, it was found that private schools are more efficient and more consistent in maintaining their efficiency over time than other types of schools. In addition, when accounting for socioeconomic factors, publicly subsidized schools were more efficient than public schools. Practical implications The Chilean parliament is currently discussing new educational reforms that focus on more efficient use of educational resources to improve educational quality and equity. The results provided in this study generate useful evidence for policymakers and other stakeholders regarding school efficiency and the appropriate allocation of public resources to support diverse students served by different types of secondary educational institutions. Social implications Education is a key factor affecting social mobility and socioeconomic improvement of societies. Schools are called upon to improve their performance to promote these social goals. Accordingly, more novel forms of research on efficiency are necessary to assess how well schools are transforming their inputs into performance outputs. Originality/value This study provides a longitudinal analysis of educational efficiency using DEA with a national data set of Chilean schools to evaluate how consistent the schools are in maintaining their levels of efficiency over time. In addition, one DEA model accounts for a social “vulnerability” index at the student level to better understand how efficiently secondary schools use their resources. The insights gained provide data-driven answers to support more informed educational decision-making and policy processes in Chile.