scholarly journals Can benchmarking Australian hospitals for quality identify and improve high and low performers? Disseminating research findings for hospitals

2020 ◽  
Vol 32 (Supplement_1) ◽  
pp. 84-88 ◽  
Author(s):  
Peter Hibbert ◽  
Faisal Saeed ◽  
Natalie Taylor ◽  
Robyn Clay-Williams ◽  
Teresa Winata ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Health Services Research ◽  
Large Scale ◽  
Logic Model ◽  
Continuous Variables ◽  
Services Research ◽  
High Performer ◽  
Research Findings ◽  
Multiple Domains ◽  
Selection Of

Abstract This paper examines the principles of benchmarking in healthcare and how benchmarking can contribute to practice improvement and improved health outcomes for patients. It uses the Deepening our Understanding of Quality in Australia (DUQuA) study published in this Supplement and DUQuA’s predecessor in Europe, the Deepening our Understanding of Quality improvement in Europe (DUQuE) study, as models. Benchmarking is where the performances of institutions or individuals are compared using agreed indicators or standards. The rationale for benchmarking is that institutions will respond positively to being identified as a low outlier or desire to be or stay as a high performer, or both, and patients will be empowered to make choices to seek care at institutions that are high performers. Benchmarking often begins with a conceptual framework that is based on a logic model. Such a framework can drive the selection of indicators to measure performance, rather than their selection being based on what is easy to measure. A Donabedian range of indicators can be chosen, including structure, process and outcomes, created around multiple domains or specialties. Indicators based on continuous variables allow organizations to understand where their performance is within a population, and their interdependencies and associations can be understood. Benchmarking should optimally target providers, in order to drive them towards improvement. The DUQuA and DUQuE studies both incorporated some of these principles into their design, thereby creating a model of how to incorporate robust benchmarking into large-scale health services research.

scholarly journals 2286

2017 ◽  
Vol 1 (S1) ◽  
pp. 14-14
Author(s):  
William G. Adams ◽  
Michael Mendis ◽  
Shiby Thomas ◽  
David Center ◽  
Sara Curran
Keyword(s):  
Health Services Research ◽  
Health Services ◽  
Community Health ◽  
Comparative Effectiveness ◽  
Large Scale ◽  
Safety Net ◽  
Services Research ◽  
Patient Level Data ◽  
Patient Level ◽  
Level Data

OBJECTIVES/SPECIFIC AIMS: The primary objective of this effort is to develop and distribute an easy to use i2b2 component that is capable of evaluating diverse complex relationships for a wide variety of exposures and outcomes over time. In this manner we are able to leverage the unique design of the i2b2 database to support health services research, comparative effectiveness, and quality improvement using a single tool. Furthermore, our novel database redesign has the potential to provide user-friendly access to individual and group CHC data for CER. METHODS/STUDY POPULATION: For this project we used software experts, clinical informatics specialists, and the existing i2b2 open-source software to convert our legacy HOME Cell into a web-client version. The tool will be used to study health outcomes within a network of Boston based Community Health Centers and the largest safety-net hospital in New England, Boston Medical Center. RESULTS/ANTICIPATED RESULTS: The new web-client HOME Cell will allow i2b2 users to model virtually any exposure (including therapeutic interventions such as medications or tests) in i2b2 against any outcome accounting for complex temporal relationships and other factors. In addition we plan to use our new Community Health Center views to enhance our community engagement activities by allowing direct access to their data for our partners. DISCUSSION/SIGNIFICANCE OF IMPACT: Our project addresses multiple national priorities related to data sharing, clinical research informatics, and comparative effectiveness. The web-client version of the HOME Cell substantially improves our community’s access to HOME Cell functionality and is a novel, sharable resource for use within the CTSA/NCATS community. Our approach provides a new way to perform large-scale collaborative research without the need to actually move patient-level data and has demonstrated that CER, health services research, and quality measurement can share a common framework. In addition, and as demonstrated in our earlier pilot work, the HOME Cell also has the potential to support large-scale multivariate analyses in a distributed manner that does not require sharing of patient-level data. We believe our approach has great promise for supporting the reuse of clinical data for rapid, transparent, health outcome assessments on a national scale. Our efforts support multiple strategic goals including: (1) support for building national clinical and translational research capacity by enhancing a broadly adopted informatics tool (i2b2); (2) enhanced consortium-wide collaborations by offering a tool that can be easily shared within the CTSA network to support multi-institutional collaboration; and (3) improving the health of our communities by offering a tool that has the potential to provide new insights into health care processes and outcomes that could drive innovation and improvement activities.

Quality Improvement: New Contributions from the Field of Health Services Research

2000 ◽  
Vol 57 (2_suppl) ◽  
pp. 5-8
Author(s):  
Peggy McNamara ◽  
Blake Caldwell ◽  
Irene Fraser ◽  
Jan De La Mare ◽  
Jill Arent
Keyword(s):  
Quality Improvement ◽  
Health Services Research ◽  
Health Services ◽  
Services Research

Quality Improvement: New Contributions from the Field of Health Services Research

2000 ◽  
Vol 57 (3) ◽  
pp. 5-8
Author(s):  
Peggy McNamara ◽  
Blake Caldwell ◽  
Irene Fraser ◽  
Jan De La Mare ◽  
Jill Arent
Keyword(s):  
Quality Improvement ◽  
Health Services Research ◽  
Health Services ◽  
Services Research

scholarly journals Opportunities for diversifying and enriching our article mix

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Bruce Rosen ◽  
Stephen C. Schoenbaum ◽  
Avi Israeli
Keyword(s):  
Health Policy ◽  
Health Services Research ◽  
Health Services ◽  
Large Scale ◽  
Policy Research ◽  
National Policy ◽  
Administrative Databases ◽  
Services Research ◽  
Health Policy Analysis ◽  
Access To Data

AbstractAs 2020 comes to a close, the Israel Journal of Health Policy Research (IJHPR) will soon be starting its tenth year of publication. This editorial compares data from 2012 (the journal’s first year of publication) and 2019 (the journal’s most recent full year of publication), regarding the journal’s mix of article types, topics, data sources and methods, with further drill-downs regarding 2019.The analysis revealed several encouraging findings, including a broad and changing mix of topics covered. However, the analysis also revealed several findings that are less encouraging, including the limited number of articles which assessed national policy changes, examined changes over time, and/or made secondary use of large-scale survey data. These findings apparently reflect, to some extent, the mix of studies being carried out by Israeli health services researchers.As the senior editors of the IJHPR we are interested in working with funders, academic institutions, the owners and principal users of relevant administrative databases, and individual scholars to further understand the factors influencing the mix of research being carried out, and subsequently published, by Israel’s health services research community. This deeper understanding could then be used to develop a joint plan to diversify and enrich health services research and health policy analysis in Israel. The plan should include a policy of ensuring improved access to data, to properly support information-based research.

How Do We Align Health Services Research and Quality Improvement?

2020 ◽  
pp. 282-291
Author(s):  
Devika Das ◽  
Lalan Wilfong ◽  
Katherine Enright ◽  
Gabrielle Rocque
Keyword(s):  
Health Care ◽  
Quality Improvement ◽  
Health Services Research ◽  
Health Services ◽  
Health Care Quality ◽  
Care Delivery ◽  
Care Quality ◽  
Health Care Use ◽  
Semistructured Interview ◽  
Services Research

Quality improvement (QI) initiatives and health services research (HSR) are commonly used to target health care quality. These disciplines are increasingly important because of the movement toward value-based health care as alternative payment and care delivery models drive institutions and investigators to focus on reducing unnecessary health care use and improving care coordination. QI efforts frequently target medical error and/or efficiency of care through the Plan-Do-Study-Act methodology. Within the QI framework, strategies for data display (e.g., Pareto charts, run charts, histograms, scatter plots) are leveraged to identify opportunities for intervention and improvement. HSR is a multidisciplinary field of study that seeks to identify the most effective way to organize, deliver, and finance health care to maximize the quality and value of care at both the individual and population levels. HSR uses a diverse set of quantitative and qualitative methodologies, such as case-control studies, cohort studies, randomized control trials, and semistructured interview/focus group evaluations. This manuscript provides examples of methodologic approaches for QI and HSR, discusses potential challenges associated with concurrent quality efforts, and identifies strategies to successfully leverage the strengths of each discipline in care delivery.

scholarly journals Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Laura J Damschroder ◽  
David C Aron ◽  
Rosalind E Keith ◽  
Susan R Kirsh ◽  
Jeffery A Alexander ◽  
...  
Keyword(s):  
Health Services Research ◽  
Health Services ◽  
Implementation Science ◽  
Services Research ◽  
Research Findings

Analysis of Novel Care Management Programs in Primary Care: An Example of Mixed Methods in Health Services Research

2016 ◽  
Vol 13 (1) ◽  
pp. 85-112 ◽  
Author(s):  
Jodi Summers Holtrop ◽  
Georges Potworowski ◽  
Lee A. Green ◽  
Michael Fetters
Keyword(s):  
Mixed Methods ◽  
Health Services Research ◽  
Health Services ◽  
Quantitative Data ◽  
Mixed Methods Research ◽  
Care Management ◽  
Large Scale ◽  
Qualitative Comparative Analysis ◽  
Data Transformation ◽  
Services Research

While health services researchers are using mixed methods research in large-scale studies with “big data” and incorporating data transformation for merging qualitative and quantitative data sets, these developments are not widely known to the broader mixed methods research community. Our purpose in this article is to introduce health services research to the broader mixed methods audience, to examine the potential for novel innovations in mixed methods research procedures, and to illustrate these points through a project on care management that used a convergent mixed methods design. In addition to traditional analytical procedures, we illustrate two qualitative to quantitative data transformation procedures, one using normalization process theory and a second, fuzzy set qualitative comparative analysis.

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