PubMed Health⌕ Search

PubMed · 10312885

Lessons from industry.

Abstract

Rapidly emerging in public debate as the key to the future of the health care industry is quality. One can hardly pick up a health care magazine or journal or listen to a discussion among health care professionals without quality becoming a concern. Despite virtually universal agreement on the importance of the generic term and secondary agreement on the importance of being able to measure it, discussion bogs down in either of two ways. It may become overwhelmed by the sheer magnitude of the task of describing all the elements of health care quality, or the different viewpoints of individuals will yield quite variable understandings of what the term "quality of health care" means. To make substantial progress in improving health care quality, we will need to come to an agreement on terms.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R W Hungate. Lessons from industry.. https://pubmed.ncbi.nlm.nih.gov/10312885/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Defining health inequality: why Rawls succeeds where social welfare theory fails.

While there has been an important increase in methodological and empirical studies on health inequality, not much has been written on the theoretical foundation of health inequality measurement. We discuss several reasons why the classic welfare approach, which is the foundation of income inequality analysis, fails to provide a satisfactory foundation for health inequality analysis. We propose an alternative approach which is more closely linked to the WHO concept of equity in health and is also consistent with the ethical principles espoused by Rawls [A Theory of Justice. Harvard University Press, Cambridge, MA, 1971]. This approach in its simplest form, is shown to be closely related to the concentration curve when health and income are positively related. Thus, the criteria presented in our paper provide an important theoretical foundation for empirical analysis using the concentration curve. We explore the properties of these approaches by developing policy scenarios and examining how various ethical criteria affect government strategies for targeting health interventions.

Efficiency↗

Statistical power analysis to estimate how many months of data are required to identify operating room staffing solutions to reduce labor costs and increase productivity.

UNLABELLED: We performed a statistical power analysis to determine how many historical data are needed for optimal operating room (OR) management decision making. The work applies to hospitals that provide service for all of its surgeons' elective cases on whatever workday the surgeons and patients choose. The hospital and anesthesia group adjust OR staffing and patient scheduling to care for the patients while minimizing OR staffing costs and maximizing labor productivity. Two years of data were obtained from a seven-OR surgical suite. The data were repeatedly split into training and testing datasets. The optimal staffing solution was calculated for each training dataset to maximize the efficiency of OR time usage and was then applied to the corresponding testing dataset. Training datasets ranged in size from 30 to 270 consecutive workdays. With 30 workdays of data, the statistical method identified staffing solutions that had an average of 35% decreased costs and 27% increased productivity as compared to the existing staffing plan. There was no significant improvement in performance with more than 210 workdays (10 mo) of data. With 30 workdays of OR or anesthesia group data, the optimization method can significantly reduce staffing costs and increase productivity compared with existing staffing. When applied routinely for adjusting staffing (e.g., on a quarterly basis), 9 to 12 mo of data should be used. IMPLICATIONS: With 30 workdays of operating room or anesthesia group data, the optimization method can propose staffing solutions that significantly decrease costs and increase productivity compared with existing staffing solutions. We recommend that, when the statistical method is applied routinely for adjusting staffing (e.g., on a quarterly basis), 9 to 12 mo of data be used.

Efficiency↗