Rocco J. Perla

Resolving the 50-year debate around using and misusing Likert scales

How Likert type measurement scales should be appropriately used and analysed has been debated for over 50 years, often to the great confusion of students, practitioners, allied health researchers and educators. Basically, there are two major competing views that have evolved somewhat independently of one another and of the associated empirical research literature on this ‘great debate’. Most recently in this journal, Jamieson outlined the view that ‘Likert scales’ are ordinal in character (i.e., produce rank order data) and that they, therefore, must be analysed using non-parametric statistics. Non-parametric statistics, however, are less sensitive and less powerful than parametric statistics and are, therefore, more likely to miss weaker or emerging findings.

A healthy bottom line: healthy life expectancy as an outcome measure for health improvement efforts

CONTEXT Good health is the most important outcome of health care, and healthy life expectancy (HLE), an intuitive and meaningful summary measure combining the length and quality of life, has become a standard in the world for measuring population health. METHODS This article critically reviews the literature and practices around the world for measuring and improving HLE and synthesizes that information as a basis for recommendations for the adoption and adaptation of HLE as an outcome measure in the United States. FINDINGS This article makes the case for adoption of HLE as an outcome measure at the national, state, community, and health care system levels in the United States to compare the effectiveness of alternative practices, evaluate disparities, and guide resource allocation. CONCLUSIONS HLE is a clear, consistent, and important population health outcome measure that can enable informed judgments about value for investments in health care.

Seven propositions of the science of improvement: exploring foundations

Context: The phrase “Science of Improvement” or “Improvement Science” is commonly used today by a range of people and professions to mean different things, creating confusion to those trying to learn about improvement. In this article, we briefly define the concepts of improvement and science, and review the history of the consideration of “improvement” as a science. Methods: We trace key concepts and ideas in improvement to their philosophical and theoretical foundation with a focus on Demings System of Profound Knowledge. We suggest that Demings system has a firm association with many contemporary and historic philosophic and scientific debates and concepts. With reference to these debates and concepts, we identify 7 propositions that provide the scientific and philosophical foundation for the science of improvement. Findings: A standard view of the science of improvement does not presently exist that is grounded in the philosophical and theoretical basis of the field. The 7 propositions outlined here demonstrate the value of examining the underpinnings of improvement. This is needed to both advance the field and minimize confusion about what the phrase “science of improvement” represents. We argue that advanced scientists of improvement are those who like Deming and Shewhart can integrate ideas, concepts, and models between scientific disciplines for the purpose of developing more robust improvement models, tools, and techniques with a focus on application and problem solving in real world contexts. Conclusions: The epistemological foundations and theoretical basis of the science of improvement and its reasoning methods need to be critically examined to ensure its continued development and relevance. If improvement efforts and projects in health care are to be characterized under the canon of science, then health care professionals engaged in quality improvement work would benefit from a standard set of core principles, a standard lexicon, and an understanding of the evolution of the science of improvement.

Large‐Scale Improvement Initiatives in Healthcare: A Scan of the Literature

Context: The goal of this article is to provide a succinct scan of the literature as it relates to the current thinking and practice in large‐scale improvement initiatives in healthcare. Method: We employed a scan of the literature using a modified Delphi technique. A standard review form was used. The scan was limited to large‐scale spread efforts in hospitals and healthcare systems. Each of the main factors that emerged during the scan was linked to secondary factors and organized using a driver diagram. Findings: Four primary drivers (factors) emerged during our scan that inform large‐scale change initiatives in healthcare: Planning and Infrastructure; Individual, Group, Organizational, and System Factors; The Process of Change; and Performance Measures and Evaluation. Conclusion: Our scan identified a tremendous amount of work being done around the world to improve healthcare. In general, our findings suggest these initiatives tend to be fragmented from an implementation standpoint. We identified primary and secondary drivers (factors) that can be used by those responsible for implementing large‐scale improvement initiatives both at a strategy level and in their daily work. These drivers could serve as a “checklist” of ideas to consider in different testing and implementation situations.

Learning Networks for Sustainable, Large-Scale Improvement

Large-scale improvement efforts known as improvement networks offer structured opportunities for exchange of information and insights into the adaptation of clinical protocols to a variety of settings.

Health care-associated infection reporting: the need for ongoing reliability and validity assessment.

Government-mandated reporting of health care-associated infections (HAIs) and new reimbursement regulations place a premium on accurate and reliable detection of HAIs. This commentary addresses the challenges and opportunities of having consistent, well-defined, and continuous methods in place to ensure the reliability and validity of HAI detection and reporting. In addition, such procedures could support the development and expertise of infection preventionists. A Web-based clinical vignette model is suggested for improving HAI reporting for hospitals participating in the Centers for Disease Control and Preventions National Healthcare Safety Network.

Sampling considerations for health care improvement.

Sampling in improvement work can pose challenges. How is it different from the sampling strategies many use with research, clinical trials, or regulatory programs? What should improvement teams consider when determining a useful approach to sampling and a useful sample size? The aim of this article is to introduce some of the concepts related to sampling for improvement. We give specific guidance related to determining a useful sample size to a wider health care audience so that it can be applied to improvement projects in hospitals and health systems.

Judgment sampling: a health care improvement perspective.

Sampling plays a major role in quality improvement work. Random sampling (assumed by most traditional statistical methods) is the exception in improvement situations. In most cases, some type of “judgment sample” is used to collect data from a system. Unfortunately, judgment sampling is not well understood. Judgment sampling relies upon those with process and subject matter knowledge to select useful samples for learning about process performance and the impact of changes over time. It many cases, where the goal is to learn about or improve a specific process or system, judgment samples are not merely the most convenient and economical approach, they are technically and conceptually the most appropriate approach. This is because improvement work is done in the real world in complex situations involving specific areas of concern and focus; in these situations, the assumptions of classical measurement theory neither can be met nor should an attempt be made to meet them. The purpose of this article is to describe judgment sampling and its importance in quality improvement work and studies with a focus on health care settings.

Elevated creatine phosphokinase levels associated with linezolid therapy

PURPOSE A case of elevated creatine phosphokinase (CPK) levels associated with linezolid therapy in a patient on chronic antihyperlipidemic therapy is presented. SUMMARY A 79-year-old Caucasian man with a primary diagnosis of acute hemoptysis secondary to pneumonia was admitted to the medical-surgical intensive care unit. A chest radiograph showed a large, right, lower-lobe infiltrate with alveolar consolidation. The patients medical history included hyperlipidemia that was chronically treated with lovastatin and gemfibrozil. Methicillin-resistant Staphylococcus aureus (MRSA) pneumonia was suspected and confirmed. Vancomycin 1 g i.v. every 12 hours was administered for approximately 10 days into the admission and switched to linezolid 600 mg i.v. every 12 hours after a lack of response to vancomycin. On hospital day 11, the patients CPK concentration was 47 units/L. Seven days later, his CPK concentration was 2584 units/L and his lovastatin and gemfibrozil were discontinued on that day. The patients CPK concentration peaked at 5369 units/L on the following day, and linezolid was discontinued at that point. One week later, his CPK concentration was 28 units/L. Approximately two weeks after the patients CPK levels normalized, he developed numerous complications. The patient died as a result of respiratory failure 11 days after being extubated, which occurred about 38 days after his admission. Although concomitant use of statins and gemfibrozil is known to increase the risk for CPK elevations, the continued rise in CPK levels after discontinuation of antihyperlipidemic therapy and the rapid time course for normalization after linezolid discontinuation are more consistent with an event associated with linezolid initiation. CONCLUSION A patient on chronic antihyperlipidemic therapy developed elevated CPK levels after receiving linezolid for the treatment of MRSA pneumonia.

Measuring Hospital-Wide Mortality-Pitfalls and Potential.

Abstract:Risk-adjusted hospital-wide mortality has been proposed as a key indicator of system-level quality. Several risk-adjusted measures are available, and one—the hospital standardized mortality ratio (HSMR) — is publicly reported in a number of countries, but not in the United States. This paper reviews potential uses of such measures. We conclude that available methods are not suitable for interhospital comparisons or rankings and should not be used for pay-for-performance or value-based purchasing/payment. Hospital-wide mortality is a relatively imprecise, crude measure of quality, but disaggregation into condition- and service-line-specific mortality can facilitate targeted improvement efforts. If tracked over time, both observed and expected mortality rates should be monitored to ensure that apparent improvement is not due to increasing expected mortality, which could reflect changes in case mix or coding. Risk-adjusted mortality can be used as an initial signal that a hospitals mortality rate is significantly higher than statistically expected, prompting further inquiry.