Quality health care is a high priority for the President, the Department of Health and Human Services (HHS), and the Centers for Medicare & Medicaid Services (CMS). CMS implements quality initiatives to assure quality health care for Medicare Beneficiaries through accountability and public disclosure. CMS uses quality measures in its various quality initiatives that include quality improvement, pay for reporting, and public reporting.

Quality measures are tools that help us measure or quantify healthcare processes, outcomes, patient perceptions, and organizational structure and/or systems that are associated with the ability to provide high-quality health care and/or that relate to one or more quality goals for health care. These goals include: effective, safe, efficient, patient-centered, equitable, and timely care.

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CMS uses quality measures in its quality improvement, public reporting, and pay-for-reporting programs for specific healthcare providers. Click on Related Links below for more information.

Data on quality measures are collected or reported in a variety of ways, such as claims, assessment instruments, chart abstraction, registries. Click on Related Links Inside CMS below for more information.

CMS is currently testing the submission of quality measures data from Electronic Health Records for physicians and other health care professionals and will soon be testing with hospitals. Click on the "Electronic Specification" link to the left for more information.

Join CMS for a two-part webinar series that covers an introduction to quality measures, overview of the measure development process, how the public can get involved, and the new Meaningful Measures initiative

CMS is committed to improving quality, safety, accessibility, and affordability of healthcare for all. We are offering an Introduction to CMS Quality Measures webinar series available to the public. On April 26th, from 1:00-2:00pm, ET, CMS will host the first of a two-part series that covers an introduction to quality measures, overview of the measure development process, how the public can get involved, and the new Meaningful Measures initiative. If you are unable to attend during that time, the same session will be offered again on May 2nd, from 4:00-5:00pm, ET. CMS is looking for your feedback and participation in the quality measurement community, so please join us during the webinar to learn what we are doing and how you can be a part of the process!

CMS is committed to improving quality, safety, accessibility, and affordability of healthcare for all. We are excited to offer an opportunity to learn about quality measures. On June 13th, from 12:00-1:00pm, ET, CMS will host the 2nd webinar , of a two-part series that covers an introduction to quality measures, overview of the measure development process, and how providers, patients, and families can be involved. If you are unable to attend during this time, the same session will be offered again on June 14th, from 4:00-5:00pm, ET. CMS is looking for your feedback and participation in the quality measurement community, so please join us during the webinar to learn what we are doing and how you can be a part of the process!

The Centers for Medicare & Medicaid Services (CMS) has contracted with FMQAI to provide services for the Medication Measures Special Innovation Project. The purpose of the project is to develop measures that can be used to support quality healthcare delivery to Medicare beneficiaries. The key objectives of the project are to:

The project currently has a portfolio of eight NQF-endorsed measures for the ambulatory care setting, five of which (i.e., NQF 0545, NQF 0555, NQF 0556, NQF 2467, NQF 2468) are undergoing NQF comprehensive review and have received recommendations for re-endorsement. In addition, one measure (i.e., NQF 2379) for the ambulatory care setting and two electronic clinical quality measures (i.e., NQF 2362 and NQF 2363) for the inpatient care setting have been submitted to NQF and have received recommendations for endorsement.

While these tools have not been independently published and would not be considered standardized, they may be useful to the research community. These reports describe how experts used the tools for the project. Researchers may want to use the tools for their own projects; however, they would need to determine their own parameters for making judgements. Details about the design and application of the tools are included in Appendix A of the reports.

Was the study described as randomized? A study does not satisfy quality criteria as randomized simply because the authors call it randomized; however, it is a first step in determining if a study is randomized

Groups can differ at baseline without raising red flags if: (1) the differences would not be expected to have any bearing on the interventions and outcomes; or (2) the differences are not statistically significant. When concerned about baseline difference in groups, reviewers recorded them in the comments section and considered them in their overall determination of the study quality.

Conversely, differential dropout rates are not flexible; there should be a 15 percent cap. If there is a differential dropout rate of 15 percent or higher between arms, then there is a serious potential for bias. This constitutes a fatal flaw, resulting in a poor quality rating for the study.

Intention-to-treat (ITT) means everybody who was randomized is analyzed according to the original group to which they are assigned. This is an extremely important concept because conducting an ITT analysis preserves the whole reason for doing a randomized trial; that is, to compare groups that differ only in the intervention being tested. When the ITT philosophy is not followed, groups being compared may no longer be the same. In this situation, the study would likely be rated poor. However, if an investigator used another type of analysis that could be viewed as valid, this would be explained in the "other" box on the quality assessment form. Some researchers use a completers analysis (an analysis of only the participants who completed the intervention and the study), which introduces significant potential for bias. Characteristics of participants who do not complete the study are unlikely to be the same as those who do. The likely impact of participants withdrawing from a study treatment must be considered carefully. ITT analysis provides a more conservative (potentially less biased) estimate of effectiveness.

The questions on the assessment tool were designed to help reviewers focus on the key concepts for evaluating a study's internal validity. They are not intended to create a list that is simply tallied up to arrive at a summary judgment of quality.

Fatal flaws: If a study has a "fatal flaw," then risk of bias is significant, and the study is of poor quality. Examples of fatal flaws in RCTs include high dropout rates, high differential dropout rates, no ITT analysis or other unsuitable statistical analysis (e.g., completers-only analysis).

Generally, when evaluating a study, one will not see a "fatal flaw;" however, one will find some risk of bias. During training, reviewers were instructed to look for the potential for bias in studies by focusing on the concepts underlying the questions in the tool. For any box checked "no," reviewers were told to ask: "What is the potential risk of bias that may be introduced by this flaw?" That is, does this factor cause one to doubt the results that were reported in the study?

NHLBI staff provided reviewers with background reading on critical appraisal, while emphasizing that the best approach to use is to think about the questions in the tool in determining the potential for bias in a study. The staff also emphasized that each study has specific nuances; therefore, reviewers should familiarize themselves with the key concepts.

Systematic reviews can be qualitative or quantitative. A qualitative systematic review summarizes the results of the primary studies but does not combine the results statistically. A quantitative systematic review, or meta-analysis, is a type of systematic review that employs statistical techniques to combine the results of the different studies into a single pooled estimate of effect, often given as an odds ratio. The guidance document below is organized by question number from the tool for quality assessment of systematic reviews and meta-analyses.

Each included study should be appraised for internal validity (study quality assessment) using a standardized approach for rating the quality of the individual studies. Ideally, this should be done by at least two independent reviewers appraised each study for internal validity. However, there is not one commonly accepted, standardized tool for rating the quality of studies. So, in the research papers, reviewers looked for an assessment of the quality of each study and a clear description of the process used.

Did the authors describe their goal in conducting this research? Is it easy to understand what they were looking to find? This issue is important for any scientific paper of any type. Higher quality scientific research explicitly defines a research question.

For some exposures, however, this question may not be applicable (e.g., the exposure may be a dichotomous variable like living in a rural setting versus an urban setting, or vaccinated/not vaccinated with a one-time vaccine). If there are only two possible exposures (yes/no), then this question should be given an "NA," and it should not count negatively towards the quality rating.

For example, retrospective self-report of dietary salt intake is not as valid and reliable as prospectively using a standardized dietary log plus testing participants' urine for sodium content. Another example is measurement of BP, where there may be quite a difference between usual care, where clinicians measure BP however it is done in their practice setting (which can vary considerably), and use of trained BP assessors using standardized equipment (e.g., the same BP device which has been tested and calibrated) and a standardized protocol (e.g., patient is seated for 5 minutes with feet flat on the floor, BP is taken twice in each arm, and all four measurements are averaged). In each of these cases, the former would get a "no" and the latter a "yes." be457b7860

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