The GAAP in Quality Measurement and Reporting
Abstract
CATALYZED BY EVIDENCE OF POOR-QUALITY CARE AND remarkable variations in processes and outcomes, the interest in quality measurement has increased exponentially. Manifestations of this interest include widespread promulgation of quality measures, an increase in public reporting of these measures, and early experiments in paying for quality. Now that quality of care is being measured rather than assumed, there seems little doubt that better quality scores will lead to major competitive advantages for clinicians and organizations. Although many quality measures are used internally by health care organizations to improve quality of care, an increasing number of measures are being reported publicly. Yet the measurement of quality in health care is neither standardized nor consistently accurate and reliable. Because any organization or company can advertise the quality of its products, it is important to hold health care quality measures to a higher standard than claims about, for example, household products. Without such assurances, publicly reported quality measures may misinform and possibly erode the trust that underlies the medical profession. If patients cannot trust what is reported, they may lose confidence in the quality of care that is provided. Invalid publicly reported quality-of-care measures pose significant risks to patients, clinicians, and potentially to payers. Patients might choose care according to misinformation and make poor decisions. Health care organizations may become overconfident about the quality of care provided and reduce or eliminate improvement efforts and introduce preventable harm. Payers may mistakenly provide financial rewards, channel patients to low-quality clinicians, or make inaccurate inferences about the value (quality per cost) they purchase.
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Improvement interventions are commonly launched without either a good outcome measurement plan or the baseline data required for meaningful time-series analyses.
“Improvement interventions are also commonly launched without either a good outcome measurement plan or the baseline data required for meaningful time-series analyses.34–36”
From Demystifying theory and its use in improvement (2015) · cites this paper as [34]
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- Authors
- Pronovost, P. J., Miller, M., & Wachter, R. M.
- Affiliations
- Johns Hopkins University (US)
- University of California, San Francisco (US)
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2007-10-17
- Volume
- 298
- Issue
- 15
- Pages
- 1800
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- OpenAlex
- W2013775035
- Cited by
- 92
- References
- 16
- Citations by year
- 2024: 1 · 2022: 2 · 2020: 4 · 2019: 3 · 2018: 5 · 2017: 6 · 2016: 4 · 2015: 8 · 2014: 1 · 2013: 9 · 2012: 6
- FWCI
- 23.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- JAMA (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Healthcare Quality and Management
- Health Sciences › Health Professions › General Health Professions › Patient Satisfaction in Healthcare
- Health Sciences › Health Professions › General Health Professions › Primary Care and Health Outcomes
- Keywords
- quality of care measurement, health care quality reporting, healthcare quality standards, healthcare quality improvement
- MeSH terms
- Credentialing, Humans, Medical Audit, Outcome and Process Assessment, Health Care, United States, Private Sector, Public Sector, Quality Indicators, Health Care
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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