The Effect of Financial Incentives on Hospitals That Serve Poor Patients
Bibliographic record
Abstract
Letters1 March 2011The Effect of Financial Incentives on Hospitals That Serve Poor PatientsIshak A. Mansi, MDIshak A. Mansi, MDFrom Brooke Army Medical Center, Fort Sam Houston, TX 78234.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-154-5-201103010-00013 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:Jha and colleagues (1) made excellent efforts to define the effects of the pay-for-performance Premier Hospital Quality Incentive Demonstration program (referred to hereafter as the “Premier program”) on hospitals that serve poor patients. However, they missed the essence of the question in their methodology, interpretation, conclusion, and discussion. The disproportionate-share index, which these authors used to identify hospitals caring for poorer populations, is also used by the Centers for Medicare & Medicaid Services to compensate hospitals for caring for poorer Medicare patients and has never been validated as a marker for hospitals that care for “poor” patients ...References1. Jha AK, Orav EJ, Epstein AM. The effect of financial incentives on hospitals that serve poor patients. Ann Intern Med. 2010;153:299-306. [PMID: 20820039] LinkGoogle Scholar2. Mansi IA, Shi R, Khan M, Huang J, Carden D. Effect of compliance with quality performance measures for heart failure on clinical outcomes in high-risk patients. J Natl Med Assoc. 2010;102:898-905. [PMID: 21053704] CrossrefMedlineGoogle Scholar3. Mansi IA. Public reporting and pay for performance [Letter]. N Engl J Med. 2007;356:1783. [PMID: 17465041] MedlineGoogle Scholar4. Ko DT, Tu JV, Masoudi FA, Wang Y, Havranek EP, Rathore SS, et al. Quality of care and outcomes of older patients with heart failure hospitalized in the United States and Canada. Arch Intern Med. 2005;165:2486-92. [PMID: 16314545] CrossrefMedlineGoogle Scholar5. Ofri D. Quality measures and the individual physician. N Engl J Med. 2010;363:606-7. [PMID: 20818853] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Ishak A. Mansi, MDAffiliations: From Brooke Army Medical Center, Fort Sam Houston, TX 78234.Disclaimer: The views expressed herein are those of the author and do not reflect the official policy or position of Brooke Army Medical Center, the U.S. Army Medical Department, the U.S. Army Office of the Surgeon General, the Department of the Army, the Department of Defense, or the U.S. government.Disclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoThe Effect of Financial Incentives on Hospitals That Serve Poor Patients Ashish K. Jha , E. John Orav , and Arnold M. Epstein The Effect of Financial Incentives on Hospitals That Serve Poor Patients Ashish K. Jha , Arnold Epstein , and E. John Orav Metrics 1 March 2011Volume 154, Issue 5Page: 370KeywordsConflicts of interestHealth careHealth care qualityHealth statisticsHeart failureMedicareMotivationPatientsPopulation statisticsSurgeons ePublished: 1 March 2011 Issue Published: 1 March 2011 Copyright & PermissionsCopyright © 2011 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".