Answers are grounded in this paper and cite their sources.
Editorials1 March 1997How Much Are We Willing To Pay for Information about Quality of CareLisa I. Iezzoni, MD, MScLisa I. Iezzoni, MD, MScBeth Israel Deaconess Medical Center, Boston, MA 02215Author, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-126-5-199703010-00009 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Once we agree-or concede-that intense, unscripted competition is now "reforming" the U.S. health care system, the pressing need for information becomes self-evident. Few sentient beings would argue the first point. In some areas, competition has rocked and upended health care landscapes that formerly shifted only with the grinding reluctance of tectonic plates. The second point follows: Information is necessary for making informed choices among competitors. To date, however, costs have been easier to quantify than quality, so dollars have dominated decision making.When driven purely by price, competition is antithetical to the fundamental mission of health care. Information, therefore, is ...References1. Brook RH, Kamberg CJ, McGlynn EA. Health system reform and quality. JAMA. 1996; 276:476-80. Google Scholar2. Brook RH, McGlynn EA, Cleary PD. Quality of health care. Part 2: measuring quality of care [Editorial]. N Engl J Med. 1996; 335:966-70. Google Scholar3. Iezzoni LI, Shwartz M, Restuccia J. The role of severity information in health policy debates: a survey of state and regional concerns. Inquiry. 1991; 28:117-28. Google Scholar4. Iezzoni LI, Greenberg LG. Widespread assessment of risk-adjusted outcomes: lessons from local initiatives. Jt Comm J Qual Improv. 1994; 20:305-16. Google Scholar5. Romano PS, Zach A, Luft HS, Rainwater J, Remy LL, Campa D. The California Hospital Outcomes Project: using administrative data to compare hospital performance. Jt Comm J Qual Improv. 1995; 21:668-82. Google Scholar6. U.S. General Accounting Office; Health, Education, and Human Services Division. Employers and Individual Consumers Want Additional Information on Quality. Washington, D.C.: U.S. General Accounting Office; 1995. GAO/HEHS-95-201. Google Scholar7. Pine M, Norusis M, Jones B, Rosenthal GE. Predictions of hospital mortality rates: a comparison of data sources. Ann Intern Med. 1997; 126:347-54. Google Scholar8. Daley J, Jencks S, Draper D, Lenhart G, Thomas N, Walker J. Predicting hospital-associated mortality for Medicare patients. A method for patients with stroke, pneumonia, acute myocardial infarction, and congestive heart failure. JAMA. 1988; 260:3617-24. Google Scholar9. Iezzoni LI, Ash AS, Coffman GA, Moskowitz MA. Predicting in-hospital mortality. A comparison of severity measurement approaches. Med Care. 1992; 30:347-59. Google Scholar10. McMahon LF Jr, Hayward RA, Bernard AM, Rosevear JS, Weissfeld LA. APACHE-L: a new severity of illness adjuster for inpatient medical care. Med Care. 1992; 30:445-52 Google Scholar11. Mozes B, Easterling MJ, Sheiner LB, Melmon KL, Kline R, Goldman ES, et al. Case-mix adjustment using objective measures of severity: the case for laboratory data. Health Serv Res. 1994; 28:689-712. Google Scholar12. Goldman ES, Easterling MJ, Sheiner LB. Improving the homogeneity of diagnosis-related groups (DRGs) by using clinical laboratory, demographic, and discharge data. Am J Public Health. 1989; 79:441-4. Google Scholar13. Davis RB, Iezzoni LI, Phillips RS, Reiley P, Coffman GA, Safran C. Predicting in-hospital mortality. The importance of functional status information. Med Care. 1995; 33:906-21. Google Scholar14. U.S. Congress. Office of Technology Assessment. Bringing Health Care Online: The Role of Information Technologies. Washington, D.C.: U.S. Government Printing Office; 1995. OTA-ITC-624. Google Scholar15. Localio AR, Hamory BH, Sharp TJ, Weaver SL, TenHave TR, Landis JR. Comparing hospital mortality in adult patients with pneumonia. A case study of statistical methods in a managed care program. Ann Intern Med. 1995; 122:125-32. Google Scholar16. Schneider EC, Epstein AM. Influence of cardiac-surgery performance reports on referral practices and access to care. A survey of cardiovascular specialists. N Engl J Med. 1996; 335:251-6. Google Scholar17. U.S. General Accounting Office; Health, Education, and Human Services Division. Health Care Reform. "Report cards" are useful but significant issues need to be addressed. Washington, D.C.: U.S. General Accounting Office; 1994. GAO/HEHS-94-219. Google Scholar18. Hibbard JH, Jewett JJ. What type of quality information do consumers want in a health care report card? Med Care Res Rev. 1996; 53:28-47. Google Scholar Author, Article, and Disclosure InformationAffiliations: Beth Israel Deaconess Medical Center, Boston, MA 02215Corresponding Author: Lisa I. Iezzoni, MD, MSc, Division of General Medicine and Primary Care, Department of Medicine, Beth Israel Deaconess Medical Center East Campus, 330 Brookline Avenue, Boston, MA 02215. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoPredictions of Hospital Mortality Rates: A Comparison of Data Sources Michael Pine , Marija Norusis , Barbara Jones , and Gary E. Rosenthal Metrics Cited byQuality improvement of International Classification of Diseases, 9th revision, diagnosis coding in radiation oncology: Single-institution prospective study at University of California, San FranciscoLetter to the Editor: Personally-Controlled Electronic Health Records Have Potential to Improve Reporting on Quality and Safety in AustraliaImproved accuracy of co-morbidity coding over time after the introduction of ICD-10 administrative dataMaximising the value of hospital administrative datasetsComparison of Clinical and Administrative Data Sources for Hospital Coronary Artery Bypass Graft Surgery Report CardsComparison of Administrative Data and Medical Records to Measure the Quality of Medical Care Provided to Vulnerable Older PatientsAuditing for quality purposesIs Risk-Adjustor Selection More Important Than Statistical Approach for Provider Profiling? Asthma as an ExampleA practical method of linking data from Medicare claims and a comprehensive electronic medical records systemAccurate case finding using linked electronic clinical and administrative data at a children's hospitalDeriving a Risk-Adjustment Model for Pressure Ulcer Development Using the Minimum Data SetUnderuse of invasive procedures among Medicaid patients with acute myocardial infarctionSocioeconomic status as an independent risk factor for hospital readmission for heart failureLength of stay and procedure utilization are the major determinants of hospital charges for heart failureModelos probabilísticos de mortalidad para pacientes hospitalizados en unidades convencionalesSocioeconomic Status Is an Important Determinant of the Use of Invasive Procedures After Acute Myocardial Infarction in New York StateDocumenting the Impact of HospicePrediction of hospital readmission for heart failure: development of a simple risk score based on administrative dataThe Department of Veterans Affairs' NSQIPManaged care for congestive heart failure: Influence of payer status on process of care, resource utilization, and short-term outcomesInfluence of race and gender on care process, resource use, and hospital-based outcomes in congestive heart failureA Computer-Assisted Management Program for Antibiotics and Other Antiinfective AgentsMEDICAL INFORMATION SYSTEMS AND THEIR IMPORTANCE IN MANAGED CARE 1 March 1997Volume 126, Issue 5Page: 391-393KeywordsComputersDeath ratesElectronic medical recordsHealth careHealth care qualityHealth information technologyInformation technologyResearch designResearch laboratoriesStatistical data Issue Published: 1 March 1997 Copyright & PermissionsCopyright © 1997 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
No takes yet. Share an insight, caveat, or question.
Lisa I. Iezzoni (1997) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: