Looking for Answers in All the Wrong Places

Editorials15 October 1993Looking for Answers in All the Wrong PlacesPeter E. Dans, MD, Deputy EditorPeter E. Dans, MD, Deputy Editor Annals of Internal Medicine, Philadelphia, PA 19106.Author, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-119-8-199310150-00014 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail An old vaudeville story describes a man groping on all fours beneath a lamppost looking for a quarter he had dropped a block away. When asked his reason for searching there, he responded, because the light's better. The article by Jollis and colleagues [1] in this issue of Annals brings this story to mind. They and other members of the cardiology division at Duke University have for years prospectively accumulated a rich clinical database for managing patients with cardiovascular disease. Consequently, they were in a unique position to answer the Agency for Health Care Policy and Research's call of PORT ...References1. Jollis JG, Ancukiewicz M, Delong ER, Pryor DB, Muhlbaier LH, Mark DB. Discordance of databases designed for claims payment versus clinical information systems: implications for outcomes research. Ann Intern Med. 1993; 119:844-50. Google Scholar2. Salive ME, Mayfield JA, Weissman NW. Patient outcomes research teams and the Agency for Health Care Policy and Research. Health Serv Res. 1990; 25:697-708. Google Scholar3. Kennedy GT, Stern MP, Crawford MH. Miscoding of hospital discharges as acute myocardial infarction: implications for surveillance programs aimed at elucidating trends in coronary artery disease. Am J Cardiol. 1984; 53:1000-2. Google Scholar4. Iezzoni LI, Burnside S, Sickles L, Moskowitz MA, Sawitz E, Levine PA. Coding of acute myocardial infarction. Clinical and policy implications. Ann Intern Med. 1988; 109:745-51. Google Scholar5. Shiff GD, Yaacoub AS. The diagnostic coding of myocardial infarction (Letter). Ann Intern Med. 1989; 110:243. Google Scholar6. Fisher ES, Whaley FS, Krushat WM, Malenka DJ, Fleming C, Baron JA, et al. The accuracy of Medicare's hospital claims data: progress has been made, but problems remain. Am J Public Health. 1992; 82:243-8. Google Scholar7. Greenberg A, Kowey PR, Bargmann E, Wolfe SM. Permanent pacemakers in Maryland. Washington, D.C.: Health Research Group; 1982. Google Scholar8. Scherlis L, Dembo DH. Problems in health data analysis: the Maryland permanent pacemaker experience in 1979 and 1980. Am J Cardiol. 1983; 51:131-6. Google Scholar9. Bargmann E, Grove C. Surgery in Maryland Hospitals, 1979 and 1980: Charges and Deaths. Washington, D.C.: Health Research Group; 1982. Google Scholar10. Zuidema GD, Dans PE, Dunlap ED. Documentation of care and prospective payment. One hospital's experience. Ann Surg. 1984; 199:515-21. Google Scholar11. Steinberg EP, Whittle J, Anderson GF. Impact of claims data research on clinical practice. Int J Technol Assess Health Care. 1990; 6:282-7. Google Scholar12. Romano P, Luft HS. Getting the most out of messy data: problems and approaches for dealing with large administrative data sets. In: Medical Effectiveness Research Data Methods. Rockville, Maryland: U.S. Department of Health and Human Services; 1992:57-75. AHCPR pub. no. 92-0056. Google Scholar13. Whittle J, Steinberg EP, Anderson GF, Herbert R. Use of Medicare claims data to evaluate outcomes in elderly patients undergoing lung resection for lung cancer. Chest. 1991; 100:729-34. Google Scholar14. Dans PE. Issues along the Potomac: efficacy and technology transfer. South Med J. 1977; 70:1225-31. Google Scholar15. Perry S, Kalberer JT Jr. The NIH consensus development program and the assessment of health-care technologies: the first 2 years. N Engl J Med. 1980; 303:169-72. Google Scholar16. Blumenthal D. Federal policy toward health care technology: the case of the National Center. Milbank Q. 1983; 61:584-613. Google Scholar17. Fitzmaurice JM. What do we do when the data for medical effectiveness research are not perfect? In: Medical Effectiveness Research Data Methods. Rockville, Maryland: U.S. Department of Health and Human Services; 1992:5-7. Google Scholar18. Bean WB, ed. Sir William Osler: Aphorisms. From His Bedside Teachings and Writings. Springfield, Illinois: Thomas; 1961. Google Scholar19. Chassin MR. Explaining geographic variations: The enthusiasm hypothesis. Med Care. 1993; 31(5 Suppl):YS37-44. Google Scholar20. Feinstein AR. The chagrin factor and qualitative decision analysis. Arch Intern Med. 1988; 145:1257-9. Google Scholar21. The Feasibility of Linking Research-related Data Bases to Federal and Non-federal Medical Administrative Data Bases. AHCPR Pub. No. 91-0003. Rockville, Maryland: U.S. Department of Health and Human Services; 1991. Google Scholar22. Automated Data Sources for Ambulatory Care Effectiveness Research. AHCPR Pub. No. 93-0042. Rockville, Maryland: U.S. Department of Health and Human Services; 1993. Google Scholar23. Heithoff KA, Lohr KN. Effectiveness and Outcomes in Health Care: Proceedings of an Invitational Conference by the Institute of Medicine, Division of Health Care Services. Washington, D.C.: National Academy Press; 1990. Google Scholar24. Wennberg JE. Outcomes research, cost containment, and the fear of health care rationing. N Engl J Med. 1990; 323:1202-4. Google Scholar Author, Article, and Disclosure InformationAffiliations: Annals of Internal Medicine, Philadelphia, PA 19106.Corresponding Author: Peter E. Dans, MD, American College of Physicians, Independence Mall West, Sixth Street at Race, Philadelphia, PA 19106-1572. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoDiscordance of Databases Designed for Claims Payment versus Clinical Information Systems: Implications for Outcomes Research James G. Jollis , Marek Ancukiewicz , Elizabeth R. DeLong , David B. Pryor , Lawrence H. Muhlbaier , and Daniel B. 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