Comparison of Mortality Risk Adjustment Using a Clinical Data Algorithm (American College of Surgeons National Surgical Quality Improvement Program) and an Administrative Data Algorithm (Solucient) at the Case Level Within a Single Institution

Journal of the American College of Surgeons

Volumn 205, Issue 6, 2007, Pages 767-777

  Hall, Bruce Lee ^a,b,c   Hirbe, Mitzi ^b   Waterman, Brian ^d   Boslaugh, Sarah ^d   Dunagan, Wm Claiborne ^d,e  

^a VETERANS AFFAIRS MEDICAL CENTER   (United States)

^b WASHINGTON UNIVERSITY   (United States)

^c WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

^d Center for Healthcare Quality and Effectiveness   (United States)

^e WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ALGORITHM; ARTICLE; CONTROLLED STUDY; FEMALE; HUMAN; MAJOR CLINICAL STUDY; MALE; MEDICAL RECORD REVIEW; MORTALITY; PRIORITY JOURNAL; RISK ASSESSMENT; SURGEON; SURGICAL TECHNIQUE; TOTAL QUALITY MANAGEMENT;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; CHILD; CHILD, PRESCHOOL; FEMALE; FORMS AND RECORDS CONTROL; HOSPITAL MORTALITY; HOSPITALS, TEACHING; HOSPITALS, URBAN; HUMANS; INFANT; MALE; MEDICAL AUDIT; MIDDLE AGED; MISSOURI; OUTCOME ASSESSMENT (HEALTH CARE); QUALITY INDICATORS, HEALTH CARE; RISK ADJUSTMENT; SURGICAL PROCEDURES, OPERATIVE; URBAN POPULATION;

EID: 36248995328     PISSN: 10727515     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jamcollsurg.2007.08.013     Document Type: Article

References (49)

1. Atherly F., Fink A., Campbell D., et al. Evaluating alternative risk-adjustment strategies for surgery. Am J Surg 188 (2004) 566-570
2. Baldwin L., Klabunde C., Green P., et al. In search of the perfect comorbidity measure for use with administrative claims data: does it exist?. Med Care 44 (2006) 745-753
3. Best W., Khuri S., Phelan K., et al. Identifying patient preoperative risk factors and postoperative adverse events in administrative data: results from the National VA Surgical Quality Improvement Program. J Am Coll Surg 194 (2002) 257-266
4. Gordon H., Johnson M., Wray N., et al. Mortality after noncardiac surgery: prediction from administrative versus clinical data. Med Care 43 (2005) 159-167
5. Hannan E., Kilburn H., Lindsey M., et al. Clinical versus administrative databases for CABG surgery. Does it matter?. Med Care 30 (1992) 892-907
6. Hannan E., Racz M., Jollis J., et al. Using Medicare claims data to assess provider quality for CABG surgery: does it work well enough?. Health Serv Res 31 (1997) 659-678
7. Hawker G., Coyte P., Wright J., et al. Accuracy of administrative data for assessing outcomes after knee replacement surgery. J Clin Epidemiol 50 (1997) 265-273
8. Humphries K., Rankin J., Carere R., et al. Co-morbidity data in outcomes research: are clinical data derived from administrative databases a reliable alternative to chart review?. J Clin Epidemiol 53 (2000) 343-349
9. Iezzoni L., Ash A., Coffman G., et al. Predicting in-hospital mortality. A comparison of severity measurement approaches. Med Care 30 (1992) 347-359
10. Iezzoni L., Ash A., Shwartz M., et al. Predicting who dies depends on how severity is measured: implications for evaluating patient outcomes. Ann Intern Med 123 (1995) 763-770
11. Iezzoni L., Davis R., Palmer R., et al. Does the complications screening program flag cases with process of care problems?. Using explicit criteria to judge processes. Int J Qual Health Care 11 (1999) 107-118
12. Jollis J., Ancukiewicz M., DeLong E., et al. Discordance of databases designed for claims payment versus clinical information systems. Implications for outcomes research. Ann Intern Med 119 (1993) 844-850
13. Klabunde C., Harlan L., and Warren J. Data sources for measuring comorbidity: a comparison of hospital records and Medicare claims for cancer patients. Med Care 44 (2006) 921-928
14. Lawthers A., McCarthy E., Davis R., et al. Identification of in-hospital complications from claims data. Is it valid?. Med Care 38 (2000) 785-795
15. Malenka D., McLerran D., Roos N., et al. Using administrative data to describe case mix: a comparison with the medical record. J Clin Epidemiol 47 (1994) 1027-1032
16. McCarthy E., Iezzoni L., Davis R., et al. Does clinical evidence support ICD-9-CM diagnosis coding of complications?. Med Care 38 (2000) 868-876
17. Pine M., Jordan H., Elixhauser A., et al. Enhancement of claims data to improve risk adjustment of hospital mortality. JAMA 297 (2007) 71-76
18. Pine M., Norusis M., Jones B., et al. Predictions of hospital mortality rates: a comparison of data sources. Ann Intern Med 126 (1997) 347-354
19. Romano P., Chan B., Schembri M., et al. Can administrative data be used to compare postoperative complication rates across hospitals?. Med Care 40 (2002) 856-867
20. Romano P., Schembri M., and Rainwater J. Can administrative data be used to ascertain clinically significant postoperative complications?. Am J Med Qual 17 (2002) 145-154
21. Quam L., Ellis L., Venus P., et al. Using claims data for epidemiologic research. The concordance of claims-based criteria with the medical record and patient survey for identifying a hypertensive population. Med Care 31 (1993) 498-507
22. van Doorn C., Bogardus S., Williams C., et al. Risk adjustment for older hospitalized persons: a comparison of two methods of data collection for the Charlson index. J Clin Epidemiol 54 (2001) 694-701
23. Weingart S., Davis R., Palmer R., et al. Discrepancies between explicit and implicit review: physician and nurse assessments of complications and quality. Health Serv Res 37 (2002) 483-498
24. Weingart S., Iezzoni L., Davis R., et al. Use of administrative data to find substandard care: validation of the complications screening program. Med Care 38 (2000) 796-806
25. Weintraub W.S., Deaton C., Shaw L., et al. Can cardiovascular clinical characteristics be identified and outcome models be developed from an in-patient claims database?. Am J Cardiology 84 (1999) 166-169
26. Krumholz H., Wang Y., Mattera J., et al. An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with an acute myocardial infarction. Circulation 113 (2006) 1683-1692
27. Krumholz H., Wang Y., Mattera J., et al. An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with heart failure. Circulation 113 (2006) 1693-1701
28. Parker J.P., Li Z., Damberg C.L., et al. Administrative versus clinical data for coronary artery bypass graft surgery report cards: the view from California. Med Care 44 (2006) 687-695
29. Hartz A., Guse C., Sigmann P., et al. Severity of illness measures derived from the Uniform Clinical Data Set (UCDSS). Med Care 32 (1994) 881-901
30. Newschaffer C., Bush T., and Penberthy L. Comorbidity measurement in elderly female breast cancer patients with administrative and medical records data. J Clin Epidemiol 50 (1997) 725-733
31. Daley J., Khuri S., Henderson W., et al. Risk adjustment of the postoperative morbidity rate for the comparative assessment of the quality of surgical care: results of the National Veterans Affairs Surgical Risk Study. J Am Coll Surg 185 (1997) 328-340
32. Khuri S., Daley J., Henderson W., et al. The National Veterans Administration Surgical Risk Study: risk adjustment for the comparative assessment of the quality of surgical care. J Am Coll Surg 180 (1995) 519-531
33. Khuri S., Daley J., Henderson W., et al. Risk adjustment of the postoperative mortality rate for the comparative assessment of the quality of surgical care: results of the National Veterans Affairs Surgical Risk Study. J Am Coll Surg 185 (1997) 315-327
34. Khuri S., Daley J., Henderson W., et al. The Department of Veterans Affairs' NSQIP: the first national, validated, outcome-based, risk-adjusted, and peer-controlled program for the measurement and enhancement of the quality of surgical care. National VA Surgical Quality Improvement Program. Ann Surg 228 (1998) 491-507
35. American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP). Data acquisition form. Available at: https://acsnsqip.org/main/program_data_collection_form.pdf. Accessed September 1, 2006.
36. Belay E., Holman R., Maddox R., et al. Kawasaki syndrome hospitalizations and associated costs in the United States. Public Health Rep 118 (2003) 464-469
37. Burleigh E., Wang C., Foster D., et al. Thromboprophylaxis in medically ill patients at risk for venous thromboembolism. Am J Health Syst Pharm 63 suppl 6 (2006) S23-S29
38. Griffith J., Alexander J., and Jelinek R. Measuring comparative hospital performance. J Healthc Manag 47 (2002) 41-57
39. Griffith J., Knutzen S., and Alexander J. Structural versus outcomes measures in hospitals: a comparison of Joint Commission and Medicare outcomes scores in hospitals. Qual Manag Health Care 10 (2002) 29-38
40. Griffith J., Pattullo A., Alexander J., et al. Is anybody managing the store?. National trends in hospital performance. Healthc Manag 51 (2006) 392-405
41. Kroch E., Vaughn T., Koepke M., et al. Hospital boards and quality dashboards. J Patient Safety 2 (2006) 10-19
42. McCollam P., Foster D., and Riesmeyer J. Outcomes in acute myocardial infarction patients: Results of the national GPIIb/IIIa inhibitor study. Am J Health Syst Pharm 60 (2003) 1251-1256
43. Sullivan K., Delay E., Durbin R., et al. Epidemiology of Reye syndrome, United States, 1991-1994: Comparison of CDC surveillance and hospital admissions data. Neuroepidemiology 19 (2000) 338-344
44. Young J., Foster D., and Heller S. Cardiac revascularization in specialty and general hospitals. N Engl J Med 352 (2005) 2754-2756
45. Agency for Healthcare Research and Quality (AHRQ). Clinical Classifications Software (CCS). Available at: http://www.hcup-us.ahrq.gov/toolssoftware/ccs/ccsfactsheet.jsp. Accessed September 1, 2006.
46. Hanley J., and McNeil B. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143 (1982) 29-36
47. Birkmeyer J., Siewers A., Finlayson E., et al. Hospital volume and surgical mortality in the United States. N Engl J Med 346 (2002) 1128-1137
48. Wray N., Hollingsworth J., Peterson N., et al. Case-mix adjustment using administrative databases: a paradigm to guide future research. Med Care Res Rev 54 (1997) 326-356
49. Agency for Healthcare Research and Quality (AHRQ). Information on Patient Safety Indicators (PSI) and Inpatient Quality Indicators (IQI) available at: http://www.qualityindicators.ahrq.gov/psi_overview.htm http://www.qualityindicators.ahrq.gov/iqi_overview.htm Accessed June 12 2007.