BACKGROUND: There is great interest in efficiently evaluating health care quality, but there is controversy over the use of administrative versus clinical data methods. We sought to compare actual mortality with risk-adjusted expected mortality in a sample population calculated by two different methods; one based on preexisting administrative records and one based on chart reviews. STUDY DESIGN: We examined a sample of patients (n = 1,234) undergoing surgical procedures at an academic teaching hospital during 1 year. The first risk-adjustment method was that used by the National Surgical Quality Improvement Program, which is based on dedicated medical record review. The second method was that used by Solucient, LLC, which is based on preexisting administrative records. RESULTS: The ratio of observed to expected mortality for this population set was higher using the National Surgical Quality Improvement Program algorithm (1.1; 95% CI, 0.8 to 1.5) than using the Solucient algorithm (0.9; 95% CI, 0.6 to 1.2) but neither estimate was notably different from 1.0. Similarly, when observed to expected mortality ratios were calculated separately for each quartile of mortality, there were no marked differences within quartiles, although minor differences with potential importance were noted. Fit was comparable by age categories, gender, and American Society of Anesthesiologists' categories. A number of actual deaths had higher predicted mortality scores using the Solucient algorithm. CONCLUSIONS: Risk-adjusted mortality estimates were comparable using administrative or clinical data. Minor performance differences might still have implications. Because of the potential lower cost of using administrative data, this type of algorithm can be an efficient alternative and should continue to be investigated.
BACKGROUND: There is great interest in efficiently evaluating health care quality, but there is controversy over the use of administrative versus clinical data methods. We sought to compare actual mortality with risk-adjusted expected mortality in a sample population calculated by two different methods; one based on preexisting administrative records and one based on chart reviews. STUDY DESIGN: We examined a sample of patients (n = 1,234) undergoing surgical procedures at an academic teaching hospital during 1 year. The first risk-adjustment method was that used by the National Surgical Quality Improvement Program, which is based on dedicated medical record review. The second method was that used by Solucient, LLC, which is based on preexisting administrative records. RESULTS: The ratio of observed to expected mortality for this population set was higher using the National Surgical Quality Improvement Program algorithm (1.1; 95% CI, 0.8 to 1.5) than using the Solucient algorithm (0.9; 95% CI, 0.6 to 1.2) but neither estimate was notably different from 1.0. Similarly, when observed to expected mortality ratios were calculated separately for each quartile of mortality, there were no marked differences within quartiles, although minor differences with potential importance were noted. Fit was comparable by age categories, gender, and American Society of Anesthesiologists' categories. A number of actual deaths had higher predicted mortality scores using the Solucient algorithm. CONCLUSIONS: Risk-adjusted mortality estimates were comparable using administrative or clinical data. Minor performance differences might still have implications. Because of the potential lower cost of using administrative data, this type of algorithm can be an efficient alternative and should continue to be investigated.
Authors: Rachel M Owen; Sebastian D Perez; Nathan Lytle; Ankit Patel; S S Davis; Edward Lin; John F Sweeney Journal: Surg Endosc Date: 2013-04-13 Impact factor: 4.584
Authors: Major K Lee; Russell S Lewis; Steven M Strasberg; Bruce L Hall; John D Allendorf; Joal D Beane; Stephen W Behrman; Mark P Callery; John D Christein; Jeffrey A Drebin; Irene Epelboym; Jin He; Henry A Pitt; Emily Winslow; Christopher Wolfgang; Charles M Vollmer Journal: HPB (Oxford) Date: 2014-06-16 Impact factor: 3.647
Authors: S Siregar; M E Pouw; K G M Moons; M I M Versteegh; M L Bots; Y van der Graaf; C J Kalkman; L A van Herwerden; R H H Groenwold Journal: Heart Date: 2013-12-13 Impact factor: 5.994