OBJECTIVE: The use of administrative data to identify inpatient complications is technically feasible and inexpensive but unproven as a quality measure. Our objective was to validate whether a screening method that uses data from standard hospital discharge abstracts identifies complications of care and potential quality problems. DESIGN: This was a case-control study with structured implicit physician reviews. SETTING: Acute-care hospitals in California and Connecticut in 1994. PATIENTS: The study included 1,025 Medicare beneficiaries greater than 265 years of age. METHODS: Using administrative data, we stratified acute-care hospitals by observed-to-expected complication rates and randomly selected hospitals within each state. We randomly selected cases flagged with 1 of 17 surgical complications and 6 medical complications. We randomly selected controls from unflagged cases. MAIN OUTCOME MEASURE: Peer-review organization physicians' judgments about the presence of the flagged complication and potential quality-of-care problems. RESULTS: Physicians confirmed flagged complications in 68.4% of surgical and 27.2% of medical cases. They identified potential quality problems in 29.5% of flagged surgical and 15.7% of medical cases but in only 2.1% of surgical and medical controls. The rate of physician-identified potential quality problems among flagged cases exceeded 25% in 9 surgical screens and 1 medical screen. Reviewers noted several potentially mitigating circumstances that affected their judgments about quality, including factors related to the patients' illness, the complexity of the case, and technical difficulties that clinicians encountered. CONCLUSIONS: For some types of complications, screening administrative data may offer an efficient approach for identifying potentially problematic cases for physician review. Understanding the basis for physicians' judgments about quality requires more investigation.
OBJECTIVE: The use of administrative data to identify inpatient complications is technically feasible and inexpensive but unproven as a quality measure. Our objective was to validate whether a screening method that uses data from standard hospital discharge abstracts identifies complications of care and potential quality problems. DESIGN: This was a case-control study with structured implicit physician reviews. SETTING: Acute-care hospitals in California and Connecticut in 1994. PATIENTS: The study included 1,025 Medicare beneficiaries greater than 265 years of age. METHODS: Using administrative data, we stratified acute-care hospitals by observed-to-expected complication rates and randomly selected hospitals within each state. We randomly selected cases flagged with 1 of 17 surgical complications and 6 medical complications. We randomly selected controls from unflagged cases. MAIN OUTCOME MEASURE: Peer-review organization physicians' judgments about the presence of the flagged complication and potential quality-of-care problems. RESULTS: Physicians confirmed flagged complications in 68.4% of surgical and 27.2% of medical cases. They identified potential quality problems in 29.5% of flagged surgical and 15.7% of medical cases but in only 2.1% of surgical and medical controls. The rate of physician-identified potential quality problems among flagged cases exceeded 25% in 9 surgical screens and 1 medical screen. Reviewers noted several potentially mitigating circumstances that affected their judgments about quality, including factors related to the patients' illness, the complexity of the case, and technical difficulties that clinicians encountered. CONCLUSIONS: For some types of complications, screening administrative data may offer an efficient approach for identifying potentially problematic cases for physician review. Understanding the basis for physicians' judgments about quality requires more investigation.
Authors: Saul N Weingart; Roger B Davis; R Heather Palmer; Michael Cahalane; Mary Beth Hamel; Kenneth Mukamal; Russell S Phillips; Donald T Davies; Lisa I Iezzoni Journal: Health Serv Res Date: 2002-04 Impact factor: 3.402
Authors: Harvey J Murff; Alan J Forster; Josh F Peterson; Julie M Fiskio; Heather L Heiman; David W Bates Journal: J Am Med Inform Assoc Date: 2003-03-28 Impact factor: 4.497
Authors: David W Bates; R Scott Evans; Harvey Murff; Peter D Stetson; Lisa Pizziferri; George Hripcsak Journal: J Am Med Inform Assoc Date: 2003 Mar-Apr Impact factor: 4.497
Authors: Sydney M Dy; Pushkal Garg; Dorothy Nyberg; Patricia B Dawson; Peter J Pronovost; Laura Morlock; Haya Rubin; Albert W Wu Journal: Health Serv Res Date: 2005-04 Impact factor: 3.402
Authors: Jay J Idrees; Charles W Kimbrough; Brad F Rosinski; Carl Schmidt; Mary E Dillhoff; Eliza W Beal; Fabio Bagante; Katiuscha Merath; Qinyu Chen; Jordan M Cloyd; E Christopher Ellison; Timothy M Pawlik Journal: J Gastrointest Surg Date: 2018-05-31 Impact factor: 3.452