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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 3 of 3 Research Studies DisplayedJafarzadeh SR, Warren DK, Nickel KB
Bayesian estimation of the accuracy of ICD-9-CM- and CPT-4-based algorithms to identify cholecystectomy procedures in administrative data without a reference standard.
The researchers sought to estimate the accuracy of two algorithms to identify cholecystectomy procedures using ICD-9-CM and CPT-4 codes in administrative data. They found that both ICD-9-CM- and CPT-4-based algorithms had high sensitivity to identify cholecystectomy procedures in administrative data when used individually and especially in a parallel-joint approach.
AHRQ-funded; HS019713.
Citation: Jafarzadeh SR, Warren DK, Nickel KB .
Bayesian estimation of the accuracy of ICD-9-CM- and CPT-4-based algorithms to identify cholecystectomy procedures in administrative data without a reference standard.
Pharmacoepidemiol Drug Saf 2016 Mar;25(3):263-8. doi: 10.1002/pds.3870.
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Keywords: Data, Surgery
Grenda TR, Krell RW, Dimick JB
Reliability of hospital cost profiles in inpatient surgery.
This study sought to discover whether Medicare payments for surgery can reliably compare hospital costs. It found that episode payments for inpatient surgery are a reliable measure of hospital costs for commonly performed procedures, but are less reliable for lower volume operations. These findings suggest that hospital cost profiles based on Medicare claims data may be used to benchmark efficiency, especially for more common procedures.
AHRQ-funded; HS000053.
Citation: Grenda TR, Krell RW, Dimick JB .
Reliability of hospital cost profiles in inpatient surgery.
Surgery 2016 Feb;159(2):375-80. doi: 10.1016/j.surg.2015.06.043..
Keywords: Inpatient Care, Surgery, Healthcare Costs, Medicare, Data
Warren DK, Nickel KB, Wallace AE
Can additional information be obtained from claims data to support surgical site infection diagnosis codes?
The authors sought to confirm a claims algorithm to identify surgical site infections (SSIs) by examining the presence of clinically expected SSI treatment. They found that over 94% of patients identified by their claims algorithm as having an SSI received clinically expected treatment for infection, including antibiotics, surgical treatment, and culture, suggesting that this algorithm has very good positive predictive value. They concluded that their method may facilitate retrospective SSI surveillance and comparison of SSI rates across facilities and providers.
AHRQ-funded; HS019713.
Citation: Warren DK, Nickel KB, Wallace AE .
Can additional information be obtained from claims data to support surgical site infection diagnosis codes?
Infect Control Hosp Epidemiol 2014 Oct;35 Suppl 3:S124-32. doi: 10.1086/677830.
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Keywords: Data, Healthcare-Associated Infections (HAIs), Patient Safety, Surgery, Injuries and Wounds, Adverse Events