National Healthcare Quality and Disparities Report
Latest available findings on quality of and access to health care
Data
- Data Infographics
- Data Visualizations
- Data Tools
- Data Innovations
- All-Payer Claims Database
- Healthcare Cost and Utilization Project (HCUP)
- Medical Expenditure Panel Survey (MEPS)
- AHRQ Quality Indicator Tools for Data Analytics
- State Snapshots
- United States Health Information Knowledgebase (USHIK)
- Data Sources Available from AHRQ
Search All Research Studies
AHRQ Research Studies Date
AHRQ Research Studies
Sign up: AHRQ Research Studies Email updates
Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 2 of 2 Research Studies DisplayedGiardina JC, Cha T, Atlas SJ
Validation of an electronic coding algorithm to identify the primary indication of orthopedic surgeries from administrative data.
The purpose of this study was to develop and validate an algorithm to identify patients receiving four elective orthopedic surgeries to promote shared decision-making. The surgeries included were: 1) knee arthroplasty to treat knee osteoarthritis (KOA); 2) hip arthroplasty to treat hip osteoarthritis (HOA); 3) spinal surgery to treat lumbar spinal stenosis (SpS); and 4) spinal surgery to treat lumber herniated disc (HD). Electronic medical records were reviewed to ascertain a “gold standard” determination of the procedure and primary indication status. Each case had electronic algorithms consisting of ICD-10 and CPT codes for each combination and indication applied to their record. A total of 790 procedures were included in the study. The sensitivity of the algorithms ranged from 0.70 (HD) to 0.92 (KOA). Specificity ranged from 0.94 (SpS) to 0.99 (HOA, KOA).
AHRQ-funded; HS000055.
Citation: Giardina JC, Cha T, Atlas SJ .
Validation of an electronic coding algorithm to identify the primary indication of orthopedic surgeries from administrative data.
BMC Med Inform Decis Mak 2020 Aug 12;20(1):187. doi: 10.1186/s12911-020-01175-1.
.
.
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Orthopedics, Surgery, Arthritis, Decision Making
Weng Y, Tian L, Tedesco D
Trajectory analysis for postoperative pain using electronic health records: a nonparametric method with robust linear regression and K-medians cluster analysis.
Postoperative pain scores are widely monitored and collected in the electronic health record, yet current methods fail to fully leverage the data with fast implementation. This article describes a trajectory analysis for postoperative pain using electronic health records. A robust linear regression was fitted to describe the association between the log-scaled pain score and time from discharge after total knee replacement.
AHRQ-funded; HS024096.
Citation: Weng Y, Tian L, Tedesco D .
Trajectory analysis for postoperative pain using electronic health records: a nonparametric method with robust linear regression and K-medians cluster analysis.
Health Informatics J 2020 Jun;26(2):1404-18. doi: 10.1177/1460458219881339..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Pain, Surgery, Orthopedics, Research Methodologies, Health Services Research (HSR)