National Healthcare Quality and Disparities Report
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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 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.
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Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Orthopedics, Surgery, Arthritis, Shared Decision Making
Gandrup J, Li J, Izadi Z
Three quality improvement initiatives and performance of rheumatoid arthritis disease activity measures in electronic health records: results from an interrupted time series study.
This study evaluated the effect of 3 HIT initiatives on the performance of rheumatoid arthritis (RA) disease activity measures and outcomes in an academic rheumatology clinic. The three initiatives implemented to facilitate performance of the Clinical Disease Activity Index (CDAI) were: 1) an EHR flowsheet to input scores, 2) peer performance reports, and 3) an EHR Smartform including a CDAI calculator. Data from 995 patients with 8,040 encounters between 2012 and 2017 was included. Electronic capture of CDAI scores increased from 0% to 64%. Peer performance reporting and the SmartForm kept performance stable. Physician satisfaction increased after SmartForm implementation.
AHRQ-funded; HS025638.
Citation: Gandrup J, Li J, Izadi Z .
Three quality improvement initiatives and performance of rheumatoid arthritis disease activity measures in electronic health records: results from an interrupted time series study.
Arthritis Care Res 2020 Feb;72(2):283-91. doi: 10.1002/acr.23848..
Keywords: Arthritis, Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Norgeot B, Glicksberg BS, Trupin L
Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis.
This study researched the use of artificial intelligence learning models to predict clinical outcomes in patients with rheumatoid arthritis (RA). Patients from a university hospital (UH) and a public safety-net hospital (SNH). The populations were quite different from each other. A total of 578 UH patients and 242 SNH patients were included in the study. Patients at the UH were seen more frequently than the SNH patients and were often prescribed high-class medications (63% vs. 28.9%). The model that was used showed a statistically random performance based on each patients’ most recent disease activity score.
AHRQ-funded; HS024412.
Citation: Norgeot B, Glicksberg BS, Trupin L .
Assessment of a deep learning model based on electronic health record data to forecast clinical outcomes in patients with rheumatoid arthritis.
JAMA Netw Open 2019 Mar;2(3):e190606. doi: 10.1001/jamanetworkopen.2019.0606..
Keywords: Arthritis, Electronic Health Records (EHRs), Health Information Technology (HIT), Outcomes