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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
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1 to 5 of 5 Research Studies DisplayedNanji KC, Garabedian PM, Langlieb ME
Usability of a perioperative medication-related clinical decision support software application: a randomized controlled trial.
The purpose of this study was assess the usability of a newly developed, comprehensive, medication-related operating room clinical decision support (CDS) software and compare it with the standard electronic health record (EHR) medication workflow. Forty participants were randomized to a CDS group (n=20) or a control group (n=20) and asked to complete 7 simulation tasks. The study found that in a simulation setting the new CDS software improved efficiency and quality of care and reduced task time, excelling over the current EHR workflow.
AHRQ-funded; HS024764.
Citation: Nanji KC, Garabedian PM, Langlieb ME .
Usability of a perioperative medication-related clinical decision support software application: a randomized controlled trial.
J Am Med Inform Assoc 2022 Jul 12;29(8):1416-24. doi: 10.1093/jamia/ocac035..
Keywords: Medication, Clinical Decision Support (CDS), Health Information Technology (HIT), Surgery, Shared Decision Making
Giardina 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
Wissel BD, Greiner TA, Holland-Bouley KD
Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.
Delay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective of this study was to prospectively validate a natural language processing (NLP) application that uses provider notes to assign epilepsy surgery candidacy scores. The authors suggest that an electronic health record-integrated NLP application can accurately assign surgical candidacy scores to patients in a clinical setting.
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner TA, Holland-Bouley KD .
Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.
Epilepsia 2020 Jan;61(1):39-48. doi: 10.1111/epi.16398..
Keywords: Neurological Disorders, Surgery, Health Information Technology (HIT), Clinical Decision Support (CDS), Shared Decision Making
Wissel BD, Greiner HM, Glauser TA
Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.
Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm trained on physician notes would produce biased recommendations for epilepsy presurgical evaluations. To assess this, an NLP algorithm was trained to identify potential surgical candidates using 1097 notes from 175 epilepsy patients with a history of resective epilepsy surgery and 268 patients who achieved seizure freedom without surgery (total N = 443 patients).
AHRQ-funded; HS024977.
Citation: Wissel BD, Greiner HM, Glauser TA .
Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.
Epilepsia 2019 Sep;60(9):e93-e98. doi: 10.1111/epi.16320..
Keywords: Neurological Disorders, Surgery, Clinical Decision Support (CDS), Healthcare Utilization, Health Information Technology (HIT), Shared Decision Making
Zheng H, Tulu B, Choi W
Using mHealth app to support treatment decision-making for knee arthritis: patient perspective.
The authors explored patient preferences on content and design of a mobile health app to facilitate daily symptom capture and summary feedback reporting, in order to inform treatment decisions, including use of total knee replacement surgery (TKR). The authors suggest that user input can inform the design and implementation of mHealth technology to meet patient needs for their treatment decisions. Patient priorities must be considered through patient-centered app design.
AHRQ-funded; HS018910.
Citation: Zheng H, Tulu B, Choi W .
Using mHealth app to support treatment decision-making for knee arthritis: patient perspective.
eGEMS 2017 Apr 20;5(2):7. doi: 10.13063/2327-9214.1284..
Keywords: Arthritis, Shared Decision Making, Health Information Technology (HIT), Surgery