Todd Lingren1, Senthilkumar Sadhasivam2, Xue Zhang3, Keith Marsolo4. 1. Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, USA. 2. Department of Anesthesia, Cincinnati Children's Hospital Medical Center, Cincinnati, USA. 3. Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, USA. 4. Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, USA. Electronic address: keith.marsolo@cchmc.org.
Abstract
BACKGROUND AND AIM: Many clinical research studies claim to collect data that are also captured in the electronic medical record (EMR). We evaluate the potential for EMR data to replace prospective research data collection. METHODS: Using a dataset of 358 surgical patients enrolled in a prospective study, we examined the completeness and agreement of EMR and study entries for several variables, including the patient's stay in the post-operative care unit (PACU), surgical pain relief and pain medication side effects. RESULTS: For all variables with a completeness percentage, values were greater than 96%. For the adverse event variables, we found slight to substantial agreement (Cohen's kappa), ranging from 0.19 (nausea) to 0.48 (respiratory depression) to 0.73 (emesis). CONCLUSION: The potential to use EMR data as a replacement for prospective research data collection shows promise, but for now, should be evaluated on a variable-by-variable basis.
BACKGROUND AND AIM: Many clinical research studies claim to collect data that are also captured in the electronic medical record (EMR). We evaluate the potential for EMR data to replace prospective research data collection. METHODS: Using a dataset of 358 surgical patients enrolled in a prospective study, we examined the completeness and agreement of EMR and study entries for several variables, including the patient's stay in the post-operative care unit (PACU), surgical pain relief and pain medication side effects. RESULTS: For all variables with a completeness percentage, values were greater than 96%. For the adverse event variables, we found slight to substantial agreement (Cohen's kappa), ranging from 0.19 (nausea) to 0.48 (respiratory depression) to 0.73 (emesis). CONCLUSION: The potential to use EMR data as a replacement for prospective research data collection shows promise, but for now, should be evaluated on a variable-by-variable basis.
Authors: S Sadhasivam; V Chidambaran; X Zhang; J Meller; H Esslinger; K Zhang; L J Martin; J McAuliffe Journal: Pharmacogenomics J Date: 2014-10-14 Impact factor: 3.550
Authors: P Coorevits; M Sundgren; G O Klein; A Bahr; B Claerhout; C Daniel; M Dugas; D Dupont; A Schmidt; P Singleton; G De Moor; D Kalra Journal: J Intern Med Date: 2013-10-18 Impact factor: 8.989
Authors: S Sadhasivam; X Zhang; V Chidambaran; J Mavi; V Pilipenko; T B Mersha; J Meller; K M Kaufman; L J Martin; J McAuliffe Journal: Pharmacogenomics J Date: 2015-01-06 Impact factor: 3.550
Authors: Hailey N Miller; Kelly T Gleason; Stephen P Juraschek; Timothy B Plante; Cassie Lewis-Land; Bonnie Woods; Lawrence J Appel; Daniel E Ford; Cheryl R Dennison Himmelfarb Journal: J Am Med Inform Assoc Date: 2019-11-01 Impact factor: 4.497
Authors: Heather F Thiesset; Karen C Schliep; Sean M Stokes; Virginia L Valentin; Lisa H Gren; Christina A Porucznik; Lyen C Huang Journal: J Surg Res Date: 2020-04-10 Impact factor: 2.192