Feasibility of establishing a multi-center research database using the electronic health record: The PURSUIT network. Issue 6 (December 2022)

• Record Type: Journal Article
• Authors: Vemulakonda, Vijaya M.; Janzen, Nicolette; Hittelman, Adam B.; Deakyne Davies, Sara; Sevick, Carter; Richardson, Andrew C.; Schissel, Josiah; Dash, Debasis; Hintz, Richard; Grider, Ron; Adams, Parker; Buck, Matt; King, Jordon; Ewing, Emily; Beltran, Gemma; Corbett, Sean; Chiang, George
• Is Part Of: Journal of pediatric urology. Volume 18:Issue 6(2022)
• Page Start: 788.e1
• Page End: 788.e8
• Publication Date: 2022-12
• Subjects: Ureteropelvic junction obstruction -- Vesicoureteral reflux -- Prenatal hydronephrosis -- Multi-center research -- Electronic health record; Pediatric urology -- Periodicals; Urologic Diseases -- Periodicals; Urogenital Diseases -- Periodicals; Urologic Surgical Procedures -- Periodicals; Child; Infant; Urologie pédiatrique -- Périodiques; Appareil urinaire -- Maladies -- Périodiques; Pédiatrie; Urologie; Pediatric urology; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); Electronic journals; Periodicals; Electronic journals; 618.926
• DOI: 10.1016/j.jpurol.2022.05.004
• Languages: English
• ISSNs: 1477-5131

Abstract

Summary: Background: Although multi-center research is needed in pediatric urology, collaboration is impeded by differences in physician documentation and research resources. Electronic health record (EHR) tools offer a promising avenue to overcome these barriers. Objective: To assess the accuracy, completeness, and utilization of structured data elements across multiple practices. Study design: A standardized template was developed and implemented at five academic pediatric urology practices to document clinic visits for patients with congenital hydronephrosis and/or vesicoureteral reflux. Data from standardized elements in the template and from pre-existing EHR fields were extracted into a secure database. A 20% random sample of infants with data from structured elements from 1/1/2020 and 4/30/2021 were identified and compared to manual chart review at sites with >100 charts; all other sites reviewed at least 20 charts. Manual chart review was standardized across sites and included: clinic and operative notes, orders linked to the clinic encounter, radiology results, and active medications. Accuracy of data extraction was evaluated by computing the kappa statistic and percentage agreement. For sites that had adopted the templates prior to 6/1/2019 (early adopters), a list of eligible patients with an initial clinic visit from 1/1/2020–7/27/2020 was generated using standardized reporting techniques and confirmed by manual chart review. Physician utilization of the template was then calculated by comparing patients with data obtained from the note template to the generated list of eligible patients. Results: 230 patient records met study criteria. Agreement between manual chart review and data extracted from the EHR was high (>85%). Race, ethnicity and insurance data were misclassified in about 10–15% of cases; this was due to site-specific differences in how these fields were coded. Renal ultrasound was misclassified 12% of the time; this was primarily due to outside images documented in radiology results but not included in the clinical note. All other data elements had >90% agreement (Figure). Template utilization for early adopters was >75% (75.5–87.5%). Discussion: This is the first study in urology to demonstrate that use of structured data elements can support multi-center research. Limitations include: inclusion of only academic sites with the Epic EHR and lack of data on utilization and sustainability at sites without a prior history of structured template use. Conclusions: Multi-center research collaboration using EHR-based data collection tools is feasible with generally high accuracy compared to manual chart review. Additionally, sites with a long history of template adoption have high levels of provider utilization. Summary Figure Agreement between EHR-based data capture and manual chart review. Summary Figure