Electronic Health Record Algorithm Development for Research Subject Recruitment Using Colonoscopy Appointment Scheduling.
J Am Board Fam Med
; 34(1): 49-60, 2021.
Article
em En
| MEDLINE
| ID: mdl-33452082
ABSTRACT
INTRODUCTION:
Electronic health records (EHRs) are often leveraged in medical research to recruit study participants efficiently. The purpose of this study was to validate and refine the logic of an EHR algorithm for identifying potentially eligible participants for a comparative effectiveness study of fecal immunochemical tests (FITs), using colonoscopy as the standard.METHODS:
An Epic report was built to identify patients who met the eligibility criteria to recruit patients having a screening or surveillance colonoscopy. With the goal of maximizing the number of potentially eligible patients that could be recruited, researchers, with the assistance of information technology and scheduling staff, developed the algorithm for identifying potential subjects in the EHR. Two validation methods, descriptive statistics and manual verification, were used.RESULTS:
The algorithm was refined over 3 iterations leading to the following criteria being used for generating the report Age, Appointment Made On/Cancel Date, Appointment Procedure, Contact Type, Date Range, Encounter Departments, ICD-10 codes, and Patient Type. Appointment Serial Number/Contact Serial Number were output fields that allowed the tracking of cancellations and reschedules.CONCLUSION:
Development of an EHR algorithm saved time in that most individuals ineligible for the study were excluded before patient medical record review. Running daily reports that included cancellations and rescheduled appointments allowed for maximum recruitment in a time frame appropriate for the use of the FITs. This algorithm demonstrates that refining the algorithm iteratively and adding cancellations and reschedules of colonoscopies increased the accuracy of reaching all potential patients for recruitment.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Registros Eletrônicos de Saúde
Tipo de estudo:
Prognostic_studies
/
Screening_studies
Limite:
Humans
Idioma:
En
Revista:
J Am Board Fam Med
Ano de publicação:
2021
Tipo de documento:
Article