Journal of pediatric rehabilitation medicine · 2018
Using structured data entry systems in the electronic medical record to collect clinical data for quality and research: Can we efficiently serve multiple needs for complex patients with spina bifida?
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Van Batavia Jason P, Weiss Dana A, Long Christopher J, Madison Julian, McCarthy Gus, Plachter Natalie, Zderic Stephen A
Аннотация
PURPOSE: The era of the electronic health record (EHR) generates the ability to systematically collect and record innumerable data for complex procedures such as videourodynamic studies (VUDS). We developed a Structured Data Entry System (SDES) that would serve as a way to better standardize VUDS for both quality improvement and research capabilities.
METHODS: A working group convened to design a SDES form for VUDS in a flow sheet format in our hospital's EHR, allowing for easy integration of the information into the clinical encounter note and for weekly export of data to clinicians in spreadsheet form.
RESULTS: Analysis of weekly VUDS data revealed that entries were missing in 3% of cells in all SDES forms completed. The availability of the data in an Excel spreadsheet allows for easy manipulation, calculation of clinical variables, and streamlined analysis in figures or graphs to identify patients at the highest risk.
CONCLUSION: Designing and implementing a SDES based on a flowsheet that can allow data to be placed seamlessly in the clinical record and to be integrated into a searchable database for quality improvement and research purposes allows one to harness the true potential of the EHR.
METHODS: A working group convened to design a SDES form for VUDS in a flow sheet format in our hospital's EHR, allowing for easy integration of the information into the clinical encounter note and for weekly export of data to clinicians in spreadsheet form.
RESULTS: Analysis of weekly VUDS data revealed that entries were missing in 3% of cells in all SDES forms completed. The availability of the data in an Excel spreadsheet allows for easy manipulation, calculation of clinical variables, and streamlined analysis in figures or graphs to identify patients at the highest risk.
CONCLUSION: Designing and implementing a SDES based on a flowsheet that can allow data to be placed seamlessly in the clinical record and to be integrated into a searchable database for quality improvement and research purposes allows one to harness the true potential of the EHR.