Design and Implementation of a Multi-Site Automated Data Acquisition Process from the Electronic Health Record (EHR) to an Electronic Data Capture System (EDC) Jeff Yearley, BA Manager of Clinical Data Management Data Coordinating Center University of Utah Slide 1/39
Novel Data Acquisition Process A significant reduction in site resource requirement Utilizes existing functionality in OpenClinica Fewer data handling steps: Increased accuracy Improved reliability Slide 2/39
TBI Traumatic Brain Injury Traumatic Brain Injury (TBI) is the leading cause of disability and death in children and adolescents in the U.S. According to the Centers for Disease Control and Prevention Almost half a million (473,947) emergency department visits for TBI are made annually by children aged 0 to 14 years. Slide 3/39 CDC Report Traumatic Brain Injury in the United States: Emergency Department Visits, Hospitalizations and Deaths (2002-2006)
Risk Attributable to Head CT Scans Slide 4/39
PECARN Supported by the Health Resources and Services Administration (HRSA), Maternal and Child Health Bureau (MCHB), Emergency Medical Services for Children (EMSC) Program through the following grants: U03MC00008, U03MC00003, U03MC22684,U03MC00007, U03MC00001, U03MC22685, and U03MC00006. Slide 5/39
PECARN Sites 1. Children s Hospital Colorado 2. Children s National MC-Washington DC 3. Children s Memorial Hospital-Chicago 4. Children s Hospital of Boston 5. University of California-Davis 6. Children s Hospital of Philadelphia 7. Primary Children s University of Utah 8. AI Dupont Hospital for Children 9. Texas Children s Hospital 10. Children s Hospital of Michigan 11. Nationwide Children s Hospital 12. Cincinnati Children s Hospital MC 13. Washington University School of Med. 14. Children s Hospital of Wisconsin 15. Children s Hospital of Pittsburgh 16. Hasbro Children s Hospital 17. Children s Hospital of New York 18. University of Michigan
TBI Study Kuppermann et al, Lancet (September 15, 2009) Slide 7/39
TBI Prediction Rules Two PECARN prediction rules < 2 years and 2-18 years Clinically important traumatic brain injury (TBI) Near perfect negative predictive value Goal appropriate diagnostic use of CT Must minimize radiation exposure, while not missing important TBIs Slide 8/39
Under 2 years Over 2 years Slide 9/39
How do you translate these rules into clinical practice?
What is Knowledge Translation? 1. The exchange, synthesis and ethically-sound application of knowledge within a complex system of interactions among researchers and users 2. To accelerate the capture of the benefits of research for Canadians through improved health, more effective services and products, and a strengthened health care system Canadian Institutes of Health Research Slide 11/39
KT and KT Research 1. Knowledge Translation (the practice): Closing the evidence to action gap (what s known and what s done) to improve health outcomes and systems 2. Knowledge Translation research: Studying how best to promote evidence uptake Slide 12/39
PECARN TBI Decision Rules + Knowledge Translation Research Development and Pilot Testing of a Computer- Based Decision Support Tool to Implement Clinical Prediction Rules for Children with Minor Blunt Head Trauma Peter Dayan, MD, MSc Nathan Kuppermann, MD, MPH And the TBI-KT team Knowledge Translation supported by American Recovery and Reinvestment Act MCHB S02MC19289 Slide 13/39
TBI-KT - Goals Develop a computer-based decision support (CDS) tool to implement the PECARN prediction rules for children with minor head trauma Test the feasibility of the clinical decision support tool in PECARN centers Slide 14/39
Two parts to getting this to work 1. Place the rule variables in the electronic health record Design the electronic health record to facilitate collection of the variables by the RN and MD in a structured, sensible manner 2. Help clinicians to make decisions using the rule variables=decision Support Physicians get real time feedback on the risk of TBI based on child s presentation Slide 15/39
KT Requirements Data collection must fit into the clinical workflow No ability to verify data collected in EHR No subject consent or provider choice about participation Intervention is to implement a best practice into the daily workflow Slide 16/39
Definitions EHR electronic health record, also EMR electronic medical record Epic an EMR system used at many study sites chosen for this project Slide 17/39
Methods The electronic health record (Epic) was set-up with an algorithm that triggers the data collection tool Criteria for this trigger was based on a grouping of chief complaints The chief complaint grouping was developed using a sensitivity and specificity analysis Slide 18/39
Methods Custom blunt head trauma template in the electronic health record used to capture the responses to the PECARN prediction rule elements Slide 19/39
Data Entry with Definitions Slide 20/39
Additional Questions in Physician Data Entry Slide 21/39
Responses entered in the blunt head trauma assessment are transferred to OpenClinica Automated transfer of this information from electronic health record into OpenClinica Include other common variables to minimize data abstraction and maximize efficiency ED presentation and discharge dates and times Disposition (partially, due to collection differences) Demographic information Slide 22/39
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Slide 25/39 Site CRCs receive three reports directly from electronic health record reporting: 1. Report for upload (csv) 2. Report for review (pdf) 3. Exclusion criteria report
The csv report is uploaded to a form in the TBI-KT I study TBI-KT I collects site information Patient records are created in the TBI-KT II study based on the uploaded csv Slide 26/39
TBI-KT I: Upload Log Slide 27/39
Uploaded files are processed hourly Person ID must be unique Patient records are created in the TBI-KT II study Information is added to the ED Data form Slide 28/39
.NET Console Application OpenClinica web services were added as Windows Communication Foundation (WCF) services This was called to schedule an event in OpenClinica This was called to enroll a subject in OpenClinica Slide 29/39
TBI-KT II: ED Data Form Slide 30/39
Slide 31/39 The other two reports are used by the site to complete the eligibility form
CRC Reports Slide 32/39
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Results The first automated upload was processed on Dec. 14 th, 2011 from Children s Hospital Colorado As of June 12 th 2013: 19,373 patient records have been created in OpenClinica Slide 34/39
Prediction Rule Elements Collected Assessment: # of assessments #patients Loss of consciousness 21871 19185 109355 Vomiting since injury 21971 19281 109855 Acting normally per caregiver 21820 19181 109100 Mechanism of injury 22910 19206 114550 Current headache 21670 19097 108350 Total GCS 22855 19357 114275 Other signs of altered mental status 21540 18943 107700 Scalp hematoma 22315 18778 93890 Signs of basilar skull fracture* 17858 16574 89290 Palpable skull fracture* 17784 16519 88920 # data values Slide 35/39 * Questions only displayed for physician providers
Completion Rates Total Eligible All 10 prediction rule elements completed All 7 age specific ED data completed Nurse only Physician only 18078 13670 (83%) 14056 (85%) 4280 (26%) 5754 (35%) From ED Data Reports on 12Jun2013 Slide 36/39
Lessons Learned Additional programming resources are required both at the study site and DCC Consistency of electronic health record configuration and/or reporting Significant change control implications Data verification questions Slide 37/39
Thank you! Implementing this process would not have been possible without these key individuals: Sara Deakyne Research and Data Analyst, University of Colorado School of Medicine Mihai Virtosu Lead Application Specialist Jun Wang Project MS Biostatiscian Hai Le Project Manager Charlie Casper Biostatiscian SallyJo Zuspan PECARN Project Director Peter Dayan TBI-KT PI Mike Dean DCC PI Slide 38/39 Q & A