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Translating Research into Evidence-Based Practice. Using Informatics to Improve Pediatric Emergency and Trauma Care (A bold new world). Outline of Presentation. Pediatric research networks & trauma care: Informatics & technology in pediatrics Leverage the EHR for data collection
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Translating Research into Evidence-Based Practice Using Informatics to Improve Pediatric Emergency and Trauma Care (A bold new world)
Outline of Presentation • Pediatric research networks & trauma care: Informatics & technology in pediatrics • Leverage the EHR for data collection • Using web services and decision support • Exporting data from multiple sites • Using EHR for benchmarking & research • How can this improve trauma care?
The Future Patient report-pre hospital ED Trauma Bay Voice capture Direct data entry Regional/ National Trauma Registry Hospital Trauma Registry • Electronic Health Record • Narrative • Non Narrative • Labs, rad, med Computerized Clinical Decision Support (CDS)
You think this is crazy right? This scenario combines: • Data extraction and transfer • EHR consistent interface with trauma registry • Comparative effectiveness research, clinical research • Performance metrics, QI, benchmarking • Telemedicine • Computerized decision support, clinical guidelines • National/regional/local registry data • Voice recognition, natural language processing • Injury surveillance ……to improve trauma care
Reality is…… Flip flops
Trauma Data and Pediatric Research Networks What’s the connection?
What Can Be Learned from Pediatric Research Networks? • Networks & registries use data to answer questions and improve care • Large amount of medical data need to get from one place to another • Can we use informatics to: • Achieve more accurate, efficient data collection? • Reduce cost of data collection and analysis? • Improve accuracy of data • Reduce bench to bedside time • Use data for QI/PI/benchmarking • How to improve trauma registry data collection, trauma research, and trauma care?
Leveraging the EHR • EHR data is becoming more accessible, valuable • Can be merged with other data sources locally and nationally • Translational research benefits from access to EHR • May increase data access in multi- center trials • Decision support needs EHR • This may become reality!
The Perfect Clinical Trauma Registry and Clinical Care Data System • Automatic identification of trauma patient • Data entered accurately in real time • Narrative & non narrative entries • Data are immediately accessible • Outcome measures produced automatically • Built in ‘decision support’ or clinical pathways • Labs, radiology, medication systems connected • EHR data exports to trauma registry accurately • Clinical alert when care deviates from national or local standard
University of Utah Data Coordinating Center • Pediatric Emergency Care Applied Research Network (PECARN) • Collaborative Pediatric Critical Care Research Network (CPCCRN) • Therapeutic Hypothermia After Pediatric Cardiac Arrest (THAPCA Trials) • National Multiple Sclerosis Society Pediatric Network • Pediatric NMO • Hydrocephalus Clinical Research Network (HCRN) • Adult Hydrocephalus Clinical Research Network (AHCRN) • NEMSIS • Utah Trauma Registry
Informatics to the Rescue? Get your pointy ears….
“Big Picture” Items That Could Affect Pediatric Trauma • Computerized Clinical Decision Support (CCDS) • Data Export and transfer • PHIS+ • PECARN Registry Project
Clinical Decision Support for Pediatric Traumatic Brain Injury 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
Clinical Decision Support Computerized˄ • A clinical prediction rule is research study where researchers try to predict the probability of a specific disease or outcome • Ottowa Ankle Rules • VTE prophylaxis in trauma • Catheter related BSI • TBI decision rules • Intra abdominal prediction rule
Examples of Trauma Decision Support • Computerized decision support system improves fluid resuscitation following severe burns: an original study. Salinas J et. al. Crit Care Med. 2011 Sep;39(9) • Performance of a computerized protocol for trauma shock resuscitation. SucherJF et.al, World J Surg. 2010 Feb;34(2):216 • Improved prophylaxis and decreased rates of preventable harm with the use of a mandatory computerizedclinicaldecisionsupport tool for prophylaxis for venous thromboembolism in trauma. Haut ER et. al, Arch Surg. 2012 Oct;147(10):901-7.
PECARN Head Injury Prediction Rules Under 2 years Over 2 years Under 2 years 2 years and over • No altered mental status • LOC (none or <5 sec) • No history of vomiting • No severe mechanism of Injury • No clinical signs of BSF • No severe headache • No altered mental status • Scalp hematoma (none or frontal) • LOC (none or <5 sec) • No severe mechanism of injury • No palpable skull fracture • Acting normally per parent Kuppermann et al, Lancet (Sept 2009)
How do you get the research to the clinician to help the kids? • EHR provides computerized decision support using patient data to execute protocol logic • Computer somewhere just knows “how” to do something; you send a message • Computer 1sends question to computer 2, & computer 2 sends back the answer Patient data
Getting this to work • Place TBI rule variables in the EHR • Design EHR to facilitate collection of variables by RN & MD in a structured, sensible manner • Help clinicians make decisions using the rule variables=Decision Support • Physicians get real time feedback on TBI risk based on child’s presentation
Blunt Head Trauma Flow Sheet Nursing Role is Key } 6 variables
CDS Development and Implementation • PECARN TBI Prediction Rules • Data Collection tool development • Input on content, language and format from study team, clinicians • Export, Testing, implementation • Flat file import, site customization, EPIC import specifications • Develop of CDS statements • Testing 2500 CDS rules, • Permutations • Centralized manual testing at site, correction of errors • Customization of site, dept. provider, workflow differences • Export/import site customization • Implementation • Head injury specific data in EHR Transfer of data to web Services based CDS • Apply EPIC based CDS • Clinician receives CDS & risk statement for ciTBI
Translating the Rules into Practice • 8 y.o. fell off bike, no history of LOC • No vomiting • Was sleepy but GCS 15 in ED • c/o moderate headache • No obvious sign of basilar skull fracture
Summary • Direct instrumentation of the EHR • Message sent to outside web service specializing in decision support engines • Message returned to clinician in real time • EHR displays the advice generated • Web-service model allows for updating risks centrally to allow for changes to be implemented • Cost savings compared to local focus • Improve on local system generated algorithms • But does this actually change clinical care?
Data and Benchmarking Can we do it better?
Network & Trauma Registries • Requires humans to gather data • Costly (more data, more humans) • Primary or secondary abstraction • Quality control varies • Under reporting of complications • Minimal interface with EHR • Outcome based • Delay in performance reports • Data dictionaries vary • Limitations based on amount of data collected • Describe disease • Improve care • Conduct research • Quality evaluation • Benchmarking • Share with local registries • Contribute to national registry Trauma Registries: History, Logistics, Limitations, and Contributions to Emergency Medicine Research. AcadEmerg Med. 2011 Jun;18(6):637-43.
Trauma Benchmarking • Trauma Quality Improvement Program (TQIP) • Uses National Trauma Data Bank (NTDB) to collect data, provide feedback to TCs, and identify characteristics associated with improved outcomes • Risk-adjusted benchmarking of TCs • PTS-Benchmarking pediatric trauma using PHIS http://www.facs.org/trauma/ntdb/tqip.html http://pediatrictraumasociety.org/
Pediatric Health Information System (PHIS) + • Pediatric database of clinical & financial data • What if you could ADD labs and radiology information to this data? Funding The Agency for Healthcare Research and Quality (AHRQ) has funded $8,693,362 for this 3-yr project
PHIS Plus (+) PHIS +lab & imaging= studies to predict outcomes and improve care of hospitalized kids • Conduct observational studies to evaluate therapeutic strategies where RCT trials not feasible • Develop quality measures to study inpatient quality across multiple sites AMIA AnnuSymp Proc. 2011; 2011: 994–1003. Published online 2011 October 22. Federating Clinical Data from Six Pediatric Hospitals: Process and Initial Results from the PHIS+ Consortium
PHIS example AMIA AnnuSymp Proc. 2011; 2011: 994–1003. Published online 2011 October 22. Federating Clinical Data from Six Pediatric Hospitals: Process and Initial Results from the PHIS+ Consortium
What else do you want?Better Registries, Benchmarking? • Capture real time data from multiple hospitals? • Ability see improvement over time • Get disease (Injury) specific information? • Could we get quick and accurate answers? (query-able) • Generate EBG-driven clinical decisions? • Feed back information to satellite/referral sites? • Get clinician level data? • Get more accurate complications?
PECARN Registry:Improving the Quality of Pediatric Emergency Care Using an EHRRegistry and Clinician Feedback Elizabeth Alpern, M.D., M.S.C.E. The Children’s Hospital of Philadelphia
How does this work? Database Data Coordinating Center • Site Electronic Health Record • XML • Narrative • Non- Narrative • Labs, rad, med • ICD9/10 • Discharge meds • Vital Signs • Vital Status • Orders Validation De-identification • Performance Measures • Insulin for DKA • Meds for SE • Trauma team arrival Natural Language Processing (NLP) Site specific Clinician specific Disease specific Real time ALL ED Visits from 8 sites Monthly data transmission Improved patient care
Your wish is granted… • Emergency care registry for all pediatric ED visits • Export data from 8 sites with different EHRs • Innovative Natural Language Processing (NLP) from free text • Collect & determine benchmarks for emergency care performance • Report performance to individualED clinicians & sites while evaluating change using a staggered time-series study • Quality improvement and future research
Natural Language Processing (NLP) • What can it do for you? Example here
Advantages • Direct transfer; EHR to db; no data entry • Validation processes help assure quality • Feedback to sites and clinicians • Use of narrative and non-narrative data • Eliminates human data extraction & entry • May reduce cost • Benchmarking in real time • Could in theory, be done for any disease
Quality Performance Measures • HRSA/EMSC Targeted Issues Grant
Could this Apply to Trauma? Establish performance measures for trauma and these could be added to report card • ICU LOS • Re-admissions • ED LOS • Time to OR • Can we find the ‘sweet spot’ between ‘human generated data’ and EHR generated data?
Study Progress • IRB approval-Completed • Database Construction-completed • Establish De-Id procedure • Extract and transmit 1day of data to DCC • Extract & De-Id one month of CY 2012 • Transmit one month of CY 2012 to DCC • Test import procedures from extract into Registry • Extract, De-Id, transmit entireCY 2012 Project work supported by: AHRQ R01HS020270 PECARN infrastructure support by: Health Resources and Services Administration (HRSA), Maternal and Child Health Bureau (MCHB), Emergency Medical Services for Children (EMSC) through the following grants: U03MC00008, U03MC00003, U03MC22684, U03MC00007, U03MC00001, U03MC22685, U03MC00006
Challenges • All of these solutions require extreme cooperation from clinical sites, and all have involved significant funding (in the millions) • None of these solutions is “obviously” portable • Actual impact on clinical care remains to be demonstrated • But future is here….
Summary • Seeing the ‘future’ using data we have today • Leveraging the EHR • Computerized Clinical Decision Support • Electronic Registries • Benchmarking • Research