Translating Research into Evidence-Based Practice

1. Translating Research into Evidence-Based Practice Using Informatics to Improve Pediatric Emergency and Trauma Care (A bold new world)

2. 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?

4. 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)

5. 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

6. Reality is…… Flip flops

7. Reality….

8. Trauma Data and Pediatric Research Networks What’s the connection?

9. 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?

10. 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!

11. 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

12. 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

13. THAPCA data

14. Informatics to the Rescue? Get your pointy ears….

15. “Big Picture” Items That Could Affect Pediatric Trauma • Computerized Clinical Decision Support (CCDS) • Data Export and transfer • PHIS+ • PECARN Registry Project

16. 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

17. 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

18. 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.

19. 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)

20. 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

21. 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

22. Blunt Head Trauma Flow Sheet Nursing Role is Key } 6 variables

23. Definitions if needed

24. Recommendation & Risk Estimate

25. 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

26. 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

27. 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?

28. Data and Benchmarking Can we do it better?

29. 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.

30. 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/

31. 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

32. 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

33. 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

34. 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?

35. 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

36. 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

37. 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

38. Natural Language Processing (NLP) • What can it do for you? Example here

39. 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

40. Quality Performance Measures • HRSA/EMSC Targeted Issues Grant

41. Report Card

42. 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?

43. 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

44. 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….

45. Summary • Seeing the ‘future’ using data we have today • Leveraging the EHR • Computerized Clinical Decision Support • Electronic Registries • Benchmarking • Research