Translating Research into Evidence-Based Practice

Using Informatics to Improve Pediatric Emergency and Trauma Care

(A bold new world)

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?

Outline of Presentation

The Future

Patient report-pre hospital

ED Trauma BayVoice captureDirect data entry

Electronic Health Record• Narrative• Non

Narrative• Labs, rad,

med

Hospital Trauma Registry

Computerized Clinical

Decision Support (CDS)

Regional/National Trauma Registry

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

You think this is crazy right?

Reality is……

Flip flops

Reality….

Trauma Data and Pediatric Research

NetworksWhat’s the connection?

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?

What Can Be Learned from Pediatric Research Networks?

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!

Leveraging the EHR

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

THAPCA data

Informatics to the Rescue?

Get your pointy ears….

1. Computerized Clinical Decision Support (CCDS)

2. Data Export and transfer◦PHIS+◦PECARN Registry Project

“Big Picture” Items That Could Affect Pediatric Trauma

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, MScNathan Kuppermann, MD, MPH And the TBI-KT team

Clinical Decision Support for Pediatric Traumatic Brain Injury

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

Clinical Decision SupportComputerized˄

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. Sucher JF et.al, World J Surg. 2010 Feb;34(2):216

Improved prophylaxis and decreased rates of preventable harm with the use of a mandatory computerized clinical decision support tool for prophylaxis for venous thromboembolism in trauma. Haut ER et. al, Arch Surg. 2012 Oct;147(10):901-7.

Examples of Trauma Decision Support

Under 2 years 2 years and over1. No altered mental

status2. LOC (none or <5 sec)3. No history of vomiting4. No severe mechanism

of Injury 5. No clinical signs of BSF6. No severe headache

1. No altered mental status2. Scalp hematoma (none or

frontal)3. LOC (none or <5 sec)4. No severe mechanism of

injury5. No palpable skull

fracture6. Acting normally per

parent

PECARN Head Injury Prediction RulesUnder 2 years Over 2 years

Kuppermann et al, Lancet (Sept 2009)

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 1 sends question to computer 2, & computer2 sends back the answer

How do you get the research to the clinician to help the kids?

EHR

Web serv

Hospital

Patient data

1. Place TBI rule variables in the EHR2. Design EHR to facilitate collection of

variables by RN & MD in a structured, sensible manner

3. Help clinicians make decisions using the rule variables=Decision Support

4. Physicians get real time feedback on TBI risk based on child’s presentation

Getting this to work

Blunt Head Trauma Flow SheetNursing Role is Key

}6 variables

Definitions if needed

Recommendation & Risk Estimate

PECARN TBI Prediction Rules

Export, Testing, implementation

Develop of CDS statements

Testing 2500 CDS rules, Permutations

Export/import site customization

Head injury specific data in EHR

Input on content, language and format from study team, clinicians

Apply EPIC based CDS

Implementation

Transfer of data to webServices based CDS

CDS Development and Implementation

Flat file import, site customization, EPIC import specifications

Centralized manual testing at site, correction of errors

Customization of site, dept. provider, workflow differences

Data Collection tool development

Clinician receives CDS & risk statement for ciTBI

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

Translating the Rules into Practice

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?

Summary

Data and Benchmarking

Can we do it better?

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

Network & Trauma Registries

Trauma Registries: History, Logistics, Limitations, and Contributions to Emergency Medicine Research. Acad Emerg Med. 2011 Jun;18(6):637-43.

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

Trauma Benchmarking

http://www.facs.org/trauma/ntdb/tqip.htmlhttp://pediatrictraumasociety.org/

Pediatric database of clinical & financial data What if you could ADD labs and radiology

information to this data?

Pediatric Health Information System (PHIS) +

Funding The Agency for Healthcare Research and Quality (AHRQ) has funded $8,693,362 for this 3-yr project

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 Annu Symp 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 Plus (+)

PHIS example

AMIA Annu Symp 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

◦ 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?

What else do you want?Better Registries, Benchmarking?

PECARN Registry:Improving the Quality of

Pediatric Emergency Care Using an EHR

Registry and Clinician Feedback

Elizabeth Alpern, M.D., M.S.C.E.The Children’s Hospital of Philadelphia

Data Coordinating Center

Site Electronic Health RecordXML• Narrative• Non- Narrative• Labs, rad,

med• ICD9/10• Discharge

meds• Vital Signs• Vital Status• Orders

Natural Language Processing (NLP)

ALL ED Visits from 8 sitesMonthly data transmission

Site specific Clinician specificDisease specificReal time

Performance Measures• Insulin for DKA • Meds for SE• Trauma team arrival

How does this work?Database

Improved patient care

ValidationDe-identification

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 individual ED clinicians &

sites while evaluating change using a staggered time-series study

Quality improvement and future research

Your wish is granted…

What can it do for you?

Natural Language Processing (NLP)

Example here

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

Advantages

Quality Performance Measures

HRSA/EMSC Targeted Issues Grant

Clinician Beta Agonist in Asthma

ATB use in Viral illness

Trauma Trauma

Clinician 1 75% 18%Clinician 2 65% 5%Clinician 3 50% 40%Clinician 4 90% 5%Site X 89% 7%Site Y 95% 10%

Report Card

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?

Could this Apply to Trauma?

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 entire CY 2012

Study Progress

Project work supported by: AHRQ R01HS020270PECARN 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

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….

Challenges

Summary Seeing the ‘future’ using data we have

today Leveraging the EHR Computerized Clinical Decision Support Electronic Registries Benchmarking Research