KUMC Biomedical Informatics Resources for your Research: a focus on HERON

Russ Waitman, PhDDirector of Medical Informatics,

Associate Professor, Department of BiostatisticsDirector, Frontiers Biomedical Informatics

Assistant Vice Chancellor, Enterprise AnalyticsUniversity of Kansas Medical Center

Kansas City, Kansas

This project is supported in part by NIH grant UL1TR000001 and NSF Award CNS-1258315

• We have tools and expertise to manage data and convert it into information

• REDCap and CRIS – enter and manage data

• HERON – fish for data from the hospital/clinic

• Biweekly Frontiers Clinical Informatics Clinics– Tuesday 4-5 pm in 1028 Dykes Library. – Next session April 30, 2013.

Biomedical Informatics Can Help Your Research

Bennett Spring Trout Park, Lebanon Missourihttp://mdc.mo.gov/regions/southwest/bennett-spring

You’re that fisherman: wanting to land data to answer your research hypothesis

The Fish: Diagnoses, Demographics, Observations, Treatments

Why so many fish? Current Goal: Build Hatchery, Manage the Fishery

Photo Credit: HuntFishGuide.comhttp://www.flickr.com/photos/huntfishguide/5883317106/

Second Goal: If you need help fishing, get a guide

Photo Credit: S. Klathillhttp://www.flickr.com/photos/sklathill/505464990/

Prepare and Analyze Data

Photo Credit: Steve Velo http://www.flickr.com/photos/juniorvelo/259888572/

Our shared goal: a tasty publication

• I’ll just enter everything in Excel….• What if I lose or accidentally sort my

spreadsheet?• How to I let students only review de-

identified data?

• Hospital/Clinic is making me use this Electronic Medical Record and I get nothing in return...

Little White Salmon River, Washington State, last Summer in July

Nightmare: looks like a nice river, but can’t catch fish

• https://redcap.kumc.edu– It uses the same username and password as your KUMC email.

• Non-KUMC researchers can request an affiliate account through Frontiers CTSA office

– Check out the training materials under videos– Case Report Forms and Surveys

• For consultation and to move project to production: Register your project with us so we can keep track of your request.

– http://biostatistics.kumc.edu/projectReg.aspx– After you register your project, a CRIS team member, likely Kahlia Ford will get in touch

with you.

• Check out other institutions using REDCap and possibly borrow from the master library.

– http://www.project-redcap.org/

Sometimes, You’re willing to enter data/buy fish: REDCap: Research Electronic Data Capture

REDCap Case Report Form Example

REDCap Survey: Think SurveyMonkey

• For clinical trials, CRIS (Velos) may be a better fit– Multiple years of experience– CRIS team builds for you with biostatistics review – Budget for CRIS team and biostatistics explicitly

• “Investigator driven” REDCap only works if you, the Principal Investigator, takes responsibility for your data– Scalability: informatics provides consultation and responsibility for

technical integrity; not your dictionary or data entry. • Underwritten by CTSA, but you “feed and talk to your fish”

– Middle model where informatics can build for you in REDCap.• Again, you budget for our team’s time

Option Two: CRISREDCap Disclaimer

http://www.flickr.com/photos/wiccked/185270913/lightbox/

REDCap: think Fish Tank you manage

Bonneville Hatchery: Trout, Salmon, Sturgeon, Columbia River, Oregon

I want to go fishing, not fill a fish tank (REDCap) Use HERON: a managed fishery

• Get a License: Develop business agreements, policies, data use agreements and oversight.

• Get a Fishing Rod and Bass Boat: Implement open source NIH funded (i.e. i2b2 https://www.i2b2.org/) initiatives for accessing data.

• Know what your catching: Transform data into information using the NLM UMLS Metathesaurus as our vocabulary source.

• Stock Different Tasty Fish: link clinical data sources to enhance their research utility.

Central CTSA Informatics Aim: Create a data “fishing” platform: HERON, https://heron.kumc.edu

• Fill out System Access Agreements to sponsor students/staff• Fill out Data Use Agreement to request data export• No Limit!!! IRB Protocol Not Required to view or pull de-

identified data• Must be on campus or use VPN or https://access.kumed.com • Check http://frontiersresearch.org/frontiers/HERON-Introduction

for more information, status, and training videos

Single sign-on using your email username

Real-time check

for current human subjects training

HERON: Getting a Fishing License

The i2b2 “Fishing Rod”: build Diabetes cohort

Drag concepts from upper left into panels on the right

Types of “fish” in folders

i2b2 : AND in Frontiers Research Registry

Dragging over the second condition

i2b2: AND a high Hemoglobin A1C

When you add a numeric concept,i2b2 asks if you want to set a constraint

i2b2 Result: 497 patients in Cohort

Run the QueryQuery took 4 seconds497 patient in cohort

I2b2: Explore Cohort, Visualize

http://www.oregon.com/columbia_gorge_attractions/bonneville_hatchery

Catch the data for JAMA, NEJMpublication

The dream: landing the big one

Without getting bit

• Goal: stable monthly process, minimal downtime• Complete rebuild of the repository, not HL7 messaging update based. • Two databases: create new DB while old DB is in use. • When the new DB is ready, switch over i2b2 to serve customers fresh data.

• Initial Files from Clinical Organizations• Export KUH Epic Clarity relational database instead of Cache/MUMPS. • Monthly file from UKP clinic billing system (GE IDX). UHC CDB, NAACCR

• Demographics, services, diagnoses, procedures, and Frontiers research participant flag.

• Extract Transform Load (ELT) processes largely SQL (some Oracle PL/SQL) • Wrapped in python scripts.

• Goals for a monthly release (20 months in a row so far): – Fresh data. Example: another month of visits = millions of facts– New types of data. Example: family history– New functionality: Example: link data by encounter across clinical and

financial sources; distinguish medication administration from prescription

How the team works: HERON Evolves Every Month

https://informatics.kumc.edu/work/blog

Monthly ReleaseBlog highlights:- Features- Size- Dates of sources

HERON’s Data Sources, Types of Data

https://informatics.kumc.edu/work/wiki/HeronProjectTimeline#Sep2012Planning - contains current plan for next several monthly releases

• Find a colleague• Talk with hospital, clinic

to understand workflow• Attend bi-weekly clinics• Watch the videos:

http://frontiersresearch.org/frontiers/informatics-training-videos

• Request a consult http://frontiersresearch.org/frontiers/biomedical-informatics

If you don’t see what you want, or you really like things, let us know:

https://redcap.kumc.edu/surveys/?s=3SBkPg&tool=1

“Who’s Using HERON” and collaboration approaches

• HIPAA Safe Harbor De-identification – Remove 18 identifiers and randomly date shifting by up to 365 days back in

time• Downside: can’t do seasonal studies without IRB approval to go back and get actual dates• In general, tack on 7 months when wanting volume for the last year.

– Resulting in non-human subjects research data but treated as a limited data set from a system access perspective. System users and data recipients agree to treat as a limited data set (acknowledging re-identification risk)

• To be addressed: – For now, we won’t add free text such as progress notes with text scrubbers

(DeID, MITRE Identification Scrubber toolkit)

• Date Shift example:– Patient was born August 13, 1968, had their blood pressure measured on

November 28, 2012.– Each month dates shifted, ex: to -15 for January release: New birthday is July

29, 1968 and the blood pressure measurement occurred on November 13, 2012.

• For another patient, their offset might be -278. Next month the Aug 13th patient’s offset might be -192.

HERON De-identification: Remove HIPAA 18 identifiers -> non human subjects research

Research Context: Medical Informatics Hypotheses

Hypothesis #1: Admin + Clinical -> Better Knowledge?

Hypothesis #2: Computer + Clinical Process-> Better Health?

• Motivation: Build a way to go beyond counting and obtain insight before you need a Data Use Agreement and release patient data. – Grows out Dan Connolly’s survival analysis tool for NCI site visit– Intermediate step of a multi-cohort generalized survival plugin– R Data Builder plugin in i2b2 and integration with RStudio Server

• (http://www.rstudio.com/ide/docs/server/getting_started)

Emerging Functionality: From Data Aggregation to Hospital Quality Preliminary Analysis

• Test Case: Antibiotic Administration for Septic patients in the Emergency Room– Past publication to bring in flowsheet

data an important foundation – University HealthSystem Consortium

CDB “gold” standard for KU Hospital– What can you solve in i2b2 “same

financial encounter” versus send to R?

Repurposing i2b2 Clinical Research Infrastructure for Inpatient Quality Improvement

• i2b2 “largely” ambulatory or population/genomics focused • Is i2b2 version 1.6 with same financial encounter and modifiers

now useful for inpatient research?

• Goal: understand medication timing and antibiotic selection• Suspect vancomycin preferred• Validate HERON medications

– Especially administration timing

Systems ArchitectureIdentified data server

i2b2 compatiblestar schema

Staged source data

De-identified server

i2b2 compatiblestar schema

Application server

de-identification processmonthly refresh ETL

Source System files (EMR dump, UHC CDB extract)

secu

re F

TP

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RStudio Server

R scripts plots,statistics

Investigator’s client

One tab in browser

i2b2 web client

Another tab in browser

RStudio IDE web client

i2b2 Hive

rgate

R Data Builder Plugin and RStudio Server

Web based for user. Just another tab in the browser

All data stays on the server so there’s no data release and risk of re-identification due to a lost file

i2b2 Plugin invokes a program that creates a Rda file in their directory on the server

UHC, Flowsheets, Medications data sources:what i2b2 could answer versus R analysis

3513 patients had a UHC-defined

septicemia diagnosis

2912 patients were an Emergency

Admission

2861 patients age were 18 years or

older

2722 patients had an exposure to an

Antibiotic in the encounter 1839 had

ED Triage documentation

during the encounter

1244 patients had 1st antibiotic admin

within 24 hours(1474 encounters)

A

993 had 1st antibiotic admin given in ED(1140 encounters)

B

316 had 1st antibiotic admin not in ED(334 encounters)

C

1836 had the Sepsis Screen

Used during the encounter

261 had 1st antibiotic admin before sepsis

screening (277 encounters)

D

1040 had 1st antibiotic admin after sepsis screening

(1197 encounters)

E

Cohorts above line defined with i2b2

Cohorts below line further refined with R

1223 had 2 SIRS criteria, organ

dysfunction and suspicion/treatment

of infection717 MD notified

Average time spent in ED is 8.7 hours, median 7.6

Average time in ED is 7.9 hours,

median 7.1

Average time spent in ED is 6.7 hours, median 6.6

Average time to sepsis screening 2.9 hours, median 49 minutes

Note: 28 patients who lacked an ED departure time were excluded from further analysis

i2b2 could define cohort

cohort refinement with R

Density Plots: Time from Arrival to First Antibiotic

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Broad Spectrum versus Vancomycin

Lag in Broad Spectrum after Vancomycin

Lag when given outside Emergency Room

Administration relative to RN Sepsis Screen

• REDCap registries into i2b2 allows intuitive exploration – Researchers may need less abstraction as data is extracted from the EMR.

• i2b2 into REDCap: inherit security model, graphical/export tools

Aligning Clinical Research Informatics for Quality: Registry Abstraction and Data Delivery

• Informatics Research and Systems for Hypothesis #1 – Administrative plus Clinical/Biomedical providers better knowledge– Current UHC models of administrative data based on linear regression

• Want to reproduce UHC models with for our data in HERON

– Then develop systematic method to evaluate utility of clinical data• Perhaps applicability of newer machine learning and statistical methods and methods for

validation (ex: bootstrapping)

• Engage with Clinical Researchers and Hospital Quality– Continue to harvest valuable data: microbiology discrete pathology results – Advance streamlined methods for self service

• Recognize though that data driven research is non-trivial and sometime the effort is underestimated by investigators

• Harvest Epic alerts (best practice, drug interaction), Orderset Utilization to evaluate Hypothesis #2 – Computer + Clinical Process -> Improved Decisions and Better Health

Next Steps

Questions?