While at Northwestern we developed a chart abstraction tool using a data mart to present EHR data to research personnel without double entry. Used in the Brain Tumor Institute. Mike Gurley did the majority of the development.

1. Assisted Chart Abstraction:
a technique to help while
we wait for Nirvana

2. The Driver
Many entities within Northwestern Medicine (NM) want to
capture data about cancer patients treated at NM.
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Research
!
Education
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Operational/Outcomes Analysis
!
QA/QC and Process Improvement
!
Marketing/Branding/Outreach Assessment

3. Challenges
NM has multiple EHR systems: Epic (NMFF), PowerChart
(NMH) and Mosaiq (Radiation Oncology).
Not all clinical systems flow into one of the EHRs
All relevant data is not discretely captured during the course of
clinical care. For example, pathology diagnosis is recorded
in a textual document.

4. Northwestern Medicine Enterprise
Data Warehouse (NMEDW)
The NMEDW is:
One stop shop for finding data from 40+ clinical systems, 10
years of data, and 2.2 Million patients (4 billion events!)
Optimized cross-system data marts representing major
biomedical entities and events: patients, providers,
encounters, labs, medications...and more.
Intelligent structures, data representations, and the ability to
identify and correlate data across patients, events and data
types

5. Who is requesting
change?
The Northwestern Brain Tumor Institute*
SPORE in Prostate Cancer
Lynn Sage Breast Cancer Center
Gastrointestinal Oncology Group
Many others - typically disease-focused
* We will focus mainly on use cases and workflows from BTI

6. What data do they need?
Demographics
Diagnosis
Treatment
Disease Progression
Survival

7. Old Solution
Data coordinator opens up EHR(s) and manually copies data
into a clinical database.
Newer solution: Data coordinator pulls data from reports run
against the NMEDW and copies/extracts/annotates them
into the clinical database

8. Command + Tab Model
A manually curated database disconnected from EHR data.
Depends on a data coordinator finding and manually copying
data from the EHR to a clinical database.

9. EHR

10. command + tab

11. Clinical Database

12. Command + Tab Model:
Pros
Depends on humans:
Humans are great at interpreting narrative documentation -where a significant portion of cancer clinical data
(unfortunately) resides.

13. Command + Tab Model:
Cons
Depends on humans:
Difficult for a human to be aware of every relevant
medical event of every patient within a cohort.
Ignores the flux that occurs within EHRs: patient medical
histories merging and splitting.
Humans get bored with rote copying discrete data.
Humans quit and get new jobs.

14. New Solution:
NBTI Data CaptureTool
The NBTI Data Capture Tool automatically pulls (via the
NMEDW) relevant EHR data for each patient.
Data points discretely captured in the EHR need no further
review.
Data points captured non-discreetly in textual documents are
abstracted via natural language process (NLP) and
presented to a data coordinator for review/revision.

15. Why not use reports?
Lots of valuable clinical data still resides in narrative
documents.
Not all discrete data contained within the EHR(s) has
been normalized into easily queryable structures in
the NMEDW.
Today an investigator cannot ask an NMEDW analyst
the question and get a quick result:
How many IDH1 negative glioma patients survived
longer than 5 years?

16. Waiting for Nirvana
NMEDW reports will not obsolete research clinical
databases until:
!
!
Clinical IT optimizes the EHRs to discretely
capture all relevant data points (ain’t happenin’)
The NMEDW normalizes all EHR data into easily
digestible formats and to reference
terminologies (limited by above step!)

17. Sources for the first
iteration
Epic: support discrete data capture of fundamental treatment/
diagnosis data points.
Epic/MyChart: embed intake form.
Cerner: support discrete data capture of pathology data points.
Cerner: support explicit association between pathology cases
and surgeries.
MOSAIQ: support discrete data capture of site, laterality for
radiation therapies:.

18. Building the Foundation

19. Analyze the Data
Started with a list of data elements and sample data from a
neuro-oncologist and a neurosurgeon
Determined obtainability of each data element:
!
Discrete in the EHR and in the EDW.
!
Discrete in the EHR but not in the EDW.
!
narrative document in the EHR and in the EDW.
!
narrative document in the EHR but not in the EDW.

20. Build an EDW
Data Mart
Engaged the NMEDW team to build a NBTI-dedicated data mart and
extract transform load (ETL) script:
patients
encounters
medications
surgeries
surgery notes
pathology cases
gamma knifes
radiation therapies
imaging exams
progress notes
labs

21. Build a Clinical Database
Build a clinical database mirroring the structure of the
EDW data mart in a PostgreSQL server
Add database structures to allow for the layering of
curated data on top of data imported from the EDW.

22. Import Data
Expose the data in the EDW data mart as
web services via SQL Reporting Service
reports.
Automate via cron jobs the pull of data
into the clinical database from the EDW
with shared EDW web service adapter
code.

23. Patients
The criteria for inclusion within the NBTI system is
determined by a list of ICD diagnosis codes. Criteria
could alternatively be determined by consent to a
protocol.
Pull from the NMEDW patient name, birth date, MRN(s),
gender, ethnicity, race, death date and last seen date
(across Northwestern Medicine - NM).

24. Integrate with Specimen
Inventory Data
Prepare data for migration into PathCore's specimen
inventory system BSI2.
Allow for ad hoc query exploration of specimen
inventory based on clinical data points.
Standardizing the structure of clinical data captures
across sites makes this possible.

25. NLP
Build an NLP pipeline to abstract from the flow of
narrative documents and textual fragments discrete
data points.
Use the Stanford NLP library for chunking and sentence
splitting.
Use the lingscope library to parse the negation scope of
sentences.
Use the NCI metathesaurus for synonym lookups and
attaching codes

26. Electronic Intake Form
Deploy an electronic intake that can be filled out by a
patient before or at their first clinic visit.
email is sent, can be filled out by web browser, tablet or
(painfully) on a smart phone

27. Navbar
Sections

28. The Results

29. Biopsy, Surgery and
Pathology Diagnosis
Pull from the NMEDW NM biopsies, surgeries, surgical
procedure reports and pathology cases (inside and out).
Abstract and allow for the confirmation/revision of surgery
type, site, laterality, pathology diagnosis, grade,
recurrence, anatomical location of primary, cancer staging
and pathology test results.

30. Present the NLP
Abstractions
Present NLP-derived abstractions as queues of work that
the data coordinator needs to confirm or revise.

31. Surgeries and Pathology
Cases

32. Surgery Detail

33. Pathology Case Detail

34. With Context

35. Gamma Knife Radiation
Therapy
Pull from the NMEDW NM radiation therapies.
Abstract and allow for the confirmation/revision of
site and laterality.

36. Intravenous
Chemotherapy
Pull from the NMEDW NM intravenous
chemotherapy treatments (from Intellidose and
Epic Beacon).

37. Labs and Other
Medications
Labs
•
Pull from the NMEDW NM labs.
Other Medications
•
Pull from the NMEDW NM non-intravenous,
prescribed/ordered medications.
•
Allow for confirmation/revision of drug, route,
duration, amount, patient parameter and
administered.

38. Imaging Exams and Clinic
Visits
Imaging Exams
•
•
Pull from the NMEDW NM imaging exams.
Abstract and allow for confirmation/revision of
response/progression declarations and lesion
measurements.
Clinic Visits
•
Pull from the NMEDW NM clinic visit notes. Abstract
and allow for confirmation/revision of performance
status declarations and tracking of outside treatments.

39. Reporting
Ad-hoc query exploration of data.
Integrate NMH quality metrics.
Generate Kaplan Meier survival curves against SEER
data on demand

40. Reporting

41. Export
Export into Word, Excel, CSV for analysis and
visualization by SAS, SPSS, R, etc