An overview of the OneSource initiative, which a new framework and approach to sourcing clinical data in electronic healthcare record systems (EHRs).

1. THE ONESOURCE INITIATIVE: AN APPROACH
TO STRUCTURED SOURCING OF KEY
CLINICAL DATA (FOR PERSONALIZED CARE)
Michael Hogarth, MD, FACP, FACMI
Professor, Internal Medicine
Professor and Vice Chair, Dept. of Pathology and Laboratory Medicine
http://www.hogarth.org
mahogarth@ucdavis.edu

2. Tailoring Care to Biology, Patient Preference,
and Clinical Performance
Personalized Medicine is the art of . . . .

3. Two Similar People Are Not . . .
Veronica
Age 51, 1 cm tumor
Screen detected
Works for Walmart
Post menopausal
Grade 1 ER+ tumor
No family history
Kim
Age 51, 1 cm tumor
Found on self exam
Self employed consultant
Recently divorced, single
mom
BRCA carrier
Pre menopausal
Grade 3 triple negative
tumor
Positive nodes

4. Key Elements to Provision Personalized Care
• Learning Healthcare Systems
– Systems that can evaluate care and continue to improve it
• Ability to adapt care based on data
– Personal level
– Systems level
• Precision Medicine:
– understand the disease and the host’s biology;
• To be more “precise” on disease and host response (precision
medicine!)

5. Structured data capture
and cancer biomarkers

6. But… Sourcing clinical data has not changed with EHR use
- we continue to be burdened by “manually abstracting data”
6
“Where is that ER/PR Result?”
“Where is that outside MRI?”
“Did the path show invasion?”
”Where is that MammaPrint report?” post-EHR
1907 – ~today
(pre-EHR)
“Where is that ER/PR Result?”
“Where is that outside MRI?”
“Did the path show invasion?”
”Where is that MammaPrint report?”

7. What is the “quality” of your EHR data today?

8. Peeking inside a HIMSS Stage-7 EHR’s data
75% of records
have unknown race?
Nobody is older than 85?
(1) Only have dx for pts. admitted after
1984?
(2) Someone is pre-admitted for 2020....
35 million procedures are “unknown” type?
We have a procedure for someone
To be admitted 12 years from now
Only 659,000 records have a diagnosis
(UCDHS repository profiling 2014)

9. Hip Fracture Registry -- Delirium
What happened Dec 14?

10. UCDHS Transfusion Registry
Missing
data
Missing data: lab results brought by patient, not entered

11. We need better data –
but can we do it?
Survey of 845 primary care providers
“48min loss of free time per clinic day per
physician”
information finding
takes time because
notes are bloated
and “new” or “key”
data is hard to find...
I don’t have time, so
I will cut & paste...

12. The EHR “clinical documentation conundrum”
o The Electronic Documentation Conundrum – “I have met the
enemy and it is I...”
• We EHR-using physicians generate large, verbose, narrative notes that
include auto-inserted unnecessary text (lab values, Rad reports) leading
to very poor readability
(the “note bloat” phenomenon)
• We physicians spend significant time foraging for key pieces of data in
our “bloated notes” which negatively impacts clinical care and physician
productivity
o The extent of productivity loss in US practices implementing EHRs
• A survey of 9 family practice physicians at 1 academic medical center.
Providers had 2+ years of experience with an EHRs
average 46 min of free time list per clinic day per physician
• Survey of 410 Internists (JAMA 2014- McDonald)
average of 42 min loss of free time per clinic day per physician
(1) http://www.redwoodmednet.org/projects/events/20130725/rwmn_20130725_mcdonald_v2.pdf
(2) McDonald, McDonald. Arch Intern Med. 2012. Feb 13;172(3):285-7

13. We believe it all starts with good data capture...

14. INSPIRE:
An ONC-funded Demonstration Project
(2013-2014)

15. INSPIRE Workflow Analysis: Data Sourcing is Highly Fragmented and Variable

16. INSPIRE’s Vision
“Improve the acquisition and
exchange of patient data in high
impact conditions in order to support
care coordination, practice
improvement, and longitudinal
disease registries”

17. INSPIRE Approach to Data Capture
Providers fill out “electronic forms”
(templates) at key points of care
Ask providers to enter structured data
for key data elements only
Data is used for structured
sharing/exchange
(caCCD, CAP eCC, CDC-CDA)

18. A breast cancer “e-checklist”
(xml form/template rendered by system)

19. The idea is not new….
The CAP Electronic checklists XML Specification (eCC XML) c2013

20. Data Element Selection for Checklist
Clinical Data
Critical
Clinical
Data
Clinical Trial
Data
ASCO
Data
• Clinical Dataset captured on all
patients
• Identify subset that is critical
for decision making, reporting
– Elements vetted by over 50
clinicians across the UC
Medical System for clinical
and research importance
– Re-vetted by 50 clinicians for
functionality, adoption and
workflow
• Compare against Community
Data Standards
– ASCO, CAP, Cancer Registry,
NCI CTEP Common Data
Elements (for Clinical Trials)

21. INSPIRE Demonstration Project  OneSource
‘capturing and exchanging key clinical data for care coordination in high impact conditions’
EHR
SDC
XML
Dynamic Form for Data Capture
(XML-driven and questionnaire like
with skip/branch, etc…. Rendered *within* EHR)
EHR 1
EHR 2
State Central
Cancer Registry
Community
HIE
Repository
ASCO/HL7
BTPS
ASCO/HL7
BTPS
ASCO/HL7
BTPS
ASCO/HL7
BTPS

22. Decision
Support
Personalized
Care
WISDOM
Trial
Matching
Clinical
Trials
Patient
Engagement
Athena
Home
Care
Biopharma
& Device
Orgs
Biomedical
Research
Payors
Regulators
EMA
Clinical
Efficiency
Others
Registries
Ancillary
Patient Care
Systems
Enter the ‘right’ data once
Using dynamic XML-based
checklists for data capture,
rendering using the IHE
SDC standard
eSource
Widget
OneSource Initiative
“enter the right clinical data once, use many times”
Good quality clinical care, clinical trials,
registries, quality improvement,
researchers, scientists, payors, regulators
and others all require the same data
elements...

23. Key Components of the
OneSource Framework
1) A data element core set for high impact
condition
2) OneSource forms delivered to EHR as
Structured Data Capture XML (SDC-XML)
3) OneSource form is loaded and rendered by
the primary system (EHR, CTMS, mobile, etc..)
4) OneSource forms can be co-authored by
clinicians on a single shared form viewable in
the primary system
23

24. Retrieving remote SDC “xml form”
using IHE RFD from an EHR
24

25. Structured Forms without custom programming:
IHE RFD and ONC SDC

26. Epic Supports Remote Form for Data Capture
26

27. OneSource: What are we really talking about?
• Fundamentally changing the documentation style
and data sourcing in EHRs!!
– Value based payment models will provide an
opportunity to think differently about
documentation
– Value-based documentation vs. volume-based
documentation
• “Clinical Data Checklists”
– A co-authored structured data documentation “tab”
in the EHR record

28. Will OneSource “clinical data checklists” just
cause more clinician angst?
• No, because OneSource forms have shared
authorship, which will decrease documentation time
• No, because OneSource data forms put key data in
one place in the chart – making it EASIER to find!
• No, because the clinical checklist has real value to
the clinician
– The effort is rewarded if clinicians document this way for
all patients with high impact conditions
– OneSource for “key data” – makes it EASIER to provide
good care

29. ONC SDC and IHE RFD –
“Remote Structured Forms”
RFD
SDC Additions
CRD DSC BFDR …
ONCSDC
Athena
core data
elements

31. OneSource I-SPY2 Pilot – QoL ePatient; Clinical Data Checklist,
Structured Pathologist forms, Structured Radiologist form
Patient
Data
Checklist
Library
Store
Select
Pre-populate
Load
Submit
Archive
Import

32. The relevance of OneSource
in Clinical Trials
• ~25% of the cost of an investigational drug
phase III clinical trial is in the *verification* of
source data against what is being recorded in
the CRF and reported to FDA
• FDA eSource guidance allows data to be
directly submitted from EHR into a “eCRF”
with appropriate audit trail/controls

33. Clinical Trial Costs Breakdown
 Operational costs of a trial are 80% of the cost of a
trial
 30% of operational costs are source verification
 In a $100M Phase III trial – source verification = $24M

34. OneSource and FDA eSource
 FDA– “source data should be attributable, legible,
contemporaneous, original, and accurate (ALCOA)”
 When using CRFs, best practice is for a first pass data
entry followed by a second pass or verification step by
an independent operator
 FDA eSource guidance – Final Guidance, Jan 29, 2014
 To encourage direct transmission of data from EHR record
to the eCRF
 To encourage eCRFs vs. “paper”
 If one sources data according to eSource guidance, source
verification/validation is not required

36. Our Quest for High Quality Structured Data Does
not End with the EHR
• EHRs are an authoritative source of clinical care data
• BUT, EHRs are NOT the only source of the needed patient
data, now and even more so in the future
• A single source of truth is constructed from multiple
authoritative data sources:
Data
Source A
Data
Source D
Data
Source C
Data
Source B
VIEW
OF THE
TRUTH

37. Patient as a Source of Data -- Engaging Patients
Implementing Electronic Patient Reported Data (ePRI)
Athena Breast Health Network Screening Cohort
- 5 UC med centers, Sanford
- To date: 90,000+ questionnaires of women undergoing screening mammograms
- Automated risk models as a web service
- Composite 15yr risk of breast cancer provided to PCP
- Risk report fully integrated with EHR record
- High-risk referred to genetic counseling

38. 38
100,000 women
① Screening/Prev:
- ePRI
- breast cancer risk
② Dx/Rx/Survivorship
- EHR data, ePRI
- structured data
- comparative eff
- clin trials matching

39. Athena iPad Application

40. Athena iPad Application

41. Athena iPad Application Technical
Review
• Questionnaire questions, answer choices,
skip/branch logic stored in XML in Health
Questionnaire System
Health Questionnaire System
Salesforce.com
<Surveys>
<Survey Id="1" Name="Exported survey 2" Description="">
<Questions>
<Question Id="1145" GroupId="2" Name="What is your gender?" SortableRank="1"
Type="Gender">
<Answers>
<Answer Id="1" Name="Male" SortableRank="1">
<Options>
<Option>
<Name>Gender</Name>
<Value>Male</Value>
</Option>
</Options>
</Answer>
<Answer Id="2" Name="Female" SortableRank="2">
<Options>
<Option>
<Name>Gender</Name>
<Value>Female</Value>
</Option>
</Options>
</Answer>
</Answers>
</Question>
<Questions>
<Survey Id="1" Name="Exported survey 2" Description="">
<Surveys>

42. Athena ePRI apps

43. Clinician Reports Patient Risk Reports

44. Scheduling Interface (out of EHR)
44

45. Reports available
in the Electronic
Health Record
System (EHR) in
Chart Review,
Notes

46. The Athena WISDOM Trial
Funded by PCORI

47. Patient Reported Data
(Athena WISDOM Trial)

48. Internet of Things (IoT) Data – Wearables and Sensors

49. Questions?
Lake Tenaya, Yosemite National Park
https://en.wikipedia.org/wiki/Tenaya_Lake