THE ONESOURCE INITIATIVE: AN APPROACH TO STRUCTURED SOURCING OF KEY CLINICAL DATA (FOR PERSONALIZED CARE)

Michael Hogarth, MD, FACP, FACMIProfessor, Internal MedicineProfessor and Vice Chair, Dept. of Pathology and Laboratory Medicine

http://www.hogarth.orgmahogarth@ucdavis.edu

Tailoring Care to Biology, Patient Preference, and Clinical Performance

Personalized Medicine is the art of . . . .

Two Similar People Are Not . . .

VeronicaAge 51, 1 cm tumor Screen detectedWorks for WalmartPost menopausalGrade 1 ER+ tumorNo family history

KimAge 51, 1 cm tumorFound on self examSelf employed consultantRecently divorced, single momBRCA carrierPre menopausalGrade 3 triple negative tumorPositive nodes

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!)

Structured data capture and cancer biomarkers

But… Sourcing clinical data has not changed with EHR use - we continue to be burdened by “manually abstracting data”

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“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?”

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

Peeking inside a HIMSS Stage-7 EHR’s data

75% of recordshave 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 someoneTo be admitted 12 years from now

Only 659,000 records have a diagnosis

(UCDHS repository profiling 2014)

Hip Fracture Registry -- Delirium

What happened Dec 14?

UCDHS Transfusion Registry

Missing data

Missing data: lab results brought by patient, not entered

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...

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 EHRsaverage 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

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

INSPIRE:An ONC-funded Demonstration Project

(2013-2014)

INSPIRE Workflow Analysis: Data Sourcing is Highly Fragmented and Variable

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”

INSPIRE Approach to Data Capture

Providers fill out “electronic forms”(templates) at key points of care

Ask providers to enter structured datafor key data elements only

Data is used for structured sharing/exchange

(caCCD, CAP eCC, CDC-CDA)

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

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

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)

INSPIRE Demonstration Project OneSource‘capturing and exchanging key clinical data for care coordination in high impact conditions’

EHR

SDCXML

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/HL7BTPS

ASCO/HL7BTPS

ASCO/HL7BTPS

ASCO/HL7BTPS

Decision

Support

Personalized

Care

WISDOM

Trial

Matching

Clinical

Trials

Patient

Engagement

Athena

Home

CareBiopharma

& 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...

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

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

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Structured Forms without custom programming:IHE RFD and ONC SDC

Epic Supports Remote Form for Data Capture

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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

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

ONC SDC and IHE RFD –“Remote Structured Forms”

RFD

SDC Additions

CRD DSC BFDR …

ON

C S

DC

Athena core data elements

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

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

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

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

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 THETRUTH

Patient as a Source of Data -- Engaging PatientsImplementing 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

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100,000 women① Screening/Prev:

- ePRI- breast cancer risk

② Dx/Rx/Survivorship- EHR data, ePRI- structured data- comparative eff- clin trials matching

Athena iPad Application

Athena iPad Application

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>

Athena ePRI apps

Clinician Reports Patient Risk Reports

Scheduling Interface (out of EHR)

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Reports available in the Electronic Health Record

System (EHR) in Chart Review,

Notes

The Athena WISDOM Trial

Funded by PCORI

Patient Reported Data(Athena WISDOM Trial)

Internet of Things (IoT) Data – Wearables and Sensors

Questions?

Lake Tenaya, Yosemite National Parkhttps://en.wikipedia.org/wiki/Tenaya_Lake