Closing Plenary: Bill Stead

8/9/2019 Closing Plenary: Bill Stead

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Recalibrating InformaticsTrue North

William W. Stead, M.D.Associate Vice Chancellor for Health Affairs

Chief Strategy & Information OfficerMcKesson Foundation Professor of Biomedical

Informatics and Medicine

Disclosures: Co-inventor of two patient medical record products one licensed toMcKesson, Inc., and one licensed to Informatics Corporation of America (ICA) fromwhich I receive royalties through Vanderbilt University. Director of HealthStream, a publiccompany, compensated by meeting fees & an annual option grant.


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Capture & re-usestandardized data

StandardizedData

UnstructuredData

Old New

Idea Take 1


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Idea take 1 The Need for a Shift

Gap between todays HCIT & what we need Root cause

The Opportunity The idea take 2 Shifting EHR computational paradigm

The Idea take 3

Action steps

Road Map


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Central Conclusions of NRC Report

Current efforts aimed at nationwidedeployment of HCIT will not besufficient to achieve the vision of 21st

century health care, and may evenset back the cause

Success will require emphasis onproviding cognitive support(assistance for thinking about andsolving problems).

In the near term, embracemeasurable health care qualityimprovement as the driving rationalefor HCIT adoption efforts.2009


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Information-Intensive Aspects of theIOMs Vision for 21st Century Health Care

Comprehensive data on patients conditions, treatments &outcomes

Cognitive support for health care professionals & patients tohelp integrate

patient-specific data

evidence-based practice guidelines & research results Tools to manage a portfolio of patients & to highlight problems

as they arise

Rapid integration of new instrumentation, biologicalknowledge, treatment modalities, and so on into a learninghealth care system

Accommodation of growing heterogeneity of locales forprovision of care

Empowerment of patients and their families in effectivemanagement of health care decisions and their implementation

Stead WW, Lin HS. 2009. Computational technology for effective health care: immediate steps and strategicdirections. Comput Sci and Telecom Board, Nat Res Council. Washington: National Academies Press.


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University of PittsburghMedical CenterPittsburgh, PA

Veterans Administration

Washington, DC

HCA TriStarNashville, TN

Vanderbilt UniversityMedical CenterNashville, TN

Partners HealthcareBoston, MA

Intermountain Health CareSalt Lake City, UT

University of California,San FranciscoSan Francisco, CA

Palo Alto Medical FoundationPalo Alto, CA

Site Visits


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Site Visit Observations

Patient records are fragmented.

Clinical user interfaces mimic paper without human factors

& safety design. Biomedical devices are poorly integrated.

Systems are used often to document what has been done,after the fact, for regulatory and legal uses.

Support for evidence-based medicine and computer-basedadvice is rare.

Clinical research activities are not well integrated intoclinical care.

Legacy systems are predominant.

Centralization is the predominant method ofstandardization.

Implementations timelines are long and course changes areexpensive.

Response times are variable and long down times occur.


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Data Mining

Automation

Connectivity DecisionSupport

Root Cause: Mismatch between ComputationalTechnique & Scale of Problem

Stead WW. Electronic Health Records. In: Rouse WB, Cortese DA, eds. Engineering the system of healthcare delivery.Tennenbaum Institute Series on Enterprise Systems, Vol. 3. Amsterdam: IOS Press; 2009.


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PATIENT CARE RESEARCH

DecisionS

upport

Medical

Logic

VirtualPatient

MedicalKnowledge

Whereclinicianswant to stay

Transactions

Raw data

Clinicalresearch

transactions

Raw researchdata

WhereHealth ITchains us

Workflow modeling and support, usability, cognitive support,computer-supported cooperative work (CSCW), etc.

Stead WW, Lin HS. 2009. Computational technology for effective health care: immediate steps and strategicdirections. Comput Sci and Telecom Board, Nat Res Council. Washington: National Academies Press.

Overarching Grand Challenge: Cognitive Support


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Burning Platform: Overwhelming Complexity

SetsofFac

tsperDecision

1000

10

100

5Human

CognitiveCapacity

2000 20101990 2020

Structural Genetics:

e.g. SNPs, haplotypes

Functional Genetics:Gene expression

profiles

Proteomics and othereffector molecules

Decisions by ClinicalPhenotype

Stead WW. Beyond expert-based practice. IOM (Institute of Medicine). Evidence-based medicine and thechanging nature of health care: 2007 IOM annual meeting summary,(Introduction and Overview, p. 19).Washington, DC: The National Academies Press 2008.


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The Idea Take 2

Work at multiple scales to manage complexity Triangulate multiple signals for robustness

Satellite

Doppler Radar

Rain Gauge


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symptom

mechanism


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OLD NEW

One integrated set of dataSets of data from multiplesources

Capture data in standardizedterminology

Capture raw signal and annotatewith standard terminology.

Single source of truthCurrent interpretation of multiplerelated signals

Seamless transfer amongsystems

Visualization of the collectiveoutput of relevant systems

Clinician uses the computer toupdate the record during thepatient visit.

Clinician & patient work togetherwith shared records andinformation.

The system provides transaction-level data.

The system provides cognitivesupport.

Work processes are programmedand adapt through non-systematic work around.

People, process and technologywork together as a system.

Shift EHR Computational Paradigm



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Data Mining

Automation

ConnectivityDecisionSupport

AggregateEHR

Diseasemanagement

dashboards

Work lists

Evidence-based

advisors

Match Computational Approach toComplexity of Data



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Use different scales to supporta systems approach to care


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Evidence-based Medicine

Consistent Process

Visualizationof Results vs. Plan

IterativeImprovement

Outcomes


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Consistent Process

Visualization

of Results vs. Plan


Outcomes


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Consistent Process

Visualizationof Results vs. Plan


Outcomes


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100

150

200

250

300

2005 2006 2007 2008 2009

Source: UHC and Vanderbilt Data

3. Mortality for VanderbiltVentilator Patients Compare to

all the other Hospitals Best in the U.S.

1. Number of VentilatorAcquired Pneumonia (VAP)

Cases/Year at Vanderbilt

Fiscal Year

2009Results c/w

2008

VAPsPrevented 108

DeathsAvoided 16

$ Saved $4.3M

HospitalDays

Avoided 1055

ICU DaysAvoided 431

2. Impact on Results

Systems Approach to Care

Vanderbilt now # 1O/E Vent MortalityO/E Length of StayO/E Cost


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Abstraction Generalization

Observation One Instance

Modelformal

relationship

FeatureSet

package of

relatedattributes

Attributestructured

information

raw signalData

The Idea Take 3


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Generic Interface

Engine & Core ofInteroperable Data

Terminology Mining & Management

ContentBuilders

Terminology

Editors

Populate Operational ToolsHED

StarFormsRxStarCHISL

Populate EnterpriseData Warehouse

Metadata

External StandardsUMLS

SNOMEDFDB

VUMC Terminology Map(Natural Language)

VUMC Health Language Server(Curated)


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Text String Matching: Ace Inhibitors

Ac?uprilAc?reticAceon

AltaceAtacandAvalid*

AvaproBenazeprilBenicar

Cand?artanCapotenCapozid*

? = wild card character* = accept any terminal ending


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Low Level Facts Combine To ProvideInformation

Nurse charts milliliters of TPN,

tube feeding or dextrose

administered per hour

Nutritionprovides

calories and fat

per milliliter

Product is carbohydrate

calories and fat per hour

Which fuels the statistical

prediction of hypoglycemia


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Action Steps

Capture data from any source in any form & archiveas raw signal

Keep data from different scales of biology or levels

of abstraction separate

Aggregate and interpret data purpose by purpose

Use statistical approaches to separate signal from

noise & correlate multiple weak signals

Share algorithms & knowledge sources

Closing Plenary: Bill Stead

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