Transcript Download slides

The Role of Informatics: Scope,
Strategy, and Tactics
Blackford Middleton
MD, MPH, MSc, FACP, FACMI, FHIMSS
Chairman, American Medical Informatics Association (AMIA)
Overview
 Motivation: the current state of US Healthcare
 Definitions in Biomedical Informatics
 Scope: Transforming Care with health informatics
 Translational Research and Informatics
 Strategy
 Care Transformation
 Infrastructure: Technical
 Tactics
 Infrastructure: Organizational
 Example: The Clinical Decision Support Consortium
Motivation for Healthcare
Transformation
US Motivation for Informatics
 Providers have incomplete knowledge of their patients
 Patient data unavailable in 81% of cases in one clinic,
• average of 4 missing items per case.
 18% of medical errors are due to inadequate availability of patient
information.
 Medicare beneficiaries see 1.3 – 13.8 unique providers annually, on
average 6.4 different providers/yr
 Delayed translation of new knowledge to clinical practice
 From bench to bedside, on average it takes > 17 years for new medical
knowledge to be routinely applied in clinical practice
 Clinical Information Needs of Practitioners are unmet
 Physicians in US urban and rural practices have on average more than 1
unanswered question per patient on optimal therapy diagnosis, or
procedure
Flexner Report
"...The curse of medical education is the
excessive number of schools. The
situation can improve only as weaker
and superfluous schools are
extinguished."
“Society reaps at this moment
but a small fraction of the
advantage which current
knowledge has the power to
confer.”
Abraham Flexner,
Medical Education in the United States and Canada.
Boston: Merrymount Press, 1910
Clinical Information Exceeding
Human Cognitive Capacity
Clinical Care Failures
ADA Guideline
Compliance
Measure blood pressure at every routine diabetes visit.
64.22%
57.86%
On average, Patients receive 54.9%
Visual foot exam at every
routine visit, comprehensive
foot
of recommended
care
44.92%
Test for lipid disorders at least annually and more often if
needed to achieve goals.
examination annually
Test for microalbuminuria in all type 2 diabetic patients at
least annually and during pregnancy.
23.62%
Dilated and comprehensive eye exam at diagnosis of Type 2
and annually.
14.21%
McGlynn EA, NEJM 2003; 348:2635.
Moody’s: National Nonprofit
Healthcare Revenue Growth Rate
Decline
Waste in US Healthcare Production
$765B
Access Problems for US Citizens
http://bit.ly/1DNp5hJ
Dilbert Wisdom…
Bending the Curve Towards
Transformed Health
Achieving Meaningful Use of Health Data
2009
2011
2013
2015
Improved
outcomes
Advanced
clinical
processes
Data capture
and sharing
HITECH Stimulus: Meaningful Use
King J, ONC Data Brief, 2012
CITL HIT Value Assessments
 Net US could save $150B with HIT adoption, or approximately 7.5% or
US Healthcare Expenditure
 The Value of Ambulatory Computerized Order Entry (ACPOE)
• $44B US nationally; $29K per provider, per year
 The Value of HealthCare Information Exchange and Interoperability
(HIEI)
• $78B/yr
 The Value of IT-enabled Chronic Diabetes Management (ITDM)
• $8.3B Disease Registries; Advanced EHR $17B
 The Value of Physician-Physician Tele-healthcare
• >$20B*
 The Value of Personal Health Records
– Approx. $20B
www.citl.org
Definitions in Biomedical
Informatics
Bill Tierney: “Clinical Informatics is
the basic science of health care.”
People, organizational, and leadership factors impacting
informatics support for clinical and translational research.
Payne PRO, et al. BMC Med Inform Dec Making 2013
AMIA Usability Rec 4.a: Adopt best practices for EHR
system implementation and ongoing management
“The EHR
implementation
experience depends on a
variety of factors
including the technology,
training, leadership, the
change management
process, and the
individual character of
each ambulatory practice
environment.”
Lorenzi N, et al.
BMC Med Inform Decis
Mak 9, 15 (2009).
Middleton B, et al. JAMIA 2013.
The BMI Fundamental Theorem
(
+
)>
Friedman, C. P. (2009). A “fundamental theorem” of biomedical informatics
JAMIA, 16(2), 169–170.
Clinical Informatics Drives High
Velocity Medicine
http://bit.ly/1dGuja6
The Scope of Biomedical
Informatics == Scope of
Translational Science from
Basic to Applied
Biomedical Informatics is the field of study which
accelerates the transformation of healthcare by
accelerating the discovery, use, and evaluation of
knowledge with information technology.
Translational Research
Translational research includes two areas of
translation.
One is the process of applying discoveries
generated during research in the laboratory, and in
preclinical studies, to the development of trials and
studies in humans.
The second area of translation concerns research
aimed at enhancing the adoption of best practices in
the community. Cost-effectiveness of prevention
and treatment strategies is also an important part
of translational science.
Rubio DM, Schoenbaum EE, Lee LS, et al. Defining translational research: implications for training. Acad Med
2010;85:470–5.
The NIH Roadmap may benefit from “blue highway” research that connects the
Research
major academic scienceTranslational
laboratories to the physicians
and patients in primary
care offices across the United States.
Westfall JM, Mold J, Fagnan L. JAMA 2007
Payne PRO et al. BMC Med Inform Decis Mak 2013
Personalized Medicine: A Key
Motivator for Translational Science
Fernald GH, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB. Bioinformatics
challenges for personalized medicine. Bioinformatics. 2011;27(13):1741–1748.
It’s Coming… Big Data
Add webber graphic
Weber GM et al.
JAMA 2014
Strategy(ies)
Determinants of Health and Their
Contribution to Premature Death
• Purchasers are
paying into a
disease system
rather than a
wellness
system
• 4% of health
care dollar is
spent on
prevention and
public health
Schroeder S. N Engl J Med 2007;357:1221-1228
Ecology of Medical Care
58% of
Americans
have a smart
phone
Green LA, Fryer GE, Yawn BP, et al. The ecology of medical care revisited. N Engl J Med 2001
Moving to the Left:
Benefits of Proactive Mitigation of
Disease Risk
20% of Population
Generates 80% of Costs
Health Status
IT-enabled
Care Redesign
Healthy/Low
Risk
At
Risk
High
Risk
Chronic
Disease
– Early
Stage
Chronic
Disease
Progression
End-ofLife Care
Acute
Disease
Value
Cost
After Daniel Kraft/George Poste
What is happening in care redesign?
Care redesign and standardization
Evidence-based care (bundles)
Standard Clinical Operating Model: Inpatient,
outpatient
Care coordination and management
Population Health Management
Team-based communications
Navigators
Emerging new business models for idependent health
care care management
Expanding Perspectives on Care –
Macro Efficiencies
From 1:1 Dr-Patient
Clinic Efficiency
Hospital Efficiency
System/IDN Efficiency
Regional Efficiency
Maximizing Value
 Improving patient safety,
reducing harm
 Improving the quality of
care
 Minimizing error and
waste
Assuming increased Risk
Tactics
With an Example
Building Organizational
Infrastructure
Payne PRO, et al. People, organizational, and leadership factors impacting informatics support for
clinical and translational research. BMC Med Inform Decis Mak 2013
Effecting provider change: a Golden Rule for Health Care
Organizations (be aware of behavioral economics)
Need to reduce
cost trend
New contracts with
risk for trend
Changes to org
structure
Investment in population
management infrastructure
1
Enhanced Care (PCMH)
Coordination for high risk
patients
2
Changes to organizational Network affiliations
incentives
3
Implement new local
incentives
4
New relationships with
community
hospitals and doctors
Enhanced access to specialty
services
Sustained cost trends
near GDP
Evidence based care improvement tactics
Longitudinal Care
Primary Care
Access to care
Episodic Care
Specialty Care
Hospital Care
Patient portal/physician portal
Optimize site of care
Extended hours/same day appointments
Reduced low acuity
admissions
Expand virtual visit options
Defined process standards in priority conditions
(multidisciplinary teams)
Design of care
Re-admissions
High risk care
management
Shared decision making
Hospital Acquired
Conditions
100% preventive
services
Appropriateness
Hand-off and
continuity programs
Chronic condition management
EHR with decision support and order entry
Variance reporting/performance dashboards
Measurement
Quality metrics: clinical outcomes, satisfaction
Incentive programs
Costs/population
Milford, CE, Ferris TG (2012 Aug). Mayo Clin Proc. 87(8):717-720.
Costs/episode
Sample Intervention
Self/Remote Mgmt
Virtual Visits
Async Services
CPOE w/CDS
Alerts/Reminders
Clin Doc
Discharge Mgt
Referral Mgt
Clinical Pop Analytics
Procedure OE
Quality Incentives
Population Mgmt
Discovery/
Survillance
Biomedical informatics and outcomes research: enabling
knowledge-driven health care.
Embi PJ, Kaufman SE, Payne PRO. Circulation 2009
Stanford’s STRIDE and a 13 y/o
female patient with SLE
http://med.stanford.edu/scci/research/stride.html
Frankovich J, Longhurst CA, Sutherland SM. Evidence-Based Medicine in the EMR Era. N Engl J Med 2011
Real Time Clinical Decision Support
with STRIDE
“On the basis of this real-time, informatics-enabled
data analysis, we made the decision to give our patient
anticoagulants within 24 hours after admission.”
“Although many other groups have highlighted the
secondary use of EMR data for clinical research, we
have now seen how the same approach can be used to
guide real-time clinical decisions.”
The rapid electronic chart review and analysis were
not only feasible, but also more helpful and accurate
than physician recollection and pooled colleague
opinion.
Frankovich J, et al. N Engl J Med 2011
Distributed Data Query
Mandl KD, Kohane IS. Federalist principles for healthcare data networks. Nature Biotechnology 2015
ASCO Vision: Blueprint for Transforming Clinical
and Translational Research
ASCO. Accelerating Progress Against Cancer. 2011
Integrated Informatics Research
Portfolio – A Learning Health System
Feedback: PCS intervention
efficacy
‘Decision Cells’ standardized
and validated reusable
building blocks of HIT
modules and functions
Effective Use of PCS
in Health IT
Feedback: portability,
adaptability
expression
‘Decision Proteins’
Essential codes and
structure for key data, well
specified software
methods and open source
code, APIs
Reference
Standards and
Architecture
translation
‘Decision RNA’ of abstract
patient state definitions,
knowledge objects,
controlled terminology,
ontology
Patient-centered
Data Abstractions
and Knowledge
Engineering
mental models, utilities,
preferences, perception, and
behavior
Feedback: case variant, atypical
Feedback: functional
expectation mismatch
Feedback: semantic
constraints
Feedback: data, lexical,
ontological variants
Feedback: knowledge engineering
efficiency
transcription
‘Decision DNA’ of concepts,
Feedback: localization,
workflow, functionality
Cognitive and
Behavioral
Foundations
Feedback: cognitiveobjective discord
Feedback: coherence, understanding,
self-determination, actualization
The Clinical Decision Support
Consortium
An Example
Knowledge - based systems
“A knowledge-based system is an AI program whose
performance depends more on the explicit presence of
a large body of knowledge than on the presence of
ingenious computational procedures…”
Duda RO, Shortliffe EH. Expert systems research.
Science. 1983 Apr 15;220(4594):261-8.
Humility
Inference Methods Used in Expert
Systems
Algorithmic
Statistical
Pattern Matching
Rule-based
(Heuristic)
Fuzzy sets
Neural nets
Bayesian
TBD…
Inference Engine
Copy
Knowledge
Knowledge
Base
Inference Engine
Knowledge Translation and
Specification
Evidence
Guideline(s)
K Repres’n
Shareable K
Executable
Experience
Decision Tables
GEM
Arden
ONCOCIN
EON(T-Helper)
GEODE-CM
GLIF2
GLIF3
MBTA
EON2
Asbru
PRODIGY
Oxford System
of Medicine
DILEMMA
PRODIGY3
PROforma
PRESTIGE
1980
1990
2000
P. L. Elkin, M. Peleg, R. Lacson, E. Bernstam, S. Tu, A. Boxwala, R. Greenes, & E. H. Shortliffe.
Toward Standardization of Electronic Guidelines. MD Computing 17(6):39-44, 2000
Three Models to Accelerate
Knowledge -> Practice
•Current paper-based approach
EMR
Guideline
•Knowledge artifact import into EMR
Computer Interpretable Guideline
•Cloud-based clinical decision support services
Web Services
CCD/VMR Patient Data Object
Decision Support
CDS Consortium:
Goal and Significance
 Goal: To assess, define, demonstrate, and evaluate best practices for knowledge
management and clinical decision support in healthcare information technology at
scale – across multiple ambulatory care settings and EHR technology platforms.
 Significance: The CDS Consortium will carry out a variety of activities to
improve knowledge about decision support, with the ultimate goal of supporting
and enabling widespread sharing and adoption of clinical decision support.
1. Knowledge Management Life Cycle
2. Knowledge
Specification
3. Knowledge Portal and
Repository
4. CDS Public Services
and Content
5. Evaluation Process for each CDS Assessment and Research Area
6. Dissemination Process for each Assessment and Research Area
An external repository of clinical
content with web-based viewer
Search Criteria
Content Type…
Specialty
Enterprise CDS Framework
ECRS
CDS Consumers
Input
(CCD)
Vendor Products
Metadata
Query
External
Consumers
Rule
Authoring
Rule Execution
Recc
Server
PHSese
PHS internal
applications
ECRS
Controller
SMArt
Rule DB
SMArt Apps
VMR
External
Consumers
Open
EHR
OpenEHR
...
Supporting Services
CCD
Factory
Patient Data
Pt Data
Access
/
Translations
Translation
Normalization
Services
Normalization
Services
Reference
Data
Partners LMR
CDS Consortium
Demonstrations
Toward a National Knowledge Sharing Service
Mid-Valley IPA (NextGen)
Salem,Oregon
Kaiser Roseville
UC Davis
Kaiser Sacramento
Kaiser San Rafael
Kaiser San Francisco
California
PHS
Wishard Hospital
Indianapolis,IN
Cinncinati Children’s
Nationwide Children’s
Children’s Hospital
Ohio
Colorado
NYP
NY
UMDNJ (GE)
Newark,NJ
CDS Consortium
PECARN TBI CDS
A Healthcare Ecosystem for Information Exchange, Care
Coordination, and Learning
v
and knowledge!
Middleton B. JAMIA 2014
Where are we?
“I conclude that though
the individual physician
is not perfectible, the
system of care is, and
that the computer will
play a major part in the
perfection of
future care systems.”
Clem McDonald, MD NEJM 1976
Thank you!
Blackford Middleton, MD
[email protected]
Mobile 617-335-7098