Data + Analysis = Decision
Support
Rema Padman, PhD
Professor of Management Science and Health
Informatics
The Heinz School
Carnegie Mellon University
MMM Program
rpadman@cmu.edu
Outline - Day 1
• Introduction
– Motivation
– Illustrative Examples
– DSS Analysis Framework
– DSS Reference Architecture
– Key Challenges
• Decision making
– Process
– Strategies used by humans
– Implications for decision support
Outline - Day 2
• Technology Enablers
– Data Management
•
•
•
•
Databases
Data warehousing
Data marts
OLAP, ROLAP, MOLAP, WOLAP
– Analysis Technologies
• Decision analysis methods
• Rule-based systems
• Knowledge discovery and pattern recognition
Outline - Day 3
– DSS Integration, Deployment, Use
• Intermountain Health Care
• Clinical Reminder System
Information – The Emerging Capital
of Healthcare
Volume of Information
(Diamond Cluster International, Inc.)
Provider Information
Processing Capacity
Available Patient
Information
Healthcare
Informatics
Time
The Future- A Decision Support
Focus
• Real time decision support at the point of care
• Customized disease management for the individual
and the population
• Knowledge nodes that leverage pervasive
computing infrastructure
– Telemedicine
– Public health
Drivers
• Demand Pull
– Value of information
– Reduction of errors
– Managing information overload
• Technology Push
– Computing and communication technologies,
decision and information technologies, medical
technologies,..
• Regulatory Push
– HIPAA, Insurance reform, Patient’s bill of rights…
Motivation: Value of Information
“In attempting to arrive at the truth, I have applied
everywhere
for information, but in scarcely an instance have I
been able to
obtain hospital records fit for any purpose of
comparison. If they
could be obtained...they would show subscribers how
their money
was being spent, what amount of good was really being
Evidence for CDSS Adoption?
DL Hunt, RB Haynes, SE Hanna, K Smith. Effects of computer-based clinical decision support systems
on physician performance and patient outcomes. JAMA 1998 280: 1339-1346.
•
•
•
•
•
Drug dosing systems
Diagnostic aids
Preventive care systems
Other medical care
All CDSS studies
-
9/15
1/5
14/19
19/26
43/65
Motivation: Reducing Medical Errors
• 1999 IOM report on medical errors - “To Err is
Human: Building A Safer Health System”
– "The stunningly high rates of medical errors - resulting
in deaths, permanent disability, and unnecessary
suffering - are simply unacceptable in a medical
system that promises first to 'do no harm,'" says
William Richardson, chair of the committee that wrote
the report and president and chief executive officer of
the W.K. Kellogg Foundation, Battle Creek, Mich.
– "Our recommendations are intended to encourage the
health care system to take the actions necessary to
improve safety. We must have a health care system
that makes it easy to do things right and hard to do
them wrong.”
Evidence for ADE Reduction?
DW Bates et al.. JAMA 1998 280: 1311-1316, DW Bates et al. JAMIA 2003 10:115-128.
•
•
•
•
BWH interventions
Applications devoted to workflow
Existing, expandable clinical systems
Order entry only way to enter orders
– 15,000 orders a day, 400 changed due to
intervention (warnings, reminders, ..)
– drug-allergy, drug-drug, drug-problem
– drug-lab, lab-drug
– drug dosing, renal dosing
– appropriate dosage
– drug substitution
• High user satisfaction
Motivation: Managing Information
Overload
“It is simply unrealistic to think that
individuals can synthesize in their heads
scores of pieces of evidence, accurately
estimate the outcomes of different options,
and accurately judge the desirability of
those outcomes for patients.”
David M. Eddy, MD, PhD
JAMA 1990, 263:1265 - 1275
Technology Drivers
• Reduced barriers to facilitated and
customized information access
– Data storage, management, retrieval, and
analysis technologies
– Input-output technologies
– Communication and networking technologies
– Internet and web technologies
Technology Drivers
Building efficiency, effectiveness, and
quality into healthcare delivery and
administration
– use data in EMRs and CDRs for more
than individual patient management
Regulatory Drivers
• HIPAA
– Administrative provisions
– Privacy, security provisions
– ….
• Leapfrog Group Recommendations
– Computer Physician Order Entry (CPOE)
– Evidence-based Hospital Referral (EHR)
– ICU Physician Staffing (IPS)
• ..
Contextual Drivers
• Increased risk-sharing by providers
• IOM reports
– To Err is Human: Building a Safer health System (1999)
– Crossing the Quality Chasm: A New Health System for
the 21st century (2001)
– Unequal Treatment: Confronting Racial and Ethnic
Disparities in Healthcare (2002)
– Patient Safety: Achieving a New Standard for Care
(2003)
• Bioterrorism and biosurveillance–related objectives
Current Initiatives
(EBM Solutions)
Payers will focus on these co-dependent
variables to reduce costs and improve care
• Reduced inappropriate variation and increased
adherence to evidence-based guidelines by
providers
• Increased comprehension of and adherence to
evidence-based guidelines by patients.
Variation Reduction + Patient
Compliance - Potential Results
• Patient Compliance…
– $20-50B opportunity (UBS Warburg)
• Inappropriate variability…
– $200-300B opportunity (Juran)
The two are inextricably related…essential to manage
how care is delivered to manage costs and improve
care
Transforming Health Care with IT
TABLE 1 Simple Rules for the 21st-Century Health Care
System
Current Approach
.
New Rule
Care is based primarily on visits.
Care is based on continuous healing relationships.
Professional autonomy drives variability.
Care is customized according to patient needs and values.
Professionals control care.
The patient is the source of control.
Information is a record.
Knowledge is shared and information flows freely.
Decision making is based on training and experience.
Decision making is evidence-based.
Do no harm is an individual responsibility.
Safety is a system property.
Secrecy is necessary.
Transparency is necessary.
The system reacts to needs.
Needs are anticipated.
Cost reduction is sought.
Waste is continuously decreased.
Preference is given to professional roles over the system.
Cooperation among clinicians is a priority.
SOURCE: Table 3-1 in Crossing the Quality Chasm, IOM, 2001
Pay-Off: Health Cost Reduction Potential
of 10% with New Care Management
Model
(Gartner)
• Necessary for system to move from adverse event
model to utilization management model with 10%
savings achievable by 2007.
• Drivers are…
–
–
–
–
Increased costs
Patient protection legislation
Increased demand for services
New technology
Using Patient Care Management to Preserve and Enhance Profitability Gartner Inc.,
September, 2002
Pay-Off: Anticipated Impact of Internet
Clinical Applications
• 20% of Office Visits Eliminated
• 30% of Physicians Time Reduced
PriceWaterhouse Survey of 400
Healthcare Thought Leaders
Healthcast 2010
Organizational Context
• Most organizational decision-making can
be classified as occurring at 3 levels:
strategic, tactical, and operational
(Anthony, 1965)
Unstructured
Strategic
Long-term impact
Tactical
Structured
Operational
Short-term impact
Illustrative Examples
• Biosurveillance – RODS
• Clinical DSS – Theradoc, Dxplain
• Administrative DSS – ThinkMed Expert,
Internetivity
• Personalized DSS – Medicalogic,
DecisionCoaches
A Conceptual Model of a
DSS
(Alter)
Supporting semi-structured
or unstructured decisions
Information
Data about
past and present
projections,
assumptions
People
Decision makers,
support staff
Goals
Work
Practices
Information
Technology Interactive systems,
software for data
analysis, model
building, and
graphical display
Clinical Decision Framework
Patient data
Domain info
Clinical
Decisions
Interventions
Patient
Responses
Admin info
Guidelines and
Decision Support
Pathways
Clinical
Guidelines and Pathways
DSS Reference Architecture
Language
System
Presentation
System
Problem Processing
System
Knowledge
System
DXPlain: Diagnostic DSS from
Massachusetts General Hospital
• Uses a set of clinical findings to produce
a ranked list of diagnoses that might
explain the findings
– provides justification
– suggests additional clinical information to
collect
– lists unusual manifestations for each
disease
Dxplain
• Information repository
– > 2000 diseases
– > 5000 clinical manifestations
– Uses a bayesian probabilistic method to
draw inferences about diseases based on
presented clinical findings
• Educational tool for clinical education
and clinical problem solving
– Electronic medical textbook and a medical
reference system
Dxplain info
• http://www.lcs.mgh.harvard.edu/privlic.htm
– to become a registered user
– limited to clinicians
Analyzing a DSS
• Is the goal problem finding or problem
solving?
• What type of activity is involved:
planning, execution or control?
• What approach is used to improve the
decision?
• What kinds of answers does the system
provide?
Analyzing a DSS
• What is the users role and degree of
interaction with the system?
• What kind of impact does the system
have on desired objectives?
• What kind of information technology is
used?
Feature of system
• Extent to which system imposes structure
– access to information/tools
– enforcement of rules/procedures
– substitution of technology for people
Feature of Task
• Level of coordination
– individual/discretionary
– individual/mandatory
– workgroup
– organizational
– inter-organizational
Approach used
• Provide background data
– individual discretionary, access to info
• Support analytic work
– individual discretionary, access to tools
• structure repetitive decisions
– individual mandatory, enforcement of rules
• automate decisions
– individual mandatory, substitution of technology for
people
• structure planning processes
– organizational, enforcement of rules
• structure control processes
– organizational, enforcement of rules
DSS Reference Architecture
Language
System
Presentation
System
Problem Processing
System
Knowledge
System
Clinical DSS: Theradoc
Antibiotic Assistant
DSS Reference Architecture
Language
System
Presentation
System
Problem Processing
System
Knowledge
System
DSS and Organizational Decision
Making
• Operational
– Theradoc: antibiotic prescription is operational DSS
• Tactical
– Theradoc: provides cost information alongside antibiotic lists
for managing infection, thereby resulting in bottom line
impact on patient outcomes and costs and revenues
• Strategic
– Antibiotic assistant: prescribes “right” antibiotic, taking costs,
demographics, drug interactions and allergies into account
• Has impact on reducing medical errors, a metric that is
compiled and made public
• See US News and World Report and University ratings
– Meets biosurveillance needs and objectives
Thinkmed Expert: Data
Visualization and Profiling
(http://www.click4care.com)
• http://www.thinkmed.com/soft/softdemo.ht
m
ThinkMed Expert
• Processing of consolidated patient
demographic, administrative and claims
information using knowledge-based rules
• Goal is to identify patients at risk in order
to intervene and affect financial and
clinical outcomes
Vignette
• High risk diabetes program
• Need to identify
– patients that have severe disease
– patients that require individual attention
and assessment by case managers
• Status quo
– rely on provider referrals
– rely on dollar cutoffs to identify expensive
patients
Vignette
• ThinkMed approach
– Interactive query facility with filters to identify
patients in the database that have desired
attributes
• patients that are diabetic and that have cardiac,
renal, vascular or neurological conditions (use of
codes or natural language boolean queries)
• visualize financial data by charge type
DSS Reference Architecture
Language
System
Presentation
System
Problem Processing
System
Knowledge
System
Administrative DSS using
WOLAP
DSS Reference Architecture
Language
System
Presentation
System
Problem Processing
System
Knowledge
System
Personalized DSS for
Automating Decisions:
Online practice profiling
DSS Reference Architecture
Language
System
Presentation
System
Problem Processing
System
Knowledge
System
DecisionCoaches
(www.decisioncoaches.com)
IT Architecture
DSS Reference Architecture
Language
System
Presentation
System
Problem Processing
System
Knowledge
System
Case for Change (PriceWaterhouseCoopers 2003)
• Creating the future hospital system
– Focus on high-margin, high-volume, highquality services
– Strategically price services
– Understand demands on workers
– Renew and replace aging physical structures
– Provide information at the fingertips
– Support physicians through new technologies
Case for Change (PriceWaterhouseCoopers 2003)
• Creating the future payor system
– Pay for performance
– Implement self-service tools to lower costs
and shift responsibility
– Target high-volume users through
predictive modeling
– Move to single-platform IT and data
warehousing systems
– Weigh opportunities, dilemmas amid public
and private gaps
Challenges
• Understanding human decision making
• Understanding the technology
environment
– status quo (islands of information, legacy
systems)
– desired status (integrated enterprise-wide
view and model of data that can be
analyzed and is ubiquitously available)