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Data + Analysis = Decision Support. Rema Padman, PhD Professor of Management Science and Health Informatics The Heinz School Carnegie Mellon University MMM Program [email protected] Outline - Day 1. Introduction Motivation Illustrative Examples DSS Analysis Framework

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Data analysis decision support l.jpg

Data + Analysis = Decision Support

Rema Padman, PhD

Professor of Management Science and Health Informatics

The Heinz School

Carnegie Mellon University

MMM Program

[email protected]

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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

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Outline - Day 2

  • Technology Enablers

    • Data Management

      • Databases

      • Data warehousing

      • Data marts


    • Analysis Technologies

      • Decision analysis methods

      • Rule-based systems

      • Knowledge discovery and pattern recognition

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Outline - Day 3

  • DSS Integration, Deployment, Use

    • Intermountain Health Care

    • Clinical Reminder System

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Information – The Emerging Capital of Healthcare (Diamond Cluster International, Inc.)

Volume of Information

Provider InformationProcessing Capacity

Available PatientInformation



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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

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  • 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…

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Motivation: Value of Information

  • “In attempting to arrive at the truth, I have applied everywherefor 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 done with

  • it, or whether the money was not doing mischief rather than good...”

  • Florence Nightingale, 1873

    • Notes on a Hospital

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Drug dosing systems

Diagnostic aids

Preventive care systems

Other medical care

All CDSS studies

- 9/15

- 1/5

- 14/19

- 19/26

- 43/65

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.

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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.”

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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

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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

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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

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Technology Drivers

Building efficiency, effectiveness, and quality into healthcare delivery and administration

  • use data in EMRs and CDRs for more than individual patient management

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Regulatory Drivers


    • Administrative provisions

    • Privacy, security provisions

    • ….

  • Leapfrog Group Recommendations

    • Computer Physician Order Entry (CPOE)

    • Evidence-based Hospital Referral (EHR)

    • ICU Physician Staffing (IPS)

  • ..

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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

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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.

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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

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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.

Transforming Health Care with IT

SOURCE: Table 3-1 in Crossing the Quality Chasm, IOM, 2001

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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

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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

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Unstructured Applications


Long-term impact




Short-term impact

Organizational Context

  • Most organizational decision-making can be classified as occurring at 3 levels: strategic, tactical, and operational (Anthony, 1965)

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Illustrative Examples Applications

  • Biosurveillance – RODS

  • Clinical DSS – Theradoc, Dxplain

  • Administrative DSS – ThinkMed Expert, Internetivity

  • Personalized DSS – Medicalogic, DecisionCoaches

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A Conceptual Model of a DSS Applications(Alter)


Supporting semi-structured

or unstructured decisions

Data about

past and present




Decision makers,

support staff






Interactive systems,

software for data

analysis, model

building, and

graphical display

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Clinical Decision Framework Applications

Patient data






Domain info

Admin info




Guidelines and Pathways

Guidelines and

Decision Support


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DSS Reference Architecture Applications



Problem Processing






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DXPlain: Diagnostic DSS from Massachusetts General Hospital Applications

  • 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

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Dxplain Applications

  • 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

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Dxplain info Applications


    • to become a registered user

    • limited to clinicians

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Analyzing a DSS Applications

  • 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?

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Analyzing a DSS Applications

  • 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?

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Feature of system Applications

  • Extent to which system imposes structure

    • access to information/tools

    • enforcement of rules/procedures

    • substitution of technology for people

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Feature of Task Applications

  • Level of coordination

    • individual/discretionary

    • individual/mandatory

    • workgroup

    • organizational

    • inter-organizational

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Approach used Applications

  • 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

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DSS Reference Architecture Applications



Problem Processing






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DSS Reference Architecture Applications



Problem Processing






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DSS and Organizational Decision Making Applications

  • 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

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Thinkmed Expert: Data Visualization and Profiling Applications(


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ThinkMed Expert Applications

  • 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

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Vignette Applications

  • 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

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Vignette Applications

  • 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

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DSS Reference Architecture Applications



Problem Processing






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DSS Reference Architecture Applications



Problem Processing






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Personalized DSS for Automating Decisions: ApplicationsOnline practice profiling

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DSS Reference Architecture Applications



Problem Processing






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DecisionCoaches Applications(

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IT Architecture Applications

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DSS Reference Architecture Applications



Problem Processing






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Case for Change Applications (PriceWaterhouseCoopers 2003)

  • Creating the future hospital system

    • Focus on high-margin, high-volume, high-quality services

    • Strategically price services

    • Understand demands on workers

    • Renew and replace aging physical structures

    • Provide information at the fingertips

    • Support physicians through new technologies

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Case for Change Applications (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

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Challenges Applications

  • 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)