Looking Back on Clinical Decision Support and Data Warehousing

Culturally-Driven Process Improvement Enabled By Technology

Guest Lecture for Health Information Science HINF 551

University of VictoriaMay 2008

Clinical Decision Support and Data Warehousing

Dale [email protected]

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• Complex, life-critical, time-critical computerized decision support• It all boils down to managing false positives and false negatives, then

optimizing your intervention and response

My background

US Air ForceCommand, Control, Communications, Computers & Intelligence (C4I) Officer

TRW/National Security Agency• START Treaty• Nuclear Non-

proliferation• US nuclear

weapons threat reduction

Director of Medical Informatics, LDS Hospital/Intermountain Healthcare

CIO, Northwestern

CIO, Cayman Islands National Health System

Product Development,Health Catalyst

20161983

Reagan/Gorbachev Summits

Nuclear Warfare Planning and Execution– NEACP & Looking Glass

3

Acknowledgements & Thanks

Robert Jenders, MD, MS Associate Professor, Dept of Medicine, Cedars-Sinai Medical

Center & UCLA Co-chair, HL7 Clinical Decision Support TC & Arden Syntax SIG

R. Matthew Sailors, PhD Assistant Professor, Dept of Surgery, UT-Houston Co-chair, HL7 Clinical Decision Support TC & Arden Syntax SIG

Clinical Decision Support and Arden Syntax

Overview

• Patient information systems trends & concepts• Enterprise Data Warehouse (EDW)

– Basic Terms and Concepts– Case Study Examples

– Intermountain Healthcare– Northwestern University

• Clinical Decision Support

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Information Systems:The Three Perspectives

Transaction Systems:Collecting data that

supports analytics & efficient workflow

Analytic Systems:Aggregating and exposingdata to improve workflow

Knowledge Systems:Organizing, sharing,

and linkinginformation

• Query and reporting tools• Enterprise data warehouses• Benchmarking data

• Document imaging• Videoconferencing • Collaboration tools • Intranets/Internet access• Search engines

• EMR’s• Billing systems• GL systems• HR systems• Scheduling systems• Inventory management systems

Goal Measurement

Goal achievement

Goal Achievement

Designed to support

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Patient Information Systems Trends Transportability and Interoperability

– Information moves with the patient Real-time alerts and reminders

– Drug-drug and drug-allergy interactions Data-driven treatment planning Disease management at the point-of-care Payer-driven data collection

– Pay for Performance (P4P) Quality of care reporting Transparency of cost is coming

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Health consumerism movement– Demands for improved and more transparent

information access– Demands for more security and privacy– The “credit report” phenomenon

Computerized patient records– Legislation and state and federal initiatives are

supporting investment in collaborative software Regional health information networks are receiving

funding – For collaborative clinical information sharing and for

pay-for-performance initiatives

Patient Information Systems Trends

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Patient Care Data “Customers”

Patient Care Data

Financial HIS Coding (HDM) A/R Management Standard Costing Materials Management Case Mix

Clinical Patient Safety Clinical Programs Clinical Support Services Case Mix

Accreditation/Regulatory JCAHO, NCQA, HEDIS HIPAA, EMTALA, OSHA, CLIA

Third-party Payers Claims information Utilization management Case management

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Meaningful,maintainable point-of-careclinical decision support

• Registration• Scheduling• Accts Receivable• Patient/payer billing• Reporting• HIPAA claims, eligibility, remittance

• Benefit plan tracking• Co-pay tracking• Referral management• COB• Risk management• Patient education

• Encounter documentation• Charge capture• Diagnostic coding• ePrescribing• Allergy alerts• D-D interactions• Medical history

• Messaging & real time collaboration• Patient portal• Self-scheduling• Self-registration• Account management• Results & history• Rx refills• Credit card payment

• Lab interfaces• Payer/clearinghouse interfaces (HIPAA)• Integrated orders• Integrated results• ePrescribing• Patient education• Clinical references within context• Affiliated referring partners

Business Intelligence/”Pay for Performance” MetricsWorkflow & Handoff Between Clinical and Business Processes

CoreBest Practices Reminders Meaningful Alerts

Advantage Differentiator Off The Edge

Regional/External Entities

Functional Framework: Electronic Health Record

Leading Edge

• Rare & difficult• The next frontier

The Future EHR User Interface

• Patient specific data– Much like current EHRs– “Tell me about this patient.”

• Disease management data– “Tell me about managing patients like this.”

• Treatment options data– “Tell me about my options for treating this patient.”– “Tell me about the most common tests and medications ordered for patients like this.”

• Cost of care data– “Tell me about how much these treatment options cost.”

• Clinical outcomes data– “Tell me how satisfied patients were with these treatment options.”

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Closed Loop Analytics

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12

Enterprise Data Warehousing

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Multiple, Collaborative Organizations

EDWA single data perspective

on the patient care processDiagnostic systems•Lab System•Radiology•Imaging•Pathology•Cardiology•Others

DiagnosisRegistration &Scheduling

PatientPerception

Orders & Procedures

Results & Outcomes

Billing &Accounts

Receivable

Claims Processing

EncounterDocumentation

•ADT System•Master Patient Index

Pharmacy ElectronicMedical Record

Surveys•Diagnostics•Pharmacy

Billing and ARSystem

Claims ProcessingSystem

Diagnostic systems•Lab System•Radiology•Imaging•Pathology•Cardiology•Others



PatientPerception

Orders & Procedures

Results & Outcomes

Billing &Accounts

Receivable

Claims Processing








PatientPerception

Orders & Procedures

Results & Outcomes

Billing &Accounts

Receivable

Claims Processing


•ADT System•Master Patient Index•ADT System•Master Patient Index

PharmacyPharmacy ElectronicMedical Record

ElectronicMedical Record

SurveysSurveys•Diagnostics•Pharmacy•Diagnostics•Pharmacy







PatientPerception

Orders & Procedures

Results & Outcomes

Billing &Accounts

Receivable

Claims Processing










PatientPerception

Orders & Procedures

Results & Outcomes

Billing &Accounts

Receivable

Claims Processing








PatientPerception

Orders & Procedures

Results & Outcomes

Billing &Accounts

Receivable

Claims Processing












PatientPerception

Orders & Procedures

Results & Outcomes

Billing &Accounts

Receivable

Claims Processing










PatientPerception

Orders & Procedures

Results & Outcomes

Billing &Accounts

Receivable

Claims Processing








PatientPerception

Orders & Procedures

Results & Outcomes

Billing &Accounts

Receivable

Claims Processing










Hospital X

Hospital Y Physician Office Z

Sanders’ Hierarchy of Analytic Maturity• Basic business reporting

– Financial and Human Resources• Legal compliance reporting

– As required by state and federal law– Cancer Registry, mortality rates, et al

• Professional accreditation reporting– Joint Commission, Society of Thoracic Surgeons, et al

• Quality of care reporting– Physician Quality Reporting Initiative, Leap Frog, et al

• Patient Relationship Management (PRM)– Borrowing from Customer Relationship Management in retail– Tailored to the entire context of the patient– Simpler, faster patient satisfaction and outcomes feedback– Clinical “Loose Ends”

• Real-time analytic fusion– Blending patient specific data with general patient type data– “Other physicians who saw patients like this, ordered these medications and tests.”

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Increasing Maturity

Healthcare Analytics Adoption ModelLevel 8 Personalized Medicine

& Prescriptive AnalyticsTailoring patient care based on population outcomes and genetic data. Fee-for-quality rewards health maintenance.

Level 7 Clinical Risk Intervention& Predictive Analytics

Organizational processes for intervention are supported with predictive risk models. Fee-for-quality includes fixed per capita payment.

Level 6 Population Health Management & Suggestive Analytics

Tailoring patient care based upon population metrics. Fee-for-quality includes bundled per case payment.

Level 5 Waste & Care Variability Reduction Reducing variability in care processes. Focusing on internal optimization and waste reduction.

Level 4 Automated External Reporting Efficient, consistent production of reports & adaptability to changing requirements.

Level 3 Automated Internal Reporting Efficient, consistent production of reports & widespread availability in the organization.

Level 2 Standardized Vocabulary & Patient Registries Relating and organizing the core data content.

Level 1 Enterprise Data Warehouse Collecting and integrating the core data content.

Level 0 Fragmented Point Solutions Inefficient, inconsistent versions of the truth. Cumbersome internal and external reporting.

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Lab

Admissions

Radiology

Registration

Pharmacy

Nursing

AR/AP

Materials M

gt

Vertical and Horizontal Strategy

Intensive Medicine

Cardiology

Oncology

Women’s Health

Neurology

Step One:Clinical Excellence

Programs

Step Two: Operational Excellence Programs

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Examples of Clinical Goals

• Decrease the total number of nulliparous elective inductions with a Bishop Score <10 by 50%

• Keep the variable cost increase of deliveries without complications resulting in normal newborns to 5.73% for 2003

• For all adult patients with diabetes, increase the percent of patients with LDL less than 100 to >=45.5%. (Currently 44.5%)

• Measured glucose values will be between 60 and 155 mg/dl 80% of the time for all ICU patients

• 100% compliance to post-surgery radiation therapy protocols for breast cancer cases with >4 positive nodes and tumor size >=5cm

• Compliance with the timing of administration of Pre-surgical Prophylactic Antibiotic Usage will exceed 91%

• For patients being treated for depression, increase the percentage continuing on prescribed antidepressant for 6 months after filling first prescription to >=44.6%

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DOQ-IT/PQRI Examples

The Advisory Board Company





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Structured vs. Unstructured Data

Representation of Human Experience & Knowledge

Com

puta

ble

Ana

lytic

Val

ue

• Text

• Video

• RecordedAudio

• Structured, discrete data

• Face-to-FaceAudio

INTERMOUNTAIN HEALTHCARE

Case Study Example

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Case Study

• Primary Care: Diabetes– Motive: Improved long-term management of diabetes patients

– RAND Study 2002: “64% of diabetic patients receive inadequate care.”– Integrates five disparate data sources

– Lab– Problem list– Insurance claims: CPT’s and pharmacy– In-patient pharmacy– Hospital ICD-9

– This one hits home– Winner

– National Exemplary Practice Award 2002– American Association of Health Plans

Measure Goal

HbA1c (test at least 2 times a year)

<7.0%

Blood Pressure (check at each office visit)

<130/80 mm Hg

LDL Cholesterol (test at least every 2 years)

<100 mg/dL

Triglycerides (test at least every 2 years)

<150 mg/dL

Foot Exam (perform at least annually)

normal

Urine Microalbumin/Creatinine

Ratio (test at least annually)

<30

Dilated Eye Exam (check annually,

or every 2 years if well controlled)

normal

Diabetes CPM:Key Indicators

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Case Study: Diabetes Management

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Case Study: Diabetes Management

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Diabetes Management Peer Comparison Chart

Case Study

• CV Discharge Medications– Motive: Basic protocol adherence

– Appropriate discharge meds ordered following CV (IHD and MI) diagnosis and treatment

–Results– 1994: 15% (estimate, no hard data)– 2004: 98% (hard data)

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Case Study: CV Discharge Meds

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Case Study: CV Discharge Meds

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The Tangible BenefitsFrom Intermountain’s

Cardiovascular Clinical Program

Case Study

• Labor and Delivery - Elective Inductions– Continue to educate physicians and patients on the safe

and efficacious practice of elective labor induction.– To maintain at ≤5% elective deliveries that do not meet

strict criteria (39 weeks gestation) developed by the Intermountain Obstetrical Development Team.

– To measure clinical outcomes of care and report quarterly by provider.

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Elective Inductions

Elective Deliveries <39 Weeks Intermountain Healthcare

0%

5%

10%

15%

20%

25%

30%

35%

1999

JanFebMarApr

MayJunJu

lAugSepOct

NovDec

2000

JanFebMarApr

MayJunJu

lAugSepOct

NovDec

2001

JanFebMarApr

MayJunJu

lAugSepOct

NovDec

2002

JanFebMarApr

MayJunJu

lAugSepOct

NovDec

2003

JanFebMarApr

MayJunJu

lAugSepOct

NovDec

2004

JanFebMarApr

MayJunJu

lAugSepOct

NovDec

2005

JanFebMarApr

MayJunJu

lAugSepOct

NovDec

Month

Perc

ent <

39 W

eeks

37Intermountain Healthcare, Steve Barlow

Elective InductionsEstimated Variable Cost Savings From Elective Induction Protocol

Intermountain Healthcare 2001-2005

$26,479

$207,772

$597,367

$380,833

$188,606

$-

$100,000

$200,000

$300,000

$400,000

$500,000

$600,000

$700,000

2001 2002 2003 2004 2005

Year

Varia

ble

Cost

Sav

ings

$-

$200,000

$400,000

$600,000

$800,000

$1,000,000

$1,200,000

$1,400,000

$1,600,000

Cum

ulat

ive

Varia

ble

Cost

Sav

ings

Yearly Savings Cumulative Savings

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NORTHWESTERN’S EDWSo far, so good…

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Data Loaded to Date

Metric Value

Number of Rows 3,173,632,200

Terabytes 2.2

Truckloads 1,233

Complete works of Shakespeare 252,483

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Early Adopters and Value of the EDWCustomer Analytic Use

NUgene Relating genomic data and clinical profiles for phenotyping high risk diseases such as diabetes and asthma

Neurosurgery A summary of new patients, encounters and diagnoses from the EDW is import daily into MDAnalyze, a Neurosurgery outcomes database

Alan Peaceman, MD Creation of a perinatal patient registry for studying clinical quality outcomes; BMI relationships to complications

Bill Grobman, MD Statistics of deliveries at NMH in preparation for a grant proposal

Dana Gossett, MD Application of Systemic Inflammatory Response Syndrome (SIRS) criteria to pregnant and postpartum women with infectious complications

Andrew Naidech, MD First adopter of the Research Patient Data Aggregator for use in research and clinical quality assessment of subarachnoid hemorrhage, intracerebral hemorrhage, and stroke patients

NMH Process Improvement A DMAIC project aimed at improving the quality of care for patients seen with bone fractures at NMH. Used the EDW to help narrow and speed their search for bone fracture patients using a query of text-based Radiology reports.

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Specific Research Example

For the last year for the women who deliver, provide…• mean age and standard deviation• percent between 18-34, inclusive• ethnic breakdown, at least by white, black, latino• % smokers• % singletons (i.e. no twins or triplets)• % who receive their prenatal care with an NMH doc• mean BMI and standard deviation• % BMI < 19• % BMI 19 - 29.9• % BMI > 29.9• % who start prenatal care in the first trimester

Rapid turnaround (<2 days) to meet a grant submission deadline…

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Other Examples

• How many patients were prescribed an NSAID and who also had a lab value which indicated reduced renal function (lab result of GFR < 50 or Creatinine > 1.5)?– Answer: 725 out of 16214 in calendar year 2007

• What percentage of patients diagnosed with multiple myeloma in remission over age 18 were prescribed bisphosphonates in the past 12 months?– Answer: 18%

• How many patients who have had 1 or more low LVEF (<40) measurements in our outpatient echo system (Xcelera) and who have received a low LVEF measurement within the last 180 days and who have not seen one of the following doctors in a Northwestern clinic office visit within the last 120 days?

– 'KADISH, ALAN H.'– 'GOLDBERGER, JEFFREY J.'– 'PASSMAN, ROD S.'– 'DENES, PABLO'– 'JACOBSON, JASON‘

– Answer: 309

Changes in quality measures during the first 3 months of the studyMEASURE Satisfied (%)

Sept 301, 2007Satisfied (%) Dec 31, 2007

Satisfied (%) April 30, 2008

Coronary Heart Disease Beta blocker in MI 0.89 0.91 0.91 Antiplatelet drug 0.90 0.89 0.91 Lipid lowering drug 0.88 0.88 0.89 ACE inhibitor/ARB in DM or LVSD 0.84 0.84 0.85Heart Failure ACE inhibitor/ARB in LVSD 0.86 0.87 0.85 Anticoagulation in atrial fibrillation 0.63 0.64 0.72 Beta blocker in LVSD 0.83 0.84 0.85Hypertension control 0.76 0.75 0.76Diabetes Mellitus Blood pressure management 0.60 0.60 0.63 HbA1c control ( < 8) 0.63 0.65 0.64 LDL control 0.51 0.51 0.52 Aspirin for primary prevention 0.76 0.77 0.83 Nephropathy screening/management 0.81 0.82 0.83

Examples

Prevention Screening mammography 0.79 0.80 0.84 Cervical cancer screening 0.80 0.81 0.80 CRC screening 0.49 0.48 0.47 Pneumococcal vaccination 0.49 0.52 0.54 Osteoporosis screening or therapy

0.76 0.79 0.82

Changes in quality measures during the first 3 months of the study

MEASURE Satisfied (%) Sept 301, 2007

Satisfied (%)

Dec 31, 2007

Satisfied (%)

April 30, 2008

-20

-10

0

10

20

30

40

50

60

70

80

90

100

%

Aspirin for Primary Prevention in Diabetes

Physician Performance(most recent 3 months)

-20

-10

0

10

20

30

40

50

60

70

80

90

100

%Anticoagulation for Heart Failure with Atrial

Fibrillation

-20

-10

0

10

20

30

40

50

60

70

80

90

100

%

Cervical Cancer Screening

Why Didn’t the Patient Follow the Protocol?

• 167 patient reasons for not following advice for preventive service– 9 have resulted in patient having service

• 2 patients unable to afford medication

• 14 patients refused medication– 0 have started medication

Why Didn’t the Physician Follow the Protocol?

• 147 cases in which medical exceptions or modifiers were given– 132 appropriate on initial review– 5 discussed with another reviewer and judged

appropriate– 4 discussed with another reviewer and judged

inappropriate: feedback given– 6 reviewed with peer reviewer and expert and

recommended change in management

Clinical Decision Support Systems

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Clinical DSS Structure

Point-of-Care DSS–Alerts, reminders

Retrospective–What happened?

Prospective–What will happen?

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Where Does It Appear?

Organization of Data– “checklist effect”

Stand-Alone Expert Systems– often require redundant data entry

Data Repository: Mining

CDSS Integrated into Workflow– push information to the clinician at the point

of care– examples: EMR, CPOE

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The Revolutions in CDSS

Phase 1: Quality and safety of care– What is “good care”?– Did we provide good care?– Barely entering this phase now

Phase 2: Economics of care– What does good care cost?– Did we provide good care at the most effective cost?

Phase 3: Genomics of care– What are the genomic influences on good care?– Did we provide personalized, tailored care?

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Key Architectural Elements

Data capture/display/storage– EMR – central data repository

Controlled, structured vocabulary Knowledge representation (e.g., Arden) Knowledge acquisition Clinical event monitor: integrate the pieces

for many different uses (clinical, research, administrative)

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Foundation and Rationale for Decision Support Models Mathematics, mathematical models and

decision making Probability and statistics (Bayesian models) Rule-based decision-making

– IF the patient has symptoms A or B or C THEN

– Prescribe medication X and treatment Y and schedule next visit for T weeks

Data-driven models– Looks for patterns within a test set of data

and then generalize

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Justification for CDSS:Medical Errors

Estimated annual mortality:Air travel deaths 300AIDS 16,500Breast cancer 43,000Highway fatalities 43,500Preventable medical errors 44,000 -

(1 jet crash/day) 98,000

Costs of Preventable Medical Errors:$29 billion/year overall 1999 Institute of Medicine (IOM) Report

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Definitions: What is an error?

Error of execution: Failure of an action to be completed as planned

Error of planning: Use of a wrong plan to achieve an aim

Adverse event: An injury caused by medical management (and not the result of the patient’s condition)

Preventable adverse event: An adverse event attributable to error

Negligent adverse event: A preventable adverse event that satisfies criteria for malpractice

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Errors in Medicine

Hospital admissions: 2.9% (UT/CO, 1992) - 3.7% (NY, 1984) have an adverse event

Proportion of preventable adverse events: 53% (CO/UT) - 58% (NY)

Extrapolate to USA (33.6M admissions in 1997): 44,000 - 98,000 deaths

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Errors in Medicine

Types of adverse events (Harvard Medical Practice Study, 1991):–drug complications: 19%–wound infections: 14%– technical complications: 13%

50% associated with operations

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Clinical DSS: The Impact

Examined randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes.

CDSS improved practitioner performance in 62 (64%) of the 97 studies

JAMA. 2005;293:1223-1238.

62

Case Studies: Examples of CDSS Effectiveness

Perioperative Antibiotic Administration – intervention: reminder re timing and type of abx– period: 1988 - 1994– result: perioperative wound infections dec 1.8% ->

0.9%– avg # doses: 19 -> 5.3– overall antibiotic cost (constant $) per treated

patient: $123 -> $52

Pestotnik SL, Classen DC, Evans RS, Burke JP. Implementing antibiotic practice guidelines through computer-assisted decision support: clinical and financial outcomes. Ann Intern Med 1996;124(10):884-90.

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Examples (continued): Preventable ADEs

CPOE Implementation– Population: hospitalized patients over 4

years– Non-missed-dose medication error rate fell

81%– Potentially injurious errors fell 86%

Bates DW, Teich JM, Lee J. The impact of computerized physician order entry on medication error prevention. J Am Med Inform

Assoc 1999;6(4):313-21.

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Examples (continued)

Reminders of Redundant Test Ordering – intervention: reminder of recent lab result– result: reduction in hospital charges (13%) – Tierney WM, Miller ME, Overhage JM et al. Physician inpatient order writing on

microcomputer workstations. Effects on resource utilization.JAMA 1993;269(3):379-83.

Preventive Health Reminders in HIV– intervention: reminders to perform screening tests or

vaccination (e.g., pap smear, HBV)– result: sig decreased time to documentation (median = 11 vs

52 days)– Safran C, Rind DM, Davis RB et al. Guidelines for management of HIV infection

with computer-based patient's record. Lancet 1995;346(8971):341-6.

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Examples (continued) Systematic review

– 68 studies– 66% of 65 studies showed benefit on physician

performance• 9/15 drug dosing• 1/5 diagnostic aids• 14/19 preventive care• 19/26 other

– 6/14 studies showed benefit on patient outcome

Hunt DL, Haynes RB, Hanna SE et al. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA 1998;280(15):1339-46.

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Other CDSS Success Stories

Point-of-Care Decision Support– Bilirubin Management in neonates– Ventilator Management in ARDS– Coumadin Management– Glucose Management in the ICU– Antibiotic Assistant– Infectious Disease Monitoring

Medical Artificial Intelligence

Just Another Term For Decision Support

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Goals of AI

Study the thought processes of humans to better understand the complexity of human intelligence

Create computer systems which achieve human levels of reasoning

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Knowledge Representation Formalisms: Their Role

Express policies (institutional, national, international) in computable format

Formulate interventions in medical practice

Make local variations in guidelines

Provide “intelligence” to a clinical expert system

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Forms of Knowledge Representation

Bayesian/probabilistic = Decision Analysis Special Issues: Guidelines & GLIF (Guideline Interchange

Format) Case-based reasoning Ontologies Decision Tables Artificial Neural Networks Bayesian Belief Networks Procedural Production rules Arden Syntax

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Roots of Medical AI

MYCIN (late 1070s)– Shortliffe, et al, at Stanford– 1970s, infectious disease and antibiotic

therapies– Rules-based

PUFF (early 1980s)– Based on MYCIN– Pulmonary data interpretation

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Roots of Medical AI

APACHE (1981)– http://www.cerner.com/public/Cerner_3.asp?id=3562– Point of care in ICU

http://www.cerner.com/public/Cerner_3.asp?id=3562

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Computers Are Good At…

Computational functions - add, subtract, multiply, divide, compare– The most familiar

Symbolic reasoning

Pattern recognition

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The Arden Syntax

A symbolic language for encoding medical knowledge Adopted by HL7 and ANSI in 1999 Used to develop Medical Logic Modules (MLMs) Each MLM can make a single medical decision

– MLMs can be chained Can be used for variety of clinical decision support

functions– E.g., alerting physicians of potential kidney failure

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Arden Syntax: Assessment

Incorporated into several vendors’ products

Growing number of installation sites

Facile for simple alerts/reminders

May not be sufficiently expressive for complex guidelines

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Support for Arden Syntax

Institutions Cedars-Sinai Medical Center

Software Vendors Eclipsys/Healthvision McKesson Siemens

Knowledge Vendors Micromedex

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Arden Syntax - History

HELPLDS Hospital

Salt Lake City, UT

CARERegenstrief Institute

Indianapolis, IN

Arden Syntax1989

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Arden Syntax - Rationale

Arden Syntax arose from the need to make medical knowledge available for decision making at the point of care.

Allow knowledge sharing within and between institutions

Make medical knowledge and logic explicit

Standardize the way medical knowledge is integrated into hospital information systems

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Pattern Recognition

Objects, events or processes are described by their qualitative features, logical, and computational relationships

Examples– Computer matches pattern found in a new x-ray to

other cases to determine diagnosis– Searching text for context-based key words

• Spam filters

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Wikipedia

Based on either a priori knowledge or on statistical information extracted from the patterns

Sensor FeatureExtraction

ClassificationEngine

Training Set

Real Data

http://en.wikipedia.org/wiki/A_priori

http://en.wikipedia.org/wiki/Statistics

81

Other AI Methods

Genetic algorithms–Selection, recombination, mutation

Search algorithmsConstraint-based problem solving

–When conditions in variables are met, then execute

Frame-based reasoning

Frame Example

82

Arden Example83

JAMIA, Volume 19, Issue 4, 1 July 2012

84

In Summary

Enterprise Data Warehouses and Electronic Medical Records work hand-in-hand to address Clinical Decision Support

Artificial Intelligence has yet to prove itself scalable beyond informatics research projects

85

Thank You!

Questions and discussion?

© 2016 Health CatalystProprietary and Confidential

Healthcare Analytics Summit 16

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David F. Torchiana, M.D.President and CEOPartners HealthCareFormer Chairman and CEO of the Mass, General Physicians Organization

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Liz WisemanPresident, theWiseman GroupBestselling Author, Speaker & Executive Advisor“Rookie Smarts: Why Learning Beats Knowing in the New Game of Work”

Anne MilgramFormer NJ Attorney GeneralSenior Fellow at NYU School of Law, VP of Criminal Justice, Laura and John Arnold Foundation ““Criminal Justice Analytics”

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Taylor DavisVP, Analysis & Strategy, KLASAssociate Professor of Statistics of Utah David Eccles School of BusinessHealthcare Analytics Mkt Overview

Don Berwick, M.D.Former Administrator, CMSFounding CEO, Institute for Healthcare Improvement

Jay Bishoff, MDDirector, Intermountain Urological InstituteIntermountain HealthcareTop Rated HAS 15 speaker

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Salt Lake CitySept 6-8 2016

The Grand America Hotel

86