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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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Enterprise Data Warehousing
14
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
Diagnostic 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
DiagnosisRegistration &Scheduling
PatientPerception
Orders & Procedures
Results & Outcomes
Billing &Accounts
Receivable
Claims Processing
EncounterDocumentation
•ADT System•Master Patient Index•ADT System•Master Patient Index
PharmacyPharmacy ElectronicMedical Record
ElectronicMedical Record
SurveysSurveys•Diagnostics•Pharmacy•Diagnostics•Pharmacy
Billing and ARSystem
Billing and ARSystem
Claims ProcessingSystem
Claims ProcessingSystem
Diagnostic 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
Diagnostic 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
DiagnosisRegistration &Scheduling
PatientPerception
Orders & Procedures
Results & Outcomes
Billing &Accounts
Receivable
Claims Processing
EncounterDocumentation
•ADT System•Master Patient Index•ADT System•Master Patient Index
PharmacyPharmacy ElectronicMedical Record
ElectronicMedical Record
SurveysSurveys•Diagnostics•Pharmacy•Diagnostics•Pharmacy
Billing and ARSystem
Billing and ARSystem
Claims ProcessingSystem
Claims ProcessingSystem
Diagnostic 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
Diagnostic 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
DiagnosisRegistration &Scheduling
PatientPerception
Orders & Procedures
Results & Outcomes
Billing &Accounts
Receivable
Claims Processing
EncounterDocumentation
•ADT System•Master Patient Index•ADT System•Master Patient Index
PharmacyPharmacy ElectronicMedical Record
ElectronicMedical Record
SurveysSurveys•Diagnostics•Pharmacy•Diagnostics•Pharmacy
Billing and ARSystem
Billing and ARSystem
Claims ProcessingSystem
Claims ProcessingSystem
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
The Advisory Board Company
The Advisory Board Company
The Advisory Board Company
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.
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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.
66
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
69
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
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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
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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
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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
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Thank You!
Questions and discussion?
© 2016 Health CatalystProprietary and Confidential
Healthcare Analytics Summit 16
Here’s a sneak preview …
David F. Torchiana, M.D.President and CEOPartners HealthCareFormer Chairman and CEO of the Mass, General Physicians Organization
Summit highlightsJam-Packed Agenda, all focused on outcomes67 Sessions and stories 8 keynotes, 27 breakouts, 32 Analytics WalkaboutsIndustry Leading Keynote SpeakersWe’ll hear from well-known healthcare industry champions. And by popular demand, we've invited back two of our top-rated speakers.CME Accreditation for CliniciansLast year’s HAS 15 summit was awarded 24.0 AMA PRA Category 1 Credits ™ and we expect a similar number this year.
Improved "How-to" Case Study SessionsWe’ve increased the breakout session times to give more time for detailed “how to” learning while also extending the Q&A time.
The Analytics WalkaboutBack by popular demand, we will feature 32 new projects highlighting a variety of additional clinical, financial, operational, and workflow analytics outcomes improvement successes.
Analytics-Driven, Hands-on Engagement for TeamsAnalytics will continue to flow through the three-day summit touching every aspect of the agenda.
Networking and FunWe will have longer breaks, frequent fun-run/walk opportunities, a night on the town, and some fun and games, including your favorite retro arcade games.
Pre-Summit Classes and TrainingAn early half-day of pre-session classes and training options specifically for Health Catalyst clients.
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”
Eric Siegel, Ph.D.President, Prediction Impact, Inc.Best Selling Author and Founder of Predictive Analytics World“The Power to Predict Who Will Click, Buy, Lie, or Die”
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
Toby Freier, FACHEPresident, New Ulm Medical CenterHearts Beat Back ™Heart of the New Ulm ProjectHAS 16 Documentary
Salt Lake CitySept 6-8 2016
The Grand America Hotel
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