The document discusses the importance of data warehousing and analytics in clinical quality improvement and decision support, showcasing case studies and lessons learned from institutions like Northwestern University. Key points include the cultural commitment required for effective data utilization and the evolution of electronic data practices in healthcare. The presentation underscores the value of structured data management and the potential for improved clinical outcomes through timely information access.

1. Clinical Quality Improvement
Data Driven Decision Support
Guest Lecture for Health Information Science HINF 551
University of Victoria
Dale Sanders
312-695-8618
dsanders@northwestern.edu 1

2. Overview
• Data Warehousing & Analytics
• Clinical Quality Programs
• Clinical Decision Support
• Case Study Examples
– Intermountain Healthcare
– Northwestern University
• Lessons Learned

3. Acknowledgements
• Any success and knowledge that I enjoy on this topic is largely a
reflection of their association and support
– Much of the material contained herein is their labor, not mine
• My current informatics team and clinical champions
– Darren Kaiser, Mike Doyle, Andrew Winter, Rex Chisholm, Warren Kibbe
– Drs. Jim Schroeder, Abel Kho, David Baker, Steve Persell, David Liebovitz, Gary
Martin
• Colleagues and teammates from Intermountain Healthcare
– Steadfast clinician champions
– David Burton, Brent James
– My former data warehousing team
– Steve Barlow, Dan Lidgard, Kristine Mitchell, Chuck Lyon, Jonathan Despain
• Colleagues from the Healthcare Data Warehousing Association
– Jack Bates, Deb Alzner, Pat Taylor, Craig Own, Jonathan Einbinder, and others
• Professional colleagues from past professional lives
– Tom Robison, Mary Carter, Rob Carpenter, Rick Sorensen, Stan Smith, Terri
Parkinson, Ron Gault, Bob Bloss
• Specific slides from Dr. John Haugom & The Advisory Board Company
3

4. Northwestern University
Medical School Campus
• Feinberg School of
Medicine
• Northwestern
Memorial Hospital
• Northwestern
Medical Faculty
Foundation
• Children‟s Memorial
Chicago, Illinois
Hospital
4

5. Key Facts
• Feinberg School of Medicine
– 360 tenured faculty
– 370 graduates
– 3,000 full-time + contributed services faculty
– 290 National Institutes of Health Principle Investigators
– 65 grants in excess of $1M
– $852M annual revenue
• Northwestern Memorial Hospital
– Sole winner of National Quality Health Care Award in 2005
– $1.1B annual revenue
– 43,000 admissions
– 71,000 ER visits
– 744 beds
• Northwestern Medical Faculty Foundation
– 600 physicians, specialty focused
– 1100 employees
– 575,000 ambulatory clinic visits/year
– $500M annual revenue
5

6. Issues Related to…
DATA WAREHOUSING &
ANALYTICS
6

7. Data Warehousing:
The Library Metaphor
• Stores all of the books and other reference material you need to
conduct your research
– The Enterprise data warehouse
• A single place to visit
– One database environment
• Contents are kept current and refreshed
– Timely, well choreographed data loads
• Staffed with friendly, knowledgeable people that can help you find
your way around
– Data architects and analysts
• Organized for easy navigation and use
– Metadata
– Data models
– “User friendly” naming conventions
7

8. EDW: The Targeted Value Areas
The data required for…
Measurement, Trends, and Patterns
Business
Performance • Minimize the cost of operations
• Maximize the quality of care
• In the Minimum time required
Clinical
Quality &
Research Safety
Compliance &
Accreditation
8

9. The Healthcare Process
Simplified
Billing &
Billing and AR Claims Claims Processing
System
Accounts System
Processing
Receivable
Registration & Orders & Encounter Results & Patient
Diagnosis
Scheduling Procedures Documentation Outcomes Perception
•Diagnostics Surveys
•ADT System Diagnostic systems Pharmacy Electronic
•Pharmacy
•Master Patient Index •Lab System Medical Record
•Radiology
•Imaging
•Pathology
•Cardiology
•Others
9

10. Multiple, Collaborative Organizations
Hospital X
Billing &
Billing and AR Claims Claims Processing
System
Accounts System
Processing
Receivable
Registration &
Scheduling
Diagnosis
Orders &
Procedures
Encounter
Documentation
Results &
Outcomes
Patient
Perception EDW
A single data perspective
on the patient care process
•Diagnostics Surveys
•ADT System Diagnostic systems Pharmacy Electronic
•Pharmacy
•Master Patient Index •Lab System Medical Record
•Radiology
•Imaging
•Pathology
•Cardiology
•Others
Billing &
Billing and AR Claims Claims Processing
System
Accounts System
Processing
Receivable
Billing &
Billing and AR Claims Claims Processing
System
Accounts System
Processing
Receivable
Registration & Orders & Encounter Results & Patient
Diagnosis
Scheduling Procedures Documentation Outcomes Perception
Registration & Orders & Encounter Results & Patient
Diagnosis
Scheduling Procedures Documentation Outcomes Perception
•Diagnostics Surveys
•ADT System Diagnostic systems Pharmacy Electronic
•Pharmacy
•Master Patient Index •Lab System Medical Record
•Radiology •Diagnostics Surveys
•Imaging •ADT System Diagnostic systems Pharmacy Electronic
•Pharmacy
•Pathology •Master Patient Index •Lab System Medical Record
•Cardiology •Radiology
•Imaging
Hospital Y
•Others
•Pathology
Physician Office Z
•Cardiology
•Others
10

11. Why Should You Care
About A Data Warehouse?
Pay for Consumer Genetic medicine
Performance: No pressure on vs. clinical
data, no money “safe medicine” outcomes
Greater
understanding of IRS: Proof of
outcomes vs. non-profit status
medications Influences driving
healthcare towards
“measurement” and
analytics
Payer emphasis
Consumer driven
to drive down
choice for quality
costs
Malpractice Greater
Sarbanes-Oxley
litigation: emphasis on
and non-profit
Where’s the data driven
versions of same
proof? clinical research
11

12. Intangible Value of the Data Warehouse
• Increased grant funding
– The EDW tools and data will help attract grants
– Already seeing the effects of same in recent grants
– CTSA
– NUgene Genome Wide Association
– Electronic Notifications at Care Transitions
– Disease Ontology
• The best clinical faculty are attracted to good clinical data
– Good data = Good research opportunities
• Commercial value of clinical data
– Funding will be available from pharmas and commercial genomics companies
– As a tool to speed their drug trials and genomic discoveries
• Preferential negotiations with payers and employers
– Transparency of lower costs with higher clinical quality
– Negotiations will move faster towards conclusion, too
• Greater national recognition
– More grants = More published papers
• Philanthropic and/or commercial branding
– Of the EDW in total, or portions of it
12

13. Data Warehousing in Healthcare
• 1980s: Isolated research databases
– Not much electronic clinical data available
– Some text based clinical data (natural language processing)
– Heavily dependent on ICD9, CPT case mix and financial data
• 1990s: Coded, structured clinical data emerges
– The value of standardized clinical vocabularies to analytics, reporting, and decision
support becomes apparent
– Top tier academic and integrated delivery systems build first version data warehouses
based on coded clinical data (LOINC, SNOMED, et al)
• 2000s: Electronic Medical Records
– Electronic clinical data is now becoming more available
– “Now that we have this data, let‟s analyze it.”
• Current state: Quality, Cost, and Translational Research
– Cultural economic emphasis on faster, better, cheaper healthcare
– Data warehousing now “the second highest IT priority” (Gartner) among medium-to-
large healthcare organizations
13

14. Issues Related to…
ANALYTIC CONCEPTS &
STRATEGY
14

15. Books to Read
• Books to read which capture the vision
– “Competing on Analytics: the New Science of Winning”
– Tom Davenport, Harvard Business School
– “Super Crunchers: Why Thinking By Numbers is the New Way to
be Smart”
– Ian Ayres, Yale Law School
– “The Wisdom of Crowds: Why the Many Are Smarter Than the Few
and How Collective Wisdom Shapes Business, Economies,
Societies and Nations”
– James Surowiecki
– “Nudge: Improving Decisions About Health, Wealth, and
Happiness”
– Richard H. Thaler
– “Programming Collective Intelligence”
– Toby Segaran, MIT
15

16. Sanders’ Hierarchy of Analytic Maturity
• Basic business reporting Increasing Maturity
– 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.”
16

17. Mean Time To Improvement
• On average, how long does it take an organization to Improve?
– What is your cultural MTTI?
• Healthcare
– MTTI is measured in years, sometimes decades
– 17 years passed before the “standard” clinical protocol for CAP to be
commonly practiced
• Examples of low MTTI cultures
– Amazon.com; Intel; WalMart; GE; Black & Decker
– MTTI measured in weeks and days
– Dramatic change in recent years: Microsoft
• What drives down MTTI?
– Evidence of a better way
– Cultural commitment to act
– Constant discontent with the status quo
17

18. Mean Time To Improvement
COMPUTERIZED WORKFLOW-BASED, ANALYTICS-BASED RECOGNITION:
BUSINESS OR WORKFLOW ALERTS TRANSACTION INFORMATION OPPORTUNITY FOR
CLINICAL PROCESS AND EMBEDDED INFORMATION SYSTEMS QUALITY
DECISION SUPPORT SYSTEMS (BI AND DW) IMPROVEMENT
ACTION TAKEN:
PROCESS AND
QUALITY
IMPROVEMENT
Mean Time To “Influence” (MTTI)
Goal: Squeeze MTTI as close to zero as possible
18

19. Examples of Clinical Goals
• Decrease the total number of • 100% compliance to post-surgery
nulliparous elective inductions with radiation therapy protocols for
a Bishop Score <10 by 50% breast cancer cases with >4
positive nodes and tumor size
• Keep the variable cost increase of
>=5cm
deliveries without complications
resulting in normal newborns to • Compliance with the timing of
5.73% for 2003 administration of Pre-surgical
Prophylactic Antibiotic Usage will
• For all adult patients with diabetes,
exceed 91%
increase the percent of patients with
LDL less than 100 to >=45.5%. • For patients being treated for
(Currently 44.5%) depression, increase the
percentage continuing on
• Measured glucose values will be
prescribed antidepressant for 6
between 60 and 155 mg/dl 80% of
months after filling first prescription
the time for all ICU patients
to >=44.6%
19

20. DOQ-IT/PQRI Examples
20

21. Vertical and Horizontal Strategy
Neurology
Women’s Health
Step One:
Intensive Medicine
Clinical Excellence
Programs Cardiology
Oncology
Materials Mgt
Registration
Admissions
Radiology
Pharmacy
Nursing
AR/AP
Lab
Step Two: Operational Excellence Programs
21

22. The Advisory Board

23. The Advisory Board

24. The Advisory Board

25. The Advisory Board

26. The Advisory Board

27. Measuring Data Quality
• Data Quality = Completeness x Validity
– Can it be measured objectively?
• Measuring “Completeness”
– Number of null values in a column
• Measuring “Validity”
– Cardinality is a simple way to measure validity
– “We only have four standard regions in the business, but
we have 18 distinct values in the region column.”
27

28. Structured vs. Unstructured Data
• Structured,
discrete data
Computer Analytic Value
Frustrated here…
Comfortable here…
• Text
• Recorded
Audio
• Face-to-Face
Audio
• Video
Representation of Human Experience
& Knowledge
28

29. Lessons Learned and…
KEY MESSAGES ON
ANALYTICS
29

30. Key Messages
• No matter what they tell you, a vendor cannot provide an
“enterprise” data warehouse solution out of the box
– They must be custom built, but they don‟t have to be built from scratch
• Mistakes are costly and the root causes are subtle
– Mistakes emerge insidiously and late in the lifecycle of data warehouses,
when they are the most costly to repair
• It‟s easy to avoid the common causes of failure in data
warehousing, if you have made them before
– Unlike EMR/EHR deployments, where making the same mistake
over and over is sometimes hard to avoid
– Collaborate with the growing membership of the Healthcare Data
Warehousing Association (www.hdwa.org)
– No membership fees
30

31. Key Messages
• Your data warehouse will only be as good as the
source systems which supply it
– Don‟t put the cart before the horse
• Technology is only half of the equation
– Culture is the other half
– The ROI from an EDW comes from a cultural willingness to use the
tool
– To drive down costs and improve quality
– Your organization must be committed to continuous quality
improvement
– Otherwise, the IT of EDW is a lost investment
31

32. Cultural Lessons
• Overprotecting access to the data
– The most secure, least accessible data is also the most useless
– Trust your data analysts who have access, but verify with audits
• Data warehouse staff that can‟t work with customers
– You need a librarian‟s personality and skill set
– But they also need to be technical
• Assuming an EDW is going to solve your business problems
– It‟s only a tool
– Must be deployed with a process improvement culture
• Failing to hire adequate numbers of skilled data analysts
– Like building a library in an illiterate community
32

33. Cultural Lessons
• Trying to justify an EDW with a traditional ROI mindset
– What‟s the ROI of a library in an academic medical center?
– What‟s the ROI of your telephone system?
• Trying to build an EDW using traditional software project
management methodologies
– Waterfall development techniques don‟t apply
– In-depth requirements analysis and use cases are a waste of time and
money
33

34. Data Content & Structure Lessons
• Blindly believing that star schemas are the solution to everything
– Star schemas are terrible for many of today‟s data analysis problems
– They can be useful, but use them sparingly and with caution
– Analysts love flat tables with lots of rows and columns– there‟s a reason Excel is so
popular
• Trying to “clean” data from the source systems before it is loaded into
the data warehouse
– For example, forcing your data to align with national standards, such as SNOMED,
when the source systems don‟t align themselves
– A never ending battle
– Push accountability where it belongs
• Standardizing the names of data structures for the sake of
standardization
– Making significant changes to the names and structure of data supplied by the source
system
– You will lose data familiarity with stewards who have used the data for years with the
old naming conventions
34

35. Data Content & Structure Lessons
• Failing to recognize that changes will be required in your source
systems to support your data warehouse and analytics strategy
– You will find that the data being collected in source systems is not optimized to support
analytics
– You will need to change human processes associated with data collection
– You will need to change the way applications are written to collect data in the source
systems
• Very poor or no metadata repository
– Where would the telephone be without the Yellow Pages?
– Maybe the most overlooked, underestimated aspect of a data warehouse
– Quite often considered a luxury item… it‟s not!
• Overly complex security roles
– Trying to be too granular with roles
– Which actually leads to greater insecurity and risk
• Sacrificing joins to minimize redundant data and storage space
– Storage is cheap
– Joins are expensive to CPUs and to data analysts
35

36. Data Content & Structure Lessons
• The importance of master data management
– For front-end data collection
– For back-end data validation and analysis
• When in doubt, extract more data, not less
– Even if you don‟t think you‟ll need the data, extract it from the source system anyway
– Chances are, you will eventually find a need for nearly all collected data
• Under appreciating the role of Data Stewards
– Formally assigning, by name, a Data Steward for each data content area
– They can assist with proper use of the data (training) as well as data quality ownership
• Assuming that all analysis must pass through a tool like Cognos or
BusinessObjects
– Those tools will appeal to a certain demographic customer, but not all
– Allow direct access to tables for sophisticated analysts… What‟s the worst that can
happen?
– Accommodate the needs of Designers, Drillers, and Clickers
36

37. Data Content & Structure Lessons
• Clinical vocabulary standards
– Failing to balance something better against the pursuit of perfection
– Many healthcare organizations, especially academics, look constantly for
the perfect vocabulary tool or semantic model, like UMLS
– In the meantime, they can‟t even managed basic terms and content around
the most fundamental issues like Patient Identity and Provider Identity
• “The art of being wise is knowing what to overlook.”
– William James, Principles of Psychology, 1890
37

38. The Case For Timely Updates
Generally, to minimize Total Cost of Ownership (TCO), your update frequency
should be no greater than the decision making cycle associated with the data.
But… everyone wants more timely data.
100
% Requests for Data
utilization
0
Today 1 year 2 years
Data Age
38

39. In Summary
• In the absence of culturally-driven process improvement, data
warehouses are simply costly IT investments with no value
• The future of data warehousing is at the frontend of the care
delivery model, affecting what‟s happening
– Not at the backend of reporting, wondering what happened
• Data warehouses are relatively simple and safe to build
– Despite their high failure rates
– Look around… ask for advice… and stop reading Kimball, Inmon, and Imhoff 
39

40. Role of the Enterprise Data Warehouse in…
CLINICAL DECISION
SUPPORT
40

41. Medical Evidence Overload?
• Medline, alone…
– 4,500 journals in 30 languages
– 11.7 million citations
– Growth rate: 400,00 per year
• No time to read
– “As a general practitioner, how many hours per week do you have time to
read to stay current in your profession?”
– ½ hour or less per week: 3%
– 1 hour: 46%
– 1 ½ hours: 23%
– 2 hours: 20%
– 3+ hours: 8%
• We need to deliver evidence at the point of care
– Embedded smoothly in the clinicians‟ workflow
41

42. Decision Support Interventions
From an acute To a health
Illness enterprise improvement system
Overall Quality of Care
Complexity & Investment
Population-
based care
Disease
management
Managing
episodes of care
“Random acts
of clinical
improvement”
Time
Dr. John Haugom
42

43. What is Clinical Decision Support?
A workflow view
• Synchronous
– Real-time pop-ups, dialog boxes & advisories
– Disruptive of workflow
– Used only for high-value/high-risk situations
• Asynchronous
– Not real-time; usually is feedback after a decision is made
– Can be as simple as a report
• Blended
– Semi-immediate, like an e-mail
– Inbox population from background surveillance
Dr. John Haugom
43

44. CDS Intervention Types
• Forms and templates
• Relevant data presentation
• Proactive order suggestions and order sets
• Support for guidelines, complex protocols,
algorithms, clinical pathways
• Reference information and guidance
• Reactive alerts (i.e., unsolicited by patient or clinician recipient)
Dr. John Haugom
44

45. Decision Support Interventions
A Workflow View
All Decision Support
Synchronous Asynchronous
Real-time •Defaults •E-mail •Population •Scorecards
alerts •Menus •Inbox Surveillance/ •Population
•Embedded infor- HMA‟s Reports
Mation ( •Chronic Care
•Order sets & Protocols
protocols) •Order/Referral
•Clinician access Follow-up
to content (“pull”)
CDSS HIS/EMR Data Mining
Increasing Immediacy
Dr. John Haugom
45

46. Examples
Changes in quality measures during the first 3 months of the study
MEASURE Satisfied (%) Satisfied (%) Satisfied (%)
Sept 301, 2007 Dec 31, 2007 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 0.85
Heart 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 0.85
Hypertension control 0.76 0.75 0.76
Diabetes 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

47. Changes in quality measures during the first 3 months of the study
MEASURE Satisfied Satisfied Satisfied
(%) (%) (%)
Sept Dec 31, April 30,
301, 2007 2008
2007
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 0.76 0.79 0.82
therapy

48. Physician Performance
(most recent 3 months)
Aspirin for Primary Prevention in Diabetes
100
90
80
70
60
50
%
40
30
20
10
0
-10
-20

49. Anticoagulation for Heart Failure with Atrial
Fibrillation
100
90
80
70
60
50
%
40
30
20
10
0
-10
-20

50. Cervical Cancer Screening
100
90
80
70
60
50
40
%
30
20
10
0
-10
-20

51. 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

52. 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

53. 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.”
• Quality of care data
– “Tell me how satisfied patients were with these treatment options.”
53

54. Case Study Examples
INTERMOUNTAIN
HEALTHCARE
54

55. 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
55

56. Big Picture
• Two forms of data driven quality improvement
– Point of care clinical decision support
– Population and process improvement
56

57. Point of Care Decision Support
Examples and anecdotes
• Antibiotic Assistant
• ICU Glucose Manager
• MRSA/VRE Alerting System
• ARDS Vent Weaning Protocols
• Drug-Drug Interaction Alerts
57

58. The Antibiotic Assistant
• Balancing quality and cost at the point of
care
Antibiotic Dosage Route Interval Predicted Average
Protocol Efficacy Cost/Patie
nt
Option 1 500mg IV Q12 98% $7,256
Option 2 300mg IV Q24 96% $1,236
Option 3 40mg IV Q6 90% $1,759
…
Option 10
58

59. The Antibiotic Assistant Impact
• Outcomes improved 47%
• Avg # doses declined from 19 -> 5.3
• The replicable and bigger story
– Antibiotic cost per treated patient: $123 -> $52
– By simply displaying the cost to physicians
– Information Technology created the
illusion and benefits of First Order
Economics…!
59

60. General Lessons
• These specific examples of decision support are not
extensible or possible for smaller, less IT capable
organizations
– Teams of MDs and PhDs build and maintain these
systems
– Vendors have not been successful in making
these systems possible for smaller organizations
• Drug-Drug Interaction alerts have generally been a
failure
– Many organizations turn them off completely
60

61. Diabetes CPM:
Key Indicators
Measure Goal
HbA1c (test at least 2 times a <7.0%
year)
Blood Pressure <130/80 mm
(check at each office visit) Hg
LDL Cholesterol <100 mg/dL
(test at least every 2 years)
Triglycerides <150 mg/dL
(test at least every 2 years)
Foot Exam (perform at least normal
annually)
Urine Microalbumin/Creatinine <30
Ratio (test at least annually)
Dilated Eye Exam (check normal
annually,
or every 2 years if well
controlled)
Intermountain Healthcare, Steve Barlow
61

62. Case Study: Diabetes Management
62

63. Case Study: Diabetes Management
63

64. Diabetes Management Peer Comparison Chart
64

65. Case Study: Asthma
• Primary Care: Asthma
– Motive: Increase controller medication use
– Reduce Asthma related ER visits
– Source of data: Health Plans Claims and
ER records
65

66. Case Study: Asthma
66

67. Case Study: Asthma
67

68. Case Study: Asthma
68

69. Asthma Peer Comparison Report
69

70. 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)
70

71. Case Study: CV Discharge Meds
71

72. Case Study: CV Discharge Meds
72

73. The Tangible Benefits
From Intermountain’s
Cardiovascular Clinical
Program
73

74. Case Study
• Labor and Delivery - Elective Inductions
– Current Care Process Goals: Continued Clinical
Program Focus
– 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.
Intermountain Healthcare, Steve Barlow
74

75. Percent <39 Weeks
19
99
0%
5%
10%
15%
20%
25%
30%
35%
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Intermountain Healthcare, Steve Barlow
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75

76. Elective Inductions
Estimated Variable Cost Savings From Elective Induction Protocol
Intermountain Healthcare 2001-2005
$700,000 $1,600,000
$597,367 $1,400,000
$600,000
$1,200,000
Cumulative Variable Cost Savings
$500,000
Variable Cost Savings
$1,000,000
$400,000 $380,833
$800,000
$300,000
$600,000
$207,772
$188,606
$200,000
$400,000
$100,000
$200,000
$26,479
$- $-
2001 2002 2003 2004 2005
Year
Yearly Savings Cumulative Savings
Intermountain Healthcare, Steve Barlow 76

77. So far, so good…
NORTHWESTERN’S DATA
WAREHOUSE
77

78. Northwestern
• 2.1 billion clinical data points
• 1.9 million patients
Northwestern Medicine
Enterprise Data Warehouse
(EDW)
Hospital Research
Data Data
Clinic
Data
78

80. 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

81. Early Adopters of the EDW
Customer 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.
81

82. Specific Example
Rapid turnaround (<2 days) to meet a grant submission deadline…
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
82

83. 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 an NMFF office visit within the last 120 days?
– 'KADISH, ALAN H.'
– 'GOLDBERGER, JEFFREY J.'
– 'PASSMAN, ROD S.'
– 'DENES, PABLO'
– 'JACOBSON, JASON„
– Answer: 309
83

85. The High Clinical and Research Value of…
DISEASE REGISTRIES
85

86. Disease Registry
A database designed to collect information about the occurrence
and incidence of a particular disease, and for which, the
inclusion criteria are defined in such a manner that minimizes
variability within the included cohort.
“Computer Applications used to capture, manage, and provide
information on specific conditions to support organized care
management of patients with chronic disease.”
--”Using Computerized Registries in Chronic Disease Care”;
California Healthcare Foundation and First Consulting Group, 2004.
86

87. History of Disease Registries
• Historically, the term implies stand-alone, specialized
products and clinical databases
– Our premise: No more stand alone registries
– They must be integrated within an overall EMR and Data
Warehouse strategy
• Pioneered by GroupHealth of Puget Sound in the
early 1980s for diseases other than cancer
– “Clinically related information system”
• Long precedence of use and effectiveness in Cancer
– 1926: First cancer registry at Yale-New Haven hospital
– 1935: First state, centralized cancer registry in Connecticut
– 1973: Surveillance, Epidemiology, and End Results (SEER) program of National
Cancer Institute, first national cancer registry
– 1993: Most states pass laws requiring cancer registries
87

88. Use Cases for Disease Registries
Disease registries can drive...
• Consistent profiling for prospective, predictive intervention
– The goal is to keep people off of disease registries, but first you have to know how those who are on
the registry, got there…
• Best practice guidelines within the EMR
– Guideline-based intervals for tests, follow-ups, referrals
– Interventions that are overdue
– “Recommend next HbA1C testing at 90 days because patient is not at goal for glucose control.”
• Outreach communication to patients
– Reminders about care and intervention
• Consistent patient education for all members on the disease registry
• Quality of care reporting to payers and employers
• Feedback reports to physicians about their care practices
• Population reporting and analysis for research
• Process improvement projects for service line clinical programs
88

89. Disease Registries Data
• How do we define a particular disease?
SCHEDULING
• Who has the disease?
• What is their demographic profile?
REGISTRATION
MORTALITY
PATH
* DISEASE MANAGEMENT
* OUTCOMES ANALYSIS
TUMOR REG * RESEARCH
* P4P REPORTING
* CLINICAL TRIALS ENROLLMENT
RAD RESULTS
LAB RESULTS
INCLUSION
CRITERIA & COSTS &
DISEASE
MEDICATIONS STRUCTURED REIMBURSEMENT
REGISTRY
EXCLUSION DATA
CODES
ICD9 CODES
CPT CODES
PATIENT
PROVIDER
CLINICAL OBS RELATIONSHIP
PROBLEM
LIST
PATIENT • Are we managing these patients according to
VALIDATION accepted best protocols?
CLINICIAN • Which patients had the best outcomes and why?
VALIDATION • Where is the optimal point of cost vs. outcome?
CARDIOLOGY
IMAGING
89

90. For Example: Heart Failure
• Inclusion codes based entirely on ICD9, which is a good place to start,
but not specific enough
– Heart failure codes for study inclusion
– 398.91, 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93,
428.xx
– Exclusion criteria for beta blocker use†
– Heart block, second or third degree: 426.0, 426.12, 426.13, 426.7
– Bradycardia: 427.81, 427.89, 337.0
– Hypotension: 458.xx
– Asthma, COPD: see above
– Alzheimer's disease: 331.0
– Metastatic cancer: 196.2, 196.3, 196.5, 196.9, 197.3, 197.7, 198.1, 198.81,
198.82, 199.0, 259.2, 363.14, 785.6, V23.5-V23.9
– † Exclusion criteria were only assessed for patients who did not have a medication
prescribed; Thus, if a patient was prescribed a medication but had an exclusion criteria,
the patient was included in the numerator and the denominator of the performance
measure. If a patient was not prescribed a medication and met one or more of the
exclusion criteria, the patient was removed from both the numerator and the
denominator.
Acknowledgements to Dr. David Baker, NU Feinberg School of Medicine
90

91. Disease Registry Exclusions
• The industry will need standard vocabularies for excluding patients
– Removing patients from the registry whose data would otherwise skew the data profile
of the cohort
• “Why should this patient be excluded from this registry, even though
they appear to meet the inclusion criteria?”
– Patient has a conflicting clinical condition
– Patient has a conflicting genetic condition
– Patient is deceased
– Patient is no long under the care of this facility or physician
– Patient is voluntarily non-compliant with the care protocol
– Patient is incapable of complying with the care protocol
• You can see that the exclusion criteria imply a connection between a
patient‟s inclusion and their managed care
– This might not be true in all cases, e.g., research
91

92. Large n Disease Registries
• Asthma • HIV
• Breast cancer • Hypertension
• Cataracts • Lower back pain
• Chronic lymphocytic leukemia • Systemic Lupus
• Chronic obstructive pulmonary • Macular degeneration
disease • Major depression
• Colorectal cancer • Migraines
• Community acquired bacterial • MRSA/VRE
pneumonia • Multiple myeloma
• Myelodysplastic syndrome & acute leukemia
• Coronary artery bypass graft • Myocardial infarction
• Coronary artery disease • Obesity
• Coumadin management • Osteoporosis
• Diabetes • Ovarian cancer
• End stage renal • Preoperative antibiotic prophylaxis
• Prostate cancer
• Gastroesophageal reflux disease • Rheumatoid Arthritis
• Glaucoma • Sickle Cell
• Heart failure • Upper respiratory infection (3-18 years)
• Stroke (Hemorrhagic and/or • Urinary incontinence (women over 65)
Ischemic) • Venous thromboembolism prophylaxis
• High risk pregnancy
92

93. Small n Disease Registries
• More and more, rare diseases will attract emphasis
from research, pharmas, payers, and families
• We can plan our disease registry strategy now
• For example…
– Amyotrophic Lateral Sclerosis
– Alzheimer's
– Hemophilia
– Hodgkin's Disease
– Rett Syndrome
– Scleroderma
93

94. Thank You!
• Questions?
Dale Sanders
dsanders@northwestern.edu
312.695.8618
94