1. The Faculty Practice Plan of Northwestern’s Feinberg School of Medicine
Users’ Group Meeting
September 20, 2007
Disease Registries: Translating P4P and other Quality
Measures into EpicCare

2. Agenda
 NMFF Overview
 Epic Implementation History
 Patient Registries History and Overview
 Our Experience building a Disease Registry
 After the Build
 Lessons learned
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3. Chicago Shoreline
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4. Feinberg Pavilion (Inpatients) & Ambulatory Care Center
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5. Prentice Women’s Hospital: Opening October 2007
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6. Our Mission
Northwestern Medical Faculty Foundation is the
regionally and nationally recognized physician group
at the Feinberg School of Medicine, Northwestern
University. Our physicians and staff use innovative
clinical practices and technology and a
multidisciplinary approach to provide optimal patient
care and service. We support the clinical and
academic activities of the Feinberg School and
create an environment where the best medical
practices are demonstrated and learned.
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7. NMFF Overview
About Northwestern Medical Faculty Foundation (NMFF)
 Private, independent academic faculty practice plan
founded in 1980
 Multi-specialty group practice for over 600 member
physicians who are all full-time faculty of Northwestern
University’s Feinberg School of Medicine (NU FSM)
 Physician led
 Not for profit
 Provides care for indigent patients
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8. NMFF Overview
Northwestern Medical Faculty Foundation Facts
 Over 600 physicians and 1,101 staff at the end of FY06
 17 departments, 34 specialties
 Just under 571,000 outpatient encounters in FY06
 Total clinical revenue $346 million in FY06
 We occupy roughly 268,000 square feet of clinical space
for outpatient care in the Ambulatory Care Center (ACC)
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9. NMFF Overview
Relationship to Northwestern University Feinberg
School Of Medicine (NU FSM)
 NMFF members are full-time NU FSM faculty
• Clinical care of patients
• Ground breaking clinical research
• Training next generation of physicians
 Faculty for over 600 medical students and 500 residents
and fellows (11,000 hours of teaching)
 NMFF’s Ambulatory Care Center provides the venue for
outpatient teaching and clinical research
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10. Epic Implementation History
Epic Implementation History
• Pilot: 1996 NLM Project to Pilot EpicCare in GIM
• Awards: 1998 Davies Award Winner
• Implementation in 31 specialty practices (2001 – 2006)
• 97% Implemented in 32 specialties (2007)
• Non-Implemented Specialties
• Reproductive Endocrinology and Infertility
• Ophthalmology
• Trauma/Critical Care
• Epic Products: Bridges, Clarity, EpicCare, Identity, MyChart
• Epic Version: Epic Fall 2006 version (Spring 2007 IU1 upgrade
scheduled for October 2007)
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11. Epic Implementation History
EpicCare Implementations in Specialty & Sub-Specialty Areas
 Allergy  Lynn Sage Breast Center
 Anesthesia/Pain Medicine  Maternal Fetal Medicine
 Cardiology  Nephrology
 CardioThoracic Surgery  NeuroBehavior
 Dermatology  NeuroSurgery
 Endocrine/Metabolism  Northwestern Ovarian Cancer Early Detection Program
 Gastroenterology  Orthopedics/Sports Medicine
 General Internal Medicine*  Otolaryngology
 General Neurology  Plastics Surgery
 General OB/GYN  Psychiatry
 Geriatrics  Pulmonary
 GI-Endocrine Surgery  Reproductive Genetics
 Gynecology Oncology  Rheumatology
 Gynecologic Surgery  Surgical Oncology
 Hematology Oncology  Travel Medicine/Immunizations
 Hepatology  UroGynecology
 Immunotherapy  Urology*
 Interventional Radiology  Vascular Surgery
*MyChart Department 11

12. Agenda
 NMFF Overview
 Epic Implementation History
 Patient Registries History and Overview
 Our Experience building a Disease Registry
 After the Build
 Lessons learned
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13. Patient Registries History & Overview
Patient Registry Definitions
“A database designed to store and analyze information about the
occurrence and incidence of a particular disease, procedure,
event, device, or medication and for which, the inclusion criteria
are defined in such a manner that minimizes variability and
maximizes precision of inclusion within the cohort.”
--- Dale Sanders, Northwestern University Medical Informatics Faculty, 2005
“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.
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14. Patient Registries History & Overview
AHRQ’s Patient Registry Definition
 “A patient registry is an organized system that uses
observational study methods to collect uniform data (clinical
and other) to evaluate specified outcomes for a population
defined by a particular disease, condition, or exposure and
that serves one or more predetermined scientific, clinical, or
policy purposes.”
 The National Committee on Vital and Health Statistics
describes registries used for a broad range of purposes in
public health and medicine as "an organized system for the
collection, storage, retrieval, analysis, and dissemination of
information on individual persons who have either a
particular disease, a condition (e.g., a risk factor) that
predisposes [them] to the occurrence of a health-related
event, or prior exposure to substances (or circumstances)
known or suspected to cause adverse health effects."
http://effectivehealthcare.ahrq.gov/reports/registry/registry.htm
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15. Patient Registries History & Overview
History of Patient Registries
 Historically, the term implies stand-alone, specialized products and
clinical databases
 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
 Pioneered by GroupHealth of Puget Sound in the early 1980s for
diseases other than cancer
• “Clinically related information system”
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16. Patient Registries History & Overview
Types of Registries
 Product Registries
• Patients exposed to a health care product, such as a drug or a device.
 Health Services Registries
• Patients by clinical encounters such as
– Office visits
– Hospitalizations
– Procedures
– Full episodes of care
 Referring Physician Registry
• Facilitates coordination of care
 Primary Care Physician Registry
• Facilitates coordination of care
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17. Patient Registries History & Overview
Types of Registries
 Scheduling Events Registry
• Facilitates analysis for Patient Relationship Management (PRM)
• Can drive reminders for research and standards of care protocols
 Mortality registry
• An important thing to know about your patients
 Research Patient Registry
• Clinical Trials
• Consent
 Disease or Condition Registries
• Disease or condition registries use the state of a particular disease or
condition as the inclusion criterion.
 Combinations
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18. Patient Registries History & Overview
Varying Benefits
How do I analyze patient
trends and outcomes for
a disease?
Clinicians
How are my clinicians
managing diseases?
Physician Organization Registries Consumer
How do I know which
How does my drug drug/procedure works
perform in disease best for me?
prevention and cure?
Drug Manufacturer
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19. Patient Registries History & Overview
Uses for Patient Registries
 To observe the course of disease
 To understand variations in treatment and outcomes
 To examine factors that influence prognosis and quality of life
 To describe care patterns, including appropriateness of care and
disparities in the delivery of care
 To assess effectiveness
 To monitor safety
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20. Patient Registries History & Overview
Current Trends measuring Quality using Registries
 The IOM defines quality as “the degree to which health services for
individuals and populations increase the likelihood of desired health
outcomes and are consistent with current professional knowledge.”
 Quality-focused registries are being used increasingly to assess
differences between providers or patient populations based on
performance measures that compare:
• Treatments provided or outcomes achieved with “gold standards” (e.g.,
evidence-based guidelines)
• Comparative benchmarks for specific health outcomes (e.g., risk-
adjusted survival or infection rates)
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21. Quality Management Reporting - Example Patient Registries History & Overview
Eligible Satisfied Rate
Preventive Services
Cervical Cancer Screen 223 146 65%
Mammogram 138 83 60%
Colorectal Cancer Screen 355 143 40%
Pneumonia Vaccine 144 33 23%
Osteoporosis Screened or on Treatment 75 44 59%
Cardiovascular Disease
HTN: good BP control (mean or last <= 140/90) 310 196 63%
CAD: antiplatelet medication 62 54 87%
CAD: lipid lowering medication 65 54 83%
CAD: Beta blocker post-MI 12 10 83%
CAD: ACE/ARB if DM or LVSD + CAD 25 19 76%
CHF: anticoagulation for AF + HF 6 5 83%
CHF: ACE/ARB if LVSD 3 3 100%
CHF: beta blocker if LVSD 3 3 100%
Diabetes
Last Hba1c <= 7 87 37 43%
Last Hba1c <= 9 87 66 76%
Good BP control (mean or last BP <= 130/80) 83 39 47%
Good LDL control (<100) 87 49 56%
Nephropathy: screened or on ACE/ARB 87 64 74%
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22. Patient Registries History & Overview
Getting the most out of your disease registry (Our Interpretation)
 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…
 Outreach communication to patients
• Reminders about care and intervention
 Ensuring a common understanding for inclusion, exclusion and disease
management.
 Quality of care reporting (e.g. P4P)
• Cost effective & treatment efficacy to payers & employers
• Feedback reports to physicians about their care practices
 Process improvement projects for service line clinical programs
• Use trend analysis to find possible process deficiencies that affect patient
care
 Population reporting and analysis for research (e.g. Epidemiology)
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23. Agenda
 NMFF Overview
 Epic Implementation History
 Patient Registries History and Overview
 Our Experience building a Disease Registry
 After the Build
 Lessons learned
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24. Target Disease Registries Our Experience Building a Disease Registry
– Amyotrophic Lateral Sclerosis – Hypertension
– Alzheimer's – Lower back pain
– Asthma – Systemic Lupus
– Breast cancer – Macular degeneration
– Cataracts – Major depression
– Chronic lymphocytic leukemia – Migraines
– Chronic obstructive pulmonary disease
– MRSA/VRE
– Colorectal cancer
– Multiple myeloma
– Community acquired bacterial
pneumonia – Myelodysplastic syndrome & acute leukemia
– Coronary artery bypass graft – Myocardial infarction
– Coronary artery disease – Obesity
– Coumadin management – Osteoporosis
– Diabetes – Ovarian cancer
– End stage renal
– Prostate cancer
– Gastro esophageal reflux disease
– Rett Syndrome
– Glaucoma
– Heart failure – Rheumatoid Arthritis
– Hemophilia – Scleroderma
– Stroke (Hemorrhagic and/or Ischemic) – Sickle Cell
– High risk pregnancy – Upper respiratory infection (3-18 years)
– HIV – Urinary incontinence (women over 65)
– Hodgkin's Disease – Venous thromboembolism prophylaxis
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25. Our Experience Building a Disease Registry
Patients exist in one of three states, relative to a patient registry
 On Registry: The patient is a member of a particular registry; i.e., they fit the
inclusion criteria
 Off Registry: Patient was once a member of a registry and fit the inclusion criteria,
but is now excluded. The exclusion could be “disease free.”
 At Risk: The patient fits the profile that could lead to inclusion on the registry, but
does not yet meet the formal inclusion criteria, e.g. obesity as a precursor to
membership on the diabetes and or hypertension registry.
Disease Registry
At Risk On Registry Off Registry
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26. Our Experience Building a Disease Registry
Patient Registry Vision
• How do we define a particular disease?
• Who has the disease?
• What is their demographic profile?
• Are we managing these patients according to
accepted best protocols?
• Which patients had the best outcomes and why?
• Where is the optimal point of cost vs. outcome?
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27. Our Experience Building a Disease Registry
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
Disease Registry
At Risk On Registry Off Registry
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28. Our Experience Building a Disease Registry
Our disease registry is populated by patient care cycle
Original Diagnosis Continued Care Continued Care Cured
Patient Data
How do I build this?
(Clinical, Business, etc)
Pay for
Pay for
Pt. included in Disease Reg. Performance
off Original Diagnosis Performance
measures
measures
Disease Registry
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29. The Healthcare Process and Transactional Systems at NMFF
Patient Data lies in various data sources
Billing &
Billing and AR Claims Claims Processing
Accounts
System Processing System
Receivable
Registration & Orders & Encounter Results & Patient
Diagnosis
Scheduling Procedures Documentation Outcomes Perception
Results Surveys
ADT System Diagnostic systems Pharmacy Electronic
Master Patient Index Lab System Medical Record
Radiology
Imaging
Pathology
Cardiology
Others
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30. The Northwestern Campus : Multiple, Collaborative, Organizations
Physician Office 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
Pharmacy •Diagnostics Surveys
•ADT System Diagnostic systems Electronic
•Master Patient Index •Lab System Medical Record •Pharmacy
•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
Pharmacy •Diagnostics Surveys
•ADT System Diagnostic systems Electronic
•Master Patient Index •Lab System Medical Record •Pharmacy
•Radiology •Diagnostics Surveys
•Imaging •ADT System Diagnostic systems Pharmacy Electronic
•Master Patient Index •Lab System Medical Record •Pharmacy
•Pathology
•Cardiology •Radiology
•Imaging
Hospital Y
•Others
•Pathology
Physician Office Z
•Cardiology
•Others
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31. Our Experience Building a Disease Registry
Original Diagnosis Continued Care Continued Care Cured
Patient Data
(Clinical, Business, etc)
Pay for
Pay for
Pt. included in Disease Reg. Performance
off Original Diagnosis Performance
measures
measures
How do I build this?
Disease Registry
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32. Our Experience Building a Disease Registry
Basic steps to build a disease registry
 Identify stakeholders
 Identify data points necessary to define, include and exclude in disease registry
 Identify source of data points
 Build registry
 Address data quality issues
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33. Our Experience Building a Disease Registry
Identifying Data Points & Data Sources
 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
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34. Our Experience Building a Disease Registry
What does a Diabetes Disease Registry Look Like Elsewhere?
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35. Our Experience Building a Disease Registry
University of Washington Physicians Network
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36. Our Experience Building a Disease Registry
Harvard Vanguard
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37. Our Experience Building a Disease Registry
Harvard Vanguard (continued)
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38. Our Experience Building a Disease Registry
Our First Design
 Keep it as flat as possible
 Use consistent naming standards throughout
 Keep design minimalist
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39. Our Experience Building a Disease Registry
Our Extensible Design
Motives
Consistent profiling for prospective, predictive intervention
Outreach communication to patients
Quality of care reporting (e.g. P4P)
Process improvement projects for service line clinical programs
Population reporting and analysis for research (e.g. Epidemiology)
Key Design Considerations
Used clinician input for building & defining institutional definition of the disease
Stakeholders input in defining inclusion & exclusion criteria
Disease Registry metadata contains inclusion, exclusion criteria
Added a reason for inclusion description for disease registry
The disease registry data model was built to tie the patient identity back to data points in the
data warehouse which includes all EMR data sources.
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40. Building our Disease Registry (i.e. Diabetes)
Epic-Clarity di abet es ( r egi st r i es_ dm)
Column Name Data Type Allow Nulls
ETL Package
Problem List mrd_pt_id int
birth_dt datetime
death_dt datetime
Orders gender_cd varchar(20)
problem_list_diabetes... int
encntrs_diabetes_dx_... int
Encounters
orders_diabetes_dx_n... int
meds_diabetes_dx_num int
Cerner last_hba1c_val float
last_hba1c_dts datetime
Inclusion
Problem List and max_hba1c_val float
Exclusion max_hba1c_dts datetime
Criteria min_hba1c_val float
Orders for min_hba1c_dts datetime
Specific tobacco_user_flg varchar(50)
Disease
alcohol_user_flg varchar(50)
Encounters Registry
last_encntr_dts datetime
last_bmi_val decimal(18, 2)
last_height_val varchar(50)
IDX
last_weight_val varchar(50)
CPT’s Billed data_thru_dts datetime
meta_orignl_load_dts datetime
meta_update_dts datetime
Billing Diagnosis meta_load_exectn_guid uniqueidentifier
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41. Our Experience Building a Disease Registry
Our First Disease Registry
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42. Agenda
 NMFF Overview
 Epic Implementation History
 Patient Registries History and Overview
 Our Experience building a Disease Registry
 After the Build
 Lessons learned
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43. After the Build
Analyzing Disease Registry Data
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44. After the Build
Recognizing Bad Data
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45. After the Build
Investigating Bad Data
Hello, CNN?
3345 kg = 7359 lbs
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46. After the Build
Strategies for managing bad data
Proactive Measures
 Define feedback mechanism to report bad data in the source system to
the appropriate data owner.
Prevent future input of bad data in the source systems
• Add data validations in the user interface.
Reactive Measures
 Define the ability to flag erroneous data in the data marts (disease
registries)
Eliminate erroneous data from analytical reporting
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47. After the Build
Tying it all together
Improvement
Opportunity
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48. After the Build
Tying Disease Registries back to Point of Care
 Ideally disease registry information should be available at point of care
• 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.”
 How do you implement this in Epic?
• Invoke web services within epic programming points to display
information inside epic
• Invoke external web solutions within hyperspace
• Write data back in epic
– FYI Flags
– CUIs
– Health Maintenance Topics
– Etc.
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49. Agenda
 NMFF Overview
 Epic Implementation History
 Patient Registries History and Overview
 Our Experience building a Disease Registry
 After the Build
 Lessons learned
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50. After the Build
Lessons Learned
 Clinical Sponsorship is necessary.
 Agile development methods are useful in getting user buy-in
• They are quick
• They demonstrate work product
 Defining registries shouldn’t be limited to only ICD-9 defined diseases
 Try to include the reason a patient is added into a registry.
 Measure and Insight can be equally significant to registries other than
disease based
 Need to prioritize which data sources have highest value (esp. when you
have more than one EMR source)
 Creating a “data bus” to traverse all available data points will create new
opportunities for discovery and research.
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