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Precise Patient Registries: 
The Foundation for Clinical Research & 
Population Health Management 
© 2014 Health Catalyst 
www.healthcatalyst.com Creative Commons Copyright 
© 2014 Health Catalyst 
www.healthcatalyst.com 
Dale Sanders, November 2014 
Follow Us on Twitter #TimeforAnalytics
© 2014 Health Catalyst 
www.healthcatalyst.com 
Follow Us on Twitter #TimeforAnalytics 
Agenda 
• Assertions and criticisms of the current state 
• What is a patient registry? 
• History and definitions 
• What should we be doing differently? 
• Designing precise registries 
• An example from our registry work at 
Northwestern University 
• Nitty Gritty data details
© 2014 Health Catalyst 
www.healthcatalyst.com 
Acknowledgements & Thanks 
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• Steve Barlow 
• Cessily Johnson 
• Darren Kaiser 
• Anita Parisot 
• Tracy Vayo
© 2014 Health Catalyst 
www.healthcatalyst.com 
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Poll Question 
Have you ever been directly involved in the design 
and development of a patient registry? 
Yes 
No
Assertion #1 
Without precise definitions and registries of patient types, 
you can’t have… 
• Precise clinical research 
© 2014 Health Catalyst 
www.healthcatalyst.com 
• Precise comparisons across the industry 
• Precise financial and risk management 
• Precise, personalized healthcare 
• Predictable clinical outcomes 
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© 2014 Health Catalyst 
www.healthcatalyst.com 
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Assertion #2 
• We can’t keep building disease registries at each 
organization, from scratch 
• It takes too long, it’s too expensive, it’s not 
standardized to support disease reporting, 
surveillance, and comparative medicine 
• Federal involvement has helped, but projects are 
moving too slowly
© 2014 Health Catalyst 
www.healthcatalyst.com 
Healthcare Analytics Adoption Model 
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Level 8 Personalized Medicine 
& Prescriptive Analytics 
Tailoring 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.
© 2014 Health Catalyst 
www.healthcatalyst.com 
Achieving High Resolution Medicine 
It starts with precise registries 
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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 
© 2014 Health Catalyst 
www.healthcatalyst.com 
Patient Registry Definitions 
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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.” 
© 2014 Health Catalyst 
www.healthcatalyst.com 
AHRQ’s Patient Registry Definition 
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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." 
© 2014 Health Catalyst 
www.healthcatalyst.com 
AHRQ’s Patient Registry Definition 
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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 
© 2014 Health Catalyst 
www.healthcatalyst.com 
Patient Registry Definitions 
Medical Informatics Faculty, 2005 
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 1973: Surveillance, Epidemiology, and End Results (SEER) 
Pioneered by GroupHealth of Puget Sound in the early 
1980s for diseases other than cancer 
© 2014 Health Catalyst 
www.healthcatalyst.com 
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 
program of National Cancer Institute, first national cancer 
registry 
 1993: Most states pass laws requiring cancer registries 
 “Clinically related information system” 
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• Intermountain, 1999: 18 months to achieve consensus 
• Northwestern, 2005: 6 months to achieve consensus, 
• Cayman Islands, 2009: 6 weeks to achieve consensus, 
borrowing from Intermountain, Northwestern, and BMJ 
© 2014 Health Catalyst 
www.healthcatalyst.com 
What’s a Diabetic Patient? 
How do we define a “diabetic” patient with data? 
borrowing from Intermountain and other “evidence 
based” sources 
• Medicare Shared Savings and HEDIS: 54 ICDs 
• Meaningful Use: 43 ICDs 
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© 2014 Health Catalyst 
www.healthcatalyst.com 
Sources of “Standard” 
Registry Definitions 
There is growing convergence, but still lots of disagreement 
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 HEDIS/NCQA 
 Medicare Shared Savings 
 NLM Value Set Authority Center 
 Meaningful Use 
 NQF 
 Specialty Groups and Journals 
 OECD 
 WHO 
 And others…!
© 2014 Health Catalyst 
www.healthcatalyst.com 
Follow Us on Twitter #TimeforAnalytics 16
© 2014 Health Catalyst 
www.healthcatalyst.com 
Precise Patient Registries Example 
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Asthma 
Supplemental 
ICD9 (38,250) 
Medications 
(72,581) 
Problem 
List 
(22,955) 
ICD9 493.XX 
(29,805) 
Additional 
Potential Rules 
(101,389) 
17
© 2014 Health Catalyst 
www.healthcatalyst.com 
Follow Us on Twitter #TimeforAnalytics 18
"It may be that a 'free-text' entry was added to the 
record, but unless it is coded in electronically, the 
patient has not been included in the diabetes register 
and cannot therefore benefit from the structured care 
that depends on such inclusion." -- Dr. Tim Holt 
© 2014 Health Catalyst 
www.healthcatalyst.com 
Medscape Summary of Article 
• 11.5 million patient records 
• 9000 primary-care clinics across the United 
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States 
• 5.4% of those likely to have diabetes in the 
databases were undiagnosed 
• Undiagnosed proportion rose to 12% to 16% in 
"hot spots," including Arizona, North Dakota, 
Minnesota, South Carolina, and Indiana 
• Patients without an ICD for diabetes received 
worse care, had worse outcomes 
19
© 2014 Health Catalyst 
www.healthcatalyst.com 
Types of Registries, Not Necessarily 
Disease Oriented 
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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
● Facilitates analysis for Patient Relationship Management (PRM) 
● Can drive reminders for research and standards of care protocols 
© 2014 Health Catalyst 
www.healthcatalyst.com 
More Types of Registries 
Scheduling Events Registry 
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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
© 2014 Health Catalyst 
www.healthcatalyst.com 
Innumerable Uses & Benefits 
Registries 
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How well am I 
managing 
diseases? 
Who else is 
treating 
patients like 
this? 
How does 
my drug 
perform in 
disease 
prevention, 
progression, 
and cure? 
How is this 
disease 
expressed in the 
genome? 
How do I 
analyze patient 
trends and 
outcomes for a 
disease? 
How do I know 
which 
drug/procedure 
works best for me? 
Who else matches 
my specific profile 
for disease, 
medication, 
procedure, or 
device… and can I 
interact with them?
Patients exist in one of three states, relative 
to a patient registry 
The patient is 
a member of a 
particular 
registry; i.e., 
they fit the 
inclusion 
criteria 
© 2014 Health Catalyst 
www.healthcatalyst.com 
On Registry 
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23 
Patient was once 
a member of a 
registry and fit the 
inclusion criteria, 
but is now 
excluded. The 
exclusion could be 
“disease free.” 
Disease 
Registry 
Off Registry 
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.
© 2014 Health Catalyst 
www.healthcatalyst.com 
Follow Us on Twitter #TimeforAnalytics 24
© 2014 Health Catalyst 
www.healthcatalyst.com 
Patient Registry Engine 
* DISEASE MANAGEMENT 
* OUTCOMES ANALYSIS 
* RESEARCH 
* P4P REPORTING 
* CLINICAL TRIALS ENROLLMENT 
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SCHEDULING 
REGISTRATION 
PATH 
TUMOR REG 
LAB RESULTS 
MEDICATIONS 
ICD9 CODES 
CPT CODES 
CLINICAL OBS 
PROBLEM 
LIST 
PATIENT 
VALIDATION 
CLINICIAN 
VALIDATION 
DISEASE 
REGISTRY 
MORTALITY 
INCLUSION 
CRITERIA & 
STRUCTURED 
EXCLUSION 
CODES 
PATIENT 
PROVIDER 
RELATIONSHIP 
RAD RESULTS 
COSTS & 
REIMBURSEMENT 
DATA 
CARDIOLOGY 
IMAGING 
 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?
The Healthcare Process vs. Supportive 
Data Sources 
© 2014 Health Catalyst 
www.healthcatalyst.com 
Diagnostic systems 
Lab System 
Radiology 
Imaging 
Pathology 
Cardiology 
Others 
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Diagnosis 
Registration & 
Scheduling 
Patient 
Perception 
Orders & 
Procedures 
Results & 
Outcomes 
Billing & 
Accounts 
Receivable 
Claims 
Processing 
Encounter 
Documentation 
ADT System 
Master Patient Index 
Pharmacy Electronic 
Medical Record 
Results Surveys 
Billing and AR 
System 
Claims Processing 
System 
Patient data lies in many 
disparate sources
Geometrically More Complex In Accountable 
Care and Most IDNs 
A Data Warehouse Solves the Data Disparity Problem 
© 2014 Health Catalyst 
www.healthcatalyst.com 
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EDW 
A single data perspective 
on the patient care process 
Physician Office X 
Hospital Y Physician Office Z
© 2014 Health Catalyst 
www.healthcatalyst.com 
A well designed data warehouse can be the platform that feeds 
many of these registries, and more, in an automated fashion 
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Mini-Case Study From 
Northwestern University Medicine, 
2006 
© 2014 Health Catalyst 
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© 2014 Health Catalyst 
www.healthcatalyst.com 
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‒ HIV 
‒ Hodgkin's Disease 
– Hypertension 
– Lower back pain 
– Systemic Lupus 
– Macular degeneration 
– Major depression 
– Migraines 
– MRSA/VRE 
– Multiple myeloma 
– Myelodysplastic syndrome & acute leukemia 
– Myocardial infarction 
– Obesity 
– Osteoporosis 
– Ovarian cancer 
– Prostate cancer 
– Rett Syndrome 
– Rheumatoid Arthritis 
– Scleroderma 
– Sickle Cell 
– Upper respiratory infection (3-18 years) 
– Urinary incontinence (women over 65) 
– Venous thromboembolism prophylaxis 
© 2014 Health Catalyst 
www.healthcatalyst.com 
Target Disease Registries* 
‒ Amyotrophic Lateral Sclerosis 
‒ Alzheimer's 
‒ Asthma 
‒ Breast cancer 
‒ Cataracts 
‒ Chronic lymphocytic leukemia 
‒ Chronic obstructive pulmonary disease 
‒ Colorectal cancer 
‒ Community acquired bacterial pneumonia 
‒ Coronary artery bypass graft 
‒ Coronary artery disease 
‒ Coumadin management 
‒ Diabetes 
‒ End stage renal 
‒ Gastro esophageal reflux disease 
‒ Glaucoma 
‒ Heart failure 
‒ Hemophilia 
‒ Stroke (Hemorrhagic and/or Ischemic) 
‒ High risk pregnancy 
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*Northwestern 
University Medicine, 
2006
• Inclusion codes based entirely on ICD9, which was a 
good place to start, but not specific enough 
© 2014 Health Catalyst 
www.healthcatalyst.com 
Inclusion & Exclusion for Heart Failure 
Clinical Study 
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31 
● 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, Northwestern University Feinberg School of Medicine
Disease Registry “Exclusions” 
Our first attempts at adjusting the numerator 
The industry will need standard vocabularies for excluding patients 
 Removing patients from the registry whose data would otherwise 
“Why should this patient be excluded from this registry, even though 
they appear to meet the inclusion criteria?” 
© 2014 Health Catalyst 
www.healthcatalyst.com 
skew the data profile of the cohort 
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On Registry 
Disease Registry 
Off Registry 
At Risk 
 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
Our View On “Exclusion” Evolved 
Excluding patients might be a bad idea in many situations 
At Northwestern (2007-2009), we found that 30% of patients fell into one 
or more of these categories: 
© 2014 Health Catalyst 
www.healthcatalyst.com 
Not all patients in a registry can functionally participate in a protocol, but 
you can’t just exclude and ignore them. You still have to treat them and 
their data is critical to understanding the disease or condition. 
• Cognitive inability 
• Economic inability 
• Physical inability 
• Geographic inability 
• Religious beliefs 
• Contraindications to the protocol 
• Voluntarily non-compliant 
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33
© 2014 Health Catalyst 
www.healthcatalyst.com 
Follow Us on Twitter #TimeforAnalytics 34
Exam 
History 
Diagnosis 
Code 
© 2014 Health Catalyst 
www.healthcatalyst.com 
Diabetes Registry Data Model 
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Diabetes 
Patient 
35 
Typical Analyses Use Cases 
• How many diabetic patients do I have? 
• When was their result for each HA1C, LDL, Foot Exam, 
Eye Exam over last 2 years? 
• What are all their medications and how long have they 
been taking each? 
• What was addressed at each of their visits for the last 2 
years? 
• Which doctors have they seen and why? 
• How many admissions have they had and why? 
• What co-morbid conditions are present? 
• Which interventions (diet, exercise, medications) are 
having the biggest impact on LDL, HA1C scores? 
Procedure 
History 
Vital Signs 
History 
Current Lab 
Result 
Lab Result 
History 
Office 
Visit 
Exam 
Type 
Diagnosis 
History 
Procedure 
Code 
Lab Type 
This data model applies to virtually all 
disease registries. Just change the name 
of the central table.
© 2014 Health Catalyst 
www.healthcatalyst.com 
Building The Diabetes Registry 
diabetes (registries_dm) 
mrd_pt_id int 
birth_dt datetime 
death_dt datetime 
gender_cd varchar(20) 
problem_list_diabetes... int 
encntrs_diabetes_dx_... int 
orders_diabetes_dx_n... int 
meds_diabetes_dx_num int 
last_hba1c_val float 
last_hba1c_dts datetime 
max_hba1c_val float 
max_hba1c_dts datetime 
min_hba1c_val float 
min_hba1c_dts datetime 
tobacco_user_flg varchar(50) 
alcohol_user_flg varchar(50) 
last_encntr_dts datetime 
last_bmi_val decimal(18, 2) 
last_height_val varchar(50) 
last_weight_val varchar(50) 
data_thru_dts datetime 
meta_orignl_load_dts datetime 
meta_update_dts datetime 
meta_load_exectn_guid uniqueidentifier 
ETL Package 
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Column Name Data Type Allow Nulls 
Epic-Clarity 
Problem List 
Orders 
Encounters 
Cerner 
Problem List 
Orders 
Encounters 
IDX 
CPT’s Billed 
Billing Diagnosis 
Inclusion 
and 
Exclusion 
Criteria 
for 
Specific 
Disease 
Registry
© 2014 Health Catalyst 
www.healthcatalyst.com 
Data Quality & The Disease Registry 
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© 2014 Health Catalyst 
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Investigating Bad Data 
3345 kg = 7359 lbs 
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Hello, CNN?
 “Recommend next HbA1C testing at 90 days because patient is not at 
© 2014 Health Catalyst 
www.healthcatalyst.com 
Closed Loop Analytics 
Ideally, disease registry information should be available at point of care 
 Guideline-based intervals for tests, follow-ups, referrals 
 Interventions that are overdue 
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 
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 FYI Flags 
 CUIs 
 Health Maintenance Topics 
 Etc.
© 2014 Health Catalyst 
www.healthcatalyst.com 
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c
© 2014 Health Catalyst 
www.healthcatalyst.com 
Geisinger & 
Cleveland Clinic 
Make It Commercially 
Available 
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Nitty Gritty Data Details 
© 2014 Health Catalyst 
www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics 
© 2014 Health Catalyst 
www.healthcatalyst.com 
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Thank you, Tracy Vayo
Does your organization have a patient registry data 
© 2014 Health Catalyst 
www.healthcatalyst.com 
Poll Question 
governance and stewardship process? 
• Yes and it’s very active 
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• Yes, somewhat 
• No, but we are talking about it 
• No, not at all 
• I’m not part of an organization that manages 
patient registries 
43
© 2014 Health Catalyst 
www.healthcatalyst.com 
Follow Us on Twitter #TimeforAnalytics 
c 
Not 
exhaustive; for 
illustrative 
purposes only
© 2014 Health Catalyst 
www.healthcatalyst.com 
Follow Us on Twitter #TimeforAnalytics 
c 
Diabetes, 
continued
© 2014 Health Catalyst 
www.healthcatalyst.com 
Follow Us on Twitter #TimeforAnalytics 
c 
Not 
exhaustive; for 
illustrative 
purposes only
© 2014 Health Catalyst 
www.healthcatalyst.com 
Follow Us on Twitter #TimeforAnalytics 
c 
Not 
exhaustive; for 
illustrative 
purposes only
© 2014 Health Catalyst 
www.healthcatalyst.com 
Follow Us on Twitter #TimeforAnalytics 
c 
Sepsis, 
continued
vendor space, but most vendors are stuck on ICD codes, 
only 
© 2014 Health Catalyst 
www.healthcatalyst.com 
In Conclusion 
• Precise registries are required for precise, high 
resolution healthcare 
• So much of what we do depends on registries and the 
dependence is growing 
• Precise registries are tough to build 
• We can’t afford to keep building them from scratch 
• Federal efforts at standardization are moving slowly 
• Precise registries are a commercial differentiator in the 
• For questions and follow-up, please contact me 
• dale.sanders@healthcatalyst.com 
• @drsanders 
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Upcoming Educational Opportunities 
A Health Catalyst Overview: An Introduction to Healthcare Data 
Warehousing and Analytics 
Date: November 20, 1-2pm, EST 
Presenter: Vice President Jared Crapo & Senior Solutions Consultant Sriraman Rajamani 
http://www.healthcatalyst.com/knowledge-center/webinars-presentations 
© 2014 Health Catalyst 
www.healthcatalyst.com 
Thank You 
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Precise Patient Registries: The Foundation for Clinical Research & Population Health Management

  • 1. Precise Patient Registries: The Foundation for Clinical Research & Population Health Management © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright © 2014 Health Catalyst www.healthcatalyst.com Dale Sanders, November 2014 Follow Us on Twitter #TimeforAnalytics
  • 2. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics Agenda • Assertions and criticisms of the current state • What is a patient registry? • History and definitions • What should we be doing differently? • Designing precise registries • An example from our registry work at Northwestern University • Nitty Gritty data details
  • 3. © 2014 Health Catalyst www.healthcatalyst.com Acknowledgements & Thanks Follow Us on Twitter #TimeforAnalytics • Steve Barlow • Cessily Johnson • Darren Kaiser • Anita Parisot • Tracy Vayo
  • 4. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics Poll Question Have you ever been directly involved in the design and development of a patient registry? Yes No
  • 5. Assertion #1 Without precise definitions and registries of patient types, you can’t have… • Precise clinical research © 2014 Health Catalyst www.healthcatalyst.com • Precise comparisons across the industry • Precise financial and risk management • Precise, personalized healthcare • Predictable clinical outcomes Follow Us on Twitter #TimeforAnalytics
  • 6. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics Assertion #2 • We can’t keep building disease registries at each organization, from scratch • It takes too long, it’s too expensive, it’s not standardized to support disease reporting, surveillance, and comparative medicine • Federal involvement has helped, but projects are moving too slowly
  • 7. © 2014 Health Catalyst www.healthcatalyst.com Healthcare Analytics Adoption Model Follow Us on Twitter #TimeforAnalytics Level 8 Personalized Medicine & Prescriptive Analytics Tailoring 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.
  • 8. © 2014 Health Catalyst www.healthcatalyst.com Achieving High Resolution Medicine It starts with precise registries Follow Us on Twitter #TimeforAnalytics
  • 9. 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 © 2014 Health Catalyst www.healthcatalyst.com Patient Registry Definitions Follow Us on Twitter #TimeforAnalytics
  • 10. 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.” © 2014 Health Catalyst www.healthcatalyst.com AHRQ’s Patient Registry Definition Follow Us on Twitter #TimeforAnalytics
  • 11. 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." © 2014 Health Catalyst www.healthcatalyst.com AHRQ’s Patient Registry Definition Follow Us on Twitter #TimeforAnalytics
  • 12. 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 © 2014 Health Catalyst www.healthcatalyst.com Patient Registry Definitions Medical Informatics Faculty, 2005 Follow Us on Twitter #TimeforAnalytics
  • 13.  1973: Surveillance, Epidemiology, and End Results (SEER) Pioneered by GroupHealth of Puget Sound in the early 1980s for diseases other than cancer © 2014 Health Catalyst www.healthcatalyst.com 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 program of National Cancer Institute, first national cancer registry  1993: Most states pass laws requiring cancer registries  “Clinically related information system” Follow Us on Twitter #TimeforAnalytics
  • 14. • Intermountain, 1999: 18 months to achieve consensus • Northwestern, 2005: 6 months to achieve consensus, • Cayman Islands, 2009: 6 weeks to achieve consensus, borrowing from Intermountain, Northwestern, and BMJ © 2014 Health Catalyst www.healthcatalyst.com What’s a Diabetic Patient? How do we define a “diabetic” patient with data? borrowing from Intermountain and other “evidence based” sources • Medicare Shared Savings and HEDIS: 54 ICDs • Meaningful Use: 43 ICDs Follow Us on Twitter #TimeforAnalytics
  • 15. © 2014 Health Catalyst www.healthcatalyst.com Sources of “Standard” Registry Definitions There is growing convergence, but still lots of disagreement Follow Us on Twitter #TimeforAnalytics  HEDIS/NCQA  Medicare Shared Savings  NLM Value Set Authority Center  Meaningful Use  NQF  Specialty Groups and Journals  OECD  WHO  And others…!
  • 16. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics 16
  • 17. © 2014 Health Catalyst www.healthcatalyst.com Precise Patient Registries Example Follow Us on Twitter 1 7#TimeforAnalytics Asthma Supplemental ICD9 (38,250) Medications (72,581) Problem List (22,955) ICD9 493.XX (29,805) Additional Potential Rules (101,389) 17
  • 18. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics 18
  • 19. "It may be that a 'free-text' entry was added to the record, but unless it is coded in electronically, the patient has not been included in the diabetes register and cannot therefore benefit from the structured care that depends on such inclusion." -- Dr. Tim Holt © 2014 Health Catalyst www.healthcatalyst.com Medscape Summary of Article • 11.5 million patient records • 9000 primary-care clinics across the United Follow Us on Twitter #TimeforAnalytics States • 5.4% of those likely to have diabetes in the databases were undiagnosed • Undiagnosed proportion rose to 12% to 16% in "hot spots," including Arizona, North Dakota, Minnesota, South Carolina, and Indiana • Patients without an ICD for diabetes received worse care, had worse outcomes 19
  • 20. © 2014 Health Catalyst www.healthcatalyst.com Types of Registries, Not Necessarily Disease Oriented Follow Us on Twitter #TimeforAnalytics 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
  • 21. ● Facilitates analysis for Patient Relationship Management (PRM) ● Can drive reminders for research and standards of care protocols © 2014 Health Catalyst www.healthcatalyst.com More Types of Registries Scheduling Events Registry Follow Us on Twitter #TimeforAnalytics 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
  • 22. © 2014 Health Catalyst www.healthcatalyst.com Innumerable Uses & Benefits Registries Follow Us on Twitter #TimeforAnalytics How well am I managing diseases? Who else is treating patients like this? How does my drug perform in disease prevention, progression, and cure? How is this disease expressed in the genome? How do I analyze patient trends and outcomes for a disease? How do I know which drug/procedure works best for me? Who else matches my specific profile for disease, medication, procedure, or device… and can I interact with them?
  • 23. Patients exist in one of three states, relative to a patient registry The patient is a member of a particular registry; i.e., they fit the inclusion criteria © 2014 Health Catalyst www.healthcatalyst.com On Registry Follow Us on Twitter #TimeforAnalytics 23 Patient was once a member of a registry and fit the inclusion criteria, but is now excluded. The exclusion could be “disease free.” Disease Registry Off Registry 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.
  • 24. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics 24
  • 25. © 2014 Health Catalyst www.healthcatalyst.com Patient Registry Engine * DISEASE MANAGEMENT * OUTCOMES ANALYSIS * RESEARCH * P4P REPORTING * CLINICAL TRIALS ENROLLMENT Follow Us on Twitter #TimeforAnalytics SCHEDULING REGISTRATION PATH TUMOR REG LAB RESULTS MEDICATIONS ICD9 CODES CPT CODES CLINICAL OBS PROBLEM LIST PATIENT VALIDATION CLINICIAN VALIDATION DISEASE REGISTRY MORTALITY INCLUSION CRITERIA & STRUCTURED EXCLUSION CODES PATIENT PROVIDER RELATIONSHIP RAD RESULTS COSTS & REIMBURSEMENT DATA CARDIOLOGY IMAGING  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?
  • 26. The Healthcare Process vs. Supportive Data Sources © 2014 Health Catalyst www.healthcatalyst.com Diagnostic systems Lab System Radiology Imaging Pathology Cardiology Others Follow Us on Twitter #TimeforAnalytics Diagnosis Registration & Scheduling Patient Perception Orders & Procedures Results & Outcomes Billing & Accounts Receivable Claims Processing Encounter Documentation ADT System Master Patient Index Pharmacy Electronic Medical Record Results Surveys Billing and AR System Claims Processing System Patient data lies in many disparate sources
  • 27. Geometrically More Complex In Accountable Care and Most IDNs A Data Warehouse Solves the Data Disparity Problem © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics EDW A single data perspective on the patient care process Physician Office X Hospital Y Physician Office Z
  • 28. © 2014 Health Catalyst www.healthcatalyst.com A well designed data warehouse can be the platform that feeds many of these registries, and more, in an automated fashion Follow Us on Twitter #TimeforAnalytics
  • 29. Mini-Case Study From Northwestern University Medicine, 2006 © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics
  • 30. ‒ HIV ‒ Hodgkin's Disease – Hypertension – Lower back pain – Systemic Lupus – Macular degeneration – Major depression – Migraines – MRSA/VRE – Multiple myeloma – Myelodysplastic syndrome & acute leukemia – Myocardial infarction – Obesity – Osteoporosis – Ovarian cancer – Prostate cancer – Rett Syndrome – Rheumatoid Arthritis – Scleroderma – Sickle Cell – Upper respiratory infection (3-18 years) – Urinary incontinence (women over 65) – Venous thromboembolism prophylaxis © 2014 Health Catalyst www.healthcatalyst.com Target Disease Registries* ‒ Amyotrophic Lateral Sclerosis ‒ Alzheimer's ‒ Asthma ‒ Breast cancer ‒ Cataracts ‒ Chronic lymphocytic leukemia ‒ Chronic obstructive pulmonary disease ‒ Colorectal cancer ‒ Community acquired bacterial pneumonia ‒ Coronary artery bypass graft ‒ Coronary artery disease ‒ Coumadin management ‒ Diabetes ‒ End stage renal ‒ Gastro esophageal reflux disease ‒ Glaucoma ‒ Heart failure ‒ Hemophilia ‒ Stroke (Hemorrhagic and/or Ischemic) ‒ High risk pregnancy Follow Us on Twitter #TimeforAnalytics *Northwestern University Medicine, 2006
  • 31. • Inclusion codes based entirely on ICD9, which was a good place to start, but not specific enough © 2014 Health Catalyst www.healthcatalyst.com Inclusion & Exclusion for Heart Failure Clinical Study Follow Us on Twitter #TimeforAnalytics 31 ● 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, Northwestern University Feinberg School of Medicine
  • 32. Disease Registry “Exclusions” Our first attempts at adjusting the numerator The industry will need standard vocabularies for excluding patients  Removing patients from the registry whose data would otherwise “Why should this patient be excluded from this registry, even though they appear to meet the inclusion criteria?” © 2014 Health Catalyst www.healthcatalyst.com skew the data profile of the cohort Follow Us on Twitter #TimeforAnalytics On Registry Disease Registry Off Registry At Risk  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
  • 33. Our View On “Exclusion” Evolved Excluding patients might be a bad idea in many situations At Northwestern (2007-2009), we found that 30% of patients fell into one or more of these categories: © 2014 Health Catalyst www.healthcatalyst.com Not all patients in a registry can functionally participate in a protocol, but you can’t just exclude and ignore them. You still have to treat them and their data is critical to understanding the disease or condition. • Cognitive inability • Economic inability • Physical inability • Geographic inability • Religious beliefs • Contraindications to the protocol • Voluntarily non-compliant Follow Us on Twitter #TimeforAnalytics 33
  • 34. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics 34
  • 35. Exam History Diagnosis Code © 2014 Health Catalyst www.healthcatalyst.com Diabetes Registry Data Model Follow Us on Twitter #TimeforAnalytics Diabetes Patient 35 Typical Analyses Use Cases • How many diabetic patients do I have? • When was their result for each HA1C, LDL, Foot Exam, Eye Exam over last 2 years? • What are all their medications and how long have they been taking each? • What was addressed at each of their visits for the last 2 years? • Which doctors have they seen and why? • How many admissions have they had and why? • What co-morbid conditions are present? • Which interventions (diet, exercise, medications) are having the biggest impact on LDL, HA1C scores? Procedure History Vital Signs History Current Lab Result Lab Result History Office Visit Exam Type Diagnosis History Procedure Code Lab Type This data model applies to virtually all disease registries. Just change the name of the central table.
  • 36. © 2014 Health Catalyst www.healthcatalyst.com Building The Diabetes Registry diabetes (registries_dm) mrd_pt_id int birth_dt datetime death_dt datetime gender_cd varchar(20) problem_list_diabetes... int encntrs_diabetes_dx_... int orders_diabetes_dx_n... int meds_diabetes_dx_num int last_hba1c_val float last_hba1c_dts datetime max_hba1c_val float max_hba1c_dts datetime min_hba1c_val float min_hba1c_dts datetime tobacco_user_flg varchar(50) alcohol_user_flg varchar(50) last_encntr_dts datetime last_bmi_val decimal(18, 2) last_height_val varchar(50) last_weight_val varchar(50) data_thru_dts datetime meta_orignl_load_dts datetime meta_update_dts datetime meta_load_exectn_guid uniqueidentifier ETL Package Follow Us on Twitter #TimeforAnalytics Column Name Data Type Allow Nulls Epic-Clarity Problem List Orders Encounters Cerner Problem List Orders Encounters IDX CPT’s Billed Billing Diagnosis Inclusion and Exclusion Criteria for Specific Disease Registry
  • 37. © 2014 Health Catalyst www.healthcatalyst.com Data Quality & The Disease Registry Follow Us on Twitter #TimeforAnalytics
  • 38. © 2014 Health Catalyst www.healthcatalyst.com Investigating Bad Data 3345 kg = 7359 lbs Follow Us on Twitter #TimeforAnalytics Hello, CNN?
  • 39.  “Recommend next HbA1C testing at 90 days because patient is not at © 2014 Health Catalyst www.healthcatalyst.com Closed Loop Analytics Ideally, disease registry information should be available at point of care  Guideline-based intervals for tests, follow-ups, referrals  Interventions that are overdue 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 Follow Us on Twitter #TimeforAnalytics  FYI Flags  CUIs  Health Maintenance Topics  Etc.
  • 40. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics c
  • 41. © 2014 Health Catalyst www.healthcatalyst.com Geisinger & Cleveland Clinic Make It Commercially Available Follow Us on Twitter #TimeforAnalytics
  • 42. Nitty Gritty Data Details © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics Thank you, Tracy Vayo
  • 43. Does your organization have a patient registry data © 2014 Health Catalyst www.healthcatalyst.com Poll Question governance and stewardship process? • Yes and it’s very active Follow Us on Twitter #TimeforAnalytics • Yes, somewhat • No, but we are talking about it • No, not at all • I’m not part of an organization that manages patient registries 43
  • 44. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics c Not exhaustive; for illustrative purposes only
  • 45. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics c Diabetes, continued
  • 46. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics c Not exhaustive; for illustrative purposes only
  • 47. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics c Not exhaustive; for illustrative purposes only
  • 48. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics c Sepsis, continued
  • 49. vendor space, but most vendors are stuck on ICD codes, only © 2014 Health Catalyst www.healthcatalyst.com In Conclusion • Precise registries are required for precise, high resolution healthcare • So much of what we do depends on registries and the dependence is growing • Precise registries are tough to build • We can’t afford to keep building them from scratch • Federal efforts at standardization are moving slowly • Precise registries are a commercial differentiator in the • For questions and follow-up, please contact me • dale.sanders@healthcatalyst.com • @drsanders Follow Us on Twitter #TimeforAnalytics
  • 50. Upcoming Educational Opportunities A Health Catalyst Overview: An Introduction to Healthcare Data Warehousing and Analytics Date: November 20, 1-2pm, EST Presenter: Vice President Jared Crapo & Senior Solutions Consultant Sriraman Rajamani http://www.healthcatalyst.com/knowledge-center/webinars-presentations © 2014 Health Catalyst www.healthcatalyst.com Thank You Follow Us on Twitter #TimeforAnalytics