Precise Patient Registries for Clinical Research and Population Management

© 2014 Health Catalyst
www.healthcatalyst.com
Creative Commons Copyright
www.healthcatalyst.comCreative Commons Copyright
Dale Sanders, November 2014
Precise Patient Registries:
The Foundation for Clinical Research &
Population Health Management

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

Acknowledgements & Thanks
• Steve Barlow
• Cessily Johnson
• Darren Kaiser
• Anita Parisot
• Tracy Vayo
• And many others…

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
• Precise comparisons across the industry
• Precise financial and risk management
• Precise, personalized healthcare
• Predictable clinical outcomes

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

Healthcare Analytics Adoption Model
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.

Achieving “High Resolution” Medicine
It starts with precise registries

Patient Registry Definitions
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

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.”

AHRQ’s Patient Registry Definition
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."

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

History of Patient Registries
Historically, the term implies stand-alone, specialized products
and clinical databases for external reporting
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”

What’s a Diabetic Patient?
How do we define a “diabetic” patient with data?
• Intermountain, 1999: 18 months to achieve consensus
• Northwestern, 2005: 6 months to achieve consensus,
borrowing from Intermountain and other “evidence
based” sources
• Cayman Islands, 2009: 6 weeks to achieve consensus,
borrowing from Intermountain, Northwestern, and BMJ
• Medicare Shared Savings and HEDIS: 54 ICDs
• Meaningful Use: 43 ICDs

Sources of “Standard”
Registry Definitions
There is growing convergence, but still lots of disagreement
 HEDIS/NCQA
 Medicare Shared Savings
 NLM Value Set Authority Center
 Meaningful Use
 NQF
 Specialty Groups and Journals
 OECD
 WHO
 And others…!

Creative Commons Copyright 16

Creative Commons Copyright17
Precise Patient Registries Example
Asthma
Supplemental
ICD9 (38,250)
Medications
(72,581)
Problem
List
(22,955)
ICD9 493.XX
(29,805)
Additional
Potential Rules
(101,389)
17

Medscape Summary of Article
• 11.5 million patient records
• 9000 primary-care clinics across the United
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
"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

Types of Registries, Not Necessarily
Disease Oriented
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

More 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

Follow Us on Twitter #TimeforAnalytics
Innumerable Uses & Benefits
Registries
How does
my drug
perform in
disease
prevention,
progression,
and cure?
How well am I
managing
diseases?
Who else is
treating
patients like
this?
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?

Follow Us on Twitter #TimeforAnalytics
Patients exist in one of three states, relative
to a patient registry
23
The patient is
a member of a
particular
registry; i.e.,
they fit the
inclusion
criteria
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
On 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.

Patient Registry Engine
LAB RESULTS
CPT CODES
ICD9 CODES
MEDICATIONS
CLINICAL OBS
PROBLEM
LIST
PATIENT
VALIDATION
CLINICIAN
VALIDATION
PATH
DISEASE
REGISTRY
MORTALITY
REGISTRATION
SCHEDULING
INCLUSION
CRITERIA &
STRUCTURED
EXCLUSION
CODES
PATIENT
PROVIDER
RELATIONSHIP
* DISEASE MANAGEMENT
* OUTCOMES ANALYSIS
* RESEARCH
* P4P REPORTING
* CLINICAL TRIALS ENROLLMENT
RAD RESULTS
TUMOR REG
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
Diagnostic systems
Lab System
Radiology
Imaging
Pathology
Cardiology
Others
DiagnosisRegistration &
Scheduling
Patient
Perception
Orders &
Procedures
Results &
Outcomes
Billing &
Accounts
Receivable
Claims
Processing
Encounter
Documentation
ADT System
Master Patient Index
Pharmacy Electronic
Medical Record
SurveysResults
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
EDW
A single data perspective
on the patient care process
Physician Office X
Hospital Y Physician Office Z

A well designed data warehouse can be the platform that feeds
many of these registries, and more, in an automated fashion

Mini-Case Study From
Northwestern University Medicine,
2006

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
‒ 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
*Northwestern
University Medicine,
2006

Inclusion & Exclusion for Heart Failure
Clinical Study
31
• Inclusion codes based entirely on ICD9, which was 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, 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
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?”
DiseaseRegistry
On 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

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.
At Northwestern (2007-2009), we found that 30% of patients fell into one
or more of these categories:
• Cognitive inability
• Economic inability
• Physical inability
• Geographic inability
• Religious beliefs
• Contraindications to the protocol
• Voluntarily non-compliant
Our View On “Exclusion” Evolved
Excluding patients might be a bad idea in many situations
33

Creative Commons Copyright 34

Diabetes Registry Data Model
35
Diabetes
Patient
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
Exam
History
Diagnosis
History
Diagnosis
Code
Procedure
Code
Lab Type
This data model applies to virtually all
disease registries. Just change the name
of the central table.

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
Column Name Data Type Allow Nulls
Problem List
Orders
Encounters
Epic-Clarity
Problem List
Orders
Encounters
Cerner
CPT’s Billed
Billing Diagnosis
IDX
Inclusion
and
Exclusion
Criteria
for
Specific
Disease
Registry
ETL Package

Data Quality & The Disease Registry

Investigating Bad Data
3345 kg = 7359 lbs
Hello, CNN?

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
 “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.

cc

Geisinger &
Cleveland Clinic
Make It Commercially
Available

Nitty Gritty Data Details
Thank you, Tracy Vayo

Poll Question
Does your organization have a patient registry data
governance and stewardship process?
• Yes and it’s very active
• 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

Not
exhaustive; for
illustrative
purposes only

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
vendor space, but most vendors are stuck on ICD codes,
only
• For questions and follow-up, please contact me
• dale.sanders@healthcatalyst.com
• @drsanders

Thank You
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

Precise Patient Registries for Clinical Research and Population Management

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