Precise Patient Registries: The Foundation for Clinical Research & Population Health Management

Dale Sanders, November 2014

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

Poll Question

Have you ever been directly involved in the design and development of a patient registry?

Assertion #1Without 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 ReductionReducing variability in care processes. Focusing on internal optimization and waste reduction.

Level 4 Automated External ReportingEfficient, consistent production of reports & adaptability to changing requirements.

Level 3 Automated Internal ReportingEfficient, 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 SolutionsInefficient, 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

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 DefinitionsThere is growing convergence, but still lots of disagreement

HEDIS/NCQA

Medicare Shared Savings

NLM Value Set Authority Center

Meaningful Use

Specialty Groups and Journals

And others…!

Precise Patient Registries Example

Asthma

Supplemental ICD9 (38,250)

Medications(72,581)

Problem List

(22,955)

ICD9 493.XX (29,805)

AdditionalPotential Rules

(101,389)

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

"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

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?

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

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

PROBLEMLIST

PATIENT VALIDATION

CLINICIAN VALIDATION

DISEASEREGISTRY

MORTALITY

REGISTRATION

SCHEDULING

INCLUSIONCRITERIA &

STRUCTURED EXCLUSION

PATIENT PROVIDER

RELATIONSHIP

* DISEASE MANAGEMENT* OUTCOMES ANALYSIS* RESEARCH* P4P REPORTING* CLINICAL TRIALS ENROLLMENT

RAD RESULTS

TUMOR REG

COSTS & REIMBURSEMENT

CARDIOLOGYIMAGING

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 systemsLab SystemRadiologyImagingPathologyCardiologyOthers

DiagnosisRegistration &Scheduling

PatientPerception

Orders & Procedures

Results & Outcomes

Billing &AccountsReceivable

Claims Processing

EncounterDocumentation

ADT SystemMaster Patient Index

Pharmacy ElectronicMedical Record

SurveysResults

Billing and ARSystem

Claims ProcessingSystem

Patient data lies in many disparate sources

Geometrically More Complex In Accountable Care and Most IDNsA Data Warehouse Solves the Data Disparity Problem

EDWA 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• 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 numeratorThe 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?”

Disease Registry

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

Diabetes Registry Data Model

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 TypeThis data model applies to virtually all disease registries. Just change the name of the central table.

Building The Diabetes Registrydiabetes (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

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 AnalyticsIdeally, 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

Health Maintenance Topics

Geisinger & Cleveland Clinic Make It Commercially Available

Nitty Gritty Data DetailsThank 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

cNot exhaustive; for illustrative purposes only

cDiabetes, continued

cSepsis, continued

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 YouUpcoming Educational OpportunitiesA Health Catalyst Overview: An Introduction to Healthcare Data Warehousing and AnalyticsDate: November 20, 1-2pm, ESTPresenter: Vice President Jared Crapo & Senior Solutions Consultant Sriraman Rajamani http://www.healthcatalyst.com/knowledge-center/webinars-presentations

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