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

& Population Health Management

and definitions
What should we be doing differently?
Designing precise registries
An example from our registry work at Northwestern University
Nitty Gritty data details

development of a patient registry?

Yes
No

have…

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

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

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

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.”

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."

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

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”

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

of disagreement

HEDIS/NCQA
Medicare Shared Savings
NLM Value Set Authority Center
Meaningful Use
NQF
Specialty Groups and Journals
OECD
WHO
And others…!

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

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

(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

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?

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.”

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.

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?

Procedures

Results &
Outcomes

Billing &
Accounts
Receivable

Claims
Processing

Encounter
Documentation

Patient data lies in many disparate sources

Solves the Data Disparity Problem

EDW
A single data perspective
on the patient care process

Physician Office X

Hospital Y

Physician Office Z

many of these registries, and more, in an automated fashion

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

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

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

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

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.

for Specific Disease Registry

ETL Package

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.

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

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

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