Upload
health-catalyst
View
1.436
Download
2
Tags:
Embed Size (px)
DESCRIPTION
Join Dale Sanders as he shares his experience in developing disease registries, the history of patient registries, and the current design patterns in data engineering to create highly precise registries to support clinical research and population health management. Topics: *How the definition of the term “patient registry" has evolved from being associated with a federal- or state-mandated reporting requirement to a hospital or health system’s own population of patients, including device registries, drug registries, and procedure registries. *Why engaging certain populations via group registries allows them to better understand their conditions and reach out for support from others who share their condition. *Several untapped benefits of registries for disease and quality management. *When to utilize patient registries to guide decision-making and drive change, especially at the point of care. *Which of the critical steps to building a disease registry is most important. *The keys to winning organizational support in order to implement a successful registry initiative. *Precise patient registries play a significant role in the management of a broad variety of healthcare processes, including chronic diseases and conditions, as well as clinical research. Understanding how registries are currently built vs. how they should be built is critical to the future of healthcare outcomes improvement, cost reduction, and translational research.
Citation preview
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
© 2014 Health Catalystwww.healthcatalyst.comCreative Commons Copyright
Dale Sanders, November 2014
Precise Patient Registries: The Foundation for Clinical Research & Population Health Management
© 2014 Health Catalystwww.healthcatalyst.comFollow 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 Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
Acknowledgements & Thanks
• Steve Barlow
• Cessily Johnson
• Darren Kaiser
• Anita Parisot
• Tracy Vayo
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
Poll Question
Have you ever been directly involved in the design and development of a patient registry?
Yes
No
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
© 2014 Health Catalystwww.healthcatalyst.comFollow 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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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.
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
Achieving High Resolution Medicine
It starts with precise registries
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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.”
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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."
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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”
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
NQF
Specialty Groups and Journals
OECD
WHO
And others…!
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics 16
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics 17
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)
17
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics 18
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
© 2014 Health Catalystwww.healthcatalyst.comFollow 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?
© 2014 Health Catalystwww.healthcatalyst.comFollow 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.
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics 24
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
Patient Registry Engine
LAB RESULTS
CPT CODES
ICD9 CODES
MEDICATIONS
CLINICAL OBS
PROBLEMLIST
PATIENT VALIDATION
CLINICIAN VALIDATION
PATH
DISEASEREGISTRY
MORTALITY
REGISTRATION
SCHEDULING
INCLUSIONCRITERIA &
STRUCTURED EXCLUSION
CODES
PATIENT PROVIDER
RELATIONSHIP
* DISEASE MANAGEMENT* OUTCOMES ANALYSIS* RESEARCH* P4P REPORTING* CLINICAL TRIALS ENROLLMENT
RAD RESULTS
TUMOR REG
COSTS & REIMBURSEMENT
DATA
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?
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
A well designed data warehouse can be the platform that feeds many of these registries, and more, in an automated fashion
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
Mini-Case Study From Northwestern University Medicine, 2006
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics 31
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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics 33
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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics 34
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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 TypeThis data model applies to virtually all disease registries. Just change the name of the central table.
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
IDX
Inclusion and Exclusion Criteria for Specific Disease Registry
ETL Package
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
Data Quality & The Disease Registry
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
Investigating Bad Data
3345 kg = 7359 lbs
Hello, CNN?
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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
CUIs
Health Maintenance Topics
Etc.
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
c
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
Geisinger & Cleveland Clinic Make It Commercially Available
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
Nitty Gritty Data DetailsThank you, Tracy Vayo
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics 43
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
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
cNot exhaustive; for illustrative purposes only
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
cDiabetes, continued
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
cNot exhaustive; for illustrative purposes only
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
cNot exhaustive; for illustrative purposes only
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
cSepsis, continued
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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• [email protected]
• @drsanders
© 2014 Health Catalystwww.healthcatalyst.comFollow Us on Twitter #TimeforAnalytics
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