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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
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
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 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…!
Precise Patient Registries Example Asthma 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
Innumerable Uses & Benefits Registries Clinicians & Researchers Physician Organization Consumer Drug Manufacturer 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 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.” 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 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 Diagnosis Registration & Scheduling Patient Perception Orders & Procedures Results & Outcomes Billing & Accounts Receivable Claims Processing Encounter Documentation 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?” 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
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 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.
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
Not exhaustive; for illustrative purposes only
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 firstname.lastname@example.org @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