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

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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.

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

  1. 1. Precise Patient Registries: The Foundation for Clinical Research & Population Health Management © 2014 Health Catalyst www.healthcatalyst.com Creative Commons Copyright © 2014 Health Catalyst www.healthcatalyst.com Dale Sanders, November 2014 Follow Us on Twitter #TimeforAnalytics
  2. 2. © 2014 Health Catalyst www.healthcatalyst.com Follow 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
  3. 3. © 2014 Health Catalyst www.healthcatalyst.com Acknowledgements & Thanks Follow Us on Twitter #TimeforAnalytics • Steve Barlow • Cessily Johnson • Darren Kaiser • Anita Parisot • Tracy Vayo
  4. 4. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics Poll Question Have you ever been directly involved in the design and development of a patient registry? Yes No
  5. 5. Assertion #1 Without precise definitions and registries of patient types, you can’t have… • Precise clinical research © 2014 Health Catalyst www.healthcatalyst.com • Precise comparisons across the industry • Precise financial and risk management • Precise, personalized healthcare • Predictable clinical outcomes Follow Us on Twitter #TimeforAnalytics
  6. 6. © 2014 Health Catalyst www.healthcatalyst.com Follow 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
  7. 7. © 2014 Health Catalyst www.healthcatalyst.com Healthcare Analytics Adoption Model Follow Us on Twitter #TimeforAnalytics 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.
  8. 8. © 2014 Health Catalyst www.healthcatalyst.com Achieving High Resolution Medicine It starts with precise registries Follow Us on Twitter #TimeforAnalytics
  9. 9. 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 Catalyst www.healthcatalyst.com Patient Registry Definitions Follow Us on Twitter #TimeforAnalytics
  10. 10. 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 Catalyst www.healthcatalyst.com AHRQ’s Patient Registry Definition Follow Us on Twitter #TimeforAnalytics
  11. 11. 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 Catalyst www.healthcatalyst.com AHRQ’s Patient Registry Definition Follow Us on Twitter #TimeforAnalytics
  12. 12. 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 © 2014 Health Catalyst www.healthcatalyst.com Patient Registry Definitions Medical Informatics Faculty, 2005 Follow Us on Twitter #TimeforAnalytics
  13. 13.  1973: Surveillance, Epidemiology, and End Results (SEER) Pioneered by GroupHealth of Puget Sound in the early 1980s for diseases other than cancer © 2014 Health Catalyst www.healthcatalyst.com 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 program of National Cancer Institute, first national cancer registry  1993: Most states pass laws requiring cancer registries  “Clinically related information system” Follow Us on Twitter #TimeforAnalytics
  14. 14. • Intermountain, 1999: 18 months to achieve consensus • Northwestern, 2005: 6 months to achieve consensus, • Cayman Islands, 2009: 6 weeks to achieve consensus, borrowing from Intermountain, Northwestern, and BMJ © 2014 Health Catalyst www.healthcatalyst.com What’s a Diabetic Patient? How do we define a “diabetic” patient with data? borrowing from Intermountain and other “evidence based” sources • Medicare Shared Savings and HEDIS: 54 ICDs • Meaningful Use: 43 ICDs Follow Us on Twitter #TimeforAnalytics
  15. 15. © 2014 Health Catalyst www.healthcatalyst.com Sources of “Standard” Registry Definitions There is growing convergence, but still lots of disagreement Follow Us on Twitter #TimeforAnalytics  HEDIS/NCQA  Medicare Shared Savings  NLM Value Set Authority Center  Meaningful Use  NQF  Specialty Groups and Journals  OECD  WHO  And others…!
  16. 16. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics 16
  17. 17. © 2014 Health Catalyst www.healthcatalyst.com Precise Patient Registries Example Follow Us on Twitter 1 7#TimeforAnalytics Asthma Supplemental ICD9 (38,250) Medications (72,581) Problem List (22,955) ICD9 493.XX (29,805) Additional Potential Rules (101,389) 17
  18. 18. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics 18
  19. 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 Catalyst www.healthcatalyst.com Medscape Summary of Article • 11.5 million patient records • 9000 primary-care clinics across the United Follow Us on Twitter #TimeforAnalytics 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
  20. 20. © 2014 Health Catalyst www.healthcatalyst.com Types of Registries, Not Necessarily Disease Oriented Follow Us on Twitter #TimeforAnalytics 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
  21. 21. ● Facilitates analysis for Patient Relationship Management (PRM) ● Can drive reminders for research and standards of care protocols © 2014 Health Catalyst www.healthcatalyst.com More Types of Registries Scheduling Events Registry Follow Us on Twitter #TimeforAnalytics 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
  22. 22. © 2014 Health Catalyst www.healthcatalyst.com Innumerable Uses & Benefits Registries Follow Us on Twitter #TimeforAnalytics How well am I managing diseases? Who else is treating patients like this? How does my drug perform in disease prevention, progression, and cure? 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?
  23. 23. 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 © 2014 Health Catalyst www.healthcatalyst.com On Registry Follow Us on Twitter #TimeforAnalytics 23 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 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.
  24. 24. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics 24
  25. 25. © 2014 Health Catalyst www.healthcatalyst.com Patient Registry Engine * DISEASE MANAGEMENT * OUTCOMES ANALYSIS * RESEARCH * P4P REPORTING * CLINICAL TRIALS ENROLLMENT Follow Us on Twitter #TimeforAnalytics SCHEDULING REGISTRATION PATH TUMOR REG LAB RESULTS MEDICATIONS ICD9 CODES CPT CODES CLINICAL OBS PROBLEM LIST PATIENT VALIDATION CLINICIAN VALIDATION DISEASE REGISTRY MORTALITY INCLUSION CRITERIA & STRUCTURED EXCLUSION CODES PATIENT PROVIDER RELATIONSHIP RAD RESULTS 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?
  26. 26. The Healthcare Process vs. Supportive Data Sources © 2014 Health Catalyst www.healthcatalyst.com Diagnostic systems Lab System Radiology Imaging Pathology Cardiology Others Follow Us on Twitter #TimeforAnalytics Diagnosis Registration & Scheduling Patient Perception Orders & Procedures Results & Outcomes Billing & Accounts Receivable Claims Processing Encounter Documentation ADT System Master Patient Index Pharmacy Electronic Medical Record Results Surveys Billing and AR System Claims Processing System Patient data lies in many disparate sources
  27. 27. Geometrically More Complex In Accountable Care and Most IDNs A Data Warehouse Solves the Data Disparity Problem © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics EDW A single data perspective on the patient care process Physician Office X Hospital Y Physician Office Z
  28. 28. © 2014 Health Catalyst www.healthcatalyst.com A well designed data warehouse can be the platform that feeds many of these registries, and more, in an automated fashion Follow Us on Twitter #TimeforAnalytics
  29. 29. Mini-Case Study From Northwestern University Medicine, 2006 © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics
  30. 30. ‒ 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 © 2014 Health Catalyst www.healthcatalyst.com 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 Follow Us on Twitter #TimeforAnalytics *Northwestern University Medicine, 2006
  31. 31. • Inclusion codes based entirely on ICD9, which was a good place to start, but not specific enough © 2014 Health Catalyst www.healthcatalyst.com Inclusion & Exclusion for Heart Failure Clinical Study Follow Us on Twitter #TimeforAnalytics 31 ● 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
  32. 32. 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 “Why should this patient be excluded from this registry, even though they appear to meet the inclusion criteria?” © 2014 Health Catalyst www.healthcatalyst.com skew the data profile of the cohort Follow Us on Twitter #TimeforAnalytics On Registry Disease 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
  33. 33. Our View On “Exclusion” Evolved Excluding patients might be a bad idea in many situations At Northwestern (2007-2009), we found that 30% of patients fell into one or more of these categories: © 2014 Health Catalyst www.healthcatalyst.com 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. • Cognitive inability • Economic inability • Physical inability • Geographic inability • Religious beliefs • Contraindications to the protocol • Voluntarily non-compliant Follow Us on Twitter #TimeforAnalytics 33
  34. 34. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics 34
  35. 35. Exam History Diagnosis Code © 2014 Health Catalyst www.healthcatalyst.com Diabetes Registry Data Model Follow Us on Twitter #TimeforAnalytics Diabetes Patient 35 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 Diagnosis History Procedure Code Lab Type This data model applies to virtually all disease registries. Just change the name of the central table.
  36. 36. © 2014 Health Catalyst www.healthcatalyst.com 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 ETL Package Follow Us on Twitter #TimeforAnalytics Column Name Data Type Allow Nulls Epic-Clarity Problem List Orders Encounters Cerner Problem List Orders Encounters IDX CPT’s Billed Billing Diagnosis Inclusion and Exclusion Criteria for Specific Disease Registry
  37. 37. © 2014 Health Catalyst www.healthcatalyst.com Data Quality & The Disease Registry Follow Us on Twitter #TimeforAnalytics
  38. 38. © 2014 Health Catalyst www.healthcatalyst.com Investigating Bad Data 3345 kg = 7359 lbs Follow Us on Twitter #TimeforAnalytics Hello, CNN?
  39. 39.  “Recommend next HbA1C testing at 90 days because patient is not at © 2014 Health Catalyst www.healthcatalyst.com 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 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 Follow Us on Twitter #TimeforAnalytics  FYI Flags  CUIs  Health Maintenance Topics  Etc.
  40. 40. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics c
  41. 41. © 2014 Health Catalyst www.healthcatalyst.com Geisinger & Cleveland Clinic Make It Commercially Available Follow Us on Twitter #TimeforAnalytics
  42. 42. Nitty Gritty Data Details © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics Thank you, Tracy Vayo
  43. 43. Does your organization have a patient registry data © 2014 Health Catalyst www.healthcatalyst.com Poll Question governance and stewardship process? • Yes and it’s very active Follow Us on Twitter #TimeforAnalytics • 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
  44. 44. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics c Not exhaustive; for illustrative purposes only
  45. 45. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics c Diabetes, continued
  46. 46. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics c Not exhaustive; for illustrative purposes only
  47. 47. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics c Not exhaustive; for illustrative purposes only
  48. 48. © 2014 Health Catalyst www.healthcatalyst.com Follow Us on Twitter #TimeforAnalytics c Sepsis, continued
  49. 49. vendor space, but most vendors are stuck on ICD codes, only © 2014 Health Catalyst www.healthcatalyst.com 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 • For questions and follow-up, please contact me • dale.sanders@healthcatalyst.com • @drsanders Follow Us on Twitter #TimeforAnalytics
  50. 50. 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 © 2014 Health Catalyst www.healthcatalyst.com Thank You Follow Us on Twitter #TimeforAnalytics

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