An Overview of Disease Registries

Disease Registries: Translating P4P and other Quality Measures into EpicCare

September 20, 2007

Users’ Group Meeting

The Faculty Practice Plan of Northwestern’s Feinberg School of Medicine

http://www.epicsystems.com/index.php

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Agenda

NMFF Overview

Epic Implementation History

Patient Registries History and Overview

Our Experience building a Disease Registry

After the Build

Lessons learned

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Chicago Shoreline

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Feinberg Pavilion (Inpatients) & Ambulatory Care Center

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Prentice Women’s Hospital: Opening October 2007

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Our MissionNorthwestern Medical Faculty Foundation is the

regionally and nationally recognized physician group at the Feinberg School of Medicine, Northwestern University. Our physicians and staff use innovative

clinical practices and technology and a multidisciplinary approach to provide optimal patient

care and service. We support the clinical and academic activities of the Feinberg School and create an environment where the best medical

practices are demonstrated and learned.

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About Northwestern Medical Faculty Foundation (NMFF)

Private, independent academic faculty practice plan founded in 1980

Multi-specialty group practice for over 600 member physicians who are all full-time faculty of Northwestern University’s Feinberg School of Medicine (NU FSM)

Physician led

Not for profit

Provides care for indigent patients

NMFF Overview

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Northwestern Medical Faculty Foundation Facts

Over 600 physicians and 1,101 staff at the end of FY06

17 departments, 34 specialties

Just under 571,000 outpatient encounters in FY06

Total clinical revenue $346 million in FY06

We occupy roughly 268,000 square feet of clinical space for outpatient care in the Ambulatory Care Center (ACC)

NMFF Overview

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Relationship to Northwestern University Feinberg School Of Medicine (NU FSM)

NMFF members are full-time NU FSM faculty

• Clinical care of patients

• Ground breaking clinical research

• Training next generation of physicians

Faculty for over 600 medical students and 500 residents and fellows (11,000 hours of teaching)

NMFF’s Ambulatory Care Center provides the venue for outpatient teaching and clinical research

NMFF Overview

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Epic Implementation History• Pilot: 1996 NLM Project to Pilot EpicCare in GIM

• Awards: 1998 Davies Award Winner

• Implementation in 31 specialty practices (2001 – 2006)

• 97% Implemented in 32 specialties (2007)• Non-Implemented Specialties

• Reproductive Endocrinology and Infertility• Ophthalmology• Trauma/Critical Care

• Epic Products: Bridges, Clarity, EpicCare, Identity, MyChart

• Epic Version: Epic Fall 2006 version (Spring 2007 IU1 upgrade scheduled for October 2007)

• 3057 Active user accounts


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EpicCare Implementations in Specialty & Sub-Specialty Areas Allergy

Anesthesia/Pain Medicine

Cardiology

CardioThoracic Surgery

Dermatology

Endocrine/Metabolism

Gastroenterology

General Internal Medicine*

General Neurology

General OB/GYN

Geriatrics

GI-Endocrine Surgery

Gynecology Oncology

Gynecologic Surgery

Hematology Oncology

Hepatology

Immunotherapy

Interventional Radiology

Lynn Sage Breast Center

Maternal Fetal Medicine

Nephrology

NeuroBehavior

NeuroSurgery

Northwestern Ovarian Cancer Early Detection Program

Orthopedics/Sports Medicine

Otolaryngology

Plastics Surgery

Psychiatry

Pulmonary

Reproductive Genetics

Rheumatology

Surgical Oncology

Travel Medicine/Immunizations

UroGynecology

Urology*

Vascular Surgery*MyChart Department


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Agenda

NMFF Overview




After the Build

Lessons learned

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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

“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.

Patient Registries History & Overview

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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.”

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."

http://effectivehealthcare.ahrq.gov/reports/registry/registry.htm


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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”


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Types of Registries

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


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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


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Varying Benefits

Clinicians

Registries

Drug Manufacturer

Physician Organization Consumer

How do I analyze patient trends and outcomes for a disease?

How are my clinicians managing diseases?

How does my drug perform in disease prevention and cure?

How do I know which drug/procedure works best for me?


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Uses for Patient Registries

To observe the course of disease

To understand variations in treatment and outcomes

To examine factors that influence prognosis and quality of life

To describe care patterns, including appropriateness of care and disparities in the delivery of care

To assess effectiveness

To monitor safety


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Current Trends measuring Quality using Registries

The IOM defines quality as “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.”

Quality-focused registries are being used increasingly to assess differences between providers or patient populations based on performance measures that compare:

• Treatments provided or outcomes achieved with “gold standards” (e.g., evidence-based guidelines)

• Comparative benchmarks for specific health outcomes (e.g., risk-adjusted survival or infection rates)


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Quality Management Reporting - Example

Eligible Satisfied Rate

Preventive Services Cervical Cancer Screen 223 146 65%

Mammogram 138 83 60%

Colorectal Cancer Screen 355 143 40%

Pneumonia Vaccine 144 33 23%

Osteoporosis Screened or on Treatment 75 44 59%

Cardiovascular Disease HTN: good BP control (mean or last <= 140/90) 310 196 63%

CAD: antiplatelet medication 62 54 87%

CAD: lipid lowering medication 65 54 83%

CAD: Beta blocker post-MI 12 10 83%

CAD: ACE/ARB if DM or LVSD + CAD 25 19 76%

CHF: anticoagulation for AF + HF 6 5 83%

CHF: ACE/ARB if LVSD 3 3 100%

CHF: beta blocker if LVSD 3 3 100%

Diabetes Last Hba1c <= 7 87 37 43%

Last Hba1c <= 9 87 66 76%

Good BP control (mean or last BP <= 130/80) 83 39 47%

Good LDL control (<100) 87 49 56%

Nephropathy: screened or on ACE/ARB 87 64 74%


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Getting the most out of your disease registry (Our Interpretation)

Consistent profiling for prospective, predictive intervention

• The goal is to keep people off of disease registries, but first you have to know how those who are on the registry, got there…

Outreach communication to patients

• Reminders about care and intervention

Ensuring a common understanding for inclusion, exclusion and disease management.

Quality of care reporting (e.g. P4P)

• Cost effective & treatment efficacy to payers & employers

• Feedback reports to physicians about their care practices

Process improvement projects for service line clinical programs

• Use trend analysis to find possible process deficiencies that affect patient care

Population reporting and analysis for research (e.g. Epidemiology)


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Agenda

NMFF Overview




After the Build

Lessons learned

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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

Our Experience Building a Disease Registry

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Patients exist in one of three states, relative to a patient registry

On Registry: The patient is a member of a particular registry; i.e., they fit the inclusion criteria

Off Registry: Patient was once a member of a registry and fit the inclusion criteria, but is now excluded. The exclusion could be “disease free.”

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.

Off RegistryAt Risk On Registry

Disease Registry


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Patient Registry Vision

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?


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Disease Registry Exclusions

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

– Patient is voluntarily non-compliant with the care protocol

– Patient is incapable of complying with the care protocol

Off RegistryAt Risk On Registry

Disease Registry


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Our disease registry is populated by patient care cycle

Disease Registry

Patient Data(Clinical, Business, etc)

Pay for Performance

measures

Pay for Performance

measuresPt. included in Disease Reg.

off Original Diagnosis

How do I build this?

Original Diagnosis Continued Care Continued Care Cured


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The Healthcare Process and Transactional Systems at NMFF

Diagnostic systemsLab SystemRadiologyImagingPathologyCardiologyOthers

DiagnosisRegistration &

SchedulingPatient

PerceptionOrders &

ProceduresResults & Outcomes

Billing &Accounts

Receivable

Claims Processing

EncounterDocumentation

ADT SystemMaster Patient Index

Pharmacy ElectronicMedical Record

SurveysResults

Billing and ARSystem

Claims ProcessingSystem

Patient Data lies in various data sources

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The Northwestern Campus : Multiple, Collaborative, Organizations

EDWA single data perspective

on the patient care process

Diagnostic systems•Lab System•Radiology•Imaging•Pathology•Cardiology•Others


SchedulingPatient

PerceptionOrders &


Billing &Accounts

Receivable

Claims Processing


•ADT System•Master Patient Index


Surveys•Diagnostics•Pharmacy






SchedulingPatient

PerceptionOrders &


Billing &Accounts

Receivable

Claims Processing








SchedulingPatient

PerceptionOrders &


Billing &Accounts

Receivable

Claims Processing


•ADT System•Master Patient Index•ADT System•Master Patient Index

PharmacyPharmacy ElectronicMedical Record

ElectronicMedical Record

SurveysSurveys•Diagnostics•Pharmacy•Diagnostics•Pharmacy







SchedulingPatient

PerceptionOrders &


Billing &Accounts

Receivable

Claims Processing










SchedulingPatient

PerceptionOrders &


Billing &Accounts

Receivable

Claims Processing








SchedulingPatient

PerceptionOrders &


Billing &Accounts

Receivable

Claims Processing












SchedulingPatient

PerceptionOrders &


Billing &Accounts

Receivable

Claims Processing










SchedulingPatient

PerceptionOrders &


Billing &Accounts

Receivable

Claims Processing








SchedulingPatient

PerceptionOrders &


Billing &Accounts

Receivable

Claims Processing










Physician Office X

Hospital YPhysician Office Z

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Disease Registry

Patient Data(Clinical, Business, etc)

Pay for Performance

measures

Pay for Performance

measuresPt. included in Disease Reg.

off Original Diagnosis

Original Diagnosis Continued Care Continued Care Cured

How do I build this?


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Basic steps to build a disease registry

Identify stakeholders

Identify data points necessary to define, include and exclude in disease registry

Identify source of data points

Build registry

Address data quality issues


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Identifying Data Points & Data Sources

Inclusion codes based entirely on ICD9, which is 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, NU Feinberg School of Medicine


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What does a Diabetes Disease Registry Look Like Elsewhere?


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University of Washington Physicians Network


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Harvard Vanguard


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Harvard Vanguard (continued)


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Our First Design

Keep it as flat as possible

Use consistent naming standards throughout

Keep design minimalist


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Our Extensible Design

Key Design Considerations

Used clinician input for building & defining institutional definition of the disease

Stakeholders input in defining inclusion & exclusion criteria

Disease Registry metadata contains inclusion, exclusion criteria

Added a reason for inclusion description for disease registry

The disease registry data model was built to tie the patient identity back to data points in the data warehouse which includes all EMR data sources.

Motives

Consistent profiling for prospective, predictive intervention

Outreach communication to patients

Quality of care reporting (e.g. P4P)

Process improvement projects for service line clinical programs

Population reporting and analysis for research (e.g. Epidemiology)


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Building our Disease Registry (i.e. Diabetes)

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

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

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Our First Disease Registry


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Agenda

NMFF Overview




After the Build

Lessons learned

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Analyzing Disease Registry Data

After the Build

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Recognizing Bad Data

After the Build

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Investigating Bad Data

3345 kg = 7359 lbs

Hello, CNN?

After the Build

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Strategies for managing bad data

Reactive Measures

Define the ability to flag erroneous data in the data marts (disease registries)

Eliminate erroneous data from analytical reporting

Proactive Measures

Define feedback mechanism to report bad data in the source system to the appropriate data owner.

Prevent future input of bad data in the source systems

• Add data validations in the user interface.

After the Build

garima.sharma

Changed post optimization metrics for problem list and medicatin list to measure the same patient population.

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Tying it all together

Improvement Opportunity

After the Build

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Tying Disease Registries back to Point of Care

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.

After the Build

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Agenda

NMFF Overview




After the Build

Lessons learned

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Lessons Learned

Clinical Sponsorship is necessary.

Agile development methods are useful in getting user buy-in

• They are quick

• They demonstrate work product

Defining registries shouldn’t be limited to only ICD-9 defined diseases

Try to include the reason a patient is added into a registry.

Measure and Insight can be equally significant to registries other than disease based

Need to prioritize which data sources have highest value (esp. when you have more than one EMR source)

Creating a “data bus” to traverse all available data points will create new opportunities for discovery and research.

After the Build

An Overview of Disease Registries

Documents

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