Understanding the High Prevalence of Low-Prevalence Chronic Disease Combinations: Databases and Methods for Research. HMO Research Network Dataset

09/20/2013

HMO Research Network Virtual Data Warehouse

References:

National Cancer Institute. HMO Research Network. 2013. http://epi.grants.cancer.gov/pharm/pharmacoepi_db/hmorn.html

Database Description
White Paper(s): Data Systems and the Prevalence of Chronic Disease Combinations & Multiple Chronic Conditions and Disparities.
Sponsorship: HMO Research Network
Description: The HMORN Virtual Data Warehouse is a series of datasets developed from data submitted from 19 healthcare delivery organizations with integrated research practices. The purpose of the HMORN VDW is to provide a means by which to conduct broad spectrum population-based research studies to ultimately improve patient health and transform health care practice. HMORN research includes the following topics: biostatistics, mental health, cancer research, comparative effectiveness research, complementary & alternative medicine, communication & health literacy research, dissemination & implementation, epidemiology, genetic research, disparities research, health informatics, health services, infectious & chronic disease surveillance, patient-centered care, pharmaco-epidemiology, primary & secondary prevention, systems change and organizational behavior.
Database: (Scope, Size, Setting, Population, Age Range) The HMORN VDW is a consortium of 19 healthcare delivery systems that submit claims and EHR data for all patients.
Database Type: (Survey, Registry, Research Study, Program Database, Claims, Administrative Data, and Clinical Databases) Virtual Database - Data is housed at individual HMOs but can be accessed from anywhere.
Database Source/Origin: Administrative Data, Claims Data, & Electronic Health Record Data (which includes clinical data).
Date or Frequency of Data Collection: n/a
Longitudinal vs. Cross-sectional Database: Longitudinal
Data Collection Methodology: Programmers at participating sites transform EHR and claims data elements from local data systems to a VDW standardized set of variable definitions, names, and codes. The common structure allows for programming code developed at one site to be used at other sites to extract and analyze data for a research throughout the network.
Sampling Strategy: All Patients
Unit of Analysis: Patient
Diagnosis Information
Diagnosis Variable Type: (Chronic Condition Status, Principal Diagnosis, Primary Diagnosis, Secondary Diagnosis, Admit/Discharge Diagnosis and Self-Reported Diagnosis) Primary and secondary diagnoses.
Diagnosis Codes: (ICD-9, ICD-10, SNOMED) ICD-9-CM (other: CPT-4 & HCPCS, NGC, CPI)
Number of Diagnoses Captured: n/a
Cost, Utilization & Clinical Information
Measures of Cost: (Claims, Out-of-pocket expenses, Self- reported expenditures, and Prescription Drug Costs) Insurance Claims
Measures of Healthcare Utilization: (Number of Visits, Any Procedures/Number of Procedures/Type of Procedure, Number of Admission/Type of Admission, Length of Stay, Hospitalizations, Emergency Department Utilization, etc.) Inpatient & Outpatient Visits
Measures of Healthcare Access: n/a
Demographic Information: (Sex, Age, Race, Ethnicity, Marital Status, Disability Status, Language, Insurance Type, Educational Attainment). Age, gender, race, ethnicity, insurance type, Hispanic vs. non-Hispanic, Educational Obtainment.
Clinical Information: (BMI, Medical Conditions [high blood pressure], Smoker Status, History of Various Conditions, Preventative Health Measures , Activities of Daily Living, Instrumental Activities of Daily Living) Height, Weight, BMI, blood pressure, Laboratory Results, Tumor Status, Tumor Staging, prescription drug use.
Measures of Socioeconomic Status: (Occupation, Employment Status, Income, Wealth, Place of Residence, Household Size & Composition, geographic location) County, State, Zip, Income
Site of Service Information: Type of encounter, provider type, facility type.
Measures of Healthcare Outcomes: (Mortality, Morbidity, Mobility, Functional Status, Quality of Life, Quality Measures, Quality of Care, Readmissions) Discharge Disposition
Strengths, Limitations & Feasibility
Data Strengths: Data submitted to this warehouse is continuously vetted and cleaned. Data maintained in this warehouse can be analyzed using programs written at any HMO.
Data Limitations: Data is only submitted from health plans in twelve states.
Data Access Restrictions: n/a
Data Linking Feasibility: (Unique identifiers or sufficient demographics to allow for data linkages) Although demographic information is available, a special emphasis of this database is to keep records anonymous.
Related Grouping Systems: All ICD-related grouping systems.

 

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