Expanding Data Warehousing into a True Management Tool
Bill McCallum, CEO/PresidentAccountable AnalyticsOctober, 2014
Operating Costs rising more rapidly than revenues
Maintaining Physician Comp against declining…
Collecting self-pay and high-deductible patients
Managing finances with the uncertainty of…
Recruiting physicians
Negotiating contracts with payers
Selecting and implementing a new EMR
Modifying physician compensation methodology
Participating in PQRI
Hiring and retaining quality staff
Top 10 Challenges Physician Response
With Shrinking MarginsEvery Dollar Counts
Quality of Benchmarks
Is the Benchmark representative of your population
Controls placed on the collection of the benchmark data
Understanding of benchmark calculations
MGMA Encounter Definition
A documented, face-to-face contact between a patient and a provider who exercises independent judgment in the provision of services to the individual. If a patient sees multiple providers on the same day for the same set of problems/diagnoses, it is considered one encounter. If a patient with multiple problems/diagnoses sees multiple providers on the same day and each provider manages a different set of problems/diagnoses, then it can be considered as multiple encounters.
Do not include: 1. Ambulatory encounters attributed to nonphysician providers;2. Encounters for the physician specialties of pathology or diagnostic radiology. (see #2 under “Include” above);3. Encounters that include procedures from the surgery chapter (CPT codes 10021-69979) or anesthesia chapter (CPT codes 00100-01999);4. Number of procedures, since a single encounter can generate multiple procedures;5. Visits where there is not an identifiable contact between a patient and a physician or nonphysicianprovider such as when the patient comes into the practice solely for an injection, vein puncture, EKG, or EEG administered by an RN or technician;6. Administration of chemotherapy drugs; or7. Administration of immunizations.
Extracts to Excel
Departmental Data Manipulations
Inconsistent Data Definitions
Data War Lords
Definitions are Consistent
Reports Tie Together
Performance is Defined by Best Practice
Problems are resolved quickly
Utopia
Does Your Report Development Remind You of Playing Battleship
Information Technology
End User
PM System Specialty Cardiology
MGMA Specialty Cardiology: Electrophysiology
Cardiology: Invasive
Cardiology: Invasive-Interventional
Cardiology: Noninvasive
Surgery: Cardiovascular
Surgery: Cardiovascular-Pediatric
Surgery: Thoracic (Primary)
Surgery: Vascular (Primary)
Medicare Specialty 06 – Cardiology
78 – Cardiac Surgery
33 – Thoracic Surgery
Apples and Oranges
Apples and Oranges
Defining a Diabetic Cohort
PQRS - Medicare Physician Quality Reporting System
HEDIS- Healthcare Effectiveness Data
and Information Set
NQF - National Quality Forum
Practice Management System
EHR
How Big is Your World
US CensusNational Provider Identifier ListMedicare Public Use DataPatient Risk Scores CDCBerenson Eggers GroupsMGMA/AMGA BenchmarksNDC Drug Classes
Looking Beyond the Your World
Geospatial Mapping
Type of referrals and where they originate
Demographics by Financial Class
Disease Management
Marketing
Recruiting
Data
Warehouse
CompensationSupport StaffMid Levels
Revenue CycleCollectionsWrite-offs
DenialsDSO
ContractingPayer Mix
Fee Schedules
InfrastructureStaffing
IT
ProductivitySpecialty Specific
QualityCoding
Disease MgmtPQRS
Collective Data Drives Decisions
Quick Wins
Downstream Revenue Dropped Charges Leakage Modeling Proposed Contracts Case Rates
Quick Wins
Compensation / Productivity Coding Avoidable Denials Adjustments vs. ANSI Denials Fee Schedules Payer Contracts Provider Panel Size and Scheduling
The Data Model
Flat File vs Multi-dimensional
Dimension Structure
Naming Conventions
Understanding the Business Case
The Data ModelFlat File vs Multi-dimensional
Crystal / Reporting Services
Flat Highly IndexedFacilitates complex subsetsRequires expert user
Multi-Dimensional
Fast EasyDesigned for specific Task
Your dimensions and their hierarchy determine the success of your deployment
Know what business cases . To many dimensions become overwhelming for the user
The Data ModelDimension Structure