SAS® Master Data Management Provides One Version of the Truth

9/13/2016

Thomas P. Donia, R.Ph.

VP, Corporate Medical Informatics

AmeriHealth Caritas

AmeriHealth Caritas Company Overview

Mission-driven organization with over 30 years of experience serving low-income and chronically ill populations

Serve more than 5.4 million Medicaid, Medicare and CHIP members in 19 States and the District of Columbia:

Full-risk Medicaid

Dual-eligible products (MMP’s/D-SNP’s)

Behavioral Health Services

Pharmacy Benefit Management

Backed by Independence Blue Cross and Blue Cross Blue Shield of Michigan

Originally founded by the Sisters of Mercy in the ER of Misericordia Hospital in 1980 as Mercy Health Plan

1992 Mercy became the first MA plan to gain full NCQA accreditation

ACFC holds 2 of the 8 NCQA Multicultural Health Care (MHC) Distinctions nationally

7 plans hold NCQA Commendable Accreditation

The Organizational Growth – Complexity Paradox

Growth since 2013: Full-risk Medicaid:

Louisiana

Florida

District of Columbia

Iowa

Michigan

MMP’s: Michigan

South Carolina

“Every successful company’s growth contains seeds of failure. At some point, organizational complexity could outweigh the business benefits. It may be almost impossible at that stage to unbundle complexity and simplify the system, due to the nature of the interdependence of all the components that that complexity has generated.”

Leandro Herrero

Architect of Organizations

Growth-induced Complexities

6 Different FACETS claims payment instances

Visual Cactus credentialing system with no interface to FACETS:

No profiling or data standardization of delegated provider rosters received from health systems

6 “Enterprise Data Warehouses” each using different reference data values

No time or budget during implementations for:

Master Data Management

Data Governance

Reference Data Management

Net Result of Rapid Growth

• Non-standardized critical demographic data elements:

• Practitioner types

• Specialties

• Board Certifications

• Mailing addresses

• Undeliverable / returned mail:

• Notifications

• Benefit and policy changes

• Payments

• Print and online Provider Directories wrought with duplication and data inconsistencies

• Provider Analytics, network adequacy reports problematic

• A plethora of short term fixes that created long-term maintenance challenges

Enterprise Challenge

“Transform AmeriHealth Caritas into a data-driven organization with world-class Advanced

Analytics and predictive modeling capabilities.”

Pathway to Advanced Analytics

Enterprise Information Management

Standard Business Intelligence

(what occurred?)

Data Exploration

(why did it occur?)

Forecasting

(what will happen in the future?)

Predictive Modeling / Advanced Analytics

(why will it happen in the future?)

Without Enterprise Information Management the accuracy and reliability of everything that follows the analytic pathway is in question

Data GovernanceMaster Data Management

Semantic Data Management

Global

Financials

Data

Warehouse

load

load publish

publish

Master Data

Registry Hub

Source A

Source B

Data

Stewards

govern

govern

govern

MASTER DATA MANAGEMENT (MDM) is the practice of cleansing, rationalizing and integrating data into an enterprise-wide “system of record” for core business activities. MDM is a critical component of an Enterprise Information Management strategy.

Business information problems MDM solves include:

Data Duplication

Lack of Standardization

Process Disharmony

Difficulty in Integrating Similar Datafrom Disparate Sources

Master Data Management

Conceptual MDM Model / Data Flow

MDM Goal:

Demonstrate that the SAS MDM product suite supports ACFC requirements for Data Quality and Enterprise Master Data Management.

Scope: Multi-Iteration Approach (2 Iterations focused on Provider Data attributes)

Iteration #1 – 60 Days (Start Date 9/15/2014)

Individual Practitioners

Iteration #2 – 90 days (start date 1/1/2015)

Groups and Facilities

177 Attributes covering Demographics, Location, Specialty and Identifiers

Data profiling and standardization

Standardize Reference Data

Removal of Duplicates

USPS Address Verification, Cleansing and Enrichment

NPI Remediation using NPPES

Trust Rules to address data value mismatches between sources

MDM Scope

MDM Implementation Timelines

11

Immediate Benefits Realized

Considerable reduction in undelivered/returned mail

Print and On-line Provider Directories are best-in-class

Repurposed 7 FTE’s from manual data manipulation for directories to data remediation

teams

Provider-based analytics turn-around time has been significantly reduced, while accuracy

is no longer in question

Advanced Analytics and predictive models are based on a single source of truth, thus are

more accurate and reliable

Estimated 3 year ROI on Provider MDM is approximately $15M

Profiling and Standardizing Data

Profile of Provider City & Zip Code Data in PA

CITY COLUMN PROFILE

INVALIDZIP CODEPATTERNS

ZIP CODE COLUMN PROFILE

INVALID ZIP CODEDATA SAMPLE

“PHILADELPHIA” DATA SAMPLE

NOSTANDARD

City Standardization in SAS MDM

REF_SRC_SYS REF_SRC_LOV_CODE REF_STD_LOV_CODE REF_STD_LOV_LONG_DESCFCTS DO DO DOCTOR OF OSTEOPATHY

VC D.O DO DOCTOR OF OSTEOPATHY

VC D.O. DO DOCTOR OF OSTEOPATHY

VC DO DO DOCTOR OF OSTEOPATHY

VC M. D. MD MEDICAL DOCTOR

VC M.D MD MEDICAL DOCTOR

VC M.D. MD MEDICAL DOCTOR

VC MD. MD MEDICAL DOCTOR

VC MD MD MEDICAL DOCTOR

VC Md MD MEDICAL DOCTOR

VC MD, MD MEDICAL DOCTOR

VC md MD MEDICAL DOCTOR

VC CNM CNM CERTIFIED NURSE MIDWIFE

VC CNMW CNM CERTIFIED NURSE MIDWIFE

VC NMW CNM CERTIFIED NURSE MIDWIFE

Profiling and Standardizing Reference Data

MDM standardized 163 Practitioner titles in FACETS /VC down to 92 standard values

Profiling and Standardizing Reference Data

Amount of Values

Decreased in MDM due to

Standardization

Parse Name Suffix in Sources & MDM

Facets Application does not provide a Name Suffix field. Suffix is included within the Last Name field.

Visual Cactus Application provides a Name Suffix but not consistently being utilized.

Finding Duplication Within Source Systems

NPI Duplicate or Practitioner Duplicate in Facets

34 NPI’s Assigned to Different Practitioners 67 Practitioners with Duplicate Set-up

*Clear Duplicates with Multiple Common Practitioner ID’s in Facets*NPI’s MUST be UNIQUE per Practitioner

Identifying Disconnects Across Systems

Mismatches in Sources – NPI & Name

NPI Mismatches – Trusted Source NPPES * 264 Instances of NPI Mismatches

*NPI Inconsistency Between Sources *Fixed in MDM using NPPES NPI

Trust rules establish NPPES as the only trusted source for NPI numbers. NPPES supersedes all sources.

Address Standardization and EnrichmentLocation Domain

Provider Location Analysis & MDM Results

Location Source Data Location MDM Data

Locations – Facets

Locations – Visual Cactus

MDM Unique Location

* All Address Types (Practice, Mail, Remit)

* All Address Types

MDM Verified Addresses

Location Comparison

* 20,691 Duplicates Merged/Removed in MDM

* Facets & Visual Cactus

FACETS SOURCE CountNumber of Locations 41,238

Number of Locations - PAR 15,729

Number of Locations - NON PAR 31,859

VC SOURCE CountNumber of locations 16,509

Location Duplicate Breakdown CountDuplicate Locations in FACETS 12,868

Duplicate Locations in VC 7,823

MDM CountNumber of Unique locations 31,127

Number of unique Locations FACETS(in MDM) 28,370

Number of unique locations VC (in MDM) 8,686

Unique

Location Count

USPS Verified

Count

USPS

Unverified

Count

GEO Code

Enriched

Count

Delivery Pt

Confirmed

31,127 27,907 3,220 31,001 27,078

ADDRESS VERIFICATION & CLEANSING RESULTS

Address Anomalies for Same Practitioner

Source - Address Anomalies

MDM – Address Cleansed, Merged & Enriched Address Enrichment

Source System Postal Code Missing Postal Code Added Postal Code Corrected in MDM

(Value provided was incorrect)

FACETS 197 126 19,558

VISUAL CACTUS 65 24 1,276

TOTAL 262 150 20,834

POSTAL CODE ENRICHMENT

Source System City Value Missing City Value Added City Corrected in MDM (Value

provided was incorrect)

FACETS 82 22 3,350

VISUAL CACTUS 37 6 532

TOTAL 119 28 3,882

CITY VALUE ENRICHMENT

SAS MDM Address Enrichment

AmeriHealth SAS MDM Screen Shots

Data Management Console Screen

28

Total Open Issues – 1,089

Identified Issues by ISSUE TYPE

Data Management Console Screen

Closed Issues

Open Issues

Using SAS Visual Analytics with SAS MDMfor Real-time Remediation Metrics

Visual Analytics Remediation Summary

Visual Analytics New Issues Summary

Visual Analytics Issues by Type

Benefits of SAS MDM

Best-in-class Provider Directories

Sample Discrepancy with Provider Directory (pre-MDM)

Snapshot of the

Online Provider Directory

Duplication

NPI First Name Last Name City State Address Line 1 Provider ID Specialty1316998412 FREIDA FISHER Reading PA 145 N 6TH ST 1ST FL 30184937 FP

1316998412 FREIDA FISHER Reading PA 145 N 6TH ST 2ND FL 20092634 CRNP

1316998412 FREIDA FISHER Reading PA 145 N 6TH ST 2ND FL 30148179 NP

1578678884 VICTORIA STELLA Reading PA 145 N 6TH ST 30017869 GP

1578678884 VICTORIA STELLA Reading PA 145 N 6TH ST 1ST FL 30184809 FP

1578678884 VICTORIA STELLA Reading PA 145 N 6TH ST 2ND FL 1006260 FP

1578678884 VICTORIA STELLA Reading PA 145 N 6TH ST 2ND FL 30148183 FP

Duplicates Removed Post-MDM

Online Provider Directory Search MDM Web Service Search

Online Provider Directory Search MDM Web Service Search

Duplicates Removed Post-MDM

Net Results

Mastered 177 provider data elements

5 entities = practitioners, groups, facilities, locations, organizations

Hierarchies established for health systems, to organizations, to groups, to providers

Standardized: Board certifications

Specialties

Titles, degrees

Name Prefix / Suffix

Addresses

Established 1 record for each entity with links to all contributing records in all source systems

Why SAS MDM?

1) Most flexible, configurable and affordable solution we reviewed

2) Phased approach enabled us to get critical pieces in production and then scale

3) Aggressive implementation was supported with staff augmentation by SAS team

4) “No party model” approach best fit our data and vision for MDM use

5) Role-based data remediation screens

6) Built-in Visual Analytics reporting engine

7) Knowledge transfer to FTE’s for in-house ownership was painless

Q & A