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Healthcare Enterprise Data Model:

The Buy vs. Build DebateNovember 18, 2014

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Perficient is a leading information technology consulting firm serving clients throughout

North America.

We help clients implement business-driven technology solutions that integrate business

processes, improve worker productivity, increase customer loyalty and create a more agile

enterprise to better respond to new business opportunities.

About Perficient

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• Founded in 1997

• Public, NASDAQ: PRFT

• 2013 revenue $373 million

• Major market locations:

• Allentown, Atlanta, Boston, Charlotte, Chicago, Cincinnati,

Columbus, Dallas, Denver, Detroit, Fairfax, Houston,

Indianapolis, Minneapolis, New York City, Northern

California, Oxford (UK), Philadelphia, Southern California,

St. Louis, Toronto, Washington, D.C.

• Global delivery centers in China and India

• >2,200 colleagues

• Dedicated solution practices

• ~90% repeat business rate

• Alliance partnerships with major technology vendors

• Multiple vendor/industry technology and growth awards

Perficient Profile

BUSINESS SOLUTIONS

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Our Solutions Expertise

Healthcare Practice

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HEALTH PLAN PROVIDER

Connected

Health

Business Intelligence

and Analytics

Interoperability

& Integration

Information

Exchange

Regulatory

Compliance

Solutions & Services

CONSUMERS

Select Clients

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David Meintel

Director of Healthcare, PerficientDavid has 20 years of experience in data warehousing

and analytics in a wide range of industries. Focusing

this experience in the last 4 years exclusively on the

healthcare industry, David has worked with a large

number of provider and health plan organizations to

assist them in better leveraging their data assets.

Our Speaker

Agenda

• Brief look at the challenge in healthcare causing the need

for a data warehouse

• Evolution of the solution to the challenge

• Discuss key factors in the decision to buy or build your

data model

• Reality!

The Challenge

Regulatory

Compliance

Population

Health

Management

Reduce

Re-Admissions

Service-Line

Management

Claims

Analysis

Other…

Evolution of the Solution

Organizations move from siloed solutions to consolidated

operational data stores to enterprise warehouse…

Operational

Enterprise Data Model

Data structures to hold all of the key decision making

information for the enterprise integrated across subject areas

such as:

Clinical Claims Financial Operational

Enterprise Data Model

• Architected to be extensible

• Phased implementations

• The “single version of the

truth” for the organization

• Have evolved greatly over

the years

Enterprise Data Model (example)

Core Provider Content Payer Content Supply Chain Content

Core Master Data

Provider Patient Data

Payer Patient Data

Supply Chain Data

•Person•Patient•Organization•Facility

•Distributors•Product Pricing Tier•Price Activation•General Ledger•Accounts Payable•Accounts Receivable•Contract (Supplier)

•Member (Insured)•Subscriber•Member Enrollment•Member Enrolled PCP

•ADT Event (Hospitalization)•Biospecimen•Encounter•Finding•Lab Result

•Payer•Regulatory Agencies•Contact•Groupings/Hierarchies•Unified Standard Codes

•Supplier •Wholesaler •Distributor•Unified Standard Codes

•Practitioner•Unified Standard Codes•Groupings/Hierarchies•Reference Data

• Allergy• Diagnosis• Discharge Summaryand Instruction • Imaging• Procedure• Surgery• Survey

•Appointment•Bill•Bill Encounter•Care Team & Role•Contract (Patient)•Contracts•Disease Management•Encounter•Episode

•EOB•COB•Provider Network•Campaign•Premium Payments

•Location•Order•Patient Life Event•Patient Medications; Includes Order, Dispense, Administration, Formulary•Problem (chief complaint, etc.)

•Event (General)•Exposure & Lifestyle•General Ledger•Location•Patient Satisfaction•Payroll•Immunization•Product Master

•Policy•Coverage•Benefit•Claims (all types)•Claims (Adjustments)

•Purchase Order•Invoice•Product Item Master•Purchase History•Contracts•Suppliers•Wholesalers

•Unified Standard Codes•Groupings/Hierarchies

•Ambulatory Encounter (date, time, type)•Ambulatory procedure•Ambulatory Test order•Ambulatory Result•Ambulatory Diagnoses•Ambulatory Billing

•Wellness Targets•HEDIS Measures•PQRS Measures•ACO Measures•Quest Measures

•Agreement•Money Item•Financial Transaction•Claim Recovery

Buy vs. Build

Key areas to consider:

– How experience plays into the equation

– How is time to value impacted

– How is risk factored in with the decision

– How easy is it to integrate other solutions

– How the decision to build vs. buy can

impact your internal team

– How will cost be impacted

Experience

Key points to consider – Buy

• Experience comes bundled in

• Top sellers of EDM have years of experience both technical and healthcare

• Clinicians, technologists and business analysts to bridge the two

– Build• Does your current staff have the requisite experience?

• If not, you will likely need to hire or contract someone who does

• In addition to IT resources with technical skills, this will require plenty of support from your line of business resources (clinicians, etc.)

Many of our customers have existing data warehouses that we end up replacing and most were built internally.

Time to Value

Key points to consider – Buy

• Large portion of design is already done for you

• Customizations will need to be done regardless of what your vendor tells you

• Often times you can implement in a phased approach to reduce time to value even more

– Build• The more experience your team has the faster this will go

• Often time subject area driven to reduce time to value

• Only build what you need, can reduce time as well

Implementing something that is already designed, will be faster than designing and building it yourself.

Risk

Key points to consider

– Buy

• Lower typically due to– Level of experience and resources that went into it

– Refined over time (customer experiences incorporated)

– Repeatable so potential issues are known

• Sometimes monolithic in nature can cause scope to grow larger than needed

– Build

• Higher typically due to– Organizations typically don’t do this lots of times, so no experience

– Common mistakes are likely to be encountered due to lack of experience

Manage scope and leverage experience to mitigate risk.

Integration / Accelerators

Key points to consider

– Buy

• Standardized models encourage accelerators

• Integration points need to be standard and often times take a lowest common denominator approach, which sometimes causes slight increase in effort

– Build

• If you build it yourself, you have the ultimate in flexibility to design interface the way you want them

• Integrating with outside solutions will likely take more effort due to no standard approach for vendors to build to

When standards exist, so does common integration points and pre-built accelerators…don’t reinvent the wheel.

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Integration / Accelerators

Staff Impact

Key points to consider – Buy

• Many key decisions on design are made for you

• Staff doesn’t have to be experts in designing model which lessens the pressure in early phases

• Learning curve for potential new technologies– Modeling tool, ETL tool, Metadata management etc

• Consulting assistance for peeks in need often more effective than hiring a large team

– Build• Every decision is yours to make, increases pressure

• Analysis paralysis is a common issue, trying to make perfect

• Leverage strengths of team in choosing technologies

Make sure staff is setup for success, not failure.

Cost

Key points to consider

– Buy

• Initial purchase price may

seem high

• Total cost of ownership

generally lower

• Other accelerators also help

to reduce total cost of

ownership

– Build

• Pay as you go

• Paying to reinvent the wheel

Buying might cost

more up front, but will

save money over time.

Summary

Experience – Leverage experience of people who have done what you are trying to do

Time to Value – Implementing a model that is already designed will be faster

Risk - Managing scope and leveraging experience will mitigate risk

Integration / Accelerators – Don’t reinvent the wheel

Staff Impact – Know your staff and make sure they are set up for success

Cost – Buying a model might have higher initial costs, but building will cost more over time

Reality, Buy and Build

Buy– Data model

• Leverage the experience of many

• Don’t reinvent the wheel

– High value initial use case• Physicalize required portions of the data model

• Connectivity to your source systems to populate model

• Gain quick value for investment made in model

Build– Custom analytics for your use case

• Leverage existing investments in tools

• This is what your end user with interact with the most

– Additional use cases• Follow patterns from initial use case

• Leverage experience and iterate

Questions?

Questions?

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David MeintelDavid.Meintel@Perficient.com | Phone: 646.457.4865