The Role of Data Lakes

in Healthcare

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About Perficient

Perficient is the leading digital transformation

consulting firm serving Global 2000 and enterprise

customers throughout North America.

With unparalleled information technology, management consulting,

and creative capabilities, Perficient and its Perficient Digital agency

deliver vision, execution, and value with outstanding digital

experience, business optimization, and industry solutions.

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Perficient ProfileFounded in 1997

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Speaker Introductions

Juliet Silver, Director, Enterprise Strategy, Healthcare

Juliet provides strategic thought leadership and leverages her more than 20 years

of healthcare industry, management consulting and technology experience to

support healthcare clients in the realization of their strategic vision.

Jill Corcoran, Senior Technical Architect, Healthcare

Jill has more than 20 years of consulting experience focused on helping clients

solve complex business challenges by providing enterprise, data and business

intelligence architectural solutions that transform the way they think about,

organize, and leverage their data.

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Healthcare Data Lake Concepts

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Data Lakes in HealthcareWhat

A Data Lake, as originally coined, is designed to

hold raw data assets of varied types as they are

received from their sources. Typically the lake is

stored in an Hadoop ecosystem with minimal (if any)

change to the original format and no content

integration or enhancement of the source data.

Why

Healthcare organizations are attracted to the

concept of a data lake as it allows for in-depth

analysis of patient outcomes, fraud, waste and

abuse, R&D for drugs and DME, and clinical trials.

How

A Data Lake offers schema-on-read access to large

amounts of widely varied information that can be

loaded and accessed rapidly. This allows skilled

data scientists to uncover hidden correlations,

obscure patterns, disease trends, and more.

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The Need for a Data Lake in Healthcare

“Do we need an enterprise data warehouse, a data lake,

or both as part of our overall data architecture?”

• A Data Lake provides the ability to manage the fluid data requirements of

contemporary healthcare organizations as they attempt to rapidly analyze large

volumes of data in batch or real-time from an extensive range of sources in a

variety of formats.

• An enterprise data warehouse provides the strategy-driven, non-volatile

transformed data used to run day-to-day operations and make informed business

decisions based on known processes and thoroughly vetted data leveraging more

traditional reporting, visualization, and analytics.

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Data Lake Traits

• Time to value in data delivery is accelerated

• Uses various tools which apply “schema-on-read"

• Introduces and reuses tools and processes that

improve search and general knowledge of the data

content

• Designed for low-cost storage for large data

volumes

• Is highly agile and reconfigurable

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Healthcare Data Lake

Use Cases

• Genomic analytics used by health plans

• Improved clinical trials

• Predictive healthcare costs

• Member/Patient 360° view

• Billing opportunities in unstructured text

• Psychographic prescriptive modeling

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Use Case: Genomic Analytics Used by InsurersThe Genetic Information Nondiscrimination Act of 2008 (GINA) protects Americans from

discrimination based on their genetic information in both health insurance and employment.

But we can, and have access to the largest-ever collection of human protein-coding genetic

variants (over 10 million variants), from the Exome Aggregation Consortium (ExAC). The challenge

for healthcare is not how to use genomics data but dealing with massive amount of data.

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Use Case: Improved Clinical Trials

The analysis and design of clinical trials can discover drug combinations with significant

improvements for overall survival and toxicity. Using these statistical models we can develop

optimization models that select treatment regimens that can be tested in clinical trials, based

on the totality of data available on existing.

Existing models can be expanded upon by using published research as an external source

of data during clinical trials.

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Use Case: Predictive Healthcare CostsThe data you thought would be useful … was not

• 113 candidate predictors from structured and unstructured data sources

• Structured data was less reliable then unstructured data – increased the reliance on unstructured data

Unexpected indicators emerged from unstructured content

• Increased the value of the Predictive Model

• 18 accurate indicators or predictors

Predictor

Analysis

% Encounters

Structured

Data

% Encounters

Unstructured

Data

Ejection

Fraction

(LVEF)

2% 74%

Smoking

Indicator

35%

(65% Accurate)

81%

(95% Accurate)

Living

Arrangements

<1% 73%

(100% Accurate)

Drug and

Alcohol Abuse

16% 81%

Assisted Living 0% 13%

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Use Case: Member/Patient 360°View

Member/Patient 360◦

• Improve decision making

• Enhance patient experience

• Provide a greater opportunity

for improved outcomes

• Improve profitability for both the

provider and the health plan

• Reduce unnecessary and

inefficient processes and procedures

When applied across a large

population of patients you can:

• Predict disease outbreaks

• Identify preventative care

• Develop cures for diseases that touch

specific demographics or patient population segments

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Use Case: Billing Opportunities in Unstructured Text

• The analysis of unstructured data can provide

significant opportunities for more complete and

fairer billing practices. This information is held

by providers and payers but rarely reviewed as

the amount of detail is overwhelming. Using

keyword searches across vast amounts of data

quickly produces meaningful insight.

• Transcripts of physician’s notes show pre- and

post-procedure exam tests, labs, and related

minor procedures performed unbilled

• Large U.S. health plan compensated on per-

patient basis discovered co-morbidities

allowing them to apply risk adjustments to

segments of their patient population

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Use Case: Psychographic Prescriptive Modeling Adding psychographic data from patient healthcare records (PHR) can provide considerable insight into

additional disease risk factors. One example of this would be The Framingham Heart Study with more than 1000

published medical papers related to the study it is one of the most widely known evidence-based studies. One of

the key discoveries was that heart disease is effected not only by measurable factors (such as blood pressure,

and cholesterol) but also demographic (age, gender, and race) and psychographic factors (values, attitudes, and

lifestyles) as well.

Basic Framingham Analysis Predictor Importance

Designing and Developing

the Data Lake

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Stocking the Data Lake

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Provider Data Lake Healthcare Sources

Provider

Data Lake

PatientRecords

PhysicianNotes

DigitalImages

MedicalDevice

Financials

HealthInfo Sys

External Sources

Health Plan

Gov’tAgencies

AccountableCare Orgs

Geo-Political

Wearables

Research

Provider Sources

SystemSources

Security

Log Data

Metadata

WebSources

SocialMedia

Email& Chat

WebContent

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Payer Data Lake Healthcare Sources

Payer

Data Lake

Provider Sources

Provider

NetworkFinancials

Health Plan SystemSources

Security

Log Data

Metadata

WebSources

SocialMedia

Email& Chat

WebContent

Claim

Encounter

Member

Marketing

Rx Claim

External Sources

Gov’tAgencies

AccountableCare Orgs

Geo-Political

Wearables Genomic

PHR / PGHD

Research Survey Standard Codes

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Big Data Landscape Components

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The Enterprise Data Landscape

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Introducing Hadoop to the Enterprise Data Landscape

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Best PracticesAssessment

• Genuine need based on the 4 Vs

• Understanding the ‘Big’ Picture

• Mature metadata procedures in place

• Active governance with majority participation

Planning

• Executive suite backing and participation

• Fully vetted use cases

• Staffing and training plan

– Infrastructure Architect

– Big Data Architect

– Data Scientist

Implementation

• Start slow in digestible portions (usable POC)

• Employ technical project management

• Maintain strong scope management

• Small set of very skilled users for initial deployment

• Bring all data that will answer the questions

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Summary• Data Lakes deliver the power to share data and rapidly explore, discover, and

predict patterns of risk, cost, and improved outcomes and engagement

• Provides the foundation for research and ad-hoc data science to occur across

a variety of large volume data sets

• Integral to evidence-based care and clinical genetics programs

• Need for genomics data

• Pedigree data

• Personal health information

• Geo data sets

• Psychographic data

• Requires advanced data management and data science skill sets

• Should be governed through an Information and data governance structure

• Sets use case and data priorities

• Oversees data risk, security, and compliance

QuestionsType your question into the chat box

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