Accelerating Insights in Healthcare with “Big Data” with HaDoop - 4152

Ed Macko – CTO Healthcare Darwin Leung – Director of Informatics Applications, IBCAlex Zeltov – Research Scientist, IBCJoel Vengco – CIO, Baystate

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IBM Smarter Care uncovers valuable insights into lifestyle choices, social determinants, clinical and financial factors that effect the overall health of an individual …

Social Demographic determinants such as where one is born, grows, lives, works and ages have direct impact on an individual’s overall health, mental health and well-being.

Lifestyle Choices have direct impact on an individual’s mental and physical wellness.

Clinical Factors such as specific medical symptoms, history, medications, diagnoses, etc are indicators of an individual’s health.

FinancialCosts, insurance, reimbursement, incentive to modify behavior, new payment models, co-pays, etc. will pay a significant role.

Every organization is on its own analytics journey

Foundational

• What happened?• When and where?• How much?

Advanced, Predictive

• What will happen?• What will be the

impact?

•Dashboards•Clinical data repositories•Departmental data marts•Enterprise data warehouse

BI Reporting

•Enterprise analytics •Unstructured content analytics•Outcomes analytics•Evidence-based medicine

Population Analytics

•Streaming analytics•Similarity analytics•Personalized healthcare•Consumer engagement•Cognitive Computing

Care Optimization

Prescriptive

• What are potential scenarios?

• What is the best course?• How can we pre-empt and

mitigate the crisis?

• How are you measuring and reducing preventative readmissions?

• How are you providing clinicians with targeted diagnostic assistance?

• Which patients are following discharge instructions?

• How are you using data to predict intervention program candidates?

• Would revealing insights trapped in unstructured information facilitate more informed decision making?

Physician notes and discharge summaries Patient history, symptoms and non-symptoms Pathology reports Tweets, text messages and online forums Satisfaction surveys Claims and case management data Forms based data and comments Emails and correspondence Trusted reference journals including portals Paper based records and documents

Over 80% of stored health information is unstructured*

Does unlocking the unstructured data help accelerate your transformation?

... Biggest blind spot still remains unstructured data

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BIOGRAPHY

Independence Blue Cross

Darwin Leung Director, Informatics Application Development and Operations Responsible for the development of analytical applications across

the Informatics Division for Independence Blue Cross.

Alex Zeltov Research Scientist, Advanced Analytics Lead the development and research of Big Data initiative and

predictive analytics.

Contact Info: Email: darwin.leung@ibx.com / Phone:215.241.2255 Email: alex.zeltov@ibx.com / Phone: 215.241.9885

Independence and its subsidiaries and affiliates

Data Warehouse

We serve more than 7 million people in 24 states as well as the District of Columbia – 4 million through our medical coverage and administrative services, and 3 million through pharmacy, dental, and vision coverage and other ancillary products.

AmeriHealth Caritas

AmeriHealth Administrators

AmeriHealth Administrators

AmeriHealth Caritas

AmeriHealth Caritas

AmeriHealth Caritas and AmeriHealth Administrators

AmeriHealth Caritas

AmeriHealth

AmeriHealth Caritas

AmeriHealth Administrators

AmeriHealth Caritas

AmeriHealth CaritasPA & NJ MarketIndependence Blue CrossAmeriHealthAmeriHealth AdministratorsCompServicesAmeriHealth Caritas

AmeriHealth Caritas

AmeriHealth Caritas

AmeriHealth and AmeriHealth Administrators

AmeriHealth Administrators

Medical, service, and ancillaryMedical and ancillary

Service and ancillary

Medical

Service

Ancillary

AmeriHealth Caritas and AmeriHealth Administrators

AmeriHealth Caritas and AmeriHealth AdministratorsAmeriHeal

th Caritas

What are key business drivers that require “Big Data” solution @ IBC ?

Apply text analytics to all information available for different business cases. 

Need to bring all information (structured and instructed) to a level where our technologies can be applied.

Use advanced predictive analytics for various business use cases.

Apply search technologies to all of our structured and unstructured data

Business Cases

Product Recall Nurse Chart Review Process Predictive models:

– Customer complaints / grievances – Diabetes– Likelihood of hospitalization

Sentiment analysis Text Search on Electronic Medical Records/Data

Business Case: Product Recall

The text mining process helps identify the manufacturers that are on recall list.

Scheduled report alerts with potential identified members that match the recall manufacturers.

Create a database of extracted patient and manufacturer information.

The OCR + Text mining process analyzes charts 300+ pages long on average

Generated reports on the OCR results in IBM BigSheets

Business Case: Product Recall

Nurse Chart Review Process The text mining process helps identify conditions and

diagnoses based on the medical ontology matches for the nurse review.

The text analytics priorities the charts for nurse review, the highest scored EMR charts are presented first for the nurse review process.

The nurse has the ability to open the text version of the chart that was created part of the OCR process to the exact location of the matched terms in the scanned version of chart.

Predictive models:

Customer complaints / grievances

Diabetes

Likelihood of hospitalization

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CTM and Grievances Rates

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Issue: Identify Members with a High Likelihood to file a CTM/Grievance

Results:– Customer Satisfaction– STAR Ratings

Likelihood to File a CTM : Pre-Intervention

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Likelihood to File a CTM

0

.50

1.0

Unlikely Very Likely

.25

.75

David Pierce

John Doe

Barbara Wilson

Jessica Smith

Mary Miller

“Benefits”

“Upset”

“Bill”

Outreach Intervention

17

17

“Hello John Doe. I see that you called yesterday about a billing issue. How can I

assist you?

Likelihood to File a CTM : Post-Intervention

18

18

Likelihood to File a CTM

0

.50

1.0

Unlikely Very Likely

.25

.75

David Pierce

John Doe

Barbara Wilson

Jessica Smith

Mary Miller

“Positive Interventio

n”

“Great Customer

Experience”

“Better Stars

Rating”

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BIOGRAPHY

Joel Vengco Chief Information Officer Baystate Responsible for xxxxthe development of analytical applications

across Baystate

Contact Info: Phone:xxx.xxx.xxxx Email: joel.vengco@baystate.com

Who is Baystate ? Give 1 page overview of Baystate (who do you serve,

# provider, # patients, demographics, etc. )

Q & A

Thank You