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SAS MELBOURNE USER GROUP28 MAY 2014

‘A Healthy SAS – big opportunities with big data’

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Health Analytics SMUG - 28th May 2014

David Cross

Bupa SMUG – D Cross

Overview

Health: Big Data, Big Opportunity

Despite the significant volume, variety and velocity of health data being generated there are few actual applications of Big Data in Healthcare.

In the short term, there is no need to create new data sources for health. There exists rich, untapped data which if used correctly can improve the efficiency and effectiveness of health interventions.

Health Analytics has some catching-up to do in comparison to other fields, but tools and approaches are ready and waiting.

28 May 2014 4


Bupa Global

528 May 2014


Bupa Australia

• Private Health Insurance• Aged Care• Health Services

Bupa Health DialogBupa OpticalDental Corp.Bupa Visa Medical

28 May 2014 6

Bupa SMUG– D Cross

Bupa Australia

728 May 2014

How is Bupa Health Dialog different?

8

• Predictive modelling - Customer stratification based on analysis of multiple data sources - clinical, socioeconomic, and

supply-side drivers (treatment variation)• Maximise engagement – Combining clinical and consumer

data, we deliver personal communications to customers/providers

1. Targeting the right people • Whole person approach aimed at building self-management

skills and GP relationship• Primary coach model

• Evidence-based tools (FIMDM, Healthwise)• Shared Decision Making (SDM) - Helping customers and

their doctors arrive at a treatment decision that reflects the customers’ own preferences and values.

2. Empowering the patient

Tight co-ordination with the whole care team including -• Health coach referrals

• Shared care plans (with consent)• SMART Registry

This image cannot currently be displayed.

3. Integrating with providers• Operational reporting - regular insight into performance

against service levels• Evaluation - Use of rigorous control-group approach, , clinical

indicators, utilisation, cost• Advocacy – monitoring customer and provider satisfaction and

advocacyThis image cannot currently be displayed.

4. Demonstrating real results

Experienced in clinical engagement and change management in diverse cultures and health systems: USA, UK, France, Germany, Spain, Australia

Informatics vs. Analytics

9

Health Informatics

IT/Computer Science

Epidemiology

Public Health

Health Informatics is a well established academic field.

Traditionally practitioners of health informatics have come from a health/clinical

background.

Analytics

IT/Computer Science

Mathematics & Business Statistics

Marketing/ Sociology

Analytics is a more recent addition to business specialities.

Health Analytics


Health: Big Data

2013 Big Data Conference

Diagnostics vs. public health

• Lots of engaging presentations

• Crowd-sourced data

• Geographic Analysis

• Quantified self

But –application was limited:

• “…if we had data we could ….”

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Health: Big Data

Does it exist?

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In the financial year ended 30th June 2013

343,970,560 MBS items were processed by Medicare

197,889,484PBS & RPBS Items were processed

At 31st March 2014

1,514,805PCeHR Consumer registrations

Bupa has 3.6 million members with > 20 million health transactions

over the last 36 months.45 Aged Care services

15 Optical services+ international services


Bupa Analytics – Value

1228 May 2014

Client Data (medical, hospital, ancillary, pharmacy etc) standardised by BHD

FACTs process applied – proprietary algorithms to identify conditions, treatments and procedures based on entire patient claims history

Predictive modelling – admission risk & financial cost, based on conditions, demographics, utilisation history

Profile of every Member compiled to enable population profiling, targeting, risk assessment etc

Performance reporting and evaluation


Our Platform

Inputs:

• HDC3 – Coaching application

• Genesys Telephony

• Claims Data

Processing:

• SAS 9.3, Linux box

• Base / Stat / Graph

Warehousing:

• Oracle

• MS SQL server

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Our Approach to Data

Use our data to create a health viewLink together the various data items which describe a persons interaction with the health system:• Hospitalisations• Medical claims• Ancillary / Allied Health Services• GP visits (where available)• Pharmaceutical (where available)

Standardised processesTransferable to other sources:• Public, Private,

Insurance/Compensation

28 May 2014 14


Our Approach to Analytics

Data is only useful if you do something with it.

• Understanding the health of a populationUse the data to uncover previously unseen featuresRecognise What needs to be done

• Targeting interventions and prevention activityOptimise the impact of the activityRecognise Who needs to be targeted

• Clinical Reporting & Pathway OpportunitiesUnderstand where change needs to occur, and where change has occurred Provide information to support the clinicians and improve care for patients

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Health: Big Opportunity

Population Analysis Generate a profile of each and every patient• Demographics• Condition attribution• Admission types• Readmissions• Frequency• Allied health provision• Social supports• Costs

Understand what is driving the essential features of the population

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FACTs

1728 May 2014


Population Profiling

1828 May 2014

SD 1 2 3 4

N (indexed) 100,000 33,077 11,054 1,482

age 51.21 58.85 66.36 68.84

Mean Cost - Indexed $100 $431 $1,906 $5,816

Complexity 0.38 1.03 2.47 3.69

hd_ihd_id 4% CANCER_ALL_CMRB 12% hd_ihd_id 25% hd_ihd_id 46%

hd_diab_id 4% hd_ihd_id 11% CANCER_ALL_CMRB 23% HYPERTENSION_CMRB 37%

CANCER_ALL_CMRB 4% hd_diab_id 11% OSTEOARTHROSIS_CMRB 23% hd_diab_id 27%

GERD_CMRB 4% OSTEOARTHROSIS_CMRB 8% HYPERTENSION_CMRB 20% CARD_AFIB_AFLUTTER_CMRB 25%

ABDOM_PAIN_CMRB 3% TOBACCO_USE_CMRB 7% hd_diab_id 19% CANCER_ALL_CMRB 23%

TOBACCO_USE_CMRB 3% HYPERTENSION_CMRB 7% KNEEOSTEOARTHRITIS_CMRB 14% OSTEOARTHROSIS_CMRB 22%

PREGNANCY_HEDIS_CMRB 3% GERD_CMRB 6% MSK_BACK_PAIN_CMRB 13% MSK_BACK_PAIN_CMRB 22%

GI_HAEMORRHAGE_CMRB 2% ABDOM_PAIN_CMRB 5% CARD_AFIB_AFLUTTER_CMRB 11% hd_chf_id 20%

HYPERTENSION_CMRB 2% KNEEOSTEOARTHRITIS_CMRB 5% TOBACCO_USE_CMRB 10% BACKPAIN_LOW_CMRB 18%

SLEEPAPNEA_CMRB 2% MSK_BACK_PAIN_CMRB 4% BACKPAIN_LOW_CMRB 9% KNEEOSTEOARTHRITIS_CMRB 13%

OSTEOARTHROSIS_CMRB 2% CARD_AFIB_AFLUTTER_CMRB 4% HIPOSTEOARTHRITIS_CMRB 8% RENAL_CKD_ALL_CMRB 13%

KNEEOSTEOARTHRITIS_CMRB 2% PREGNANCY_HEDIS_CMRB 3% GERD_CMRB 7% TOBACCO_USE_CMRB 12%

MSK_BACK_PAIN_CMRB 1% GI_HAEMORRHAGE_CMRB 3% OSTEOPOROSIS_CMRB 7% DEPRESSION_MH_CMRB 10%

BACKPAIN_LOW_CMRB 1% SLEEPAPNEA_CMRB 3% hd_chf_id 7% OSTEOPOROSIS_CMRB 9%

CARD_AFIB_AFLUTTER_CMRB 1% BACKPAIN_LOW_CMRB 3% ABDOM_PAIN_CMRB 6% UNSTABLEANGINA_CMRB 9%

CANCER_PROSTATE_CMRB 1% OSTEOPOROSIS_CMRB 3% UNSTABLEANGINA_CMRB 6% hd_copd_id 9%

IBD_CMRB 1% hd_chf_id 2% hd_copd_id 5% PNEUMONIA_CMRB 9%

OSTEOPOROSIS_CMRB 1% hd_copd_id 2% OBESITY_CMRB 5% STENOSIS_SPINAL_CMRB 9%

hd_chf_id 0% UNSTABLEANGINA_CMRB 2% PNEUMONIA_CMRB 5% HIPOSTEOARTHRITIS_CMRB 8%

hd_copd_id 0% OBESITY_CMRB 2% DEPRESSION_MH_CMRB 5% GERD_CMRB 8%



Targeting

Intervention vs. Prevention

• Predictive modelling

• Costs

• Admissions

• Readmissions

• Conditions

• Preference Sensitive Conditions

• Population Insight

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$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

55 60 65 70 75 80 85 90 95

Actual Average

Cost a

t tim

e= t+

1

Risk Percentile ‐ predicted at time=t

BHD Risk Model Cost Model



Clinical Reporting & Service Utilisation

Improve service delivery with information

• Provider attribution

• Consolidated treatment reporting

• Care pathway opportunities

28 May 2014 20

Reduction in care gaps in population group with clinical data reporting

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

Time Reported Time & Reported

Mea

n Ef

fect


Conclusion

Big Data in Healthcare

From a public health perspective big data is hard to define. For various reason the accessibility of data lags behind other commercially orientated fields.

The case for big data has been made and demonstrated in many other fields. Furthermore we have techniques and processes ready and waiting once the data catches up.

But – we shouldn’t get dissuaded by the lack of ‘big’ data. The opportunity to use these techniques and processes on any data will improve the efficiency and effectiveness of healthcare provision.

28 May 2014 21


David Cross, Health Analytics Manager

Bupa Health Dialog

[email protected]

28 May 2014 22

SAS Melbourne User Group Q2 2014

Naomi Rafael

Loading and Presenting Data in SAS Visual Analytics: BioGrid’s Experience

Loading and Presenting Data in SAS Visual Analytics: BioGrid’s Experience (so far)

I. BioGrid needs an automated method: why?II. Three parts to the method

i. Technical Transfer Mechanism: The Infrastructure

ii. SAS Enterprise Business Intelligence (EBI) Server V9.4 Data Refresh Automation

iii. SAS Visual Analytics (VA) V6.2 LASR Refresh Automation

III. Future development

I. Purpose of automation• BioGrid aims to provide a data collection report in VA for each of over 80 databases in the federation

• Many databases in BioGrid have regular data updates to be reflected in the VA reporting

• Automation saves approximately 5 minutes per table/project if refreshed by a human: > 6 hours uninterrupted work for 80 items.

• Conclusion? Only automation is scalable!

II. The Method – Infrastructure

1. MySQL database is installed on the SAS Visual Analytics Linux server

2. Define SAS Library on EBI Server (“Shared EBI VA Data”) pointing to the MySQL Server

3. Create table(s) in this library via EG or code to be available to SAS Visual Analytics

II. The Method ‐ Infrastructurei. The EBI/Enterprise Guide View

II. The Method ‐ Infrastructurei. The VA View (1)

• Register table in VA environment

• Start by selecting “Manage Environment”

• On left, navigate to “Shared EBI VA Data” folder as shown

• Right click on “Shared EBI VA Data” library and choose “Register and Update Tables”

• All tables located in the MySQL database will be displayed.

• Choose the table(s) you would like to have access to within VA and click OK.

• Once the table(s) are registered you will receive a confirmation

II. The Method ‐ Infrastructurei. The VA View (2)

The transfer infrastructure is now established!

II. The Method – Refresh Automation EBIii. Using Enterprise Guide GUI (1)

• Create EG process flow/project to prepare data to import into VA

• Save as SAS source file (.sas)• Save the final output to the SAS VA library

II. The Method – Refresh Automation EBIii. Using Enterprise Guide GUI (2)

• Create a process flow to refresh the data table

• From file menu, select “Export, Export All Code in Project”

• Save as SAS source file (.sas)

II. The Method – Refresh Automation EBIii. Using Code/Program

• Write code to refresh the data table saved in the SAS VA Library

• Save code as a SAS source file (.sas)

• Requires the Schedule Manager plug‐in• Right‐click Schedule Manager plug‐in, select Deploy SAS Data

Step Program

II. The Method – Schedule the Job EBI Management Console (1)

II. The Method – Schedule the JobEBI Management Console (2)

• Select your .sas file as source file and define deployed job options

II. The Method ‐ Schedule the JobEBI Management Console (3): New Flow

• Right‐click Schedule Manager, New Flow

• Move required item(s) from left to right

II. The Method ‐ Schedule the JobEBI Management Console (4): Schedule the New Flow

• Right click on the flow you just created and select Schedule Flow


• Select the Manage button to define triggers

• Select New Time Event to define the properties


• Select your schedule option from the dropdowns and select OK.

EBI Job Scheduling is now complete!

II. The Method – Visual Analyticsiii. Update LASR Table(s)

• Manually unload and reload table or• Use “Prepare Data” to create a scheduled process

• Must have the role – Visual Analytics: Data Building

• Steps shown in following slides

II. The Method – Visual Analyticsiii. Update LASR Table(s) (1)

II. The Method – Visual Analyticsiii. Update LASR Table(s) (2): Choose table

• Click and drag SAS data table onto data builder, right click to select all columns• Save query• Scheduling icon becomes active; click it

II. The Method – Visual Analyticsiii. Update LASR Table(s) (3): Select trigger/time event

• Click New Time Event

II. The Method – Visual Analyticsiii. Update LASR Table(s) (4): Specify refresh interval

• Set required refresh interval

• Click OK and save query with schedule attached

II. The Method – Visual Analyticsiii. Update LASR Table(s) (5): Success message

Success!LASR table update is now complete.

III. Future development

• Explore alternative methods for loading data into SAS Visual Analytics

• Explore and exercise analysis capabilities of VA

Acknowledgements:

Michael Dixon, SelerityAlice Johnstone PhD, BioGrid

SASThank you.Questions?

• 7 Employees located Melbourne, Sydney & Canberra

• 65 years combined SAS experience• Clients throughout Australia

www.selerity.com.au

www.selerity.com.au

aws.amazon.com/testdrive

SAS Visual Analytics Test Drive

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Members of local SAS Users Groups

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Please join SANZOC & participate

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WHEN: 9-10th July

WHY IS CUSTOMER SEGMENTATION IMPORTANT?

identify your most and least profitable customers

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LEARN:

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sas melbourne user group groups... · 2016-03-11 · sas melbourne user group 28 may 2014 ‘a...

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