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Intermountain Healthcare Big Data Update February 5, 2016

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Page 1: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Intermountain Healthcare Big Data Update

February 5, 2016

Page 2: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

• 22 hospitals

• 33,000 employees

• 600,000 members

• 25% market share

• 200 clinics

• 1,000 employed

physicians

Intermountain Healthcare Profile

1975 1983 1994

Page 3: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Intermountain – Mission

Page 4: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

• Heart Failure Mortality Rates Less than half the national average

• Reduction of Elective Inductions Elimination of elective inductions prior to 39 weeks. NICU utilization reduced by nearly 50%. Projected $5.3B annual savings if adopted nationwide.

• Colon Surgery $1.2 million annual savings, LOS decreased from 8.44 to 6.75 days, while maintaining or improving clinical quality. - Computerworld Business Intelligence Award – Driving Process Change with BI

• Surgical Price Reduction Nearly $60M cost reduction for knee and hip replacement over 3 years while improving clinical outcomes

• Other Clinical Quality Improvements Diabetes, asthma, community acquired pneumonia (CAP), blood utilization, 50+ standardized care processes

• Healthcare Operations Improvements Lab operations, supply chain, operating room (OR), hospital operations, patient satisfaction, core measures, meaningful use, population health, shared accountability

Intermountain Analytics Supporting Clinical and Cost Improvements (Illustrative Examples)

Page 5: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Using additional data sources and new analytic tools to produce superior, actionable analytic insights (not previously possible or cost effective) leading to

• Improved healthcare outcomes

• Reduced cost

• Healthier people

What is Big Data? INTERMOUNTAIN’S DEFINTION

Page 6: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Sample of vendors evaluated

• 3M • 4medica • 10Gen • Actian • Actuate • Alteryx • Allscripts • Alteryx • Amazon • Analyltics MD • Apertiva • Apixio • Aptible • Archimedes • Atigeo • Attevo • Attunity • Attivio • Avado • Axial • Ayasdi • BA Insight • Balanced Insight • Beyondcore • BioSignia • Birst • Bitwise • Bluedata • bPrescient

• Cambridge Semantics • Cask • Chiliad • Cisco • Clarabridge • Clear Data • Cloudera • Cognitive Scale • Composite Software • Couchbase • CSC • Cytolon • Datameer • Dataskill • DataStax • Datu • Dell • Deloitte • Denodo • DNA Nexus • Domo • Dossia • Elastic Search • EMC • Enlightiks • Exact Data • Explorys • Fortel • Futrix Health

• GNIP • GNS Healthcare • Google • H2O • Health Catalyst • Health Language Intl. • Health Management

Academy • Healthcare Data Works • Healthline • Hitachi Data Systems • Hortonworks • HP • IBM • Illumina • Impetus • Index Engines • Informatica • Information Builders • Intel • Intelligent Bus. Systems • Intelligent Medical Objects • Intersystems • Kyvos • LifeDox • MapR • Marklogic • Mastadon • Metric Insights

• Microsoft • Microstrategy • Neo4J • Nextgate • Nuance • Objectivity • Oncora • Open Health Tools • Optum • Oracle • Pentaho • Perceivant • Platfora • Prime Technology Group • Proskriptive • Pure Predictive • Qlikview • Redpoint • Rock Health • Saavy Sherpa • Saffron • SAS • Skytree • Smartlogic • Smart Tek • Snowflake • Solr • Spectralogic • Sqream

• Stibo • Streamsets • Syapse • Syncsort • Talend • Tamr • Tascet • Teradata • Tervela • Think Big Analytics • Tibco • Tolven Health • Trifacta • Truven • Unifi • Verisk Health • Viewics • Viral Heat • Virdatint • Vitreos • Wandisco • Wipro • Wired Informatics • Yarcdata • Zato • Zoeticx

Page 7: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Industry Validation – Healthcare Data & Analytics Association Intermountain Approach: Fast follower of proven results

• Adventist Health • Advocate Health Care • Ascension Health • Banner Health • Baylor Health Care • Blue Cross Blue Shield • Boston Children's Hospital • Boston Medical Center • Brigham and Women's Hospital • Cancer Treatment Centers of

America • Carolinas HealthCare System • Catholic Health Initiatives • Cedars-Sinai Medical Center • Child's Health Corp of America • Children's Healthcare of Atlanta • Children's Hospital Colorado • Children's National Medical

Center • Christiana Care Health System • City of Hope • Cleveland Clinic • Dartmouth-Hitchcock Medical

Center • Deaconess Health System • Defense Health Agency • Department of Veterans Affairs

• Dignity Health • Duke University Health System • Emory Healthcare • Essentia Health • Fairview Health Services • Fox Chase Cancer Center • Geisinger Health System • H. Lee Moffitt Cancer Center • Harvard Medical Center • Hawaii Pacific Health • Henry Ford Health System • Hospital Corporation of America • Huntsman Cancer Institute • Integris Health • Inova Health System • Intermountain Health Care • Johns Hopkins Medicine • Kaiser Permanente • Legacy Health • MD Anderson Cancer Center • MaineHealth • Mayo Clinic • MedStar Health • Memorial Health System • Memorial Sloan Kettering

Cancer Center • Mercy Health

• Methodist Health System • Military Health System - DoD • Montefiore Medical Center • Mount Sinai Health System • NYC Health & Hospitals

Corporation • National Jewish Health • Nationwide Children's Hospital • New England Quality Care

Alliance • Northwestern Medicine • Novant Health • OSF HealthCare System • Ochsner Health System • Oregon Health and Science

University • Palo Alto Medical Foundation • Partners HealthCare • Peace Health • Penn Medicine • Piedmont Healthcare • Presbyterian Healthcare Services • ProHealth Care • Providence Health & Services • Rush University Medical Center • Saint Luke's Health System • Samaritan Health Services

• Scripps Health • Seattle Children's Hospital • Sharp HealthCare • Spectrum & Priority Health • Stanford Hospital & Clinics • Stony Brook Medicine • Sutter Health • Texas Children's Hospital • The Ottawa Hospital • Trinity Health • UC Irvine • UC Los Angeles • UC San Francisco • UW Health • United Health Care • University of Michigan Health

System • University of Pittsburg Medical

Center • University of Utah Health Care • VCU Health System • Vancouver Coastal Health • Veterans Health Administration • Walter Reed National Medical

Center • WellSpan Health • Yale Health System

Page 8: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Significant Accomplishments and Pursuits

• Data Governance

• Production Data Lake

• Data Preparation

• Machine Learning / Cognitive Learning

• Cloud

• Federated/Virtual Search/Indexing

Page 9: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Business

Intelligence

(BI),

Reporting,

Analytics

and

Applications

Relational

EDW

Se

ma

ntic M

an

age

me

nt

Data

In

tegra

tio

n L

aye

r Federated

Search

Data Lake

Data

Sources

(Operational and

clinical systems,

external systems,

research, medical

devices, etc.)

Processes: Data Governance, Data Quality, Master Data Management, Semantic and

Metadata Management, Security, Compliance, Legal

Intermountain Healthcare Enterprise Information Architecture

Ma

ste

r D

ata

Ma

na

ge

me

nt

Cloud

Data Quality, Metadata and Data Lineage Management

Insights, Algorithms, Decision Support, Embedded Analytics

Page 10: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Intermountain Enterprise Information Management Data Governance Organization

Intermountain Management

Committee

Intermountain Management

Executive Committee

Intermountain Management

Council

Governance Enterprise Information Management Organization

Enterprise Data

Governance

Enterprise Data

Integration

Enterprise Business

Intelligence

Enterprise Analytics

Team

Enterprise Data

Dictionary

Data Innovation

(big data)

Chief Data Officer

CIO

Page 11: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Business

Intelligence

(BI),

Reporting,

Analytics

and

Applications

Relational

EDW

Se

ma

ntic M

an

age

me

nt

Data

In

tegra

tio

n L

aye

r Federated

Search

Data Lake

Data

Sources

(Operational and

clinical systems,

external systems,

research, medical

devices, etc.)

Processes: Data Governance, Data Quality, Master Data Management, Semantic and

Metadata Management, Security, Compliance, Legal

Production Data Lake

Ma

ste

r D

ata

Ma

na

ge

me

nt

Cloud

Data Quality, Metadata and Data Lineage Management

Insights, Algorithms, Decision Support, Embedded Analytics

Page 12: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

• Centralized storage of genomic data

• Storage of ICU device data

• Storage of large data not persisted in another location or repository

Production Data Lake Initial primary use cases

Page 13: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Production Data Lake Retaining physiologic monitor data for analysis

Page 14: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Data Preparation Graphical approach to reducing SQL and R coding

Business

Intelligence

(BI),

Reporting,

Analytics

and

Applications

Relational

EDW

Se

ma

ntic M

an

age

me

nt

Data

In

tegra

tio

n L

aye

r Federated

Search

Data Lake

Processes: Data Governance, Data Quality, Master Data Management, Semantic and

Metadata Management, Security, Compliance, Legal

Ma

ste

r D

ata

Ma

na

ge

me

nt

Cloud

Data Quality, Metadata and Data Lineage Management

Insights, Algorithms, Decision Support, Embedded Analytics

Data

Sources

(Operational and

clinical systems,

external systems,

research, medical

devices, etc.)

Page 15: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Data Preparation Graphical approach to reducing SQL and R coding

PERSON DATA SQL: WITH DIM_DATE as ( SELECT trunc(date_dt, 'month')- interval '11' month as period_start_date, date_dt as period_end_date, 'year' as time_period FROM hrp.effective_DATE_DIM WHERE date_dt between trunc((trunc(sysdate, 'year' ))- interval '2' year) and trunc(sysdate + interval '1' year, 'year')- interval '1' day AND end_of_qtr_flg = 'Y' and qtr_no = 4 UNION -- quarter SELECT trunc(date_dt, 'month')- interval '2' month as period_start_date, date_dt as period_end_date, 'quarter' as time_period FROM hrp.effective_DATE_DIM WHERE date_dt between trunc((trunc(sysdate, 'year' ))- interval '2' year) and trunc(sysdate + interval '1' year, 'year')- interval '1' day AND end_of_qtr_flg = 'Y' UNION --month SELECT trunc(date_dt, 'MM') as period_start_date, date_dt as period_end_date, 'month' as time_period FROM hrp.effective_DATE_DIM -- WHERE date_dt between trunc(trunc(sysdate, 'year' )) and trunc(sysdate + interval '1' year, 'year')- interval '1' day WHERE date_dt between trunc((trunc(sysdate, 'year' ))- interval '2' year) and trunc(sysdate + interval '1' year, 'year')- interval '1' day AND end_of_mnth_flg = 'Y' ) , ORG_ASSIGNMENT as ( SELECT measure_type, period_start_date, period_end_date, time_period, org_short_nm , job_family_txt ,job_cd, job_title_txt , assignment_id, person_id , range_penetration, working_hrs, dept_tenure_yrs, job_family_tenure_yrs, job_cd_tenure_yrs, hourly_rate_amt , benefits_elig , grade_level_txt, supervisor_flg, compa_ratio_val, eeo_group_nm, eeo_group_cd FROM ( SELECT start_dt, end_dt, extended_end_dt , 'assigment_data' as measure_type , CASE WHEN regexp_substr(org_dept_nm, '([0-9])+-([0-9])+') is not null then trim(regexp_substr(org_dept_nm, '([0-9])+-([0-9])+')) WHEN regexp_substr(org_dept_nm, '([A-Z])+\s([0-9])+ ') is not null then trim(regexp_substr(org_dept_nm, '([A-Z])+\s([0-9])+ ')) END AS org_short_nm , job_family_txt, job_title_txt, job_cd , assignment_status_txt, prmry_assignment_flg, assignment_id , person_id -- facts , range_penetration , working_hrs , dept_tenure_yrs , job_family_tenure_yrs , job_cd_tenure_yrs , hourly_rate_amt , benefits_elig_flg as benefits_elig , grade_level_txt , supervisor_flg , compa_ratio_val , eeo_group_nm, eeo_group_cd FROM hrp.assignment_dim_hr WHERE end_dt > (sysdate - interval '3' year) AND prmry_assignment_flg = 'Y' AND assignment_status_txt in ('Active Assignment', 'Additional Assignment', 'Paid LOA Assignment', 'Transfer, Active Assignment')) asg JOIN DIM_DATE ON period_end_date BETWEEN start_dt AND end_dt AND period_end_date between start_dt and extended_end_dt ) , ORG_PERSON AS ( SELECT 'person_data' as measure_type , 'http://foo_bar' as url_link --date dim , period_start_date, period_end_date, time_period -- org dim

, org_short_nm , job_family_txt , job_title_txt, job_cd -- facts , ihc_tenure_yrs , perf_rating_txt , perf_rating_short_txt , age_yrs , ethnic_origin_txt , sex_cd , vet_status_txt , supervisor_flg , grade_level_txt , eeo_group_nm, eeo_group_cd FROM hrp.person_dim_hr per JOIN ORG_ASSIGNMENT asg on per.person_id = asg.person_id and period_end_date BETWEEN per.start_dt AND per.end_dt WHERE per.end_dt > (sysdate - interval '3' year) and person_typ_active_flg = 'Y' ) , DENORMALIZED_CT_PERSON as ( SELECT -- dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt , job_title_txt, job_cd , url_link , measure_type , ' IHC Tenure Yrs' as measure_name , 'AVG' as calc_type , 'The average number of years employees <in group> have worked for Intermountain Healthcare.' as measure_definition , 4000 as sort_order -- measures , ihc_tenure_yrs as fact_number_format , null as fact_decimal_format , null as fact_currency_format , null as fact_percent_format , ihc_tenure_yrs , null as age_yrs , null as ethnicity_white , null as ethnicity_other , null as gender_female , null as gender_male , null as veteran , null as retirement_eligible , null as retirement_not_eligible , null as female_manager , null as ethnic_manager , null as female_executive , null as ethnic_executive , null as head_count FROM ORG_PERSON UNION ALL SELECT -- dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt , job_title_txt, job_cd , url_link , measure_type , ' Individuals Age' as measure_name , 'AVG' as calc_type , 'The average age in years of employees <in group>.' as measure_definition , 4000 as sort_order -- measures , null as fact_number_format , age_yrs as fact_decimal_format , null as fact_currency_format , null as fact_percent_format , null as ihc_tenure_yrs , age_yrs , null as ethnicity_white , null as ethnicity_other , null as gender_female , null as gender_male , null as veteran , null as retirement_eligible , null as retirement_not_eligible , null as female_manager , null as ethnic_manager , null as female_executive , null as ethnic_executive , null as head_count FROM ORG_PERSON UNION ALL SELECT -- dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt , job_title_txt, job_cd , url_link , measure_type , ' Ethnicity White' as measure_name , 'SUM' as calc_type , 'The percent of employees who have identified their ethnic origin as white <in group>.'

as measure_definition , 4000 as sort_order --measures , null as fact_number_format , null as fact_decimal_format , null as fact_currency_format , 1 as fact_percent_format , null as ihc_tenure_yrs , null as age_yrs , 1 AS ethnicity_white , null as ethnicity_other , null as gender_female , null as gender_male , null as veteran , null as retirement_eligible , null as retirement_not_eligible , null as female_manager , null as ethnic_manager , null as female_executive , null as ethnic_executive , null as head_count FROM ORG_PERSON WHERE ethnic_origin_txt = 'White (Not Hispanic or Latino)' UNION ALL SELECT -- dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt, job_title_txt, job_cd , url_link , measure_type , ' Ethnicity Other' as measure_name , 'SUM' as calc_type , 'The percent of employees <in group> who have identified their ethnic origin as one of the following: Black or African American, Two or More Races , Native Hawaiian/Other Pacific Islander, Asian, Hispanic or Latino, or American Indian or Alaska Native.' as measure_definition -- use in tool tip , 4000 as sort_order --measures , null as fact_number_format , null as fact_decimal_format , null as fact_currency_format , 1 as fact_percent_format , null as ihc_tenure_yrs , null as age_yrs , null as ethnicity_white , 1 AS ethnicity_other , null as gender_female , null as gender_male , null as veteran , null as retirement_eligible , null as retirement_not_eligible , null as female_manager , null as ethnic_manager , null as female_executive , null as ethnic_executive , null as head_count FROM ORG_PERSON WHERE ethnic_origin_txt != 'White (Not Hispanic or Latino)' UNION ALL SELECT -- dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt, job_title_txt, job_cd , url_link , measure_type , ' Gender Female' as measure_name , 'SUM' as calc_type , 'The percent of employees <in group> who have identified their gender as female.' as measure_definition , 4000 as sort_order --measures , null as fact_number_format , null as fact_decimal_format , null as fact_currency_format , 1 as fact_percent_format , null as ihc_tenure_yrs , null as age_yrs , null as ethnicity_white , null as ethnicity_other , 1 AS gender_female , null as gender_male , null as veteran , null as retirement_eligible , null as retirement_not_eligible , null as female_manager , null as ethnic_manager , null as female_executive , null as ethnic_executive , null as head_count FROM ORG_PERSON WHERE sex_cd = 'F' UNION ALL SELECT -- dim period_start_date, period_end_date,

time_period , org_short_nm , job_family_txt, job_title_txt, job_cd , url_link , measure_type , ' Gender Male' as measure_name , 'SUM' as calc_type , 'The percent of employees <in group> who have identified their gender as male.' as measure_definition , 4000 as sort_order --measures , null as fact_number_format , null as fact_decimal_format , null as fact_currency_format , 1 as fact_percent_format , null as ihc_tenure_yrs , null as age_yrs , null as ethnicity_white , null as ethnicity_other , null as gender_female , 1 AS gender_male , null as veteran , null as retirement_eligible , null as retirement_not_eligible , null as female_manager , null as ethnic_manager , null as female_executive , null as ethnic_executive , null as head_count FROM ORG_PERSON WHERE sex_cd = 'M' UNION ALL SELECT -- dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt, job_title_txt, job_cd , url_link , measure_type , ' Veteran' as measure_name , 'SUM' as calc_type , 'The number of employees <in group> who have identified themselves as a veteran.' as measure_definition , 4000 as sort_order --measures , 1 as fact_number_format , null as fact_decimal_format , null as fact_currency_format , null as fact_percent_format , null as ihc_tenure_yrs , null as age_yrs , null as ethnicity_white , null as ethnicity_other , null as gender_female , null as gender_male , 1 as veteran , null as retirement_eligible , null as retirement_not_eligible , null as female_manager , null as ethnic_manager , null as female_executive , null as ethnic_executive , null as head_count FROM ORG_PERSON WHERE vet_status_txt is not null AND vet_status_txt != 'Not a Veteran' UNION ALL SELECT -- dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt, job_title_txt, job_cd , url_link , measure_type , ' Retirement Eligible' as measure_name , 'SUM' as calc_type , 'The percent of employees <in group> who are eligible for retirement. <CR><CR> Tenure is 5 or more years and age is 57 and older.' as measure_definition , 4000 as sort_order --measures , null as fact_number_format , null as fact_decimal_format , null as fact_currency_format , 1 as fact_percent_format , null as ihc_tenure_yrs , null as age_yrs , null as ethnicity_white , null as ethnicity_other , null as gender_female , null as gender_male , null as veteran , 1 as retirement_eligible , null as retirement_not_eligible , null as female_manager , null as ethnic_manager , null as female_executive , null as ethnic_executive , null as head_count FROM ORG_PERSON

WHERE ihc_tenure_yrs >= 5 and age_yrs >= 57 UNION ALL SELECT -- dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt, job_title_txt, job_cd , url_link , measure_type , ' Retirement Not Eligible' as measure_name , 'SUM' as calc_type , 'The percent of employees <in group> who are not eligible for retirement. <CR><CR> Tenure is 5 or more years and age is less than 57.' as measure_definition , 4000 as sort_order --measures , null as fact_number_format , null as fact_decimal_format , null as fact_currency_format , 1 as fact_percent_format , null as ihc_tenure_yrs , null as age_yrs , null as ethnicity_white , null as ethnicity_other , null as gender_female , null as gender_male , null as veteran , null as retirement_eligible , 1 as retirement_not_eligible , null as female_manager , null as ethnic_manager , null as female_executive , null as ethnic_executive , null as head_count FROM ORG_PERSON WHERE ihc_tenure_yrs >= 5 and age_yrs < 57 UNION ALL SELECT --dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt, job_title_txt, job_cd , 'http://foo_bar' as url_link , measure_type , ' Managers Female' as measure_name , 'SUM' as calc_type , 'The percent of managers who identified as female <in group>. <CR><CR>(Manager = EEO Groups: First/Mid Level Officials and Managers and Executive/Senior Level Officials and Managers)' as measure_definition , 4000 as sort_order -- measures , null as fact_number_format , null as fact_decimal_format , null as fact_currency_format , 1 as fact_percent_format , null as ihc_tenure_yrs , null as age_yrs , null as ethnicity_white , null as ethnicity_other , null as gender_female , null as gender_male , null as veteran , null as retirement_eligible , null as retirement_not_eligible , 1 as female_manager , null as ethnic_manager , null as female_executive , null as ethnic_executive , null as head_count FROM ORG_PERSON WHERE eeo_group_cd in (1,10) and sex_cd = 'F' UNION ALL SELECT --dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt, job_title_txt, job_cd , 'http://foo_bar' as url_link , measure_type , ' Managers Ethnic' as measure_name , 'SUM' as calc_type , 'The percent of managers who identified as ethnic <in group>. <CR><CR>(Manager = EEO Groups: First/Mid Level Officials and Managers and Executive/Senior Level Officials and Managers)' as measure_definition , 4000 as sort_order -- measures , null as fact_number_format , null as fact_decimal_format , null as fact_currency_format , 1 as fact_percent_format , null as ihc_tenure_yrs , null as age_yrs , null as ethnicity_white

, null as ethnicity_other , null as gender_female , null as gender_male , null as veteran , null as retirement_eligible , null as retirement_not_eligible , null as female_manager , 1 as ethnic_manager , null as female_executive , null as ethnic_executive , null as head_count FROM ORG_PERSON WHERE eeo_group_cd in (1,10) and ethnic_origin_txt != 'White (Not Hispanic or Latino)' UNION ALL SELECT --dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt, job_title_txt, job_cd , 'http://foo_bar' as url_link , measure_type , ' Executives Female' as measure_name , 'SUM' as calc_type , 'The percent of executives who identified as female <in group>. <CR><CR>(Executive = EEO Group: Executive/Senior Level Officials and Managers)' as measure_definition , 4000 as sort_order -- measures , null as fact_number_format , null as fact_decimal_format , null as fact_currency_format , 1 as fact_percent_format , null as ihc_tenure_yrs , null as age_yrs , null as ethnicity_white , null as ethnicity_other , null as gender_female , null as gender_male , null as veteran , null as retirement_eligible , null as retirement_not_eligible , null as female_manager , null as ethnic_manager , 1 as female_executive , null as ethnic_executive , null as head_count FROM ORG_PERSON WHERE eeo_group_cd in (10) and sex_cd = 'F' UNION ALL SELECT --dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt, job_title_txt, job_cd , 'http://foo_bar' as url_link , measure_type , ' Executives Ethnic' as measure_name , 'SUM' as calc_type , 'The percent of executives who identified as ethnic <in group>. <CR><CR>(Executive = EEO Group: Executive/Senior Level Officials and Managers)' as measure_definition , 4000 as sort_order -- measures , null fact_number_format , null as fact_decimal_format , null as fact_currency_format , 1 as fact_percent_format , null as ihc_tenure_yrs , null as age_yrs , null as ethnicity_white , null as ethnicity_other , null as gender_female , null as gender_male , null as veteran , null as retirement_eligible , null as retirement_not_eligible , null as female_manager , null as ethnic_manager , null as female_executive , 1 as ethnic_executive , null as head_count FROM ORG_PERSON WHERE eeo_group_cd in (10) and ethnic_origin_txt != 'White (Not Hispanic or Latino)' UNION ALL SELECT --dim period_start_date, period_end_date, time_period , org_short_nm , job_family_txt, job_title_txt, job_cd , 'http://foo_bar' as url_link , measure_type , ' Total Persons' as measure_name , 'SUM' as calc_type

, 'The total number of people <in group>. <CR><CR>(Person Type: Active Employee, Active LOA Employee, Employee & LTD Recipient, Employee & Retiree; Primary Assignments : Active Assignment, Additional Assignment, Paid LOA Assignment, Transfer, Active Assignment)' as measure_definition , 4000 as sort_order -- measures , 1 as fact_number_format , null as fact_decimal_format , null as fact_currency_format , null as fact_percent_format , null as ihc_tenure_yrs , null as age_yrs , null as ethnicity_white , null as ethnicity_other , null as gender_female , null as gender_male , null as veteran , null as retirement_eligible , null as retirement_not_eligible , null as female_manager , null as ethnic_manager , null as female_executive , null as ethnic_executive , 1 as head_count FROM ORG_PERSON ) -- create the measure subsets to use in creating sets for denominators --subset for all measures but supervisor and leaders , MEASURES_SUBSET_1 as (SELECT distinct measure_type, measure_name, measure_definition from DENORMALIZED_CT_PERSON WHERE measure_name not in (' Managers Female', ' Managers Ethnic', ' Executives Female', ' Executives Ethnic')) --subset for supervisors , MEASURES_SUBSET_2 as (SELECT distinct measure_type, measure_name, measure_definition from DENORMALIZED_CT_PERSON WHERE measure_name in (' Managers Female', ' Managers Ethnic')) -- subset for leaders , MEASURES_SUBSET_3 as (SELECT distinct measure_type, measure_name, measure_definition from DENORMALIZED_CT_PERSON WHERE measure_name in (' Executives Female', ' Executives Ethnic')) -- denominator for entire data set , SUM_DENOMINATOR_1 as (SELECT --dim period_start_date, period_end_date, time_period, org_short_nm --, job_family_txt --, job_title_txt, job_cd , SUM(1) as DENOMINATOR FROM ORG_PERSON GROUP BY period_start_date, period_end_date, time_period, org_short_nm ) -- denominators for managers , SUM_DENOMINATOR_2 as (SELECT --dim period_start_date, period_end_date, time_period, org_short_nm --, job_family_txt --, job_title_txt, job_cd , SUM(1) as DENOMINATOR FROM ORG_PERSON WHERE eeo_group_cd in (1,10) GROUP BY period_start_date, period_end_date, time_period, org_short_nm ) -- denominator for executives , SUM_DENOMINATOR_3 as (SELECT --dim period_start_date, period_end_date, time_period, org_short_nm --, job_family_txt --, job_title_txt, job_cd , SUM(1) as DENOMINATOR FROM ORG_PERSON WHERE eeo_group_cd in (10) GROUP BY period_start_date, period_end_date, time_period, org_short_nm ) -- Cartesian join of appropriate denominators with the corresponding measures and union all to single set , FINAL_DENOMINATOR as ( SELECT period_start_date, period_end_date, time_period, org_short_nm, measure_type, measure_name, measure_definition, denominator FROM SUM_DENOMINATOR_1 JOIN MEASURES_SUBSET_1 ON 1=1 UNION ALL

SELECT period_start_date, period_end_date, time_period, org_short_nm, measure_type, measure_name, measure_definition, denominator FROM SUM_DENOMINATOR_2 JOIN MEASURES_SUBSET_2 ON 1=1 UNION ALL SELECT period_start_date, period_end_date, time_period, org_short_nm, measure_type, measure_name, measure_definition, denominator FROM SUM_DENOMINATOR_3 JOIN MEASURES_SUBSET_3 ON 1=1 ORDER by period_end_date, org_short_nm) -- summarize the data , SUM_ORG_PERSON as (SELECT period_start_date, period_end_date, time_period , org_short_nm --, job_family_txt --, job_title_txt, job_cd , url_link , measure_type , measure_name , measure_definition , calc_type , sort_order , SUM(fact_number_format) as fact_number_format , SUM(fact_decimal_format) as fact_decimal_format , SUM(fact_currency_format) as fact_currency_format , SUM(fact_percent_format) as fact_percent_format , SUM(ihc_tenure_yrs) as ihc_tenure_yrs , SUM(age_yrs) as age_yrs , SUM(ethnicity_white) as ethnicity_white , SUM(ethnicity_other) as ethnicity_other , SUM(gender_female) as gender_female , SUM(gender_male) as gender_male , SUM(veteran) as veteran , SUM(retirement_eligible) as retirement_eligible , SUM(retirement_not_eligible) as retirement_not_eligible , SUM(female_manager) as female_manager , SUM(ethnic_manager) as ethnic_manager , SUM(female_executive) as female_executive , SUM(ethnic_executive) as ethnic_executive , SUM(head_count) as head_count , SUM(1) as number_observations FROM DENORMALIZED_CT_PERSON GROUP BY period_start_date, period_end_date, time_period , org_short_nm -- , job_family_txt, job_title_txt, job_cd, , url_link , measure_type , measure_name , measure_definition , calc_type , sort_order) --- left join the denominator with the summarized data SELECT d.period_start_date, d.period_end_date, d.time_period, d.org_short_nm --, job_family_txt --, job_title_txt, job_cd , url_link, d.measure_type, d.measure_name, d.measure_definition, calc_type, sort_order , fact_number_format, fact_decimal_format , fact_currency_format, fact_percent_format, ihc_tenure_yrs, age_yrs, ethnicity_white, ethnicity_other, gender_female , gender_male, veteran, retirement_eligible, retirement_not_eligible, female_manager, ethnic_manager, female_executive , ethnic_executive, head_count, number_observations, denominator FROM FINAL_DENOMINATOR d LEFT JOIN SUM_ORG_PERSON p on p.period_end_date = d.period_end_date and p.time_period = d.time_period and p.org_short_nm = d.org_short_nm and p.measure_name = d.measure_name

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Data Preparation Graphical approach to reducing SQL and R coding

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Data Preparation Robust set of SQL and Analytical tools and adapters

• Input / Output • Preparation • Join • Parse • Transform • Report/Present • Document • Spatial • Data Investgation • Predictive • Time Series

• Predictive Grouping • Connectors • Demographic Analysis • Behavior Analysis • List Count Retrieval

Engine • Laboratory • Interface • In-Database

Page 18: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Data Preparation Robust set of SQL and Analytical tools and adapters

Page 19: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Business

Intelligence

(BI),

Reporting,

Analytics

and

Applications

Relational

EDW

Se

ma

ntic M

an

age

me

nt

Data

In

tegra

tio

n L

aye

r Federated

Search

Data Lake

Data

Sources

(Operational and

clinical systems,

external systems,

research, medical

devices, etc.)

Processes: Data Governance, Data Quality, Master Data Management, Semantic and

Metadata Management, Security, Compliance, Legal

Machine Learning – Cognitive Learning

Ma

ste

r D

ata

Ma

na

ge

me

nt

Cloud

Data Quality, Metadata and Data Lineage Management

Insights, Algorithms, Decision Support, Embedded Analytics

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Machine Learning – Cognitive Learning BI, Reporting, Analytics and Applications

• Descriptive (What has happened) Financial and operational reporting, cost analysis, quality and compliance, meaningful use, etc.

• Diagnostic (Why things happened) Outcomes analysis, gaps in care, fraud detection, etc.

• Predictive (What will happen) Population health risk stratification, contract forecasting and modeling, diagnostic clinical decision support, etc.

• Prescriptive (What should happen) Care process models, prescriptive clinical decision support, precision medicine, etc.

Analytic Types Methods and Tools

• Delivered Reports Emailed, scheduled, etc. (Cognos)

• Self-Service Reports User executed on demand (Cognos)

• Self-Service Dashboards Analyst configured and user queried on demand (Tableau)

• Self Discovery Analysts and data managers exploring cubes and indexes (Power BI, Solr search)

• Predictive and Algorithms Statistical analyst developed (R, SAS, SPSS, etc.)

• Analytic Applications Purpose built analytics (Optum, Archimedes, etc.), custom alerts and embedded analytics (Cerner Healthe Intent)

• Machine Learning / Cognitive Learning Advanced analytics (Ayasdi, GNS Healthcare)

Page 21: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Deriving Cohorts and Care Process Models

Page 22: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Alvimopan

Ketorolac Ambulation Foley Catheter

Oral fluids well tolerated

2010-2015 Colon Surgery (~4500)

Fac: 128 Lowest

LOS

Found

Found

Found

Found

2015 Colon Surgery (~530) • 3 groups • >4 day LOS (~200)

Found in Best group

Found

More freq ambulation

Removed earlier in best group

Found – trend to earlier fluids for best group

Colon Surgery CPM Results (From Current CPM)

Page 23: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Colon Surgery CPM Results (New insights not found in current CPM)

Cohort: Lap Sigmoidectomy (~683)

Pre-op lab tests – chem panel

Midazolam (anti-anxiety)

Ondansetron (anti-nausea)

Surgical Supply (grounding pad)

47 patient group at 2.47 days, $4738

Found

14 patient group at 2.07 days, $4571

Found Found Found

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Tracking Adherence to Care Process Models Summary View – Facility Level

Page 25: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Tracking Adherence to Care Process Models Detailed Views – Surgery Level

Page 26: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Driving Variance Out of Care Process Models

• Behavioral health • Cardiovascular • Collaborative Pharmacy • Imaging Services • Intensive Medicine • Musculoskeletal • Oncology • Pain Services • Pediatric • Primary Care • Surgical Services • Women and newborns • Etc.

SPRING – Reducing variance from knee and hip replacement surgeries alone resulted in $60M savings over 3 years AND delivered improved outcomes

Nearly 60 CPM’s Today

Page 27: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Creating New Care Process Models Consensus, Data Driven Models

• Behavioral health • Cardiovascular • Collaborative Pharmacy • Imaging Services • Intensive Medicine • Musculoskeletal • Oncology • Pain Services • Pediatric • Primary Care • Surgical Services • Women and newborns • Etc.

Deriving detailed “Consensus” care process models from clinical event , cost and quality data

producing the best outcomes

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Business

Intelligence

(BI),

Reporting,

Analytics

and

Applications

Relational

EDW

Se

ma

ntic M

an

age

me

nt

Data

In

tegra

tio

n L

aye

r Federated

Search

Data Lake

Data

Sources

(Operational and

clinical systems,

external systems,

research, medical

devices, etc.)

Processes: Data Governance, Data Quality, Master Data Management, Semantic and

Metadata Management, Security, Compliance, Legal

Cloud Pilot

Ma

ste

r D

ata

Ma

na

ge

me

nt

Cloud

Data Quality, Metadata and Data Lineage Management

Insights, Algorithms, Decision Support, Embedded Analytics

Page 29: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Monotherapy

Dual Therapy

Triple Therapy

Pilot to be conducted on Amazon Cloud using full PHI data and over 1,000 CPU’s

Cloud Pilot Diabetes – Multi Drug Treatment (Primary Care CPM)

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Business

Intelligence

(BI),

Reporting,

Analytics

and

Applications

Relational

EDW

Se

ma

ntic M

an

age

me

nt

Data

In

tegra

tio

n L

aye

r Federated

Search

Data Lake

Data

Sources

(Operational and

clinical systems,

external systems,

research, medical

devices, etc.)

Processes: Data Governance, Data Quality, Master Data Management, Semantic and

Metadata Management, Security, Compliance, Legal

Federated/Virtual Search/Index

Ma

ste

r D

ata

Ma

na

ge

me

nt

Cloud

Data Quality, Metadata and Data Lineage Management

Insights, Algorithms, Decision Support, Embedded Analytics

Page 31: Intermountain Healthcare Big Data Update - HDWA Update... · Intermountain Healthcare Big Data Update ... • Microsoft • Microstrategy ... • Stibo • Streamsets • Syapse

Federated Search

RDF

Graph

(Master data

management,

semantic data

alignment, cross

system data

mapping, etc.)

Content Index

Structured

Index

Link Index

UnStructured

Index

Extensive

Ingest

Methods

(Change data

capture,

crawlers,

message

queues,

incremental

updates,

transaction

logs,

backup/replicati

on, update

event

notifications,

etc.)

Fast SQL

Federated/Virtual Search/Index

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Federated/Virtual Search/Index Conventional Enterprise Data Life-Cycle

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Federated/Virtual Search/Index Eliminates many steps and data duplications

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Federated/Virtual Search/Index Creating a single integrated view of the patient

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QUESTIONS