Intermountain Healthcare Big Data Update
February 5, 2016
• 22 hospitals
• 33,000 employees
• 600,000 members
• 25% market share
• 200 clinics
• 1,000 employed
physicians
Intermountain Healthcare Profile
1975 1983 1994
Intermountain – Mission
• 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)
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
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
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
Significant Accomplishments and Pursuits
• Data Governance
• Production Data Lake
• Data Preparation
• Machine Learning / Cognitive Learning
• Cloud
• Federated/Virtual Search/Indexing
Business
Intelligence
(BI),
Reporting,
Analytics
and
Applications
Relational
EDW
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age
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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
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Cloud
Data Quality, Metadata and Data Lineage Management
Insights, Algorithms, Decision Support, Embedded Analytics
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
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
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me
nt
Cloud
Data Quality, Metadata and Data Lineage Management
Insights, Algorithms, Decision Support, Embedded Analytics
• 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
Production Data Lake Retaining physiologic monitor data for analysis
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.)
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
Data Preparation Graphical approach to reducing SQL and R coding
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
Data Preparation Robust set of SQL and Analytical tools and adapters
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
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)
Deriving Cohorts and Care Process Models
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)
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
Tracking Adherence to Care Process Models Summary View – Facility Level
Tracking Adherence to Care Process Models Detailed Views – Surgery Level
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
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
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
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)
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
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
Federated/Virtual Search/Index Conventional Enterprise Data Life-Cycle
Federated/Virtual Search/Index Eliminates many steps and data duplications
Federated/Virtual Search/Index Creating a single integrated view of the patient
QUESTIONS
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