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Healthcare institutions are aggressively moving towards meeting compliance with MU1 and MU2 with the implementation of full-featured Electronic Health Records. Concomitantly, there will be a massive increase in the amount of clinical data captured electronically. Business intelligence (BI) which traditionally has focused on financial data can be leveraged to use clinical data to support providers in delivering high quality, efficient care. In addition, BI coupled with population health analytics can help meet many Accountable Care Organization needs. This presentation will discuss the Denver Health journey in using BI in a variety of was to facilitate the attainment of high quality care.
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Business Intelligence:
Supporting Delivery of High
Quality Care and
Attainment of ACO Goals
iHT2 Summit in Atlanta
April 25th, 2012
Andy Steele, MD, MPH, MSc
Director, Medical Informatics
Denver Health, Denver, CO
Learning Objectives
• Identify the impact of business
intelligence (BI) on clinical areas
• Understand unique ways to leverage BI
for supporting ACO goals
Denver Health
Integrated public safety net
institution
5,300 employees
Closed medical staff
500 bed hospital
Extensive primary care
network
Level I Trauma Center
Public Health Department
Denver Health Over 160,000 patients
25% of Denver population
Payer mix
– 35% Medicaid
– 28% Uninsured
– 10% Medicare
– 27% Other
$2B in unsponsored care
since 1992
~$400M in 2011
Clinical Technology Strategy
Clinical
Documentation Medication
Administration
Check
Data
Warehouse
Enterprise
Document
Management Immunization
Tracking
CPOE and
Clinical Rules
PACS/Imaging
Systems
Results
Repository
Enterprise
Master Patient
Index
Dashboard
Single
Sign-on
Workflow Analytics / BI
Dashboard
Patient and Provider
Centers for Medicare and
Medicaid Services: ACO
"an organization of health care providers that
agrees to be accountable for the quality,
cost, and overall care…
ACO Original Core Principals
Provider-led organizations
– Strong base of primary care
– Accountable for quality and total per capita costs
– Provide full continuum of care for a population of
patients
Payments that are linked to quality
improvements that also reduce overall costs
Use sophisticated performance
measurement
– Support improvement
– Show savings via improved care
Payment Reforms Will Motivate and
Reward Innovation at a Whole New Level -Todd Park, Chief Technology Officer,
U.S. Department of Health and Human Services
• Shared savings; redesigned care processes for high quality, efficient delivery
Accountable Care
Organizations
• Organized outpatient care, coordination and team-based approaches
Patient Centered
Medical Homes
• Pilot program for episodes of care; incentivizes reduced costs around eight conditions
Bundled Payments
• Motivates hospitals to engage with care coordinators and better organize delivery systems
Readmission Reduction Programs
Timely Clinical Data, Decision Support Care Integration Tools Technology to Extend Physician Reach Consumer Engagement Tools/Platforms/Apps Data Mining/Analytics
IT Innovations Needed:
BIG DATA
Big Data: 3 “V’s”
• Selective data retention
• Offload “cold” data
• Outsourcing Volume
• Data caches
• Point-to-point data routing
• Balance data latency with decision cycles
Velocity
• Inconsistency resolution
• Data access middleware and ETLM
• Metadata management Variety
http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf
Goals for Enterprise Business
Intelligence Strategy Baseline, documented strategy that includes the
standards, processes, definitions, and approach that
can be developed over time as business needs
change
– Organization wide consistency and coordination for
business intelligence, analytics, and reporting efforts
– Lower costs (people, systems, and software) by reducing
redundancy and unbeneficial activities
– Have an architecture that supports the Enterprise BI
Strategy
– Include plan for Governance of the BI environment
– Communicate consistent vision across the entire
organization
High-level Vision: Data Integration
Integrated Reporting, Registries and Analysis
Single source for complex data analysis and reporting
EDW Financial
Data
Clinical
Data
Claims&
Eligibility
Data Warehousing: Denver Health
Data Warehouse
Pharmacy Radiology Laboratory
Demographics Financial
Pathology Encounter Pulmonary GI Lab
Ultrasound EDM Forms
Fetal Monitoring ED Med Recon OR
2009
2007
2008
1998
Orders Med Administration
Vaccinations Wait List/Referrals Scheduling Nursing Documentation
2010
Workflow
Custom Interfaces
Data Warehouse Overview
Data Warehouse Model
Network & Hardware Infrastructure Basic Application Structure / Reporting Tool Implementation
Security and Auditing Tool Implementation
Foundation Cubes / Data Structures Development
Maintenance, Upgrades and Support
Foundation
Financial Interface Development & Testing
Clinical Interface Development & Testing
Financial Data Validation & Rpt Development
Clinical Data Validation & Rpt Development
Quality
Disease Management / Registries Patient Value
Web Rpts
Portal Design & Implementation
Decentralized Reporting / Training
Executive Reporting
Internal Rpts
External Rpts
End-user Value
Data Facing Methods Excel “spreadmarts” and Data Cubes
Crystal Reports/Data Cubes in Web
Publishing
Microsoft SQL Server Reporting
Services (Microsoft SharePoint
Integrated mode)
VPSX delivery
Microsoft Performance Point
Dashboards
Geo-coded Maps via ArcGIS
Microsoft Report Builder ad hoc
reporting model
Microsoft Power Pivot
Clinical
Registry (DW & EHR)
Financial, Quality, Safety
Reports
Ad-hoc
Reports
Point of Care
Support
Outreach
Programs
Employee
Evaluation
Research
mHealth
Clinical
Registry (DW & EHR)
Financial, Quality, Safety
Reports
Ad-hoc
Reports
Point of Care
Support
Outreach
Programs
Employee
Evaluation
Research
mHealth
CEO Dashboard
CEO Dashboard
Quality Scorecard & Registries
2010
– Electronic interface
– 102 measures – all with trend lines
– Ability to drill down to clinic level
– Most measures updated automatically from the
data warehouse (others inputted into intranet
site)
– Much broader audience for most measures
– Ability to secure access to sensitive metrics
Registries completed for:
– Colon Cancer
– Hypertension
– Diabetes
– Amiodarone
Registries in progress for:
– Breast Cancer
– Cervical Cancer
– Narcotic Users
Data Warehouse-
Medical Quality and Safety
Quality Dashboard
4/24/2012
Electronic Quality Scorecard
Printable Graphs
Printable Grids
Registry Reports
CHS Colorectal Cancer Screening Indicator
Colorectal Cancer Screening is defined as having a colonoscopy in the last 10 years or a flexible sigmoidoscopy in the last 5 years or a fecal occult blood test in the last 15 months.
All Clinics
Summary By Clinic
(Eligible Patients with visit to SGU < 6 months)
% Current with
FOBT < 15
months
Eligible Patients with visit to SGU < 6
months Eligible Patients (50 - 75 years old)
% Current with Colonoscopy
<10 years
% Current with
screening % Refused
Colonoscopy Total
Number
Site of Greatest Use
(SGU) Total
Number % Current
with Screening
Webb FIM 3,390 49 1 28 26 2,169 57
Westside Adult IM 2,977 51 1 29 26 2,017 57
Eastside Adult IM 2,599 50 1 36 19 1,828 56
La Casa/Quigg Newton
1,699 26 0 8 20 1,036 30
Lowry 1,501 37 0 22 19 949 44
DHMP 1,197 47 1 8 42 687 50
Park Hill 1,140 49 0 33 21 749 55
Westwood 945 41 4 25 19 611 45
Montbello 569 42 0 34 12 337 53
SGU Unassigned 23 30 0 17 13 0 0
Others 9 33 0 22 11 8 38
16049 45 24 26 10391 51 Total 1
(Eligible Patients with visits in last 18 months)
Report validated by DSS Development Data Current As of: 08/15/2009
Colorectal Cancer Screening
Registry
CHS Colorectal Cancer Screening Indicator
Colorectal Cancer Screening is defined as having a colonoscopy in the last 10 years or a flexible sigmoidoscopy in the last 5 years or a fecal occult blood test in the last 15 months.
All Clinics
Summary By Clinic
(Eligible Patients with visit to SGU < 6 months)
% Current with
FOBT < 15
months
Eligible Patients with visit to SGU < 6
months Eligible Patients (50 - 75 years old)
% Current with Colonoscopy
<10 years
% Current with
screening % Refused
Colonoscopy Total
Number
Site of Greatest Use
(SGU) Total
Number % Current
with Screening
Webb FIM 3,390 49 1 28 26 2,169 57
Westside Adult IM 2,977 51 1 29 26 2,017 57
Eastside Adult IM 2,599 50 1 36 19 1,828 56
La Casa/Quigg Newton
1,699 26 0 8 20 1,036 30
Lowry 1,501 37 0 22 19 949 44
DHMP 1,197 47 1 8 42 687 50
Park Hill 1,140 49 0 33 21 749 55
Westwood 945 41 4 25 19 611 45
Montbello 569 42 0 34 12 337 53
SGU Unassigned 23 30 0 17 13 0 0
Others 9 33 0 22 11 8 38
16049 45 24 26 10391 51 Total 1
(Eligible Patients with visits in last 18 months)
Report validated by DSS Development Data Current As of: 08/15/2009
CHS Colorectal Cancer Screening Indicator
Colorectal Cancer Screening is defined as having a colonoscopy in the last 10 years or a flexible sigmoidoscopy in the last 5 years or a fecal occult blood test in the last 15 months.
All Clinics
Summary By Clinic
(Eligible Patients with visit to SGU < 6 months)
% Current with
FOBT < 15
months
Eligible Patients with visit to SGU < 6
months Eligible Patients (50 - 75 years old)
% Current with Colonoscopy
<10 years
% Current with
screening % Refused
Colonoscopy Total
Number
Site of Greatest Use
(SGU) Total
Number % Current
with Screening
Webb FIM 3,390 49 1 28 26 2,169 57
Westside Adult IM 2,977 51 1 29 26 2,017 57
Eastside Adult IM 2,599 50 1 36 19 1,828 56
La Casa/Quigg Newton
1,699 26 0 8 20 1,036 30
Lowry 1,501 37 0 22 19 949 44
DHMP 1,197 47 1 8 42 687 50
Park Hill 1,140 49 0 33 21 749 55
Westwood 945 41 4 25 19 611 45
Montbello 569 42 0 34 12 337 53
SGU Unassigned 23 30 0 17 13 0 0
Others 9 33 0 22 11 8 38
16049 45 24 26 10391 51 Total 1
(Eligible Patients with visits in last 18 months)
Report validated by DSS Development Data Current As of: 08/15/2009
Colorectal Cancer Screening Registry Colorectal Cancer Screening
Registry
Performance Point Dashboards
and Reporting Services Reports
ArcGIS Heat Maps
Performance Point Strategy Maps
Clinical
Registry (DW & EHR)
Financial, Quality, Safety
Reports
Ad-hoc
Reports
Point of Care
Support
Outreach
Programs
Employee
Evaluation
Research
mHealth
Data Request Method -
Historical
Examples of clinical informational
queries:
Return to ED and Admit within 7 days
Unexpected transfers to Critical Care
Hypertensives on HCTZ who develop
Acute Gout
Data Warehouse-Medical Quality
and Safety
Data Request Process:
Outcomes
548 requests in 6 months
40% quick strike
30% critical priority
Average report completion
– 6.3 days for quick strike requests
Clinical
Registry (DW & EHR)
Financial, Quality, Safety
Reports
Ad-hoc
Reports
Point of Care
Support
Outreach
Programs
Employee
Evaluation
Research
mHealth
4/24/2012 41
Clinical
Registry (DW & EHR)
Financial, Quality, Safety
Reports
Ad-hoc
Reports
Point of Care
Support
Outreach
Programs
Employee
Evaluation
Research
mHealth
Navigator Report Community Health outreach workers contact
patients on our hypertension or diabetes
registries in an effort to improve their
preventative care and disease management
Desire for patient lists:
– Need to be contacted
– Already contacted
– MOGED or Opt out
Need ability to “write back” to DSS
Navigator Encounter Report
Brings forward patient
demographics
Displays clinical
characteristics for this
patient’s registries
Shows registry statuses
for this patient
Allows the Navigator to
log contact and activity
with the patient
Patient Outreach letters
Letters sent to patients if they need to be
screened for breast, cervical, or colorectal
cancer based on national guidelines
English or Spanish version mailed based on
patient’s preferred language
Clinical
Registry (DW & EHR)
Financial, Quality, Safety
Reports
Ad-hoc
Reports
Point of Care
Support
Outreach
Programs
Employee
Evaluation
Research
mHealth
Premature births between 33 and 36 weeks included (ICD-9 codes 765.27 and 765.28).
Neonatalogist Competency - Length of Stay in Premature Births (33-36 weeks)
For First Quarter 2008
Length of
Stay (days) Discharge
Date Admit Date Patient Name Patient ID
Medical Record Number
CHAMBERS, BETSEY
2940827 000105032718 PARAMO-TERRONES ,KARLA M 01/24/2008 01/29/2008 5
2941282 000105056055 MENENDEZ ,JOSE ALEJANDROD 01/25/2008 02/08/2008 14
2941288 000105056188 MENENDEZ ,ANTONIO MIGUELD 01/25/2008 02/14/2008 20
2944325 000105203970 GANO ,BOY D 02/01/2008 02/08/2008 7
2951097 000105518252 ARELLANO ,GIRL 02/16/2008 02/18/2008 2
JONES, M DOUGLAS
2931037 000104560032 CERRILLO-ZAPATA ,ANDY D 01/02/2008 01/15/2008 13
2934945 000104757307 BUSTOS-ARAIZA ,YOSAJANDID 01/11/2008 01/24/2008 13
2936290 000104812250 MONZON-GARCIA ,ADRIAN EMD 01/15/2008 01/26/2008 11
2940709 000105024517 PORTALES-MARZO ,JESSICA D 01/24/2008 01/28/2008 4
LANGENDOERFER, SHARON
2929548 000104504386 GONZALEZ ,GIRL 12/29/2007 01/04/2008 6
2931034 000104560024 CERRILLO-ZAPATA ,EMILY D 01/02/2008 01/16/2008 14
2949559 000105437925 RUBIO-GUTIERREZ ,ELIZABED 02/13/2008 02/27/2008 14
2955130 000105704316 DOMINGUEZ-CEBAL ,LIZBETHD 02/26/2008 03/19/2008 22
Certified by DSS Data Warehouse Report Date: 06/30/2008 Page 1
Denver Health CONFIDENTIAL - DO NOT copy, disseminate or distribute this document.
Physici
an A
Physici
an B
Physici
an C
Physici
an D
Physici
an E
Physician A
Physician B
Physician C
Clinical
Registry (DW & EHR)
Financial, Quality, Safety
Reports
Ad-hoc
Reports
Point of Care
Support
Outreach
Programs
Employee
Evaluation
Research
mHealth
Data Sharing/Comparative
Effectiveness Research
HVHC: High Value Healthcare Collaborative
(HVHC)
UniversityHealth Consortium (benchmarking)
SAFTINet: Scalable Architecture for
Federated Translational Inquires Network
HMO Research Network
CCTSI - Colorado Clinical & Translational
Sciences InstituteHRSA Collaborative
AHRQ “ACTION” (accelerated research)
Clinical
Registry (DW & EHR)
Financial, Quality, Safety
Reports
Ad-hoc
Reports
Point of Care
Support
Outreach
Programs
Employee
Evaluation
Research
mHealth
55
55
Chronic Care Management: Using a “Customer
Relationship Management (CRM)” Application
Clinical
Registry (DW & EHR)
Financial, Quality, Safety
Reports
Ad-hoc
Reports
Point of Care
Support
Outreach
Programs
Employee
Evaluation
Research
mHealth
Is It All Worth It?
52% 54%
71%
64%
39%
35%
56%
52%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Diabetes Blood pressure <130/80 mm HG
Diabetes LDL < 100 mg/dL All Hypertension BP < 140/90mm HG
Breast Cancer Screening
Denver Health
HEDIS (50thpercentile)
Clinical Quality Indicators
Low O/E Mortality
“Obvious” Lessons Learned
DSS can improve efficiency and provide easily
accessible data for quality and safety initiatives
Executive staff must be fully engaged and
supportive
Clinical leadership needs to believe that IT efforts
will improve patient safety and quality
Patience is required to develop and maintain
appropriate infrastructure
Developing clinical registries is a challenging
iterative process
Integrated strategy needed to avoid silo solutions
“Obvious” Lessons Learned
Gain physician, financial and administrative buy in
Allocate appropriate funding
Clinical development takes much longer then
financial
Primary care is multi-factorial, solutions need to be
multi-pronged
“Model” is better
– The more model the source is, the easier it is to validate
DSS
– Customizations should be done outside the DSS database
“Surprise” Lessons Learned Start with small wins at high levels
Determine type of BI model the organization can
support
Getting end users involved to early can cause loss of
interest and support
Grab as much data as possible
Look for seed/grant money to start
Data Warehouse data is e-discoverable (Litigation)
and must be in compliance with HIM policy
Physicians don’t know what they want until they see
it
“Surprise” Lessons Learned Almost every “project” can be leveraged
Registry “engine”
Data Management “engine”
Business Intelligence “engine”
“These reports are wrong”
Data is wrong/different at the source
The report is defined incorrectly
The data doesn’t mean what you think it means
Not all Super Users are “super”
Training does not imply proficiency
More difficult the more data that is available
Future
New hardware and software platform to
leverage the advancements in BI tools
Extensible data model to support new and growing
data sources
Predictive and “google-like” analytics
Migrate from static reports to self-service BI
tools
Transition “reports” team to BI tool development and
expansion
Revise governance model
More visionary role
Transfer data warehouse functions into EHR
