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Copyright © SAS Inst itute Inc. A l l r ights reserved.
Health User GroupGreg Horne, National Lead, Healthcare
Greg.Horne@SAS.com
mailto:Greg.Horne@SAS.com
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Copyright © SAS Inst itute Inc. A l l r ights reserved.
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Copyright © SAS Inst itute Inc. A l l r ights reserved.
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Copyright © SAS Inst itute Inc. A l l r ights reserved.
Triple Aim, Cost, Quality, Access (Experience)
New buildsNeed for long term care bedsHome carePrimary care priority eConsult and managementOutcome driven funding
Bending cost curve Innovative funding modelsDriving qualityEvidence based medicinePatient safetySupply chain management
Management of chronic conditionsAccess to dataLocal servicesMore aware Interoperability
Infrastructure
FundingReform
Patients
Global Health Care Issues
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• Specific education to gain analytics expertise
• Very articulate in models and mechanisms
• Using information for informed decisions
• Not educated as a data scientist
Right knowledge, right stakeholder in a meaningful manner
Everyone is an Analyst
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Visualization, the Citizen Data Scientist
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TODAYTHE
FUTURE
DA
TA
SIZ
E
Analytics Reality For Health• EMR
• Claims data
• Device generated
• Genomic
• Finance
• Operational
• Environmental
• Social determinates
• Research and journal
• Criminal Justice
• Personal apps
• Education
• Social services
• Social media
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PROACTIVEINFORMATIONALAN
ALY
TIC
MA
TUR
ITY
StandardReporting
Ad HocQuery andReporting
QueryDrilldown
Alerting
StatisticalAnalysis
Forecasting
MachineLearning
ArtificialIntelligence
What % of patients have had a recent interaction with a social worker?
How many male patients live in a specific postcode?
Which physicians have better performance?
Which patients are not adhering to their medication?
Who is at risk of cardiac disease?
What would offering pharma care cost next year?
What impact could we expect from outreach in driving screening rates?
What is the best outcome?
Decision-Making SpectrumWhat is the next best action for this patient?
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Forecast Paradox
• No one gets rewarded for delivering failure
• Focus is key
• Change management
• Measured outcomes
• Know when successful
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How Does AI Learn?
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IOT Feeds AI, and Repeat….
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Adapting the Personal Health Record
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Activated: I’ll take your help
• I am inspired by my relationships with family members and friends
• I am driven by my peers to accomplish goals that are difficult
• I am motivated by my family history
• My friends are an important source of support and encouragement
• I have conversations about health outside of the healthcare environment
• I am comfortable talking about health issues with friends
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Independents: I’ll do it myself
• I focus on small actions and achievements but I am avoiding the bigger problem
• I want to do the research myself before making decisions
• I don’t think I need help from others to make myself healthier
• I know what to do
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Procrastinators: I have no time for this
• I am too busy for healthy behaviors. I put things off for another day
• I wait too long to visit my health provider, and then feel its too late
• I don’t need to do anything to change my behavior. I am in denial
• I have no internal motivation or incentive to succeed in health goals and I am not interested in trying
• It’s all so boring and tedious
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Analytics Playing a Role Throughout The Journey
Treat
Coordinate Engage
Identify Care Gaps
Execute Outreach Campaigns
Prevent
Reward & Sustain
ProfilePatients
Predict Risk Score
PersonalizeCare Plan
Measure Treatment Outcomes
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Case Study San Bernardino County Behavioral Health
CHALLENGE• Guide service strategy and community outreach efforts.
• Reduce stigma for residents who need behavioral health services.
• Reduce hospital readmissions.
• Improve quality of care.
• Enhance decision making across DBH
SOLUTION• SAS Advanced Analytics
• SAS Data Management
• SAS Enterprise BI Server
RESULTS• Determined what services to offer in what locations and where to provide
community outreach.
• Dispelled misconceptions about consumers requiring behavioral health services.
• Developed process improvements to reduce hospital readmissions.
• Helped clinicians evaluate program targets and outcomes to improve care.
“We need to make good decisions about the community we’re serving, and the best way to do that is to collect, manage and analyze data.”
Sarah Eberhardt-Rios
Deputy Director for Program Support Services
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Patient Flow Optimization
Results as a Service – Case Study ExampleHealth Care Industry
Business Issue
Hospitals have capacity issues in recovery units – surgery to PACU
or ER and moved to recovery units (Physical beds and
appropriate staffing)
Some recovery units are over or underutilized and there is risk
that patients may be assigned to an improper recovery unit.
Results
Predict patient demand to support the daily
management of patient flow to identify patient flow
problems.
-Predicted hourly census-Admissions and discharges by hour-Number in service and off service-Number of patients waiting and avg wait time
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Case Study Dignity Health Sepsis Reduction
CHALLENGE• Reduce hospital readmissions.
• Improve quality of care.
• Enhance decision making across DBH
SOLUTION• SAS Advanced Analytics
• SAS Data Management
• SAS VA
RESULTS• Sepsis mortality rates have dropped between 5% and 13% across hospitals
• ICU LOS down 10%
• Downstream improvement in clinical quality
“Nothing more worthwhile than the work this system has done using analytics to recognize symptoms of sepsis and prevent situations where patients are going over the waterfall, and far downstream.”
Joseph Colorafi, M.D., Vice President and CMIO
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Thank you!www.linkedin.com/in/gregihorne
Greg.Horne@SAS.com
http://www.linkedin.com/in/gregihorne
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