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Transcript of Ryan uitti
©2014 MFMER | 3334306-1
Ryan Uitti, M.D.Deputy Director, Kern Center for the Science of Health Care Delivery
Economic Disruption in Healthcare – April 3, 2014
Disruptive Delivery:How Mayo Clinic is Combining Big Datawith the Voice of the Customer to RedefineSuccess on Consumers’ Terms
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The Science of Hitting – Ted Williams
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The Science of Hitting – Ted Williams
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Use of Home Telemonitoringin the Elderly to Prevent Readmissions
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Comparison:Telemonitoring + Versus Usual Care
Telemonitoring Intervention RN/MD team oversaw apx 100
patients and communicatedwith them via phone or video-conference if alerts arose
Daily telemonitoring sessions(5-10 minutes) includingweekends and holidays
Collected weight, blood pressure,blood sugar, pulse and peak flow data
Could arrange outpatient visits
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Results: Telemonitoring +Versus Usual Care
Telemonitoring + Usual Care Statistics
Emergency Dept Visits 35% 28% No difference
Hospitalization 52% 44% No difference
ED + Hospitalization 64% 57% No difference
Note: Results are for a one-year period
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Results: Telemonitoring +Versus Usual Care
Telemonitoring + Usual Care Statistics
Emergency Dept Visits 35% 28% No difference
Hospitalization 52% 44% No difference
ED + Hospitalization 64% 57% No difference
Deaths 15% 4% Very significant
Note: Results are for a one-year period
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Epilogue – What Next?
Not ready for prime-time
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Center for the Scienceof Health Care Delivery
Improve patient health experience
Improve population health
Improve quality, control cost
Improve medical practice throughanalysis and scientific rigor
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Value Framework
Patient
Provider Payer
Quality
Cost over time(outcomes, safety, service)
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Quality Measures
PatientSatisfaction
Costs
Big Data Health andQuality of Life
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Value:In the Eye of the Beholder The importance
of reflecting andrespecting multipleperspectives
Appreciating whatwe don’t know aboutthe care experience
Embracing multipleaims for improvement concurrently
Source: Bellows J, Sullivan MP. Could a quality index help us navigate the chasm? http://xnet.kp.org/ihp/publications/docs/ quality_background.pdf. Accessed July 11, 2012.
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Patient Satisfaction
CostsQuality Measures
Big Data Health andQuality of Life
Telestroke Example
Quality
Cost over time(outcome, safety, service)
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Mayo Clinic Telestroke Network
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More patients transferred to hubs
Fewer with access to IV thrombolysis and/or endovascular therapy
Fewer patients transferred More patients receiving IV
thrombolysis and/or endovascular therapy
Patient Flow in Hub-and-Spoke Telestroke Network
Spoke vs. Hub Hub Hospital
Spoke: No telenetwork
Spoke: With telenetwork
Patient presentsat Hospital
Emergency Room
HUBSpoke
Spoke
Spoke
Spoke
Spoke
SpokeSpoke
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Mayo ClinicTelestroke Quality Metrics
Effectiveness High accuracy for diagnosis and correct decision making (96%)10-fold increase in thrombolysis rates (from 2% to 20%)
TechnologyTechnology problems prevent clinical decision making in fewer than 2% of consults
Performance1-minute median stroke neurologist response time (swift response)22-minute median consult time(rapid assessment)
Disposition60% reduction in patient air/ ground ambulance transfersfrom spoke to hub
Safety 5% post thrombolysis symptomatic intracranial hemorrhage
Morbidity & MortalityTelestroke treated patients have approximately the same outcomes as those treated at a stroke center
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Telestroke: Estimated Cost Savings
Conclusions—The results of this study suggest that a telestroke network may increase the number of patients discharged home and reduce the costs borne by the network hospitals. Hospitals should consider their available resources and the network features when deciding whether to join or set up a network.
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Savings to Medicare and Medicaidfrom Broad Diffusion of Telestroke Overall, telestroke networks result in reductions in Medicare
reimbursements, considering initial hospitalization, recurrentstroke and rehabilitation revenues
Changes in Medicare and Medicaid reimbursements,including dual eligibles, by setting and type of care
Telestroke Networks (no.)
Initial Hospitalization
Recurrent Stroke
Nursing Home* Rehabilitation Total
Current $ 8.4 M - $ 3.3 M - $ 1.8 M - $ 10.9 M - $ 7.6 M
by 50% $ 12.7 M - $ 5.0 M - $ 2.6 M - $ 16.3 M - $ 11.2 M
by 100% $ 17.0 M - $ 6.6 M - $ 3.5 M - $ 21.8 M - $ 14.9 M
by 150% $ 21.2 M - $ 8.3 M - $ 4.4 M - $ 27.2 M - $ 18.7 M
* Nursing home costs for those patients who are dual eligible (Medicaid and Medicare)
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Conclusions:Telestroke Analysis Telestroke networks achieve net annual
cost saving for Medicare patients andfor all patients
Expansion of telestroke networks acrossthe country will improve patient outcomes and quality, benefitting patients, hospitals, Medicare and Medicaid
Financial modeling of the cost savings is essentialto complete the value equation Valuable in payer negotiations and public policy advocacy
Value work requires partnerships
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3 months to collect datato answer 2 questions
Seconds to collect and answer the same questions
20 Years Ago Today
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2003First Human Genome
Time: 10 YearsCost: $1 Billion
TODAY Genome Sequencing
Time: 1 WeekCost: $1,500
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$1,000
$10,000
$100,000
$1 million
$10 million
$100 million
2002 2004 2006 2008 2010 2012 2014
Cost of Whole Genome Sequencing
?
$1,000 to sequence one human genome
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OPTUM LABS
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Types of questions that may be pursued
Comparative Effectiveness
Behavioral and Policy Research
Variation in Care Research
Heterogeneity of Treatment Response
Optum Labs
H E A LT HC A R E
R E S E A R C H A N DI N N O V A T I O N
Provider
Academic
Professional/Consumer
Organization
Government
Payer
Pharma/Life
Sciences
An open, collaborative center for research and innovation for health care stakeholders interested in improving patient care.
Projects must be primarily to improve patient care and lower the cost of improved care, and be transparent to the entire collaborative.
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Optum Labs — Data and ToolsAdvanced Analytics and Data Visualization Data Growth Through Partnership
>149M“Administrative”
>30MClinical
315MUS Population
MayoHealthSystem
2
HealthPlan 1
HealthPlan 1
HealthSystem
3
ClinicalResearch
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Optum Labs — Research Process
Data sets and resources are integrated into a separate “sandbox.” Data contributions are tagged and valued.
Contributor data is de-identified and stored in standardized data sets, on secure, private environments.
Project research is done in the “sandbox” environment only according to the Research Proposal.
Upon work completion,the “sandbox” is dissolved. Publications and clinical translation proceed as appropriate.
Integration Research & Analytics OutputsData
Health Economics Biostatistics
ActuarialEpidemiology
InnovativeHealth Care Insight
ClinicalData
AdminData
PharmacyData
PopulationData
Data Sets
Project“Sandbox”
Researchers
Real Estate
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Focuses on understanding the underlying behaviors driving patient and provider behaviors, as well as the evaluation of alternative policy initiatives
Example: Can the application of economic theory to the analysis of claims data improve our understanding of patient medication adherence? Does the use of copays alter conclusions about the effects of benefit design on initial prescription fills and refills?
Behavioral and policy research
Explores the well-documented extensive variations in treatment patterns by geography and other dimensions
Example: How are measures of geographic variation in care affected by the definition of geographic region?
Variations in care
Seeks to understand what patient subpopulations are most likely to respond to a particular treatment
Example: Is a drug equally safe among all patient subpopulations? How could such information be used to design more efficient trials for future clinical development?
Heterogeneity of treatment response
Improves the quality of research from observational studies more generally through fundamental research on data infrastructure and statistical methodologies
Example: What is the potential value of multiple imputation methods to fill gaps in the data?
Methodology research
Research Themes: Areas of Focus
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Use of new anticoagulants in atrial fibrillation
Longitudinal variation in care analysis of hip and knee surgery
• National trends in the screening, diagnosis, and treatment of localized prostate cancer
• Unplanned hospital readmission and emergency department care for acute diabetes complications
• Utilization and variations in uses of proton beam therapy
Step-down protocols in asthma medication
• Diagnosis, treatment, and service utilization for spine-related problems
• GLP-based anti-hyperglycemic medications and risk of acute pancreatitis and pancreatic cancer
Currently underway or awaiting publication
Likely candidate for clinical translation project
Sample Research Projects
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• American Medical Group Association, Alexandria, Va.• Boston University School of Public Health, Boston, Mass.• Lehigh Valley Health Network, Allentown, Pa.• Pfizer Inc. (NYSE: PFE), New York, N.Y.• Rensselaer Polytechnic Institute (RPI), Troy, N.Y.• Tufts Medical Center, Boston, Mass.• University of Minnesota School of Nursing, Minneapolis, Minn.
Seven Leading Health Care Organizations Join Optum Labs
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Patients are seen by outside providers/physicians.Optum Labs data
Patients call and are given an appointment at Mayo.
Example in Action
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Patients are seen by initial Mayo team.
Document patient expectations – “Pt Exp’n”
Patients indicate their expectations.
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Patients are presented medical vs. surgery informationDocument education
Patients make a decision about their care: medical/surgery
Shared decision making – SDM
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Patients receive care … some being treated medically, others with surgery
Collect risk factors and other dataPatients see medical/pre-operative Mayo team
Collect treatment data
Mean length of stayfor primary TKA
OPTUM (x age = 56.6)
3.0 daysMAYO CLINIC (x age = 70)
2.85 days
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Patients complete care at Mayo
Collect discharge disposition data
Patients might be seen by outside providers
Post-Mayo – Optum Labs data
Patients later report their outcomes from medical care/surgery
Patient-reported outcomes – PRO
Discharge to homeOPTUM (x age = 56.6)
81.4%MAYO CLINIC (x age = 70)
63%
30-day readmissionsOPTUM (x age = 56.6)
4.4%MAYO CLINIC (x age = 70)
1.6%
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Surgical Process Flow
for Costing- TDABC method
C (Circulator Nurse)Surgical AssistantScrubs Technician
RN Anesthetist-NARadiology TechnicianS (Surgeon)
A (Anesthesiologist)AR (Anesthesiologist Resident)R (Resident/Fellow)
Inpatient SpaceOperating Room
SurgeryProcess
PostSurgery
E22
Patient Prepfor Surgery
C AR20
A5 20
20 R20
C20 E22
Operation(Incision
to Closure)
CAR91
A46
S73
91 R86
C91
91
E28
Operation(Incision
to Closure)
CAR88
A44
S71
88 R83
C88
88 10
E30
EMR documentation
and contact family, supervision time, post procedure
note, order tests
S10
R5
Hip orKnee?
Hip
Knee
FLOW 1
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The Value Equation Comes to Life
Quality outcome data:
Patient-centric outcomes
Practice performance outcomes
Cost:
Outside Mayo
At Mayo
“Cost avoidance”
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Data are collected from all Mayo Clinic sites
Comparing and adopting best practicehelps improve value for all
THA +22 +120% TKA +14 +110% PHM +0.96 +5.36% HD +3.34 +5.23% DHI +0.81 +3.79% LEN +1.34 +3.66% MAS +0.66 +3.69% EXPD -3.45 -8.61% APOL -0.66 -3.70% FSLR -1.21 -3.70% TDC -2.32 -3.69% OKE -1.42 -3.06% THA +22 +120% TKA +14 +110% PHM +0.96 +5.36% HD +3.34 +5.23% DHI +0.81 +3.79%
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Age BMI Strength Exercise
85% probabilityof going home 3 days postopAND being able to stand/walk
without pain for 30-min 3 months postop
Knee Replacement Value Proposition