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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