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©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 Data
with the Voice of the Customer to Redefine
Success on Consumers’ Terms
©2014 MFMER | 3334306-2
©2014 MFMER | 3334306-3
The Science of Hitting – Ted Williams
©2014 MFMER | 3334306-4
The Science of Hitting – Ted Williams
©2014 MFMER | 3334306-5
©2014 MFMER | 3334306-6
Use of Home Telemonitoring
in the Elderly to Prevent Readmissions
©2014 MFMER | 3334306-7
Comparison:
Telemonitoring + Versus Usual Care
Telemonitoring Intervention
RN/MD team oversaw apx 100
patients and communicated
with them via phone or video-
conference if alerts arose
Daily telemonitoring sessions
(5-10 minutes) including
weekends and holidays
Collected weight, blood pressure,
blood sugar, pulse and peak flow data
Could arrange outpatient visits
©2014 MFMER | 3334306-8
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
©2014 MFMER | 3334306-9
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
©2014 MFMER | 3334306-10
Epilogue – What Next?
Not ready for prime-time
©2014 MFMER | 3334306-11
Center for the Science
of Health Care Delivery
Improve patient health experience
Improve population health
Improve quality, control cost
Improve medical practice through
analysis and scientific rigor
©2014 MFMER | 3334306-12
Value Framework
Patient
Provider Payer
Quality
Cost over time
(outcomes, safety, service)
©2014 MFMER | 3334306-13
Quality
Measures
Patient
Satisfaction
Costs
Big Data
Health and
Quality of Life
©2014 MFMER | 3334306-14
Value:
In the Eye of the Beholder
The importance
of reflecting and
respecting multiple
perspectives
Appreciating what
we don’t know about
the care experience
Embracing multiple
aims 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.
©2014 MFMER | 3334306-15
Patient
Satisfaction
CostsQuality
Measures
Big Data
Health and
Quality of Life
Telestroke Example
Quality
Cost over time
(outcome, safety, service)
©2014 MFMER | 3334306-16
Mayo Clinic Telestroke Network
©2014 MFMER | 3334306-17
 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 presents
at Hospital
Emergency Room
HUB
Spoke
Spoke
Spoke
Spoke
Spoke
SpokeSpoke
©2014 MFMER | 3334306-18
Mayo Clinic
Telestroke Quality Metrics
Effectiveness
High accuracy for diagnosis and
correct decision making (96%)
10-fold increase in thrombolysis
rates (from 2% to 20%)
Technology
Technology problems prevent
clinical decision making in fewer
than 2% of consults
Performance
1-minute median stroke neurologist
response time (swift response)
22-minute median consult time
(rapid assessment)
Disposition
60% reduction in patient air/
ground ambulance transfers
from spoke to hub
Safety
5% post thrombolysis symptomatic
intracranial hemorrhage
Morbidity & Mortality
Telestroke treated patients have
approximately the same outcomes
as those treated at a stroke center
©2014 MFMER | 3334306-19
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.
©2014 MFMER | 3334306-20
Savings to Medicare and Medicaid
from Broad Diffusion of Telestroke
 Overall, telestroke networks result in reductions in Medicare
reimbursements, considering initial hospitalization, recurrent
stroke 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)
©2014 MFMER | 3334306-21
Conclusions:
Telestroke Analysis
Telestroke networks achieve net annual
cost saving for Medicare patients and
for all patients
Expansion of telestroke networks across
the country will improve patient outcomes and
quality, benefitting patients, hospitals, Medicare
and Medicaid
Financial modeling of the cost savings is essential
to complete the value equation
Valuable in payer negotiations and public policy advocacy
Value work requires partnerships
©2014 MFMER | 3334306-22
©2014 MFMER | 3334306-23
3 months to collect data
to answer 2 questions
Seconds to collect and
answer the same questions
20 Years Ago Today
©2014 MFMER | 3334306-24
2003
First Human Genome
Time: 10 Years
Cost: $1 Billion
TODAY
Genome Sequencing
Time: 1 Week
Cost: $1,500
©2014 MFMER | 3334306-25
2002 2004 2006 2008 2010 2012 2014
$1,000
$10,000
$100,000
$1 million
$10 million
$100 million
Cost of Whole Genome Sequencing
?
$1,000 to sequence
one human genome
©2014 MFMER | 3334306-26
OPTUM LABS
©2014 MFMER | 3334306-27
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 H
C A R E
R E S E A R C H A N D
I N N O VAT 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.
©2014 MFMER | 3334306-28
Optum Labs — Data and Tools
Advanced Analytics and Data Visualization Data Growth Through Partnership
>149M
“Administrative”
>30M
Clinical
315M
US Population
Mayo Health
System
2
Health
Plan 1
Health
Plan 1
Health
System
3
Clinical
Research
©2014 MFMER | 3334306-29
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
Innovative
Health Care Insight
Clinical
Data
Admin
Data
Pharmacy
Data
Population
Data
Data Sets
Project
“Sandbox”
Researchers
Real Estate
©2014 MFMER | 3334306-30
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
©2014 MFMER | 3334306-31
 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
©2014 MFMER | 3334306-32
• 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
©2014 MFMER | 3334306-33
Patients are seen by outside providers/physicians.
Optum Labs data
Patients call and are given an appointment at Mayo.
Example in Action
©2014 MFMER | 3334306-34
Patients are seen by initial Mayo team.
Document patient expectations – “Pt Exp’n”
Patients indicate their expectations.
©2014 MFMER | 3334306-35
Patients are presented medical vs. surgery information
Document education
Patients make a decision about their care: medical/surgery
Shared decision making – SDM
©2014 MFMER | 3334306-36
Patients receive care
… some being treated medically, others with surgery
Collect risk factors and other data
Patients see medical/pre-operative Mayo team
Collect treatment data
Mean length of stay
for primary TKA
OPTUM (x age = 56.6)
3.0 days
MAYO CLINIC (x age = 70)
2.85 days
©2014 MFMER | 3334306-37
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 home
OPTUM (x age = 56.6)
81.4%
MAYO CLINIC (x age = 70)
63%
30-day readmissions
OPTUM (x age = 56.6)
4.4%
MAYO CLINIC (x age = 70)
1.6%
©2014 MFMER | 3334306-38
Surgical Process Flow
for Costing
- TDABC method
C (Circulator Nurse)
Surgical Assistant
Scrubs Technician
RN Anesthetist-NA
Radiology Technician
S (Surgeon)
A (Anesthesiologist)
AR (Anesthesiologist Resident)
R (Resident/Fellow)
Inpatient Space
Operating Room
Surgery
Process
Post
Surgery
E22
Patient Prep
for Surgery
C AR
20
A
5 20
20 R
20
C
20 E22
Operation
(Incision
to Closure)
CAR
91
A
46
S
73
91 R
86
C
91
91
E28
Operation
(Incision
to Closure)
CAR
88
A
44
S
71
88 R
83
C
88
88 10
E30
EMR
documentation
and contact
family, supervision
time, post procedure
note, order tests
S
10
R
5
Hip or
Knee?
Hip
Knee
FLOW 1
©2014 MFMER | 3334306-39
The Value Equation Comes to Life
Quality outcome data:
Patient-centric outcomes
Practice performance outcomes
Cost:
Outside Mayo
At Mayo
“Cost avoidance”
©2014 MFMER | 3334306-40
Data are collected from all Mayo Clinic sites
Comparing and adopting best practice
helps improve value for all
THA +22 +120% TKA +14 +110% PHM +0.96 +5.36% HD +3.34 +5.23% DHI +0.81 +3.
©2014 MFMER | 3334306-41
Age BMI Strength Exercise
85% probability
of going home 3 days postop
AND being able to stand/walk
without pain for 30-min 3 months postop
Knee Replacement Value Proposition

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Ryan uitti

  • 1. ©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 Data with the Voice of the Customer to Redefine Success on Consumers’ Terms
  • 2. ©2014 MFMER | 3334306-2
  • 3. ©2014 MFMER | 3334306-3 The Science of Hitting – Ted Williams
  • 4. ©2014 MFMER | 3334306-4 The Science of Hitting – Ted Williams
  • 5. ©2014 MFMER | 3334306-5
  • 6. ©2014 MFMER | 3334306-6 Use of Home Telemonitoring in the Elderly to Prevent Readmissions
  • 7. ©2014 MFMER | 3334306-7 Comparison: Telemonitoring + Versus Usual Care Telemonitoring Intervention RN/MD team oversaw apx 100 patients and communicated with them via phone or video- conference if alerts arose Daily telemonitoring sessions (5-10 minutes) including weekends and holidays Collected weight, blood pressure, blood sugar, pulse and peak flow data Could arrange outpatient visits
  • 8. ©2014 MFMER | 3334306-8 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
  • 9. ©2014 MFMER | 3334306-9 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
  • 10. ©2014 MFMER | 3334306-10 Epilogue – What Next? Not ready for prime-time
  • 11. ©2014 MFMER | 3334306-11 Center for the Science of Health Care Delivery Improve patient health experience Improve population health Improve quality, control cost Improve medical practice through analysis and scientific rigor
  • 12. ©2014 MFMER | 3334306-12 Value Framework Patient Provider Payer Quality Cost over time (outcomes, safety, service)
  • 13. ©2014 MFMER | 3334306-13 Quality Measures Patient Satisfaction Costs Big Data Health and Quality of Life
  • 14. ©2014 MFMER | 3334306-14 Value: In the Eye of the Beholder The importance of reflecting and respecting multiple perspectives Appreciating what we don’t know about the care experience Embracing multiple aims 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.
  • 15. ©2014 MFMER | 3334306-15 Patient Satisfaction CostsQuality Measures Big Data Health and Quality of Life Telestroke Example Quality Cost over time (outcome, safety, service)
  • 16. ©2014 MFMER | 3334306-16 Mayo Clinic Telestroke Network
  • 17. ©2014 MFMER | 3334306-17  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 presents at Hospital Emergency Room HUB Spoke Spoke Spoke Spoke Spoke SpokeSpoke
  • 18. ©2014 MFMER | 3334306-18 Mayo Clinic Telestroke Quality Metrics Effectiveness High accuracy for diagnosis and correct decision making (96%) 10-fold increase in thrombolysis rates (from 2% to 20%) Technology Technology problems prevent clinical decision making in fewer than 2% of consults Performance 1-minute median stroke neurologist response time (swift response) 22-minute median consult time (rapid assessment) Disposition 60% reduction in patient air/ ground ambulance transfers from spoke to hub Safety 5% post thrombolysis symptomatic intracranial hemorrhage Morbidity & Mortality Telestroke treated patients have approximately the same outcomes as those treated at a stroke center
  • 19. ©2014 MFMER | 3334306-19 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.
  • 20. ©2014 MFMER | 3334306-20 Savings to Medicare and Medicaid from Broad Diffusion of Telestroke  Overall, telestroke networks result in reductions in Medicare reimbursements, considering initial hospitalization, recurrent stroke 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)
  • 21. ©2014 MFMER | 3334306-21 Conclusions: Telestroke Analysis Telestroke networks achieve net annual cost saving for Medicare patients and for all patients Expansion of telestroke networks across the country will improve patient outcomes and quality, benefitting patients, hospitals, Medicare and Medicaid Financial modeling of the cost savings is essential to complete the value equation Valuable in payer negotiations and public policy advocacy Value work requires partnerships
  • 22. ©2014 MFMER | 3334306-22
  • 23. ©2014 MFMER | 3334306-23 3 months to collect data to answer 2 questions Seconds to collect and answer the same questions 20 Years Ago Today
  • 24. ©2014 MFMER | 3334306-24 2003 First Human Genome Time: 10 Years Cost: $1 Billion TODAY Genome Sequencing Time: 1 Week Cost: $1,500
  • 25. ©2014 MFMER | 3334306-25 2002 2004 2006 2008 2010 2012 2014 $1,000 $10,000 $100,000 $1 million $10 million $100 million Cost of Whole Genome Sequencing ? $1,000 to sequence one human genome
  • 26. ©2014 MFMER | 3334306-26 OPTUM LABS
  • 27. ©2014 MFMER | 3334306-27 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 H C A R E R E S E A R C H A N D I N N O VAT 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.
  • 28. ©2014 MFMER | 3334306-28 Optum Labs — Data and Tools Advanced Analytics and Data Visualization Data Growth Through Partnership >149M “Administrative” >30M Clinical 315M US Population Mayo Health System 2 Health Plan 1 Health Plan 1 Health System 3 Clinical Research
  • 29. ©2014 MFMER | 3334306-29 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 Innovative Health Care Insight Clinical Data Admin Data Pharmacy Data Population Data Data Sets Project “Sandbox” Researchers Real Estate
  • 30. ©2014 MFMER | 3334306-30 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
  • 31. ©2014 MFMER | 3334306-31  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
  • 32. ©2014 MFMER | 3334306-32 • 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
  • 33. ©2014 MFMER | 3334306-33 Patients are seen by outside providers/physicians. Optum Labs data Patients call and are given an appointment at Mayo. Example in Action
  • 34. ©2014 MFMER | 3334306-34 Patients are seen by initial Mayo team. Document patient expectations – “Pt Exp’n” Patients indicate their expectations.
  • 35. ©2014 MFMER | 3334306-35 Patients are presented medical vs. surgery information Document education Patients make a decision about their care: medical/surgery Shared decision making – SDM
  • 36. ©2014 MFMER | 3334306-36 Patients receive care … some being treated medically, others with surgery Collect risk factors and other data Patients see medical/pre-operative Mayo team Collect treatment data Mean length of stay for primary TKA OPTUM (x age = 56.6) 3.0 days MAYO CLINIC (x age = 70) 2.85 days
  • 37. ©2014 MFMER | 3334306-37 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 home OPTUM (x age = 56.6) 81.4% MAYO CLINIC (x age = 70) 63% 30-day readmissions OPTUM (x age = 56.6) 4.4% MAYO CLINIC (x age = 70) 1.6%
  • 38. ©2014 MFMER | 3334306-38 Surgical Process Flow for Costing - TDABC method C (Circulator Nurse) Surgical Assistant Scrubs Technician RN Anesthetist-NA Radiology Technician S (Surgeon) A (Anesthesiologist) AR (Anesthesiologist Resident) R (Resident/Fellow) Inpatient Space Operating Room Surgery Process Post Surgery E22 Patient Prep for Surgery C AR 20 A 5 20 20 R 20 C 20 E22 Operation (Incision to Closure) CAR 91 A 46 S 73 91 R 86 C 91 91 E28 Operation (Incision to Closure) CAR 88 A 44 S 71 88 R 83 C 88 88 10 E30 EMR documentation and contact family, supervision time, post procedure note, order tests S 10 R 5 Hip or Knee? Hip Knee FLOW 1
  • 39. ©2014 MFMER | 3334306-39 The Value Equation Comes to Life Quality outcome data: Patient-centric outcomes Practice performance outcomes Cost: Outside Mayo At Mayo “Cost avoidance”
  • 40. ©2014 MFMER | 3334306-40 Data are collected from all Mayo Clinic sites Comparing and adopting best practice helps improve value for all THA +22 +120% TKA +14 +110% PHM +0.96 +5.36% HD +3.34 +5.23% DHI +0.81 +3.
  • 41. ©2014 MFMER | 3334306-41 Age BMI Strength Exercise 85% probability of going home 3 days postop AND being able to stand/walk without pain for 30-min 3 months postop Knee Replacement Value Proposition

Editor's Notes

  1. Challenge intuitive assumptions: Example of telemonitoring study
  2. Proponents of telemonitoring suggest that it has the potential to improve patient outcomes, but recent studies suggest such optimism may be premature. Paul Y. Takahashi, MD, MPH, an former associate professor of medicine at the Mayo Clinic in Rochester, Minn, and colleagues found that tracking older patients at home through telemonitoring did not lead to fewer hospitalizations or emergency department visits. The findings are based on a study of 205 individuals with an average age of about 80 years and multiple chronic conditions who were randomly assigned to receive telemonitoring or usual care at 4 Mayo Clinic sites in Minnesota
  3. A telemonitoring device in the home that can measure and transmit information about indicators of health such as blood pressure and oxygen saturation coupled with videoconferencing can allow a health care team to assess a patient’s condition remotely and discuss ongoing treatment plans with the patient.
  4. Background from Dr. Takahashi:“It’s really important to try to change our care models offered to older and sicker patients. Taking care of older people to help keep them out of the hospital is important because every time they go, they usually lose a little more of their health. We looked at this population and thought using a monitoring system and taking measurements every day would help these patients avoid the hospital. Unfortunately, we did not find that. We still have to figure this out.“Telemonitoring is designed to provide access and help people living in rural areas where they can’t easily just get up and go visit their doctor or a specialist. Maybe telemonitoring is not as important in Rochester [Minnesota, where the study took place] or other urban areas, where patients have easy access to their doctors. So maybe we need to look at different populations than the one we studied. Maybe the sickest population is not the right population. Maybe it’s those not quite as ill for whom you can make changes in treatments that will make a real difference.“Telemonitoring will become a bigger part of medicine because the technology continues to grow, but the question is when is the appropriate time to use it. You want to make sure the time you’re investing in something is really helping. We do a lot in medicine because we think it is the right thing to do, but it might not be.”
  5. A Kaiser Permanente study found this:Providers and health system leaders most frequently associated value with “traditional” metrics of clinical structure, process, and health outcomes.Patients identified aspects of the care experience, such as convenience, respectful providers, and trusting relationships with their clinicians, as most critical for them in terms of engagement and as meaningful attributes of valuePayers, including public payers, private health plans, and major employers, identified financial metrics - especially affordability plus return on their health-associated investments - as their leading indicators of value. No payer suggestedreducing costs by relaxing clinical quality standards. Rather, cost impact was generally identified as attainable through improved efficiency and reduction of waste. Each stakeholder’s perspective recognized and granted importance to the elements valued by other stakeholder perspectives, albeit with lower priority.Each stakeholder assigned prime “value” to substantially different metrics. Each stakeholder perspective did “get quality,” but in its own manner.
  6. What’s missing in health care research and innovation?Collaboration across the health care ecosystemAvailability of high quality, high volume data sourcesOpportunity to prototype and test new findingsVehicles for widespread adoption of new knowledgeOptum Labs: A seat at the tableAn open, collaborative center for research and innovation for healthcare stakeholders interested in improving patient care.Established by Optum and Mayo Clinic,January 2013, in Cambridge MA.All projects must be primarily to improve patient care, lower the cost of improved care, and be transparent to the entire collaborative.
  7. .