Disruptive Delivery: How Mayo Clinic is Combining Big Data with the Voice of the Customer to Redefine Success on Consumers Terms Ryan Uitti, M.D. Deputy Director, Kern Center for the Science of Health Care Delivery Economic Disruption in Healthcare April 3, 2014 2014 MFMER 3334306-1
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The Science of Hitting Ted Williams 2014 MFMER 3334306-3
The Science of Hitting Ted Williams 2014 MFMER 3334306-4
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Use of Home Telemonitoring in the Elderly to Prevent Readmissions 2014 MFMER 3334306-6
Comparison: Telemonitoring + Versus Usual Care Telemonitoring Intervention RN/MD team oversaw apx 100 patients and communicated with them via phone or videoconference 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 3334306-7
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 3334306-8
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 Deaths 15% 4% Very significant Note: Results are for a one-year period 2014 MFMER 3334306-9
Epilogue What Next? Not ready for prime-time 2014 MFMER 3334306-10
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 3334306-11
Value Framework Patient Quality (outcomes, safety, service) Cost over time Provider Payer 2014 MFMER 3334306-12
Patient Satisfaction Quality Measures Costs Big Data Health and Quality of Life 2014 MFMER 3334306-13
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 3334306-14
Patient Satisfaction Quality (outcome, safety, service) Cost over time Quality Measures Costs Telestroke Example Big Data Health and Quality of Life 2014 MFMER 3334306-15
2014 MFMER 3334306-16 Mayo Clinic Telestroke Network
Patient Flow in Hub-and-Spoke Telestroke Network Patient presents at Hospital Emergency Room Spoke vs. Hub Hub Hospital Spoke: No telenetwork Spoke Spoke Spoke HUB Spoke Spoke Spoke Spoke More patients transferred to hubs Fewer with access to IV thrombolysis and/or endovascular therapy Spoke: With telenetwork Fewer patients transferred More patients receiving IV thrombolysis and/or endovascular therapy 2014 MFMER 3334306-17
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%) Performance 1-minute median stroke neurologist response time (swift response) 22-minute median consult time (rapid assessment) Safety 5% post thrombolysis symptomatic intracranial hemorrhage Technology Technology problems prevent clinical decision making in fewer than 2% of consults Disposition 60% reduction in patient air/ ground ambulance transfers from spoke to hub Morbidity & Mortality Telestroke treated patients have approximately the same outcomes as those treated at a stroke center 2014 MFMER 3334306-18
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 3334306-19
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 3334306-20
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 3334306-21
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20 Years Ago Today 3 months to collect data to answer 2 questions Seconds to collect and answer the same questions 2014 MFMER 3334306-23
2003 First Human Genome Time: 10 Years Cost: $1 Billion TODAY Genome Sequencing Time: 1 Week Cost: $1,500 2014 MFMER 3334306-24
Cost of Whole Genome Sequencing $100 million $10 million $1 million $100,000 $1,000 to sequence one human genome $10,000 $1,000? 2002 2004 2006 2008 2010 2012 2014 2014 MFMER 3334306-25
OPTUM LABS 2014 MFMER 3334306-26
Optum Labs 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. Pharma/ Life Sciences Provider H E A L T H C A R E R E S E A R C H A N D I N N O V A T I O N Academic Types of questions that may be pursued Comparative Effectiveness Variation in Care Research Payer Professional/ Consumer Organization Behavioral and Policy Research Heterogeneity of Treatment Response Government 2014 MFMER 3334306-27
Optum Labs Data and Tools Advanced Analytics and Data Visualization Data Growth Through Partnership 315M US Population Health Plan 1 Health Plan 1 Mayo Health System 3 Health System 2 Clinical Research >149M Administrative >30M Clinical 2014 MFMER 3334306-28
Optum Labs Research Process Data Integration Research & Analytics Outputs Clinical Data Admin Data Data Sets Health Economics Biostatistics Pharmacy Data Population Data Researchers Project Sandbox Epidemiology Actuarial Innovative Health Care Insight Real Estate Contributor data is de-identified and stored in standardized data sets, on secure, private environments. Data sets and resources are integrated into a separate sandbox. Data contributions are tagged and valued. 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. 2014 MFMER 3334306-29
Research Themes: Areas of Focus Behavioral and policy research 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? Variations in care 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? Heterogeneity of treatment response 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? Methodology research 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? 2014 MFMER 3334306-30
Sample Research Projects Currently underway or awaiting publication 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 Likely candidate for clinical translation project 2014 MFMER 3334306-31
Seven Leading Health Care Organizations Join Optum Labs 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. 2014 MFMER 3334306-32
Example in Action Patients are seen by outside providers/physicians. Optum Labs data Patients call and are given an appointment at Mayo. 2014 MFMER 3334306-33
Patients are seen by initial Mayo team. Patients indicate their expectations. Document patient expectations Pt Exp n 2014 MFMER 3334306-34
Patients are presented medical vs. surgery information Document education Patients make a decision about their care: medical/surgery Shared decision making SDM 2014 MFMER 3334306-35
Mean length of stay for primary TKA OPTUM (x age = 56.6) 3.0 days MAYO CLINIC (x age = 70) 2.85 days Patients see medical/pre-operative Mayo team Collect risk factors and other data Patients receive care some being treated medically, others with surgery Collect treatment data 2014 MFMER 3334306-36
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 3334306-37
Surgical Process Flow for Costing - TDABC method FLOW 1 Surgery Process C AR 20 E22 A 5 Patient Prep for Surgery 20 R 20 20 Hip or Knee? Hip C 20 E22 Knee E28 S 71 88 S 73 91 A 44 Operation (Incision to Closure) R 83 88 10 A 46 Operation (Incision to Closure) R 86 AR 88 C 88 AR 91 C 91 C C E30 EMR documentation and contact family, supervision time, post procedure note, order tests S 10 R 5 Post Surgery 91 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 2014 MFMER 3334306-38
The Value Equation Comes to Life Quality outcome data: Patient-centric outcomes Practice performance outcomes Cost: Outside Mayo At Mayo Cost avoidance 2014 MFMER 3334306-39
THA +22 +120% TKA +14 +110% PHM +0.96 +5.36% HD +3.34 +5.23% DHI +0.81 +3. Data are collected from all Mayo Clinic sites Comparing and adopting best practice helps improve value for all 2014 MFMER 3334306-40
Knee Replacement Value Proposition 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 2014 MFMER 3334306-41