transforming data for sustainable global health
TRANSCRIPT
Transforming Data for Sustainable Global Health
April 13, 2016 * Gothenburg, SwedenChange. Save. Sustain. In Partnership with Patients
International Forum on Quality & Safety
Lucy A. Savitz, Ph.D., MBAAssistant Vice President, Delivery System Science
Intermountain HealthcareResearch Professor, Epidemiology
University of Utah, School of Medicine
‘The Promise of Data’22 to 27 March, 2015
Data Sharing Beneficiaries & Benefits
Beneficiary Example Benefits
Patients Better health, including longevity and higher levels of functionGreater transparency and accountabilityDirect feedback of personal health information
Providers Better health outcomes for patients at lower costImproved communicationNew knowledge to inform resource allocation
Policy Makers Better outcomes for populations at lower costNew knowledge to inform resource allocation
Researchers Comprehensive, representative, high-quality data to accelerate creation of new knowledgeNear real-time data
Industry Better employee health at lower costNew business opportunities through new knowledge paired with information and communication technologies
Group Discussion in the Max Reinhardt Library and Study
“In a world of infinite demands and finite resources, we need to figure out how we move forward in a sustainable way.”
Work Group: Kristen AntonRebecca EmenyAmel FarragYael HarrisLucy SavitzWilliam RileyVeronique Roger
Data-Driven Health Initiative:Using Data to Provide Knowledge & Global Answers that Advance Health
Sources of Data Knowledge &Ultimately Improve Health
Acknowledging that
Health ≠ Health CareDifferent kinds of available data related to • health & wellbeing (physical functioning, social connection) • clinical healthcare encounters, • the environment, • Social/cultural norms • socio-demographics• others…that can be used in new and better ways.
New Kinds of Data• Satellite images can pinpoint poverty where survey
results cannotfinding the poor via night sky
• App-enabled health tracking applications on smartphones & wearable devicesClue for studying menarche
– Near real time data that is more accurate & detailed
People have turned data into knowledge and translated that knowledge into health/healthcare improvements.
How do we use an action-oriented approach to enable others to do this to increase value and/or health
via shared learning?
Problem Statement:
Roadmap for Data Driven Value (DDV)
• Convene those with resources to contribute with those interested in learning
• Connect entities to advance new data-driven ideas into practice
• Disseminate knowledge, best practices and resources
• Advocate on how to overcome obstacles in moving from data to knowledge to value
Connecting data to drive value in health: An Example Using the
Area Deprivation Index
“Adversity is not randomly
distributed: instead it tends
to cluster and to accumulate
present on top of past
disadvantage”
David Blane, MSc MD
Social determinants & health care
• People with a higher standard of living have better
health outcomes.
• Social determinants of health includes factors that
influence where we live, work, play and pray
• The majority of health is driven by non-care
delivery factors – genetic, social, environmental,
behavioral
Perceived barriers to healthcare for people in
poverty
• Living conditions
• Poor quality of interaction with providers
• Complexity of health system organization and
functioning
(Loignon, 2015)
Linking deprivation with health care delivery
outcomes
• Clinical outcomes/mortality (Kim, 2014)
• Higher levels of ED utilization (Tozer, 2013)
• Increased readmission risk (Kind, 2013)
• Delays in time to diagnosis and time to treatment (Gattrell,1998; McKenzie,2008; Dialla,2015)
Development of deprivation indices
• An area deprivation index is a geographic area-based measure of the disadvantaged position of residents relative to society
• Used extensively in Europe, Australia and New Zealand
• Early measures compositional but have been evolving to include more contextual information
• Most common early measure is the Townsend Index proposed by Dr. Peter Townsend in 1988
http://www.theguardian.com/news/datablog/2011/mar/29/indices-multiple-deprivation-poverty-england
What is the Singh area deprivation index (ADI)?
• Index developed and validated by Singh
(2003) based upon 17 U.S. census
measures
– Education
– Employment
– Income
– Living Conditions
• Developed at the U.S. census block group
level for the state of Utah
• Patient assigned an ADI score based upon
the census block group they live in
• Surrogate measure for impact of
deprivation/social determinants
Knighton, 2015
•Not-for-profit hospitals,
physician group, and
health plan
•Founded in 1975
•22 Hospitals
•185+ Clinics
•Serves upwards of 50%
Utah’s population of about 2.9 million
Example Result
• Geocoded patient address + 17 U.S. Census variables
To assign an ADI score that can be applied at – Population level
• Health care delivery
• Connecting with other community-based organizations
• Civic planning
– Patient level• Targeted needs screening
• Tailored discharge planning
Your Ideas?
Create global health through disruptive, data-driven knowledge.
DDV Mission
Leverage existing knowledge and experience to:(1) Support effective integration of existing data
(2) Convert resulting information into actionable knowledge(3) Share and disseminate to improve global health
Goals
Conceptual Model:
Capture Organize Integrate Interpret
Manage Relate Model Disseminate
Validate Improvement/change
Data Value/
Health Knowledge Info
Culture
Disruptive
Model adapted from Nathan Shedroff
Conceptual Model
Challenges
Limited capacity to turn this information into knowledge
Limited understanding of available knowledge from data
Strategies to Realize Potential Value
Convene those with resources to contribute with those interested in learning
Examples:• coalition of EHR users• model health systems to mentor others
Roadmap for Data Driven Value (DDV)
Connect entities to advance new data-driven ideas into practice
Examples: • Pipeline mechanism for new ideas• Incubator or laboratory to test or simulate
Roadmap for Data Driven Value (DDV)
Disseminate knowledge, best practices and resources
Examples:• Training patients to manage their health with data-
driven knowledge• EHR plug in (e.g. sepsis predictive analytics tool)• Curated, dynamic compendium of best practice (e.g., National Library of Medicine, NCBI genomics platform)• Mapping data across registries
Roadmap for Data Driven Value (DDV)
Advocate on how to overcome obstacles in moving from data to knowledge to value
Examples: • revisit policies to address barriers (e.g. privacy,
interoperability)• guidelines for how data should be shared, integrated and
organized
Roadmap for Data Driven Value (DDV)
Roadmap for Data Driven Value (DDV)
• Convene those with resources to contribute with those interested in learning
• Connect entities to advance new data-driven ideas into practice
• Disseminate knowledge, best practices and resources
• Advocate on how to overcome obstacles in moving from data to knowledge to value
Approach
Model after existing entities: • Institute for Healthcare Improvement• Semiconductor industry NGO
Existing Resources:• Committed work group• Salzburg Global Seminar resources:
network of international fellows dissemination channels and meeting facilitation
Achieving Health @ Value
• Using data to provide global answers to advance health
– Measure what matters
– Leverage big data together with other available data sources
– Manage and promote population health
– Better meet the needs of individual patients
Step 1: Establishing a Steering Committee to Move Forward
• Well intentioned, smart people needed to
– Help to prioritize available opportunities
– Identify places/groups that would benefit
– Match opportunities
– Jumpstart or accelerate abilities in places of need
– Capture learning to facilitate continuous improvement
Next Steps
Identify key partners and interested stakeholders
Seek support of organizational entities
Increase outreach & work group membership
Create a sustainable organizational model
Time for Discussion
Please, Your Ideas & Suggestions
Interested in learning more later?
Contact: [email protected]
References• Bradley EH, Taylor LA. The American Health Care Paradox: Why Spending More is Getting Us Less. New
York: Public Affairs; 2013.
• Dialla PO, Arveaux P Ouedraogo S, et al. Age-related socio-economic and geographic disparities in breast
cancer stage at diagnosis: a population-based study. Eur J Pub Health, 2015.
• Gatrell A, et al. Uptake of screening for breast cancer in south Lancashire. Public Health.1998; 112(5):
297-301.
• Kim JH, et al. The association of socio-economic status with three-year clinical outcomes in patients with
AMI who underwant percutaneous coronary intervention. J Korean Med Sci. 2014 Apr;29(4):536-43
• Kind AJ, Jencks S, Brock J, et al. Neighborhood socio-economic disadvantage and 30-day re-
hospitalization: a retrospective cohort study. Ann Intern Med. 2014; 161(11):765-74.
• Knighton AJ, Savitz L, VanderSlice J et al.. Introduction of an area deprivation index measuring patient
socio-economic status in an integrated health systems: implications for population health. Submitted
manuscript.
• Loignon C, et al. Perceived barriers to healthcare for persons living in poverty in Quebec, Canada: the
Equi-healthy Project. Int J Equity Health. 2015; 14:4.
• Marmot M, Wilkinson R. Social determinants of health. Oxford: Oxford University Press; 2006.
• Singh G. Area Deprivation and Widening Inequalities in US Mortality, 1969–1998. Am J Public Health.
2003;93(7):1137-1143.
• Tarlov AR. Public Policy Frameworks for Improving Population Health. Annals of the New York Academy of
Sciences, 1999;896:281-293.
• Townsend P, Phillimore P, Beattie A. (1988) Health and Deprivation: Inequality and the North Croom Helm:
London
• Tozer AP et al. Socioeconomic status of emergency department users in Ontario, 2003-2009. CJEM.
2013;15(0):1-7.