Transforming Data for Sustainable Global Health

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


Connect entities to advance new data-driven ideas into practice

Examples: • Pipeline mechanism for new ideas• Incubator or laboratory to test or simulate


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


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



• 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.

Transforming Data for Sustainable Global Health

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