DHI, BMSF DDC, & CMS SEDI
A trio of projects supporting the design, implementation, and evaluation of a population health improvement platform that
utilizes real-time spatially enabled data architecture and analytics
Durham Health Innovations
• A partnership between Duke Medicine and
the Durham community that seeks to improve the health status of Durham County residents. • In 2009, DHI funded 10 planning teams to find
ways to reduce death or disability from
specific diseases or disorders prevalent in the community.
Durham Health Innovations
The teams identified 7 common elements that could improve the health and health care delivery in Durham, including:
– Increase health care coordination and eliminate barriers to services and resources.
– Integrate social, medical, and mental health services.
– Expand health-related services provided in group settings. – Leverage information technology.
– Use "social hubs" such as places of worship, community centers, salons and barbershops as sites for clinical and social services and information.
– Increase local access to nurse practitioners, physician assistants, and certified nurse midwives.
• Spin-off of DHI Diabetes Group • Start Date: 01July 2011
• Total Budget: $6.2 million over 5 years
• Goal: Use of a spatially enabled system model to inform prevention and treatment at the individual, neighborhood and community level
Durham County, NC
• Partner: Durham County Department of Public Health
• 39% African American
• 16.6% of individuals live in poverty • 21% of adults are obese
• Diabetes is the 5th leading cause of death
From Clinic to Community: Achieving
Health Equity in the Southern US
From Clinic to Community: Achieving
Health Equity in the Southern US
• 4 sites – Durham County,NC, Cabarrus County, NC, Mingo County, WV, Quitman County, MS
• BMSF Durham Diabetes Coalition expanded to three additional counties in the Southern United States
• Focus on reducing costs while improving health
Cabarrus County, NC
• Partner – Cabarrus Health Alliance • Existing relationships with Duke
(MURDOCK Study)
• Urban/Rural Mix with 33% population growth in last 10 years (bedroom
community for Charlotte, NC) • 10% of the adult population has
diabetes
Quitman County, MS
• Partner: Mississippi Public Health Institute • Declining population approx. 8000
• Ranked 81 out of 81 MS counties in terms of overall health outcomes
• Diabetes prevalence of 22% • 70% African American
• 35% living below poverty level • 40% obese
• 63% use tobacco
Mingo County, WV
• Partner: Williamson Health and Wellness Center • Population approx. 26,000
• Ranked 53 out of 55 WV counties in terms of overall health outcomes
• Diabetes prevalence of 14% • 97% Caucasian
• 38% smokers
• 27% living below poverty level
• Lowest 1% in US for life expectancy • Geographically isolated
Significantly Higher than WV Prevalence Higher than WV Prevalence but Not Significant Lower than WV Prevalence but Not Significant Significantly Lower than WV Prevalence
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Project Themes,
Components, Foci, and
Goals
Themes
• Community basis
• Surveillance: “The health of a community” • Geospatially enabled and location specific • Use of secondary data for surveillance
Spatially-enabled data architecture and analytics
(who, what, where)
COMMUNITY PARTNERSHIP ZONE CLINICAL CARE
Accountability: real-time monitoring and evaluation
(e.g., weight, HbA1c, vision, CVD, cancer, nutrition, nephropathy, neuropathy, physical activity, self-care/management, health system trust)
Decision support systems
Fe e d b ac k loo p Fe e d b ac k loo p
Applying Teutsch’s Spectrum of Health & Strategies to Improve It Population Health Strategies Clinical Strategies Society Dead Well Individual County-wide strategies Neighborhood strategies Improve general clinical practice In-home team Inpatient care
Evidence-Based Prevention: From Evidence to Policy to Practice. Teutsch, Steven.
Social/Medical Risk Algorithm
Drives Intervention
• Different intensities of intervention
• High-intensity clinical teams vs. lower-intensity community-based teams
• Different modes of intervention
• Patient basis, neighborhood basis, community basis
• Targeted intervention
• Stratifying patients based on risk, both at patient and neighborhood levels
The Intervention Spectrum
• Knowledge and access to community resources • Community mobilization and pilot interventions • Real time information exchange
Higher Intensity Lower Intensity
Multidisciplinary Home Care Team
Neighborhood & Community Interventions
Neighborhood Selection Process
with Community Input
Brainstorm
Geospatial Mapping
Boundary Definition/Refinement Connect with the Community
Diabetes Control
Hemoglobin A1c Random glucose
Incidents of hypoglycemia Incidents of hyperglycemia BMS-CMS risk algorithm score
Microvascular Complications
Foot infection events
New diagnosis of kidney disease
Progression of kidney disease to dialysis New diagnosis of peripheral neuropathy New diagnosis of retinopathy/blindness
Risk Factors Blood Pressure BMI Cholesterol Smoking Status Fitness Status Mortality Macrovascular Complications
New diagnosis of hypertension New diagnosis of heart failure Amputation events
MI events Stroke events
Patient-Reported Outcomes
Global health scale score (PROMIS) Problem areas in diabetes score (PAID) Patient depression score (PHQ-9) Medication adherence score Nutrition assessment score
Health Services Utilization
Number of ED encounters
Number of inpatient encounters Number of outpatient encounters Length of stay for inpatient admissions Cost
The SEDI
Integrated Data Mart
Harvesting Data from Multiple
Source Systems for Risk
Modeling, Financial Modeling,
and other Applications
Using the SEDI Integrated
Datamart: Risk Algorithms that
Predict Outcomes
Known Diabetic Patients >35 in 2010 = 14,330
With Any Encounter in 2011 = 11,548
With An Inpatient Encounter in 2011 = 2488
With A Serious Outcome in 2011 = 1742
80% Modeling Set
12% of 2010 patients; 15% of modeling set Serious Outcome = EITHER: at least 1 inpatient stay in 2011 with diagnosis of at least one of:
• MI, cardiac arrest, ventricular fibrillation, ischemic cardiomyopathy, CAD, revascularization
procedure
• stroke
• vascular disease, PVD
• diabetic renal complications, kidney disease, dialysis
• amputation, foot infection/ulcer
OR: death
Methodological Issues
• Clinical research
– Ascertainment bias – In and out-migration
– Various intervention strategies
– Cognitive issues in decision support display
• Statistical
– Modeling methods for different types of data
– Models at level of individual, neighborhood, county – Missing data
– Multivariate outcomes models – Others?